diff --git "a/notebooks/05.09 PCA \354\243\274\354\204\261\353\266\204\353\266\204\354\204\235.ipynb" "b/notebooks/05.09 PCA \354\243\274\354\204\261\353\266\204\353\266\204\354\204\235.ipynb" new file mode 100644 index 000000000..2976dd00f --- /dev/null +++ "b/notebooks/05.09 PCA \354\243\274\354\204\261\353\266\204\353\266\204\354\204\235.ipynb" @@ -0,0 +1,1368 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# In Depth: Principal Component Analysis\n", + "\n", + "- unsupervised algorithms\n", + "- dimensionality reduction\n", + "- noise filtering\n", + "- feature extraction, engineering" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns;\n", + "\n", + "sns.set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introducing Principal Component Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.29352182, 0.75265179],\n", + " [-0.10657667, -0.12111696],\n", + " [ 0.26932804, 0.50817416],\n", + " [-0.03242678, -0.20872671],\n", + " [-0.10710985, -0.27514933],\n", + " [ 0.22559036, 0.55202259],\n", + " [ 0.39791653, 0.97242155],\n", + " [ 0.24327528, 0.61174521],\n", + " [-0.01494767, 0.04968946],\n", + " [-0.15945656, -0.31620475],\n", + " [-0.60128802, -1.27676463],\n", + " [-0.68473856, -1.39718969],\n", + " [-0.10097711, -0.25599696],\n", + " [ 0.3056781 , 0.97482823],\n", + " [-0.10160989, -0.39322195],\n", + " [-0.05720377, 0.04942747],\n", + " [ 0.12323801, 0.32501636],\n", + " [-0.06318145, -0.11003777],\n", + " [ 0.35378105, 0.69025198],\n", + " [ 0.58209366, 1.53100458],\n", + " [-0.72777952, -1.6480327 ],\n", + " [ 0.10346911, 0.31460901],\n", + " [ 0.08409478, 0.2313791 ],\n", + " [ 0.32072219, 0.6950672 ],\n", + " [ 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[-0.20999094, -0.51017815],\n", + " [ 0.10192973, 0.23440678],\n", + " [-0.41487397, -1.03981884],\n", + " [-0.14389764, -0.29223445],\n", + " [ 0.16632184, 0.27035094],\n", + " [-0.13531854, -0.24595754],\n", + " [ 0.042983 , 0.14468985],\n", + " [ 0.29546664, 0.71627698],\n", + " [ 0.16773118, 0.26579321],\n", + " [ 0.31704399, 0.71927871],\n", + " [ 0.51792747, 1.42491639],\n", + " [ 0.18068254, 0.45542694],\n", + " [-0.82661923, -1.91019811],\n", + " [ 0.21533263, 0.60266864],\n", + " [-0.63189826, -1.4435989 ],\n", + " [-0.73466503, -1.60755731],\n", + " [-0.38232908, -0.83941334],\n", + " [ 0.34976549, 0.62219904],\n", + " [-0.33389232, -0.81878835],\n", + " [-0.03150357, 0.04256053],\n", + " [ 0.02526209, 0.07338478],\n", + " [ 0.09389769, 0.20182276],\n", + " [-0.28076219, -0.69073475],\n", + " [-0.11253394, -0.33854364],\n", + " [ 0.26920649, 0.64402956],\n", + " [-0.21045759, -0.50587162],\n", + " [ 0.62569068, 1.37314493],\n", + " [ 0.14335361, 0.23867693],\n", + " [ 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0.41424482, 0.82186512],\n", + " [ 0.14528102, 0.40876798],\n", + " [-0.0787854 , -0.25310363],\n", + " [ 0.2911692 , 0.66540805],\n", + " [-0.86055792, -1.91352176],\n", + " [-0.06610614, 0.06899055],\n", + " [-0.00954869, -0.17264019],\n", + " [ 0.66495355, 1.52532741],\n", + " [-0.81294613, -2.00525477],\n", + " [ 0.30265853, 0.58698878],\n", + " [ 0.25091282, 0.57911515],\n", + " [ 0.14364649, 0.21700615],\n", + " [ 0.19815577, 0.44332604],\n", + " [-0.08023976, -0.15187034],\n", + " [-0.10013811, -0.27333805],\n", + " [-0.12208962, -0.323719 ],\n", + " [-0.78786478, -1.91133603],\n", + " [ 0.53832446, 1.30316292],\n", + " [-0.78802507, -1.72171951],\n", + " [-0.08980546, -0.18022454],\n", + " [ 0.23705986, 0.62112194],\n", + " [ 0.54421118, 1.14722622],\n", + " [ 0.50081169, 1.20405082],\n", + " [-0.16310817, -0.34915349],\n", + " [-0.31622854, -0.73759493],\n", + " [ 0.33375214, 0.85677111],\n", + " [-0.64235525, -1.677248 ],\n", + " [ 0.24707034, 0.43895371],\n", + " [ 0.03316757, 0.07558037],\n", + " [ 0.09463487, 0.19872134],\n", + " [ 0.70109322, 1.69423996],\n", + " [ 0.71543743, 1.49110111],\n", + " [ 0.28669072, 0.65801902],\n", + " [-0.30757904, -0.57944406],\n", + " [-0.25120784, -0.63426605],\n", + " [-0.31709545, -0.69982233]])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rng = np.random.RandomState()\n", + "X = np.dot(rng.rand(2, 2), rng.randn(2, 200)).T\n", + "X" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.3034404218473117,\n", + " 1.0645287389146634,\n", + " -3.0244456603409886,\n", + " 2.6248965784351874)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X[:, 0], X[:, 1])\n", + "plt.axis('equal')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- unsupervised learning problem attempts to learn about the relationship between the **x** and **y** values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,\n", + " svd_solver='auto', tol=0.0, whiten=False)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.decomposition import PCA\n", + "pca = PCA(n_components=2)\n", + "pca.fit(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-0.39172647 -0.92008172]\n", + " [-0.92008172 0.39172647]]\n" + ] + } + ], + "source": [ + "print(pca.components_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**components** to define the direction of the vector" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.97521766 0.00153957]\n" + ] + } + ], + "source": [ + "print(pca.explained_variance_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**explained variance** to define the squared-length of the vector" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.3034404218473117,\n", + " 1.0645287389146634,\n", + " -3.0244456603409886,\n", + " 2.6248965784351874)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def draw_vector(v0, v1, ax=None):\n", + " ax = ax or plt.gca()\n", + " arrowProps = dict(arrowstyle = '->',\n", + " linewidth = 2,\n", + " shrinkA = 0,\n", + " shrinkB = 0)\n", + " ax.annotate('', v1, v0, arrowprops = arrowProps)\n", + " \n", + "#### plot data ###\n", + "plt.scatter(X[:, 0], X[:, 1], alpha=0.2)\n", + "\n", + "for length, vector in zip(pca.explained_variance_, pca.components_):\n", + " v = vector * 3 * np.sqrt(length)\n", + " \n", + " draw_vector(pca.mean_, pca.mean_ + v)\n", + " \n", + "plt.axis('equal')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Principal Components Rotation" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(-3, 3.1),\n", + " Text(0,0.5,'component 2'),\n", + " (-5, 5),\n", + " Text(0.5,0,'component 1'),\n", + " Text(0.5,1,'principal components')]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.RandomState()\n", + "X = np.dot(rng.rand(2, 2), rng.randn(2, 200)).T\n", + "pca = PCA(n_components=2, whiten=True)\n", + "pca.fit(X)\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n", + "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n", + "\n", + "### plot data ###\n", + "ax[0].scatter(X[:, 0], X[:, 1], alpha=0.2)\n", + "\n", + "for length, vector in zip(pca.explained_variance_, pca.components_):\n", + " v = vector * 3 * np.sqrt(length)\n", + " draw_vector(pca.mean_, pca.mean_ + v, ax=ax[0])\n", + "\n", + "ax[0].axis('equal')\n", + "ax[0].set(xlabel='x', ylabel='y', title='input')\n", + "\n", + "### plot principal components\n", + "X_pca = pca.transform(X)\n", + "ax[1].scatter(X_pca[:, 0], X_pca[:, 1], alpha=0.2)\n", + "\n", + "draw_vector([0, 0], [0, 3], ax=ax[1])\n", + "draw_vector([0, 0], [3, 0], ax=ax[1])\n", + "\n", + "ax[1].axis('equal')\n", + "ax[1].set(xlabel = 'component 1', ylabel = 'component 2',\n", + " title = 'principal components',\n", + " xlim = (-5, 5), ylim = (-3, 3.1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PCA as dimensionality reduction" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "original shape: (200, 2)\n", + "transformed shape: (200, 1)\n" + ] + } + ], + "source": [ + "pca = PCA(n_components = 1)\n", + "pca.fit(X)\n", + "X_pca = pca.transform(X)\n", + "\n", + "print('original shape: ', X.shape)\n", + "print('transformed shape: ', X_pca.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.6324695629319352,\n", + " 1.2860611429535573,\n", + " -1.8560118962395435,\n", + " 1.4694475882638003)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X_new = pca.inverse_transform(X_pca)\n", + "\n", + "plt.scatter(X[:, 0], X[:, 1], alpha=0.2)\n", + "plt.scatter(X_new[:, 0], X_new[:, 1], alpha=0.8)\n", + "plt.axis('equal')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- light points are the original data,\n", + "- dark points are the projected version\n", + "- reduced-dimension dataset is in some senses **good enough** to encode the most important relationship between the points" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PCA for visualization : Hand-written digits" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1797, 64)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.datasets import load_digits\n", + "digits = load_digits()\n", + "digits.data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1797, 64)\n", + "(1797, 2)\n" + ] + } + ], + "source": [ + "pca = PCA(2)\n", + "projected = pca.fit_transform(digits.data)\n", + "\n", + "print(digits.data.shape)\n", + "print(projected.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(projected[:, 0], projected[:, 1],\n", + " c = digits.target, edgecolor='none', alpha=0.5,\n", + " cmap = plt.cm.get_cmap('prism', 10))\n", + "\n", + "plt.xlabel('component 1')\n", + "plt.ylabel('component 2')\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What do the components mean?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Digits Pixel Components" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_pca_components(x, coefficients=None, mean=0, components=None, imshape=(8,8),\n", + " n_components=8, fontsize=2, show_mean=True):\n", + " \n", + " if coefficients is None:\n", + " coefficients = x\n", + " \n", + " if coefficients is None:\n", + " components = np.eye(len(coefficients), len(x))\n", + " \n", + " mean = np.zeros_like(x) + mean\n", + " \n", + " fig = plt.figure(figsize=(1.2 * (5 + n_components), 1.2 * 2))\n", + " g = plt.GridSpec(2, 4 + bool(show_mean) + n_components, hspace=0.3)\n", + " \n", + " def show(i, j, x, title=None):\n", + " ax = fig.add_subplot(g[i, j], xticks=[], yticks=[])\n", + " ax.imshow(x.reshape(imshape), interpolation='nearest')\n", + " \n", + " if title:\n", + " ax.set_title(title, fontsize=fontsize)\n", + " \n", + " show(slice(2), slice(2), x, 'True')\n", + " \n", + " approx = mean.copy()\n", + " \n", + " counter = 2\n", + " \n", + " if show_mean:\n", + " show(0, 2, np.zeros_like(x) + mean, r'$\\mu$')\n", + " show(1, 2, approx, r'$1 \\cdot \\mu$')\n", + " counter += 1\n", + " \n", + " for i in range(n_components):\n", + " approx = approx + coefficients[i] * components[i]\n", + " show(0, i + counter, components[i], r'$c_{0}$'.format(i + 1))\n", + " show(1, i + counter, approx, r'${0:2f} \\cdot c_{1}$'.format(coefficients[i], i + 1))\n", + " \n", + " if show_mean or i > 0 :\n", + " plt.gca().text(0, 1.05, '$+$', ha='right', va='bottom', transform=plt.gca().transAxes, fontsize=fontsize)\n", + " \n", + " show(slice(2), slice(-2, None), approx, 'Approx')\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'NoneType' object is not subscriptable", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_style\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'white'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot_pca_components\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdigits\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshow_mean\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m\u001b[0m in \u001b[0;36mplot_pca_components\u001b[1;34m(x, coefficients, mean, components, imshape, n_components, fontsize, show_mean)\u001b[0m\n\u001b[0;32m 32\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 33\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_components\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 34\u001b[1;33m \u001b[0mapprox\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mapprox\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mcoefficients\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mcomponents\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 35\u001b[0m \u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mcounter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcomponents\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mr'$c_{0}$'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 36\u001b[0m \u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mcounter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mapprox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mr'${0:2f} \\cdot c_{1}$'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcoefficients\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set_style('white')\n", + "\n", + "fig = plot_pca_components(digits.data[10], show_mean=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Digits PCA Components" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pca = PCA(n_components = 8)\n", + "Xprj = pca.fit_transform(digits.data)\n", + "sns.set_style('white')\n", + "fig = plot_pca_components(digits.data[10], Xprj[10], pca.mean_, pca.components_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Choosing the number of components" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pca = PCA().fit(digits.data)\n", + "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n", + "\n", + "plt.xlabel('number of components')\n", + "plt.ylabel('cumulative explained variance')\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "first 10 components contain approximately 75% of the variance" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PCA as Noise Filtering" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_digits(data):\n", + " fig, axes = plt.subplots(4, 10, figsize=(10, 4),\n", + " subplot_kw = {'xticks':[], 'yticks':[]},\n", + " gridspec_kw = dict(hspace=0.1, wspace=0.1))\n", + " \n", + " for i, ax in enumerate(axes.flat):\n", + " ax.imshow(data[i].reshape(8, 8),\n", + " cmap='binary', interpolation='nearest',\n", + " clim=(0, 16))\n", + "\n", + "plot_digits(digits.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html\n", + "- https://matplotlib.org/api/_as_gen/matplotlib.pyplot.imshow.html" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "components = pca.transform(noisy)\n", + "filtered = pca.inverse_transform(components)\n", + "plot_digits(filtered)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example: Eigenfaces" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Ariel Sharon' 'Colin Powell' 'Donald Rumsfeld' 'George W Bush'\n", + " 'Gerhard Schroeder' 'Hugo Chavez' 'Junichiro Koizumi' 'Tony Blair']\n", + "(1348, 62, 47)\n" + ] + } + ], + "source": [ + "from sklearn.datasets import fetch_lfw_people\n", + "faces = fetch_lfw_people(min_faces_per_person = 60)\n", + "\n", + "print(faces.target_names)\n", + "print(faces.images.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- RandomizedPCA contains a ranomized method to approximate the first $N$ principal components much more quickly than the standard PCA estimator" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, iterated_power='auto', n_components=150, random_state=None,\n", + " svd_solver='randomized', tol=0.0, whiten=False)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.decomposition import RandomizedPCA\n", + "pca = PCA(150, svd_solver='randomized')\n", + "pca.fit(faces.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- http://darkpgmr.tistory.com/105 \n", + " - 고유값(eigenvalue), 고유벡터(eigenvector)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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j1PuXub8KjmoU7rPWoRQBPtldhGD0KgiQWHGL2yuhZwnvAwQiE6+uVYnGAcY2CuFXA1s4/2srrDWU6ipGw/xyb1aiUqCIRy3dS2qc0hjPfmsUuZJQu+4TDGdmTJtZ+n/r3hcg1TSH3RmUOdP3w3ysRXMNVqLbuiaV/na9r1nGScpvrFyy1qj3OH4uYNQtJI9b0irkefpJ8tEOxlrU9tte3+cGzcdTFOlFiq576i+ijVde/njvkvduw3ncvYf1ybhGr43GOBeKCHDEqRnUNiDl9TNvlHuGnosLRpH1fgrk7Q+Ay1LsEK0b0lz6/fUcOC3Le78VeobyLA2giH47VCk53/eIrG2rDYaxALVURK582lLC87oiv+Hxv6n40xZRZg6hJ1Eo9GE1E4K/cE5/B/q5+ioP07mgoX5aCi5dQuUF9bDw/TC11vP+vKqWS8YoB2hQSka89NCLDDeA8ATV/7XDslKje3yJHBWlr4LnlejSCGIZ815mOOAULqKfBZhUcscBvGYIvAQN7decUiaEQr9lNDTAEnbCjve5C+t3rMOr12gNFaT8Zf3qFWiPFP7L98r1R0S3GltSLuacrtdrny3rfssYcRSa6zRUiiehUAL2+V6q5/wuLzoCAM1VOF73UwMLsV7GN/68uycqIVAAIUoPcV5PlHKRBcDX733JvZZUNGz9WbRyNTDmCrjUrr6vNNFWG8xrIfMrQ/rFR7QGa8xO6QN7tL/eu8RMv2EQknreYUVabQpG3AO0+lwVsxMcgWsDdq+XsUtdGiiK/0WueHhfLQ3WgWTH+H4uYx6H9RYphZvnL/K341CuQs+W5L2CNK/mZ50ZDAG6VkUFgzx4QpXmAwmLf97RuZZvO7AfRsDgAKa89eCjl6MKRkyMKd0PZTAwxcGRMlwnulCmWXdnQ8ZYGmtEvXF6whijWBcAu7Wplcvia0VrFrUUJNZvFwZbr/Ebc/yS40uRAEUAWf4C4BsVj/UOZlD+soBNgD3VAqQGXnrEcKxMiuZGyPs3Ly1m7OudK+MKUsxwW5+wHAZr7c2K31kL9yL3SkLot5SdIjxLf7gjKEat3c8IBADqtRhnYUvPBwuC3FrH9b9XOeHBuCDL9zZgo+Vc3x6E2D17YwhsVQcPrdQBY8DCWj0Zc5UnlIOs763IbBCUAfA1CnoDXo+rmubRUBzX4FvwFmRg+t1+FoPTOvqbrP+Yp98L9X5YDQoKK3T621B7DAeg7HKVBq8oWb227cq/NVQXEKaXCq+U2/a9eGlUJ174WmW3FnsjtHuC8v7Wek66tS9Hmat3Owwp7wMAC/NC+V97nDpu9Pqmw6TenhrSFJ8g+UR/2QG0nHMwvnvh14bQ+HUcqjiujQy8Ig+M2X1vLZdRsoEu73HeIocJtwyKsjlcg0Wv17I1ekoS4dQohihp0yQ80yMEdsj/S/jmxkHyVBR9xzVpROG18/NFg9bPBa/gPuevQNJqGBnClCQL9fpqI+6BARw7Yhb0/l+JgBhrYXPZ4SWstVQyz2XhzlsUdhDlrpTDomSSn2+s69uKv1aqT09DPe+V1T9uQOcsWvAw5qr2F4CtDcaEnTDcATCMgXsFdiB10fJZfUGcClDZbCX1mk65T+vMzeC+0dv+3CYdz4aEXlQAipIfQFEyD6nZ1J9Hb0d/3oM9DETx86E0hDKnyMnrpS23e7wv70vQ1TJXGEFbd0S+Am8ah3KHC47K/LUIwGgMlFp3pUHjz7tyGRU8DRjChfSeBvOlGudqOOdQ6x7Y2RH0Rr1/EZLyTK693lYLmrVokDRVxVhtQUp2H7EwxnCp3FU1yfAsDYwaJq5dCRYWhhRu//o0lyhy4aUAuP54/AzXw8DjfPX72gB7lar7XJUKo+THvbY3MgbD29BKNmMU/FbHzx7u89Zwb5gClYFKtAIAYHcldrUQHwlxOvAr3D40ToZ+24EiceX9XZMByfuv10fWpEdPLTlbxgC2A/wc78XpVtpZPfNXsnhMLRiupODQ9DhUBrKnW6+iIpqOozwPLMhzba7BFJFlZbfXRFcIidW107D7fOxl7dfPH0o4JXN7zamgpTKDg8XcLbkgb4mJv65kle2RkFfvrwkgebxehkuk7H1w7EheGVCyTmKEviH03lT8JVHNcFw3RPamU8zdc5Oww/BQ5WHTjXS2PplsP9h1b0QMSqa1tst79pcYVcZGkJNl7/GJgVKsRasbjIES6tw6xrB2Q0MeFJcIyVrqwOB1XavaXrfir0J2QHmhcKlcTdDSlnKpavTsMQX7UHLVVMqtczbWaAoC2NfY51K0dE/mSkqdfpaIbm1tV7In12lNIgE9MuCseIdDPksELRuRrVROhXSlJGugRgegeddr7/FLB7HuORSbVDGPzIiGoy6OFT/A4Tv25K0tqJUjArwHDF565eN+ud4bxoxhXdsNECn9a+Aa9vDi/Q0VxjS8ke57dRBxT1XDlW/0RSRlp6BeE7YaDRn39HWom4Wh24e2XzMQaP8ZVDTAWtRGhp01ZjhzeN37/4oRuHSrlqrWFkUt97lpSb2Ay7sUAzFcS7ASglwfR2sNKC/Lz7r9z+skhD6carPWalqQSMycRiYFk3RrtOu3wu3qiAyEQnYwSuhrX6NrpX+tBFX5O2hEWRT3uEbi1b/gRUDXF/LhEqm6NcVpr3hUaO70aSLnJe2ljm3qqfCSMnImR3nUd6SI9uu5n19/7vI5AABdR7k/cbRfTwEbY+Dtb8v9tz3+VpUpLQ8Tk1x6yZTrz0xVm4S9jm+6E35Ay/lqFY+/pw/khpWpiklOrLc7C7k1OgTee1hnKBWBvFOCanTUitbo528pZ1elr6COLmCqhKYbUzdeWfN6CAYFdn3tcUhZiBo41sK69uJ1tOF7HfsupPyKB3bTsHQPo6IqrXaPnZV8kc8Uj16MOWth0bS87zpy0n/m8KS1GjZ91ZLXQ93UspXf8wUBNcYaHZBmb9X78D5AyFZGUOo1N0JtFUaNOipLG+/rpTf3kpK3AzX3JZr70aWGMa5HBSD75WVJH3Cb4m8s2EZ63p1wMnuhI4brZ5XGkAsVD1U/i4VjuxLU15EmN16foz/yt+tIQa2fCf1/4RBFu7uGNfuIy7CfxdhQsqlaVe5dcyIY2z0z+ZtcR2SX0B0LZa9l79NPXhUtcUl0WTiu57e5OZ8fIqOtcy/2ade9/eeRzOb6PCg+QtanXEWAh9H0TO0N5GYsxjpemTm97rZV8N4pvfS4x1Xpl6KMlpnp2K9D+WL0jOWXxpCxqtccvH8ZtTZYc7VvWQ5bTxEekce1UETOGINqe3TbW4vyRrTnC3L8FUDZCVoBOSgxz7UVOyo/zneMNfqENuf8PKOTGzqgicgjmA4VXB+pFh5YuYtSH/KkTh4SfbQ8PGsN4N6c6uvzV53yMh8lCrCqQHyZEhjl4LWHPgor6wyhnxnNWT1tMOvsqzXBjWljr5XI9ffX9/M1Qw9XqbsoSnFk7NTX0MqD8aaMfEJWJM/FOtQ2CogG5B5FKq3X5dZGZD4AGRg7QYm9chg9Tvo6gC5vGN5PaE0iW/wM2NjSKAT/npQuIY33XqvVnGn/T3u8tTEqtA8lvlaxseNtsA0Wbvd5FCHjaAR/ZruxlnkXnt6t9/U9cciXqXk/y5Kpwu/1P9OcB3InCIiU3jByQrTWKMTfOqfGLtKxW8dvUP4GKucAaFgacKiGOeWZqlWoWyWyKBz6muuVe5F9M5AiiWdYMjkPxhBlq3UdtOcnj4m5BVprjOInUh0S9p25MNfC5ELfrv5fRGDMy2d4rfCvvwewu5fxb7VyOilTfxOJINexVh+0t2vFDnPzW8+X0i+3zd9NXhH83aFip6OQLiQyusJpcEovq0HmHMLUuuwWGSkRG3FuGQCZc1GdSefJMklZp92V6EqrFF1INqsj1FpTdlJnLCbvkb7V4wejcK23sByadUJSYQxKKrAcnkXpof0ijVUS8YkT21jSm5UFlbIktKbKjDwtUSgHYkxSHukhn6JhJMAE4a3vOV3rDJVhBP9NjAWfQw6rwH3lMLw2XtuovSSnh5BdayhJPCjK03ZK1O4BSUmfAFpEYexyc2P86IbRmhhubFFehdquBwli+l6EtXfUgOZ6HUfefmstknBdt4bJe5RGHpSEMIWatXqas6sOJZVdeGy871YBY6EH7WsHKVKLWjLqleFIQrwSVSvGUH1HQiuGxXn9W6fs7Ra5spWx8ZtzHn4eBW/tBhHVjnape+Xx+zDBlII6zTfNXUK6uzQS/0GY+sjz431q94bsdSrQ5NHjB/Xq4Os1AfDWhjp4O2JQ63pfrX9r3fj+/3uoELa4elbUiCxfMjOYRmIsXSPSFhGZ3VSAWKTU+5qOawNQ9DBvCbU2pC0qBTrlzi3CTAC96TBhOS443C+4SwWoTaMCmK9TLrQu8RsUvxi7YtiOSlTkrMypz+c6AtB2VQ8SFelGM3n5mZvyNOlDUXj/D969kFXpdVvj91S0+jqo99Zt4V1vANcDTGSgqLIdUtzq7LCICJKL570iaQFjjBoUco92IjCf/EzNxhhYKORBE7EGUkqM2G9l/XylzxcgapX7eUPmv634+aAS0Ikfvmns9WSulQXylgau/owUiS9/285IOWonJXloisiXemT2hLwLHT3cGiGordndqeTR5eGSYAEEuy5WsvBp++BQ2m3I9r4MfSHHKIDjPKMzFvkVr0Ny0rr5K/o6DsNai+Z7HswFDz+kLLRxjPMUarJZBRKF3OmQtlo13GUthblv1fuNqSKt2yNMZS1IsX+GV/qVMSqFkX61NaIaBtPzVuZdvxbost6b756lGppF8CKjN1EpYnTjAgho0joPN3jn1Pwmd++UFURrew+8K86ekrGGqgG8C7vSU3lurTWE0IVfHamp235urRZUZfOzqLUrKRGWt/KV9/DkGDHpER0JQV9vLr3H0pHFrWVU10uQRk/KOnIohMpXQJ3yvSpIa7WbI12+IheqkihXtLk7ZfMNrJUA1NOSuZWUkWND2hK284bL8wWXpwvOT2dcni+IF8qrZK6CEsVvDLSJ1IjeL7mQzMwJ23ZGjKtW4RATJPUJOBwecPfhDvcf7yl1acipmZZJFZwfFGMqvWnMLaPWglI7ulz6qpRrI9o7OMYAGY38Wir3sxXVGjRmnJMKEWl4JanektNunwNydno1lXSzHFOakn7b6ZPaz11rN+59t39G4z2B9Yl44WXKyCkw5uhqrXKFC06jQbg6xwKGF8fWhZ7OGfsCKIAT2M2/br3iRl67bRFbSngrs/2m4p/mAOcmtBZRho0kAApxCEqpiGvEdlkR44oYCUm8rif2+LtV11ihN1TdnNZahDB3GlQmQUG8oKHCZamNZqWwozpkT1dKh8Q7tQYuOBizKPXkLcMYA2cMqggrQ0pvGg/aIMxa7QDDnAnoUVKmfBCHfq6xAEL9CQ+45nagqpGDntY9wNUeGqeWrEUtYB2ZLMpbR63UlMXXvvGcl94FTksdLRsAk/famAeAhmZrA+HiB2vUWQPHHkIZcoBhaLYiij7w9SamT96Sx0lSHBIeZFyJGTzGUTHfMkhx7vdNLRnVFqARwtvA6H71oXfiA4AQFng/KY0vkft42udzLxcTx128p37v3bCRuRbOg5IhTVE0eu4vPWLKAd+47z/DHbFLrQz3WoeQ9vi64oqWmjlHpEtu6KJoOaRpGxjEZFFsRXIMcoWcuZdofzUc8bL5i3hnt+b55bw57xQolXJBzgXbhbpEXp4vOP16wtNPT3j66QnnpxNipOohiXLKvUqkx7l9h72cMzlHaUOMF8R4UZnonMOy3AEAYlw1Ouonj8P90nPLlcCzs1ALG4PS6s1c/Y2Vak4bEtNP+zIhRoNSKnzxqKXCJera2eZA3RdFPloDE5w6hbIvcsxIacW2XXZzIp4WCs855+BdgHUeXvEN1BHRWt8jSXyuO9KfDCSJMgFANbcbPrKH3FAi6QPhK/wUNP9fMukwCfkDQNyoO2TaEsxqODLtdmF/uaYLnkpHuQvktEzaeldbMhuzjzgwtiBtCZUjABvT6V8+XfD0sOL4Cq/JON4pe9/H+3gf7+N9vI/f0XjTHfBz0PB7ihxmyOx1pMwhHAY7xIxtu2BdT9g27g2EZ6ZsAAAgAElEQVQdL8g5ao6DQH7SdKFq2AetKQXqPB0QplnpTlPaBjY0p1+lQQlAYSPDYVQBG6aY4bkn92tsYV8yhIp2BK2NKGJnDRI4t82hmzyEiWquSGtCipQCGaMmI/2jNnoYvCwJiQrwB4CyqdGSDahYzscpZgI9pRD8bQxetRGOgvLHdF/BeSwh4DhNOEwTJu/gbW/JO3pmsj4A9ojsV4aEZcXLb2CPdQTYcM6/1Io5BORF2ng25FRgrjowds/gpunD2QDrgoadAaBaqd3vREpTWHTveh/grFBt0tlxQ4c+F8hLCvO0ZwVjFkJhCJP7F08a6Jwa2v87JmycW845IqWo4c9e0vRtw1goleouN9v2uXwZ19/rOeHcZpi80p8CFEpHa7yuFqbUoTys38drrU61lMx2uQRAu5iNTKJfO0hm2F1ZlABLc8wU5n+64PzricL9zxecz59U7qUUNWRvjHS6m9RzlVFKQowXbNsF23bWSAF5eUE7PVaOlqINlS257NgUJdTvnSPyrBtBrdIlNaYNjiO3lDraYIyF32Q/B+R5go+ZnivvZ/H+BQMCgL3UqPoBAKc2KMIhqakQFmACJueHxljCktk9/sJcDdXvUwRoFSlzA7d62/xbqRqhlTHNAdNhJo/8OGuLZoDxHDEjcjv49bzh/OmMy9OZol3WwHApfN4SNB0ZKII6H2YcH464+3iHu8cjlocDpjnAsxwptSJGiiJQGj1RNMEaaobH0RQAuDxf8MvTM9obz/5NxW9tB/NInlnCGpmFT46JEOjW8MYecpcD+KGDIooCn0RR5ZxQasE0ER2pyVbzOEKUAkApUr33KMUrOrq1TmNqLR0WCqU4VqC3CQBnqC/4CFrzznH+0SLXogrKGtOpNxXAQe+ppSJtZACgtU6zyTp55N321mtns1YrcjSfPcRNYsToKY8uoIvmn28ZImidd8oKJmC9wN355hA0vC8h/uvc/DW7YKkVdWjnKSWRgvSX769rdgt/7yx9/mGaIG1Rc8rwsVP3lsLVCLdNnYYad77jL1qD9x7TdMA0HbAsR0zTASHM1CY39DC2IN57bpD+7oNHmCk/C3DNuP4uwHHIlhR/2ZXRxpUAZAIek1zzelpxOZ1V8ZSy6jm7aeqvUJF2qtC218qm792Rr0Mcgr4WlM8UwwcApqXpGhSWFQUcbs610wWXniuXs+O83VUTXCt5oRG/ZThPz8kYg6zd3pjC+Kp8q5erDWmZ2oFgnoluQpg05SNDnCIBtbXWNKc9zwccDg8AgHm5o302k/FQmak0xaRVATIm54B5xjLdxtyXc1QnKqVN5y6jt5qeEVZKb82HZbefRV7IXmmtIeWIbTvjcnkGAFwuT1jXEwSQPE8HGOMQwszG4pgyG/BMtTHqn96naWG+z9rqzgH62kF8NfueMoKnONwvWI4LjvOE4DyCczAGyKXizHWz53XD6eGE559nXJ7OuDyvOP16Qs0FqUF73tAZO2C5W/Dhh0d8+IsP+PjhAR8OB0wh6JqfY8Sv5zMb+7kDZo1RWSHnLG0J51/PyG88+jcVf04F4DK8sQlGrU0F0XpasZ42Bj3RDYmHXkpAihWlJvVK5EFLHfq4wFLuJL8joEqEHcqSgjeo1aLWuAM+eS/9ydHfzzlg629Htr/mrZor2lBjeh5SqHYBAYp0dGiv5d0TW9DcX6KjJdcrqHolRBpyqZLLzWzpjtcsJaPk2zS/eD1uKEEBKCcvHrCzhlCwCqhpO2a/UkGMepB90zn69XNAVL/izadSkMpLUJs11CbZ1rr7vJwzwhSQp26lG2J12pFJfe1QmmJr4dlCs5x3vTt+wPHuA1no9wdMh0nrqV/kwOvAyW2ox/d0mDDNvQGItIGdlgk+eAWSpZi1HXa8RNK3xqDmijAH9qA7Ali8zJwj5QRvLOejqJbMYY/CFnyKIvTNAADUjd5rnmshrEirDZ5zmgd5pozDEQ5/KeuSuQtWpjIDo2WFXAxjfEznP+g3b9TwvvXZey9CvaPDR1Cx92TECYmOn8gYHMuLpWw5hBl3d4843N1rHldGjhmnTyecnj5xxOCCUhKMMZjnI+b5AACYplkbkEnVVI78P5cd66W1FrN5m8TlcyPnBGM8vM89IotepZKS22EWQpgxrYviEcQIIMMJQ+SyIqWoHv/p9Cu27YzWGuaZjGeRZd71aIdUkSnuRUDMsgethYM0EaOICumh28hbtvMGZ2c+V72cMsyBEPauN+zy1iJ4DzcbfH9H8y+t4vnjhp++f8avPz/h+ednWGuwXTbFhgBEjnd8vMO0THj8wyP+yQ9/wD96fMT9QniFCwPk5dmmLeL8dMZ23tgQ7vMXXIO8Jk6/ve+/CPmjyPSBea6Wim2N2M4bzk8UpqoDClRD8mbv/Yty8n7CiNwMYRo20oJpWrSsT4CCco2Uo6KZ5YCRJzLzNRg05YTjuMLdCHITdU9KbFDSHAUw6F93ClteOzB8ueBgkyMUa647ClRrDdzidJNXM9RHe6u/t7tD0HQNKVTmOUz4EgV+0/iMxyRrUZpw85edx69rJwCmWpl73qjXfv06Ufq1dcNADAo5aJP3RCiVMrbcS1rWKSCGCB86ErZwxKhyGO6WQdEk+l68j2lacDw+4u7+I+4+3OHuwx2Wu1kF+ogCp0NJJFcS7Wmlknc/eL3zccbhfsF8t2BeJljv1LtcT6sqk5LyYNRSimCMLgmZFgCtoEHebpo70J36EVyoBDO2l/FJxcL42pKpilEje5wWFA9eONDnQ9sZDkUiY2tE3NILZs/Q9gBIV+nMtNYbScmNf2m1yWuj1qoVO92jp88mj9bSGebIS1w3pLSh5O4phrBgng+4v/+ADz98wOMfHnF4OFKKh+8rbgmnX054+ukep08fsJ7O2OJFo4JapcE9AqREsC+JAGT3BrVnAO4tQ1Ilu9+1Xl4nacXWNirDTSty7k6YtXQO7cKKkg3cbV1hjEFKBIAUj18cwGk67JRZB4B2ivJaK2w2KNYNkeMMyaCO4MlbU3xx2xCmxOeZ1jBMAT4Q8HiLCTFnlKnBO4uDtTjOE+5mBkJahzUlfDgc8A/zjH8IHttlQ/u7P+P06Rm//vonAMBhfcDdxzvUWjEfZ/zw8IAfHh6wBHYIeP65VJy2Daf5AmPOFFnLnN40Btb2Z1UrnZ+3dv2bit95C9PMznIupQxle5Szl2Gshbe9Nan+PgnrnZAdWJScNB/TWsOyBMzzEdO04HC4p89JK4eVyAoKYSIFUjMul2fNidGkM4d56PAdyj2mOTA74O1lTcDL8LV62iwcACLOyDFRTp9zOoWb9ADMtOUd4hpZiPfGHGP9ZmsNrhES1IeGMAW0Y2cLJHRnR3frPTFpiwo+03BrORfAnNPcjEnG2JmvtYZ8hR4doyPOGg3Zx9yb7ozeugxhArTG4jBNqvCNMVrfv+WMmDO8cyTonENgpH86TBwCa7ruJVdUlJuFP5ii0xgDz2kk8cLEowEoCjAfFxwfjyQsFAlMVym5YrtQ3q8MZB2K+A4O892C+4/3uF9IeJRKazstHQuwLhPCPBF99krnD6bAN695vhzp/TEe0WpVIXvL6PgREfhSiiuMkr1Rksynh7pJyTfGKGTu9VErhfvbVQrASglnqYjsGcU1XZE1AZvdNGUyzYFC3xPFY8bQey3f8txfroN8vp880hYJuxMTIbjXiJQ2tFYRmDchhAXH4z0evn/Ex3/0AR//8Xf4+MNHHB8PcMHrvNbTiuefn/H8yzO280bXWqNWAiU2WkvK8BMhv5d7Cg8f7pducI5VRUNK7JZR29ATY0gbKl8IetSRlKxVdL0M6xymZcJyf4Dz9H2OGZ8+TbpHYlwZvzX1fVSr4sAUr5I9/IEIjRwcGvPVK3te7nX8KW0dX/AK5fuXjLgmBB+x3C276hMxPKj6iCqYgqN052GaEVxP0QEceQnU86HkgsunC3788e/w449/DwA4LPc4Hh9w+c8vVJXSqAzTGINcirYaPkwTPt7dYU2JuuUmwpikLWkEeIcNM0ZZZj833s7xO4dWyMMXYFpmWl4pX7h7vIe1D4hbwnq+kOVb2NNgD329PCOmDa0WOB94cklzksZYTNPSw/tpQ4wb1vUJ3s+qwKy18H7SWn8RNq0RZXDOCSl1jtIQFiz3G+a72zbB5D0p91fKIwT4B5BXG4d868ZAjxzTizyrtZbymK9Y1bQW3OlwAO4IV77kkKVZDJWIcf5LDuuVvVdu7M6nVKOtl8R5S8xQk3NUkmet5vhHwCNAhogzIE51Xr/aKqRtwnUo3xiDwGWCUvKXS8WaSABsOSHmglyK1nSLddxaAxjkBwBpjW+yV701SHAIFSYdleAneD9rG+VpmXB8POJwf8B8mDnkz4qfFWNlWs0cE7Yz9c4eQZ5pm3Qud/MMZ0ZjsmK9o3Dv+WPEel61XGh9vmA9U5ptBTAtExITyEzLrELwlkElcnUXRVAGumooVVWr1hiPZUaA1DIzjfVwVoVnXjEjjG3wwSl5TWvMwWGNkoZROROd8xxzb0gTRNj26JzO4Rsoe8WoccZgknCvd0S4EzMZa8cZj+0BM4dqjTEIC+3Hu8c73H93j8c/POLDDx/w/fcf8N3xiIVLOKVt6qf1gl+eTlifL0x7LvzvZACdnzgsnIumScRBkEjRMk+YA6UmZBhjMIfbQL17gOZYlpyQ0qZRW2s9pmmhHLwLCFL6N3lMS+D7OxCGxVmcfz0xaLuH5UOYcTw+4nj8gMPhHvN8IEMAZjA6mkaKxOj0zsBkg9Sosc84OpvpbfgO0W+UdhpbUjeE4HFcZnw4HnCYZhynCUfGUjyvq379tF5QasOnywWXTxecfjnh04+f8PNP/0YV/7Lc4f7hO/z4r/8xfv63P+Pvjwc8rytKrdhy7i3PjcHzuuLpdGZ2R/Lq19PKjkSfp0QT3fzbqv1NxW+MQU4F22VTL/byfEHaEsI84fjhDiUXnH45Ybus7IEnrCsp9KenH3E+PyHGC0pOmDSX43a5aulwtq4nXC7PjHZd8fz8C7wPOB4fAZDHJbSNggQlBG2BcwGuddKSlDaqGz1vWE+3KYHJe3hnmSFMvBTHFhkJuC0lbCnp5qi1hzQFjW+ke6GnkH+tAkTsqQESfPS5AgYUVjC5nhCDFA7lChKWEN2ZG7Z07m/6chuyXXKa1loVfssUcAgBSwjqkedKZC3WAK312vdJQq+1As5pdIAiAHnHte40elKBil3XtWvmvU7laonZzxosISAfinpI23nTNqHf4vl5H2DQMSvWUU59Oc54+P4BD98/kqd/mCjV4J0qIwmDZ0hdcMV2Jm+2Nei9GmtxuD/gdH9AcA7Hedp1BBR07908U8qEqVx9IIS8sRwGjklDk8J0ditroTZYvFo78fogCrz2XH/JnbGzjJ46g/GqJUYzAfgBlK4Q8KhrUODjaHQCDHLl0KZUCXhpEqM4Gt7kQ3XMreDG4Kh+3xqjxiUAxJyRHzMe4gPm4wznnD57Y63iNubDjOM84XE54A8P9/h4vMPsPRoazlvEaSPHYJnI2Ns+cO2+GtEU9VnZiSm1MZjWY0sZny4X5FwwT3QW5T3yzK5xNF8zTL+QcgrUkrFx9UFrhb307oyFacY8HwGAo1QTV284xlyQMxPCgsNyDwC4v/8OtRbc333E3f1HHA73WOY7TPNCFV2cCvOe5H2KiRgLfU/p0W2OUanOZnrruc8xsyfdsJ1WnaP1lvZma9hSRqkNKWesKSKXip9PhF345fmE7bzptT79+AnnpzNVu3GVGwCk5PH8/At+/Lsf8ff/99+jloLlnoz8tKV+lnKl9z9fID0oCjcpS1tSGQ0AxvYqu98abyp+ySuVNJQrnFaUlGGdw3pa8fTrL/j06Ues6wm1FmzbBefzrwCAyGQNhdHvIcw4HB40HCObRWhLhdBhXU84nX7B09NPsNbi06c/08SYEtU5wgOQxTkjhIWAgWbald01DoGKJ/S1w1pqvzp2nsylIHGkIfN/KZ8Jc1BrcRzU6azBeY9pJrrNmot6PgAhl0WwllwpbbBFxEvvjCgtkmktukXofUApgUAvTsLdWYXyLUMYwqy3mEXxhwnBS1va4fOtxWEKCMywBYA9d4I5pJwZ2New5YTzFpXZzXFYf/IOQMDkO7+/N3vKZGsSRQ0KGQ8pZ8RciKoSA6hSw554tdfBlwyJLhGNNCOWw4zluODu4z2Oj3eYD/NOCTvmWJd7qLUS4l5Kvp4umrcXAh9rLaYlwFqDtCUsdws8R1By3XvRUlGjoc4hH/sSO+F2pWNfM17LjxtrqFsqh/ylZoIwKJ1lDOjRjtaa7ufWGjyH6L3M3RMzJ8kDAvr5ySuQcIwGQaqL+D0whtH3TtH9wP55t3Kb8HcceRLSKKAbEXfHA47/2YI5eNzNM2Yf4J2Ft5R6Avqenr3HMk2Y+cxI2mrsl05hY6fVKnMI+noxlktrHFp2iDnjEiOe1hVpiEQKBqmgwH6D4pd0LFFRs8NRqBppmmb19OfpAOcDpunw6j4j5yXCWKtG2zQtOBypUkHSvMfjAw6HB8zzEctyh+VIUYKR2VJkY6kVjbnxBbxNTXP2bIDAnnjqa4YA1smw602SpmUCWsNzed6F1yVCI/suxURt7Pne1tOKuEZYa3E4POB4pKoGAiFmfPrlZ/yb/+dfI8eEwyPpwxyzggDF+VtPK6UZJk9yZwoaWZPqMCkLf2u8rfjZe6UuREMtea54/vUZnz79iKenH3E6/aqKP8Z1COEbLXWa5yNZdcsdvJ9wPD7qxorxwoKsqEUU4wXeT8g5IrPlq14ih7pT2jBNB9zfO9Q6KV0tv0gRsHG7oUUZ9hgX9T7Q65OBju4s3sG3hunQUbt+CoqJkAvKdTIGDEFtzFzWO3mRsASHPhk0JaVEQ8/pjnp3VGM+5to4XH2LHBjBnMKXf32YqKyOclxLCLtwI9CpVVtrsPw15oI1JS2TMiDWv1q90iFTjp85/nkfe2vRvNdoS0NjhjKqApCGKOOor/zuS4ciiweLWr7mmHB5vuj6G4OhrGxgdNwSzp/OlMf9+RnnTycO0VUFuMk1UsxYTysOD0dMS/fkd41frhrDpC3i8kyGxXbeFMg4RtJuGrUpY+TYYluGdaR4wVEnH3rXMBlmiGaJ9+4c8c/bKwNN0ls+eNSJPreYslP81K3OqeLXLpZSQiuls6NAvlEBekN7UjAtMmZPyv5hWYhlEk0jf6/1DBDj78KlXjFnnGNUxPaWOrtfqRV+ENrjZ5vhNQ0N3jk8Hg7YctbmVZL+KpVMshvL+Ic93ZkfSWkfKNWlZYkBY28KwVoJRW1cCfDnvNOUp7UO00RerVYBMK4rBGK9pKhQ2EVtyGjsdfwdVwI09PK9kU77VnxTXCNWtyIsoSt+74Cf6IznmLBdpGpmaJTDz0r2p5Y0NmFhPOL+/rsdWN37CbVknD9d8Gn5pNHAnEgWANA0UEkZxlkk77Gdtx3oUXROOxAmzH9rOR/l9BrnJ2mzNlGmTM1LdZ8GzjouBTGa76HaVaIufbj/DvcP3+Gw3GOej7DOaZ1ojBfKzbeGUguc9dTUp1Zs21lzrEAHMJCX4NkKlc5n3eMcPaFb832tNfLqS1FFVeqobMmyD96jcKWBHz7LeYecMqSFpgjM1hrs8DoxUKRDl4CftJZdkMVl4EHgNsdUHmmGRj9M+sF1/NT84uvnnlMmSzsXxCKWf9HwujGUV5x84Jz/y4NWakUsRb3zLSdcYsSW8w4YKGtcNJ0ieIH9Wo/CgJ4D/V6xHgIua9QX4VuePRH1dAphgAhXVrbE81Vdba0N5TArOU2tDetpxacfP+GXP/2Cp5+eiM993dBq1UoBUbAlF6Q1YbtsWI4Lh/FNr2Vn6148ChGu62nFdqLc/6joxIv+1qEGrnjxA5DM2K6QSQnvFToApdMlwb8Py9NzG8CvwcFlB1+IHlX+1uqekEY+VxS+RAQAwHHJ4zc9e7k/NPXSg/fKXTFzX4lcKlLOuCQKuWoFinNYuPSrcj8Byd2usYf6Yy67c0OGckXmVNpoFEj4PvP3Vs+I3e1RgIC15gXq4cuGApcZQyS/8z7sqKedkOwYs6OGFrxHzQUtOJRM4erGlOoSHZJrE7ER/RcQ3ev3BQXOjh0Pc85aTSEU1t8yClOrpzXh8rzynCj8bi3R9FKb+jyUZ3egdpgCEXW1xvS+VgG8Dw/fKeC2loLD4QE+EOAxbokNxaYG/u6ZeNoP6vyxM0k8IMKbwFTTb3j975S97+N9vI/38T7ex+9ovOnxSylaYppKgMFZkYh4pPaSmpEsOHC4Xrxt5wOFAv2M+/vvcHf3SKGdZYKxBvEiiNmJOlOVrJSRx/QIayzWbdnn+kD5S7FC5bOnsOjPAHlsY1joliElaxvnqIFOqCDWdmDgmrcOxRLlqJZ6sRfjg0eSkpDSe1CPYD4BR5VStZtTk37VVyVVxpjOmGYs53P33unYseqWkSJ3XIx5B8yjsCZb3rw+0VrtqibeUm0NG+cjJS953iK2nNT7ASiK4J1D9i9b346pg8JevXg+8lnU1dG9eK98/RZwH3mNBSn1trtjimW09AmJ3VNKpRRcni/49R9+wc//9hecn56RS+Ka5/qi5FVy+GlL2O42TMus9eJyPYkMZCZwEbBgWpP+XdfGum9qTgUOo/d8lwG80XSV7EEJtUuLXvl8WT8L6shnq9VW2SO2RSM6zsDDAzNjCIaOoPJ5u1bAHNEwbu8lNlN7qeGttLWt4RIjVfX4vX9UKrG01VqRKnn8FAWzWtFirSWSr1qRW0MqWVNcp21TBHjMWStirKGqFsEMTcMaScSg1sqRRTr+wiNS2v5ceOvgbsxxkzdP3r2E4w179cST4hSMLXXz1nqEMFBQM5NiLQ2mcXVH3ctwxYVYy1UBTMt7VYcvLW9r2cvxolVcKyJHjoUqmfTDTdPnTntFaXEBPpspa2RLUk8lAxb0POSc1Fph65DCchaBSzHv7j5q1VkpCctyh3k+UgfZVLC1TUnHtKLLWursnaHRYPq4Xm2lUTlPvC9v7fs3pQIpIgo7CMpQFj8EAT55TGFGLklDPteKWjaRKH0/ec2JAoDdhGefMAI5RZTjI4WXpmXHICXMf1Kr6f2E4CcC+DAJENCRzdeC4WuGKDZBogN7YM5r4DkSSPQ9KWMhlXH6YOywoQDswsXCxV5yUeazcT3HufSa1Q6w6utUYGCBG4lra5H2kkVBRBKKJwQ/rU1iJHKz0qbX6DolDunHTKQ7qRQKj5ZuTORaNHS5MW+3hClFoMqzUNBRoyoCIVKStcDVQfgWxS9r3YABi7Lu/gZAjbPtEgm0yco3x4zz0xlPPz3h9PSJudiTgi69vwKAFsrnxzXi8kyc4CN6WUPmmhaIzNWfuEa+V28YQwyBNn6D4pd5CmlUbSxQ6OmMAlzJptxLxa8C2/X0g4RsJUcp+VoxDADsMBsi5EbwpvD0C7JfwvoFHYB4a6rDALrPtLSS9+tYLtsaU0h7j2D3cqa0htO2aQrrtG34dFlxXjckxhzlVDQUbLnqIZWClDPmEBRYeJ1GE2KhOuAY5D6lBPlaHnzpsNYh+AnTdFDwdSfFsRrel34povTluYGdm5wKjKO9XlKnORYgoFDtOhdgndO+8z74jiGB4Bu6cS2pMer2t+1KDEuhdJd3byu/zw3Zj5m5VuRzhUvBT35nANTG+00wJqWiDaF2OYtS3pjzB77XQiDJw6yVQImB6Dm+DUY3hrv7LbPm+MPkEaawI/V5bbwpFVLKqJktfPFsktAFOgRv1eKbWtVNMR58tKblHl4pRtkbF0CEczCzQWt0SzlHQpLyBOPGuRYlltiXa+SS4AG0QZha47qF+gVIx7eGNmoZgDxiyRO6v29uMwhAyW1d0/Hq+oCMxVY6eCttUUlPRCjKZ49GgNT0S3mktdRDgK55e6SDPozz2IkawwCUkyyNuPSFbMKVgmQNjAkwjUQmwHnJ1ql4xQDIrPyLenMATH+d0PbalHYAJTpkDWPa9oVBd+3pf4Pip0hVQKxlx+NAOJaex5fSS6VUZQ89RcIDXC5Uoip4GAK5dW9fvHMROCkyc90aKec9tH+m63cgo1SLwEgjHMm9M/uhv034/xbVbd93xExoGGk/tmbVB8e5eXoUe6pfuVb3WobPcGREusHwGSN3HeMjNNf9M1vruc5bR/CEtBeMD8Bg0iG3LvdB7anp5xELQyBUImVZU8LzuQMwRfHLOguXgQse0UfEw7zz4gVI2BrjZtSILkqKJVGvtxpivTWsddwKN2CaiDjNuZctcZ31HNG1MK7jDIiEp8AWg8LEbYQXqrvnbfmags9S3AYbkTIkalSrVJkVagGvTX7St8m5q/Gao1hzQawd6EqUwTQvZTeVRmYTBnIvMpbC5JlwKmBZpJKtKVW3AH1LKogtIsbe5ImeiedIjNV7pEoipv+WPglMaGXDb1cyfYHHX5C2rCxcwD7EAGPg4OG4fhWtobaOxhUQmjVOO5OJRUcZgf661jpFrGwGfaDyefrZFkIuUUtWg8Axze84vsXrk7raxEhyoIf6S6uIuaj3qgCWgdveGKKpHTvvCaBJajJfXXcO+2dJMQz3X1sP/YvSb42AU611GuSxm9cto7aq4D4BmojSrrUCgjp/Zb2BnhYQ5T8+gzElMI7x97K+hvsseAYQttbQrEUuPaUgYdBOryrecXuVfOlLhoQ7hSVR16Vk5CvjLcW0814BbqqzSQ+LoqFJSs1klGL0c2SPikefYiYPONldT/BR+AJUSje5oHX1/R4pbGjsbQYvsaJxFCpLN0gQoY6WOBn9XO0xP3jk9DP0XJfCTG9XRvhuX6AbjnKd3fUwhP2vrjNGB8Tow40AN+GoGFNLpe55ASyn+by1KJYooi9MNiWefuaQsaRkpJObKH4tc2RPTbzJ+TBjezzgwA0LUh0AACAASURBVEyOcwgwpof8U6EUnMhL8RgBoPme4rxlSDTVWaf8FcKcKg5XV9j8XGov/SvGUD15GerJByP8c2k4wyBNo8Ycy4FcND2amCmRwOAr0Z8zwLlfR5zP2/a+lIz64JWQCehRpBwTShJAcVMSHevGKBYp4eW4AJz2EP6BsEjHxaqOMEX3PEou9JxTxuXSO20aWFTuTyF6VDx9YnHkSok76hzY3nj0XxQH7EJJBHrZCTgzWJilJG3EI68Vy1BaGlpmcqPGHfuDRB26ehlJzsTgN3py9Nn9+rBjWVzRHMo0USvLb7EGrTUvDpAofaFVzQO7nZRYaGMJSOle55ZvrKBeGCTWaPrDeYscRXn2kp99mJ9K3byXdck7JG+rlY2wGxU/3+eYb5Lwp/DqA27XlGc0BOTr2NpYKiDo9z2cD4DL9+h1VC1A5EljU4za2gslIApz7Csg+5N+f9P0Cb3MvR+sFWrp2hHjnLs0iCjFUudIvzf6rHMIYUKtFJYUY3YnAJn7HOAoAxPcfE5pU17bXe2FPQ1y2hLV/9/o+MnaScpJfiekPa02GBPgwt7jvs7fXmNTWmswGajD33dEPSDUspComCtD4rWcPtDTIPR9xTcceQCdJ720qmXMonRlkEOQ4YxF4R4S5wvn7i+RnCZuxy0ocMFwiOJPMWvzImm2ZFnxH09HbI8k0OeBPlZZEfm5GF6TfdOw25v0AHyuBgNMWyBjH0GqTCZzLWOs21caiBwhOl6ShYJud85hmha01pU/0A25mmkdqTPlxlS/KzlFrTtAdJ+eS0w9zI173zqjnvh8IMNLqmtGRkop5atsvMl5tdYiLBOOH464e7zTc+ScU6prep1RA2M+zKq8fSCGSNl363pS9lWJDIZ5wnI3Yz5Sn5D5yAYi84rE9Nvl619A2WuVgak7+ZYsP1Us3cMThr7dNWbHpWwEFCqJvJldXTBbzFKuBlCDn1orUtpQuUTjOr9mjUW1lbvyWRCBA5Pd5LQTBrcOAZBJOM8YA7SXOAYJUXnr9orJ9RzZOK7DnM45beLimd/cSd9pdOG7f59TgWyM5ciHKOne7vOWoUyEZfSkRwU/zAPj2vTyOwE5yRCK37ajF+VaV2k2xFGWUfnT9UiYpIHHWoyQ0qRr3Ovre8twzsOHieuEpVyoKLZEO0my9yOWuAouZqSzG5W5yutl38ootfC+zTDWsDU/ac50FKAjuG2MBMjrRiXt1ttDvuLtjk1hWmNyGAnFtobWyEs1xsCZBsuhec2vVihORIzeMnj9vvi9gmh9HmNzJTKi+HwFBxPMgDnoUQWg8x0QMPlbI32dGEf4KOR+cilImVIB8bIpnwIAbVteckarRHkrPTziumE9MRiNDQDrHMJC4DbnLea7hXj7+e/LJWKaiSDKWLOLFL72jL9l7xsDSMv0Pl+KKuwiWhINzGlHCy7e7WjcFk5jjgQ7ZDAUpER8LGmlvhTWtx1lb+PUDfHyR/6fe+netWOjUahb8R0GPjjypB9IGYuzujGZTovE5CpGgB3Oo588jg8H3H+4x3ycsZ23XZRDhuWujtKOe7lbCOvhLPXAGSJYKa0c6S0AqFxwOpBhIv8BijI4b4HtG3P8ML0msDdAIcR5ynGHwhegheMcEQCt9TTGaAgT6GjHyD+nLSkiMyVqepFyRM5pZ0wQm5SQgTg4F6iLH7fAlIfEK9a9FHfbJpBe8m4IQTtGsFe2iq9DlWO4+trckG5qzlsO6zG9cDRorYNJZCM5Z1GrpQbzuAqLXoVBd7gK/loKK5Qbpi8Al5Jz73M/VDR4a+GdHWhGzYv766x8nrr0SdgfAyKVuQGstZic2xlJo1FhOYTX0BAzrXGTf4NBcr1Ot+f4LXd+mxGCcIXnHajJe67Z5da6Qt0q915S5u6V0jXMwaACg+IXJUvXJ8COn4Iyh9W6n5PkVMd1Eg9dfs4xc0voG5tTsXIhb+uKFMhZONdTcBp2HyocjIGCUpXGmrFB1kJTRxImpWdH7xs7se2qFKqFB1CsgTEFJhg9YLvomaRMhpzs1w7HezFli2ZeOg0S2cqZ8s2nX094+ulJSVeEaz8zyQs1UEraz0N6eZRUIGQv1lrluT8mSrWI8RO3hOMD9YRwYR/tkWFH2cFn79ZBsmNv6Ijx1RrJ8laqRmQFcE1r53vqltNthdlfc87diWs9/ZXSim2bELZJDUmtlhlaM4sjU0qi3H7tToDcJ63FvpX41ww3ebjQUy4A1ebXWuE8gdKjjUS2Uynq6SePwz2lQ+4/3uH4eKcMlHHdEC9RwYLC2EnKe2I90PP1PngFeAPM1vpE0WvZ18BeVggIeARF/tb4olC/9GSX0Roxh6VE6PuUo4ZfWmvExV97+deyNGzbhpTS4BkFau3LB2DbzkhpRYoboZ9LRs4btvVEoQ7eLCOHuQABAWhFQGtNFYUo5VuVPtA91GyHNpHWDuFtqBEwcs7rWl0JL11TCc3q66RsTzb3vjTrtVwnfX578VXSJ8KESH0Mvn7umo+Mvc+9GjwiENCb8rRG3s/1PGUNDyFo2H/LVolRZJ1n73GYJm3SM85rvB5RKLtdNEE+UiM8uTeGaTd6fTAUQnWtKQKZnj1RxFJPdrLYZwbYjG15rTPKvpg28k68n9QY7f3GKZ1w93CP44c7HB+k4U8H/QDkRQqNsjHoBoYo6aHN8xh5uWnq6IJ3R4rU0D8jEaHKDlshXwEN2WcuC5VIH7WnFoM3wa1kLLVCTX9a6diCPJCYOM/4Dl4D2LILf8t0a2k7FsxbhvRZD6VAer5W56h0j8vpJOd8/kSVG59+/IS4cgifiZjGCg/pxX65PClxGdDPtHPUnfR4f8eKpzssUhLsvEVok6Y8Otp+SLNYohou3wBoJlxLHjqfSipFuqwWxBgRNyqla60qHqDUQEYBpzFaJXKauG1Y1xPWjTjtt+0MA4viHGJc4VxAigvmPKP5a9yQGLelf63lxdlW7IHzNyt+a7hTqneqpJe7Wc8eACW1q8XCWWqtfXyk0sfHPzzi7sMRzjtK9bDB64PH8eGoEY8wkdIOc6AmX8eZQHrLPuKV2WCKK0cOatNorDEM6tUurxxVfwPX9bbib71sQJR/ShQKyjlhiyu29cTKmpiHcu5lfYfDPb1uO0NM+hAm5LigtaoHgCy+izbpkfyPKG8p5xsfpjF2x8t83ZjBoKOPb0X5WnBv61o115wltPPKxrr2ugFhPBsMMXFt0Jv4aDeo3C39HBNS2vckf83SF49JGP0k7Fa5veXNXarGUs48ePxXh+0aZX/NROYsef3k5U/ETZ4SEq9n4Gc4OYdpaIjS6Zm70UNhcqPGgR0iArs1aVCu91vBjdJkBx7wlZQ0KW/K5fvBSp+PM2ZuTmJ9NxBrpZrysATcn++1JDbMk5bgCHJ/OhAYKCxB2/uOBjd1b5MQ9ghyk2qAjJy6MXBzkhMS7XA7IJ58bgWAUlEttRv1k+8CqQ7hdq5OiRcqOxQPxgXXQ9WNhBg1/QqKhi4MlpXzTeBAC5cr2hw6+Ms2Ak+V7gkVBtXVgf/ia4dlY3Ua2PNqayjWathaOqRdns5YT6v2UQDAnm1VYG5OGSkmXC5PeH7+WcvPjKFn712AmTm6KI2M5mnXqAYgeWFsJiOAS7YkVyznbnIOwTmkG+maSZYWjbwCUNyUhM9TokjsFi+IcdXooozWGuK2KeCtlITL5Rnn8684nX7la2xkULPR04b8uSsjqn9/fy9BgaMz1Mu832Kv+9wQrARhtqQrqld0A6WiMnKceA8aUvwPhNZf7g+q0OX9kjrYzpvuacuR32mZqcPnwwHWWuVPkQjC5Zne15g+23LanYz/l+e8EZ/5b44vIPCpMGBKzqvaRFqEQqQkReqTSRmH0AVljBcVUgCV6l0uz8g5ahOfmAi0kTm8X2umbnscyhfu/13Xr50nXOAcpRwyb8AwlPzdGu41rOD9kOP3zlHTjFIU0Tt+xghwI1Ib4eyv8NzVENgDYHJMqqTowZPSl/IvGdfhXX1OovTLYKVflTx+7aipKDWsCG0Jq0sJkdyH1NtfYxmITrcraG8pQmCNwSbArWSU/IfeA42eSCpB7+k69Ghf7kl9BuIF3xruZRSulEcCgM0cXuPSKwEBLceFDAAG1wDk8WcG9Rwfj7tUyMTvA6ibmfLOW+H77qkTUbh+ovArBsCl5LlrqbB2aBxS20uJ+RXDeqcGSY8syHoCzUgonTxzKuHqZ7NwgxaJFokR0BpVKkjJY44ZYUmav3bODhznvU9ByQWmVDRHhlROFjCJ+NurUVwAgB5WTuXmJj2CL9mYYAeA4k5qrcjoZEqVn0GYg5YfhnlCe2h673GN8L96Lu21ihkR7vrleMDd4x2OH444Phy11bOgyqUk2XI6xbDkt5L2MUaxMN65oenV1w/D2Kqco/LKp3Tk+/BsFGy7GvrKOBWAZFGMa0/TGQJjb9sZ23ZWDJcxhFUJYSKOl+UOYe401srLMEQ/+z0awFgy+dt1lNFRuvlGj7/mqmXMY0M0HzpGR+jVqYTWYlq6wQ/ei8YY3B0P+Phwj/QXBaczteeVvi3GELZjPhI631qrpFxE7S0ln5W5HjwMp0RFXmhkbzCk6eK/Pccv4uo3Bi84lMfmDAIEkQUiVHsHJm0b1S97P8G7gNoIBHg+P+Hp6ScAUMUueVBqACFNe9ILC885D2sEQFKBJoKoKB5gfN+tuU5nOhpdPf5aNXctnn8dlL6UrwGkKL118FMHYvng9eFK/irHDLslNK7zFXKK7hlI7jTAGmmIIg17Guc191UMUnpzq/IXBsEcs7bnLLljG67nbFqDN2Ys4aZKDS6JKhJ6b02JfGg9C/HuowGZaqglO1Nqgx/IKCSfp97u4PWPhq825/kG5UctYjtXOQAkw94VK/1pDpiXifqPc9XKyCf+/7H37rCSLV2a0Bev/cjM86iqe/9HM0ML4YDUSBgtJFxwsLGQ2hnhjEVjgzXW+Gg0EsLAawMJzBFuuxgjnBaDhNTq5r//o27VrTqPfOy944Wx1oqInafqVp28zTD0PVEq1alz8mTuiB071utb36djLrKtxlqWoNUFEwCgpPMlMyVZIDlgBA6g+fABUNqIUiLHphV8aselXPUieyvtZTSf2Did9N4ShcSQABWQYoM8Z6OruEslJdvUa8WgB2Di9Y4Z0WrYhpe/lJQaoFYMcdVtY6wp1wII90QsnUOXDMdRc2ernLS0kibGq4hGgesdxkzlmfac6YYOrrNIKcNPCw4PRxwfDlimJounFLreYXOzxe5mi80N1YaLCiEbk8xg3xRjBa9xCVaUBEW+Wc4kay4jbyLjntnwc5cCOwDSYRTCUqTBz88YAt35gvavr6F17HsBzFkWWLvBdnuLcbspz4J0OwCM/5p9+TxaNg0hqclZQYtznkUP4nJUf0qxlGYEXCmARShy8LqxI6e+RPS2nDWn/VSxJVcbjNsqnSzGXYbY1egj9o973L+7x/27O+zvqrSvZJGsM1RaaDqHEu8L21XHKOdPnwXteOHqfxkv42W8jJfxMn5G48up/pRgHdUz2xRzAZQI4lqbEumLIp+MGAPX7A/cE21KuigUVSWqnVKK1gOZSBliIO+4lRcVNbri+WqzQkmH0s63FDDITxlCDVuYsWIoIL6OwU1tRJtyLmlPpdeAP8XAEcJCBMRAEeVKmcmHQlARQuDuhcZHKykejZypjp9yTb3mJiqWz7xkFAnYJuUlSN+U19mN0sKWc0Faf0qmVBj8PLP40feEtQ8Iitays0+3ptGqKCOuAIZnYEIZP/W+C7nG+XsprWG5/9Z2nKJmoJ8g+QGKWiTCjUpBG4pkJdqXiL+C6CiFLzoNpVQh87e6pnc5Ko4hlWzM+fx/ylBaFxCuRDXB68JLLh+TM9OZcjYgYU2ioxTRlYocbwzr51HqnNJ5I21Y0g5mbXM/zwh6UkpQoV5IkQ0PcdVOd9H8lSpRdEsJrbWGzaQhEZwp99D1rqiyAQTcGjYDNn0HozSWGHE4TZhPM8Lsa0ZWAbZ32FyNuB5G9IxxWZj/v/CAxITJE6NjZAY83aybkFsBa96MS4bRlkq43FEFoKTuYySysBACYqq1amts6eTKnH2kTLAv3SZGW1jninJr53oM4w6bzTXry1OrooA6JSsUllBA5JBMXwugbkqaKdVe90vnHxYqS81H07RderjOFptjLGF7YsHU6NL9Itnc48MR427E6dUOm+sNdUE5s8oIRh9w3J+w//iIj9/f4cPvfsDHt3clzQ8wqyPzehjNsr+mgixjqBisUgL8wtn3VTV+PdDmasUqSp1Gm1LPF978Ydii45sriP82RVRTPxUJKjKQkqqPKQJRQ2lT5EsBMup08Hy6R7llWRNgW/CV/e25Q5wbAfsA1CoidX9nLILhPvKcP4l6T5wGF6OfuH7aOlN07Xl1qAn6++lrIgDDffrS4lFR7E+u/0IGK0KW+lU/cfSxYS5k5HRZFwYsprXjEXNt/xM8QAsCJNwA9UUnvmbdpJVb4A6Aop8ghkWAfisHoEkFX+oAdGNHNdb0dF2lBl5IVxivsUxLSeXNxxlFitZqdItD9D0i8yKUVLUzDF4jkJufznAd0q8fqVdeDv2U1AppfD5/qMtFamivyhzp+SNDiqIN3+7V6OOTsoLcA2MMdGeLTPK5dDCh12kNLZdRKiC2Xn/rCInDoZQCc2OVwy+w4ZBugp8yKGXOALVMqPacSX7XsVMo+uvd2GNgwz12HUbn0DkHpzV8Slg2G5y8h49hZfidsRhZhrcFsAolL30I4JIBhg6K28KMNSUgMbo+TyTcc3k617oOKVNQ5pcqmy5cIch5Bbx1rmfCHME6JSzL2rkjPn/i/xca4L7fkK0YOgKocnlI5K4FUCi9/ykGpBzLmWa04tJf1SMRp4Acg8uee794zNMCpXVpz5yP84pqXhsy4qU7i599gMibcs4wVuP0eMRpf8L2ZlN67AUzIwyOx8cT7r6/w93bj7j7/h77h/sS8NHYUFmHDT5hDZhEjEuapfOmFXH7sXv8NQshD6DQQlJtXWrPisAU3JIkQjxrj0xYzjwEoS/167X4Tq3hZCoiQmn7pFblcy5AQt2Q5aQUEXw9NK11BBQMESKU89zR9oiWPlk5sPlQsEYjRA2jpRWvBaqx4ENTayyMaIxcBtig+lgOM2stcu4+2Y1AB2dq/t9S+K7JdGgTXE7bGjzTZLLqXFg8QqqcAsSd315L8/W5sWTjr5yDM6ZE/D5qKIQV+E2cKKUUlPStNoZceNNlnpcCeX5s9GOPrnd0SOu182Ust/NZwVtQLc4voR4Wp5m46i0ZCamHpzPHiACEuay1RMVSx0WokZVke6qx//z1Cwbi0mGs5pYjFlVZ6DrbNlNqrxPCnDOmvVSdFoB7jKFgUA8/qVNLa1qJ/M/0NeSzpKVRRutIyHWJw0othBfSNfOzrWNtGTSKevrJ2VTorYNRGsFZOGPQO4teKG4Z/xNYcGfyHnOQoGWdMfExwMdQlPYEQxNiqqqY/D2lUB0O7lV3xjD5Fa9zwbxcdu+d62kN4cuz7f3C2Vq9MvCEf3Iw1pV1InC3gM16QBEOwbkBznWljdW5vhgwoS4WhcoYfMncEpHQmbNopJMkctBTf0aBmsWFPi8Fj9xZJcZ8Ps3oNz2kewUgYJ5knledT0VFkzJynvECrhciPOnoiizIRaC/6TAjhAAF7qg5C1yky6hlys153eVQz5QfN+1fNPzFa2/6k+WhlQ+ktqU1yroYAL5JBNbT3OvfAhHqYlljS8peKfLmVBaClvZ3mv7cnAsLXD4jKC4EFI0a2HNHjJRuazeRkNXQwUoPoDWGHk7+V4YY/lXv70SUnWL8ZS0FsastyRi3c8hnxrWCZgjAtkp7F6OgoVTGFyGenxk5SztfwCze7OTPBHaatP9ZD7/8XNahXFfOSIqAigChkEWONHwCjCWHT0xnUX3ztVYK5096jUgv8/z7sUc/9PBLJUCSyFzrp6JLRTyEuSn8RI6saVrN2muTLIqxtlKCMj++UgqwmT+jPhNfSmGK4S3o75/iECnFe1G6FAy0rpFFmUuqzJLt562dMQMt6nympjtLulprPtga2usmw0iRjELSGko4mDP19EOjIOwBcqJDWLehXjKMVqtuHlp7XhpUFL1SCp0lojJxZpcQyn6eF2r7iz4UB+f8oD5fM+nUaAGMAgq1Wrj0WciIs5+tw/9TSj5dNxDnCpdKAVG9CwAslKoRpfTMtxG/ZGONcfWc5KjfWlekoql0kKi0y2shlL4rGuAme6qUKceZsMYqtXYKxDm+lK5aRkpEwgRQxO93C5Tqy7UCKBS8mcGeADlmwdeuD3FoPJdzZQRPjoWo8WmjCCCqdVHbpDWmfScaAEKWlTNWAZ8MYww0ftzefdHwO2dL64Ap/cmmGJv2b2St8bYeXzYut+ZJ6pr6LG15bYtezzlBJVNKA4TO53pPqC1u0uNv4UpKlQxo7TYAUPqtLxmitiXpfZr/mqWu1AO5Vx2wK8R7SBTZl0ifo5bWGBhL/M7dEkpPf0qW50HKVO0QuuSC6odEeGtWr59S516WmfnGQ2lB8bPHEsKTsoIYfdt4o2LsDQDFwiFStwwxrdYopkTUp8y/LT9PKZXcRsooh7A4G/I5596xpLxWQiHPHG5w6MYeSiv4hd7fdglY/JODVXq2c8rl8LbOrFLxVI+PUIsnNG6Rpl1I3Yxb8BQjtclp1iXTRinxGtnJKOtgVEHDa+YA0PZyw9+m++nzzRqRbOQ5AMAtbauUoznfewbG6bUcN6PX26xiYdpcTXS93+jLXM/2JvIXZ1WyEJeMxEqjrqmdl3mXTIqCgMklLe+lj5/TuPNxxvHxhOPjEX7iNi426gAKL4PQMFtnSxnQdlUkZtyNxNpnDLS16Kwphj7lTMnusxJMOD80vnL0/YYcpybqLqqjnzlbKMqWc5/V9prMACBne17R+wKxGHAJaIR0TN5PKc1MfLUUklKEEjKrMwtfO84u5O/g7EnLWjkfZyyTL7wElJHO5MQaDdV3pYvFWAPLdfc2YytSu+I0B8ai5JxhnEWvSJJazv8gdfsQ2Rnm+82qnFHHKkfdBkFWw6haHv/U+KI1tJ2jh9/WA8hyejMn4loWLXny4Ij4odbuDcZxAwWFmEIBiWhtkVIoaR9h/asgjcjgPKaxzXKwp+IItK9t0z9rRjSzqvc+d1DNvgJmAAH7icESoyvtPmbFLriEuErHSAQq/PflPTkKcoNDipXG1HsqgZTrYbrk9IS+USNnSQWf97ZfNvcie7kM5dBapgXeB5Igbh58wTVkZJiGOlkkS6XlsUbveZU1iClhDnSvZ+8pUyGOghi2nJF1pXOtjle5iPJQCS+88LZfMhy3s7VaBamrdKTF2Df7tk2xRWtWaXHRXJfrU1H2tIKWQ5WNresd3OCKYhvAnrzVNdJF07YrkU4DIv2UmM1zhuKsRisqcv52kvmAUk9bJ8UYh3qNOqmz5zxBJw1lmjJOziCfff06YYErbYKmYhhyzitugCAO9qWGn59Na3QRlTLewzfkK2QIae/Pi8d0nHB6JDDctD9hf3/Aww8PONwdcHw8snBPoAO/oa0FKDDqBmJu68cO/XbA5mqDq9dX/HODcTcSGRTrgQC1FVYrBdsYGaE2v2QMwwYxUt+9GOn2vK3GNwEI0Iq4TKpIjeEgT8B+Va68VbkULYAa4dfMZtsuTtgxC60tBONE1/L0uf4c9us5oyhIpsoNQTgnD9c5KF2J15ymDK0zpkjj5py5XMEtpUxKJFG8vKdjTEuW8k8x7Lm0UQNUZhD5XxHvEWZSx4DIOn8W/vnCvf8Kw89pmTaSsxStZ1HSiiQ7SoaXeuoN37SuG1ba4xT5B84QNMCdaAm1iQr0oxJBhxgDAjNIZWR4JvvJWbTNTdkkkl4CAGsccQd09pPsbl8zitxrE1VyBY2uJ1cDRsQ0dgXuM6pRqQpAyDVabh2S1WaVdKizfKNrxkM84upIiOE3JevRvmeJyC4YZPgn+AbcR57vwg7NUyAh0JDsSCYIWPVTG0VEPudResdgJa0AFw1nAWJxrmSNV+JAEk0nFumRem9gWdBGUvi5QyJvooplpzflUqIpxj8kKMcgNq1XRB4AinKcZM2UpGptLZ1RT7hmERoS6elGFi0x65Q30Sj7mt5O9UBWbSRtapTw3FGcLa1KR4m25skh8ynNAABIirjcc4pIiEjJVAc4AdFUhzfZVPqZNfdGP8morIB9a9Ar0RWTNDIAeAZYygF5yRBdCa0qqJfKj5WsqmSiEqHcp8OExw+PAIC7d3e4//4O9+8f8Hh3h8PhAafTnvXjKw7JGAMFDdf1cK5H140Yxx121zcI34aVMTGGqHglC0GdMHz+/B1iXPqBIn7n+oafpcn20qwBRvenGBCiL3oWznVsrAkPJuP8zBKp6pwiYqo9/3Tu14yBsx2MdTCGWBNDiKvMwJqorKU5v6y8K/gd6rqiOYnKYkoJKqsV1qTrHczQoRNejt4V5yg2JSfh6JAuHMH81OulI6OVHwZAHQKPR0yPp/K5UYiuJCso2UOjYK2B+0J588up/s4i+bVxKrVGECOTgC9iDHVTS71HG2KTC76I+nhWWBKkJkDo/7bupbVC140Q2lMh+ElM8xuZjpa6BRKMyU82p2UUqevdqg70nEHa15ElY+l7pjl0qlCPtPxxOqupyZNADTPrMauY/G2NZ+GG5pRvihFhWbMCCpBPHqA6ZylBNIa/1Pz0RcZ/Yipm76+xcKp/Ps2YDhPXMGvUr1d7gr5HoKVKKiWMfET+05C4nHVCiKFvSZNkDZSiOqrPQuCDIo9MbS1SEgpMk0r/XjKIi9/AxPqYpJRgnCn3IPrIrFqVWU2ZdUlI2O3A3rg4B5IdELY6cQwk+1Miq8bgCQOeaLG3Udga/7DGOwOCFAAAIABJREFUe1w8VJ0PsccZKBVX5SVBG7dMlJImlQg9hYjAc8gxletqHR7NTkSMTSmtZHByORek1U8GsQbGUo5apgV+8fSMXRjx+xgxh1CcUQBFk2Kd7ma0/5mQ0nJaMB1mbr9jJzmnJ79PDiHVtikLugbqtgqMayOhStBRwI2y9pmfrwupuserETkqTNOh6n406Xk6kyUIoxa7PB9KhG8tBVzOdoUzX66XWFlr11W7DhLwWduh6wb0PbEFdl1XQKIFD8D3PBVw37rM8VOG7UxpLSxql0xmlhIpwQpGJ8VUyrSWDf/2aoPrcSTch6rZzfMMTHsfpb154vLQdJhwfDjy66jEekzH0uboFHWESUtxi7/T1kB9Idj5qlR/yNyr2dVaYwVR1Dqf8DuTVy9eXSglgBDmJz3H0vvpZIZl86bSQ0q/z978UikixRu11hEPgAipsDG0tkPfE4d6zFUU4zkjNulpMaStGh2A0ltehBKUKp0kiuuBAsCRSN71HUdoTWpWwE0N0jV4kq1Uai11nJtDRLMaVkvZCqBgILS+LOoX7YRlmQqL1HSgr4/LjNkH7Pq8KoPE5oOk5U44DsRxqn37cmClYuzl9SI00lm70hUnoKVarb0PlD5NMZb9JboHfvalr/65oxurF+/F0UsZwYaiNCeGWfa7MrqC4Rra3cyqfWL4hQ4XwKrFB0A5cHJKyFMjDJJzSWGLQRPAj9z2fGbozv//3CHALADc0lsdkrbUIgZcbE3i5yP4CKhIaUruXFEKVZaXVdCsy9TWqJuuh5b7nx0621k4pUqEm4Q50FfDP3Natu0Ff+6Y/ILDPCOmJlupqG005Lza50bRYdsNHbY3JNQSFuLT395uEZZvitMfGoyPzBEAc7Z3TN/aY9wN2Fxvsb2l9xu2A5Sqz1TOlcsiZopCZUia319478erESo5TPOhYK/m+dhkgSwUIjzofBdO//pzTQGX69F1PZwbSgY4prbM0WAE2Ja0nWGijCeKl4lFnxLjm7JE/IkyS/RG9Ty9NAkiynztkBR9Trm0hqeYEcMCpVXh2weA3jm83m1xM24wdiTKNYeAOYTVfiRsGL3X7AOOy4L9NOGOHVuJ+GnuIrG+phCmToEGM2Map/lHxpcj/p5SLGGpaSfXd9Qu0YAtKAJPMIbIdhwDgqyltE/XKeS8RWC53cpV3UZTActC/M/zfCy0jwDKZjmeHjBNh1VLlNTaoBSsdeiYEnKzucLmZotu7DBNa8P5tSOz90zUsdXjF+NfUPvSenYGKNFKoeceXWctJgbsdEOHZVpg2KBKZFg5+z8dyZX35bU3hTTj6fxqSeCiqWOeDjgc7rHZ3GA+XQMgw3/an0hadBfgY2QMhBjGCtp70u8PrDZlAU0l0nkXch9Js87Bo7eu9EZb9rRXwMkYsURq35L+X4AIkZYTlSXCpTX+3sEN3cqRjZ4cYAGOCSBPxFOUKiVtqKhqqvkswlVLo/ZoPpEqj2v8BtDWAFN5Ly2AEwB5qRkkAY9+ikTpa0ZRfTOt4TcoVLmlzBGRXUVpS3aVHBkpEypEFbkuifLarxkVA6PqGsg1NNfhl9pyupwIKR1jLA74c8dpWXB/PCL0PTYilGMMrNaYeQ+2MrgiwiKtdpvrDX7xb/+iEBsJ1bKQNJ1napTiEiof+vJ9KRt1vYMzdnXeFN6QmFdN+yEmzMFjWioJzHPGZreBURGnwzWmiZT0KKBr8ERKwdoOxgj4OhfjmyBnT+QyiAcsYAqg+2lXGL2vRed6ag8061KY0go58Hks0X5OxehLcKZbw38hTbt1FlorpAbTImn7nBKBaBmQ6pcAPakSpQPA4+aIzhjElLHx7IwGj9mT8RdV0kUAobEh4AmRSkZ3+1I22t/tSemRy3raGhYB6opT1GbllFJfVOd7oex9GS/jZbyMl/EyfkbjixG/NgrDuEEKCac9pd77TY++73E6rRWQqidXKXad6wuRTs4JXT/CWMdKfFW+VymFEABjIteV5wLiU6q2CS3LtAKcCLBP2gMpvS8R/w1GkUjMP97e8LlRosoQoBrJWKv1qo+3Bfmt1q+kc6jndgUKDDU9OR0nHO+J5Wk6TFRHnxYs01KIj+SzJBojIKMutbIKeJEyS0KK4WKQy7JMOJ0ecTo9YD69pus8TJj2J5z2Ew43M67HAdvUl4xHOlsHpRQ0csmDfC4FRXS89LVE/ksA1yrPMgTymkTesg+hMrVJ98GJamVUY70s8hG5U2V0aeMxzsKxIMwye0BX1HkKlP7MfE9J5YuicN3U/SW1L+lEY/Sq/q80pxEbBLn8W2laa7mg7XKQ5ZXU8sX1TlX3tTZNGlFaBBfFIjgkzWtYxXCNM5ByYOUCkUisrAcDKLVRJdIqGJUW1Mj1/kL0YzjaZ4rr+TgX/gRhmsyZ2uUuGfPiceKIWfadYE5oXVLJdBmtkZIGHNAzy2F/65q2v/UQXBAts3rys7rf1apDpo30hSYcaDJB/P/Je+hJ4zBfVt4ctj2MBrbXW5yOOwBgUKIvHVRaazg3FPxC20lkrYW1PWcETOm2yshQuQIFaXtUcChR+naEIzEVlyQ4EdK2XxDCzNngUKL9tsygtYaCvjjil2fe5DVfRWowRIZLtpqR+n5eSkdHTgmnhyPeDl0Bxkrr3jL5kpUsJFMhICcUee1lImCfZBCm/Ql+8hBJ3m7oMGwH9Jv+Cd4FwGdtUTu+wvBTG0kMEcMjUS3Kh3bHkUAcrqPavFIlBS2MfPN0QO5HSvn4uXQCxOAxz0dMTS5O+Pun6YDj8YFr+WuWs3aTEUe0q2AS12MYtthubwEA26tdVUD7ApPR54YY/tQssKT6rTYsG6sQGuO/AuzxoRFYjS7lqj9Of5v05LwUYy8KfYSL8AX7oJnAgpDhcujHgpAVYw+AKS4TzBfIHD43Fj9Dn/Y4Hh8xc8qPeKgnHB+POJwmHMYB274v8rlaKeQChhKKXQ0gFCwEQIdoVfEzmL2HVqkcpLKWLaGPtAUCZPiXQA6ZD5TiX2ZfDv/5NGOZfUEBXzKEnMMw8hxgvnxUB7BoMgjmoCFlEunOWo5SVdGtq3TNhbiJjb68n2AAiiFkAKc2utCHKjbQaVk/J8IieKksrWoMtuAPRAmugBYzz9FoRLu+VgAF+5DOQIg0l5omVxorjAO0Kt0U5fs5F8dGNU5JYgdsPs0lMJkOE5aFQGc+XFbim0LAyXvEXHUjtrrKRMvBKo591BohJKQCepQ9zmBUDh4Cswq2pFJkSGtAIEyPvXVAA6I/Z/SLmVDiSilk3fTte4+Y0sWGvxs6WKMxXm0w3JPhP0370j6ccyYjbYG+G6CUQt9VbRZj3RM2P5mb1PLbIa175CS4QmXegnJzTPDeY15O8MtcWgEFz1CfJQNrHDnWFxr+1b0QHY2cC5+J8C5YZ+BnPqtiKobaLx6n/VTad3POpZWv1T0RkG7pEpJ21STdALVsKWeKdYaUQDc9l+FruR2oHBb+Cw7vF62h9A73Y4dxR5F0P/botwPGwxan0x7WHlYeFy0UTy4sdGLlhMDo/phIdGGej6WFT+r5y3KiFrJlKo4E1XWq7G+bJQBHvn2/oTaY7S12u1cAgM31SKIIRkN9QvTla8YKPXxWl3PWwhkLqwOCTsXot78jtUBxFjTX884PVdF3FwEfAOVBezIaB0vQ/RTtM+sVr1XgTIG1l0lUej9DKY3j4R6P+48AgKvHVzjtT4WD+rAdMS2e64/UY3we9RmloJlQoqXZbZ2ic8PwqXWHHPa5omDnEAqCX6J8gByUwDXvS+YOcD2ea43R1/az9hoV99fKkH5d+nl7n+lwN8wN0Pbnt5K9XLZeAdzOPXrNhrGK+0itM8FPte3SNwfGJXNvI30AVZ+AhUaki8IvgWqw2a3uvQylFXV/caSqtCprlhS1XWWVkGLd16XGfdbKWNYiUwtfZPaz+VABqH72DDarAOHnjsPhiG484tRSDjecFG0kL0Q/7X0idksywNNMbI5+8vzvcmb4UTIZIvUchg5xSCWDIN0tEoh4lmROIRJfg1ZInKGwxkCz43LJ6IYO6B2RBm03vB4DI/hJOM1yn77rqHMrrVD/tkTeWpmC7D9/xmU+9XWmBjMNcx3tm1gywH6l95JLK7ncI20snHPQlyV5YZ0DslrhD2KsUr0xRjjryvNArcOpEPTQGZBgginOOV3bmkE2cxZaKQUs3AKrFTJ3DwQvdf/ELI4Etu8GblFvMn0th8VpPxW7+tk5fmkRHKN4becKyrIbKdUwbweMpx21fTAAKmNt/BQigiJgSIqRjD4bf5o8vS4EXwCCFBk5VFY//+TwyyzOQP2iDl03Yru5wdX1a2y3N3SdQ0eMSJse4UJwsxioNuWs2JgbpdBZSv+ZGFdI9U/x+udM4h62c+hGBr5F8f48rFtWDE0/NtpNL+tG7ZS+Kh4GYd2KMBcE/ZJxORwfcDjcAQAOh0ecHm9wuD9i2p9wuJ1xXBYMnXtyIMacS/dDm66mdaltfgCVTjIfnkJ7LI5NMbQgoKCAl5YQMHvPFMiesyX18KdU91Ma4a8d2giBTRWqEW/ctl62qrTLqmlV00YBTFPrOlLvI+57t9K5L4JNEmkI6yAf7EV1rskutBwQKXNE1HCLC+HIpRwGXe9gO0ftSkaQ59RuGroA4zzUzJoKIRZmw3PK3kL5zWRGdE5kJMU3v7QqUrkjJV34HxKhanktdXEgZK1TYh50BlaJ4ZdUcKsu99xxfDjB6EdYZ0tq1iiNm82mOO5yHcA6wpdBDmog4ClnJebjTHwDWUCcHDAxHa+xBjEm2LMAQjNgK2WK7EMIld45pqetnUZjujTiHzto1ZfMLiAgbV14W1yOMNqVcmMI6qzkyPdIJRhk5Ex9+eeI8zYAkkxAjLVdmdaIPnNZTqXsSX9jifxLJokF1VzfwXSXZbv6TY/ka3mZrokMugQU3dBRq3tw8GqtM4KYEBBK6t44W8pYAIpDEzlT6eeFonpWQl0AYPalTBU8tcUqdgylfU9ogAFU8HGI8ItHxk80/KbxNIU+sh979GMP13cYxy3mmdil5vlICO3UGuqAuAhhD/X5x0DiD0K/S4shxDRElVnR6uv+WEAeOg2jDWMIqK4/bq4wjlcYrygz4XqqsfSbHuZyyu5PRqHiBFht0BmDYMwqzV82oqrCG0pRZOAtMTaFpvWLFN5MOSzp85rOhaYkosAPWSb6yxg8gpfDbqlrKix58VLD76FUxDQdCrr3dHzA4f6I48MBh4cjtvsJ+2HC4By0cHQ3cwe35tE6oLAgtqPVPmj/mhiZK4BeJ22AVP+nPmvvQzH683EuyO7IEcOlNV6APH/jLLom8izUpUTdWO53m86WUerZWhPOhLs5ut6tWoZMg+aW9HVlBax1xvaAk/+DDW/wkTIeRVRkwcKMX5eMYSQWOekyAQDjqyKhOAApLkVelLJxvP819bdoaGhb5Xab5M3TwXPXWiGVPUQ/SqjGXxyj6EPhlTjtp3IIhkAMod7P8Bca/vkwwZoTtFYlhZtSQvwmFSEe6lhJhdDLNgp5LR4lj5LFoewTRYlVRhWg73U9IbWFuMk5W8oMWilEoPJVMCmMvI8HCr2s4ozQ6XTZ3PtND2tG6lIQ2WEm5ZHz2/ulieYBY6qRTDEUGyB4L630qqef5kTKqwrr5/88y5ky0wf7pRh65HUJrR1aa7jBwfSXGf5h2yPMtFeFFCrFBOSMhZ238WqEsRa2S4WOXaW6ZzU4YlfE2KlU7U6Q81GUPQ1ne+fjXLqFUkhVwI3bCJ0Tym4RrIpNtxGXd9NEmUT948/9Fw1/x/37OaayCTY3G6qpPR7h5wGb5QbSzuHDAoPKTlRpGeMZ8U5apYdI5IEAHlS3qSA1rXSTcqHf0drAMb6g68Yi8TgM2+KliqTosBkQ82X1HgBFgrfIXrIxl2xA5xzV2xpCGhlZipKooAsCbgn9Yo0WAazkVMVDphtdr0ckjgkg6LH4mf4uhAeQSF8UDKV18vkjc4/uVAiUTtMeh8cHPH7c4urjHtubLR52wzril8OK5XaBdaQPCEdRg5lQFQi1ilyigud7Hpiox8cIHynaF4Mv4C4hGhLD6Zko6pIx9B36sUf0oWBEpIUthMh1RINsc2nrE2Ms88halcxBC8yUFq6y0hLFFPKmxMxen9DWlkXMta9fDGCb7l7mueyF545N12HoO0yoTqxwI1hn4XrHbGYeMdS0uxwpOmtoJXt6fWABa8eYIj6Ufd+O4ig0r42plhjm44yJ20tbCdl5OmBeJsR4WdS7TAty2q++N59m5JRx+/oaHVOtpkxtvpbZ9KQv2zR1bGL0JAMQlp6IYEJ16qVtT3j6RYjHnXFYVBxJ4HWvdNSCDZLXGaMxz4eL5t71Hbp+oJSyKLJqC8Nl15ACTFhgjEXXCPQI14jPGZlpiXOKiIoUDnWKMCzqAwBKGC5Ve9bVs6Jw1aeEmEJp3wMqR4mMFa7KOIqMK+zgWaPfDJCMfCv8lDjqnk+kYWLGGqjlkBCWxmFRCrZpyc0xceR/rqVSgd5hqZlLPy+ldCDOu0MtpUm2OGeNFKswWAyRSvLmJxr+7TCgH0Ycmo28udowM9WE4NesSWo6YJ4zRMWL0jYJLdJfyBzamyfOAR10AlRjmkOti/GUiVvbwVoH5waM4w6bzTWGYYvN1XZVR7GdxXi9QUyXAdzkoBaZTYDPXa2hmW2KIn6L3jHYJDeCIamy0oVEKWq5qWGpqVjxGuUzhSo1RgutU7NOqdT+E5dNSu1LpI4LuC8CiJxiumz+ALFtzTMDp6YDjsdHHO6usb/b4+rhCofdiHuWKG2JjawxME0fLFYb/ikWgur3tVTSOgmyliLks4SImVmuRBFPUv4APSwxEp1oq2z4nLHte2wZw7D4isRdpgVoKIuF0x6ofdryNQAEyg1DawXPKToTYgEfCad+Aaxxej+GuMoqSAaBAD1cdogRy0zqb9Nxqr3s0wIfFiz+MsM3dh2uxxFGa5yM4HDI2AruQZj8UmxS8wKktICKgKSaBIlfDr0nhr9mBYyzRYyoAAttBUpJtC/3fTpMdGhypmuaDjhNeyzLtAounjPmaYGfUAwsQP3UktnZ3W6LKieAYvTPUfi9tTBawxkL39O+9TGUFL+UNrXRBdgqfAGrrIHMmwOG3ADFwuJxeDiWzASVVjTChU4PpZJFW6J5ZiEAMsrcWuOQbMc8+u21VkKdpMThpahf6eYc1eaJI0z7g7kOSvraF3uAnIrdaBn7pLyZkeEc4RO6zWXB3rgbkUdy7AQw6ucFnjET0nUl4lVKqRVlNPis88bD9Q6RVTtFeKkFDCYmthJw6vHxWPAqtXQVSR8HtcwH0PkSE7333fdUik0x4frNNfrdT1Tne7Xdwozj6sYus8e4G7G92WI5UR2YDJek9P0THXlCbibkbMsBlxsPjtSYNGIMWBZfyWewjhC0JhEcMfrDsMVu9wrbzTU2mxsMm36F5nQdbQI/XXb4q8YAdQ3KOqZUJXitRc+RWYgRCyqVrRidwjDHanfLRK0cslkiRwFysChNokgmkiNUUZsidhHKA7gsJ3g/FQcgFMAkp/4CAIzPnjsBbchp85415ucjGf/9ASdWHdvsN3gcXBE0sS2a1higdGYI4KeisoFq9Fvu8ZBiQS8Lsl/IL3yMBJgqbY8EmJqOU/GSCRxUqHSePXcAuBoGvNpd4TDPeOC0qe89XEfAuRTaNiLRkI+1vKCJZjXNvkjuemZ0W7W0nRlF4fYv0aAYP6YSFVBgXGjPtDXuIgnsJ3g/YVmmi+a+6Xtcj0OJYAFKe1pnC72wZo5wmdu5GJLUIXPJWogDVOdeDnxTsSBC4Sspa6AS+FB2ITFBE7dsLoSWlz06TQccDg9YlhOUAvr++fNPMSF40kr3DflXaiLAcTegH3soRQI5EtAAFZyplChWAs4YOBMRYlXvbMGuAvw1TYazBBCZn4UQinOZYqI24IcjHj884vRIWTnpCki4LNtjG9CojMwYIqFJD0HDmwUu9VAYGZNVxaSMcaV0C1BaX+r4ZT9rWwB90sYpDmTwCxa+n4XiPXhEbicsGBgOflpFWNs57G63GHaXAbp3r3bQ6BB9wP172nfLslCHWkroNz2ODwd0Y1f2J5GvSZZbROMMg/7IWRbtj5XhFpsgzjs/x0Q8Vu+fbai+jTWl3BWWgPt39/jw/Tu+DmbGtNsfneMXV+bNboc0jiuE+amf0I1ETzntTxC5WXkNaTezQfdVXlG8NORcvLYWpSupa0oRpZLyX9f3K59z34+4vn5Df6/e4PrVNYbtgG7s+LWq1Mvm04X9zKi9tnIw99YW4JrRCrMPCIYUkSTlV9LTjbxsaNphcqoUpgAKvWg57KVfWXpSy0FQvcUYPDwzHQZOyUsdDEDBUtBlP9/wG2NK9Oo9R5LsWHg/l57+aX9CP3Z4tBZWm2IstnzgZq1LJsQBhW61OHNYU/3KSLmy/gHcwhcjJu9LXZdSnE9r/N77kpqzF3Z0bPset5sN4xfoWufFs2LlvELwk6QqkJJ9IrmapKUtJWhvkDqS2SxpVKNJoe6sVlkMIUcWbiDEtxjbnCnKmI9TiRIKuNEvmOfTxeC2wTns+gHO2JJun73HbOcnKXutqf6cQi3PSUuWUpTxEJEvo1E6HIC1wS9aB6WNUq9osEnkJMJzWlvAcpElZMXJOZ0ecTzewy8zrHPo+8vuv58WHB8oAgPI6V6mmcoMs8ftL26xe7VD4m4nrapMb6s9IUPAvy3XxRPOC34Pzb8rbVkhRswsFBM9AcLE4D9+eMT9+/sS8buORJ7MhaluCsAkCynOOWdhowhUzVzzH5FyRGf6UgrOsSOBpLTu1mnbsOl7fAY0qT3KYpGei/e1dCPaLi3uKUZfOsCE198Yh3E34PqbG4zXl8H6b26voN2A0/5U5dxzxmnaY15OcB0DH7cDttfbulclDR8pgMmmqngGmauqoEE5GwLrifjZV6Pvl8J+YoxDNxKdcz90RdY5+ojD3R4f373Hx49v+TIjnOvQX/34nv/iE3E1jojDgPvjET0T2Gx3G8zTAttZbG+pzzOlxJzB0tcoYIMIoDoCSiniZo+hIG8BlK8Fmd7eYLQpYO7T7LoRV1dvcHPzLa6vv8H1ayLr6caudB+IJnlb/3ruMGpdmwU4hZ2Je14Q/zZGmBgo3dek/AJ3OSSp3zZArSe1W9Q+7YrcZlBIXhsFyi4ELBzVzcsE75cC9gOq8JG1l6X5ifgnkOPTpNTm+YgQaIMeH0443UwYtgNs5/DACmK0dhq6X6csgSrW08aHpV0JmepWOUOrGu0AdPhNnkpMp8cjE/T4Khx0WjAzklmyT4IduWQMzuF6HDF29QA5LjOm44TpYNnwhpJa1dagA8paLROgdIYRa6AqIZAI2wBMEWpadLMg5BVajQzXu8pHwenuuYn059NcDkvJzOR8Waq7dw67YcDQYAyWECjCPMNiSNo+Nfs7pHUnjlJ1D5X/g0BQaMR9Kr5l3bmQGc/gZ1+4+KVzI3h6Do7HBwDA4+NHHA73SCmixwBg9+z5U7YiU+fKgahTvZ/w+KjWnBunGVevr+F3A/yGsC4AA3Dluc81iyUSra34EH2eKpkUAEVnvWCAuMwixF6n/QkPPzzg/vs7fPjDR9x//KFgefp+g83uCltzQaoDDZYgVCpZyTIK3XoIdN1dNyClHaRzBaDzSkh3PnfOtZ9T3z8Wgx6jL3uZyNwWSIs4IADOE06nPUJYCk37MGxx84tbvPrlK4zDZan+X15fQ2022N/tMWzJe9LG4Hh8QIrUOjhse4y7Ef3Yw3LnWI1hM7fSJuTsV/e1BS+2Mr3RxxIwSPZQcQv0sCHdhs0VkdFJFm06Tvj4/R0+fPgD7u7E8CcMww43TLH+ufFC2fsyXsbLeBkv42X8jMaXmfuUwtD36J0tntum6/DgLLre4er1FUf53PrHHk3LHneKvtSH2kifatKUihQ6yE8pN1HUWttK+mGL6+s3ePP617i6fo3rV9fY3mwZOGHQCd/AQHKOYfY/SaxEevbPI1erDbQiAA8xz2kCuK3qWBK9kuiDDEmRttHdE6TzJ/qFRbM6JVI7LIA+ViyUOj8g9R61UkR8zui7oQLVIBG/pJCnknI97U8YdwOLNynccfTuWJiIAH8GClXMyDZtLVCK35/rZRnQqSmRxEpFOh3mIhLkF190slt6Y5o780FoC2Mui/g7Y7AbBnovLveclgX7acbx/lDkOUn7XaNXBExzzVbLKZdODRFzIaImsyLw0aLaZ+sekwxAbfvjLFLBiXC6m8sdYQnleZqnA0KYgQtVajprsek6VkOk74UUsZ8m7LsDp97XXQxAQzyUFYDQ7N+mcyG78jvaGOiUAdMuWmZwoIJKte1N0OxhCaXFSZT5pumAPZNM7fcfMZ32hKy+sMyjeM9473E6UcR/Oj3C+wWn0x7e+xKBn/YTrt9cY74eMXDaX6LfnAloSkycJCQloM12UHZS2r7WAEiASFyWacbx8YTj/QH7+wPu3n7E+9+9x8ePf8B+f1dee3X1iiXNn1/eA+QerjEGUoaUMp/Qg3fdSCXGsCn7ngjTzJN5EmDvEyW9AlbOJeNL3USUpSXb0HQyRWrXPB7vcTw+whhq6waA2zdv8ObXb/Dql7e4+oIm/efGm90Ou9ev8HF/wNu/oUja9R1i9Hh8pD02jjsMuxHDpsfu1RWpJ551YAjeqMXpPG1Ll9Kugk66gP9MsEXYadiN2N5sSrdaWgjj8vD+AR8+/A4fPvwODw8/0Nprg3k6VKDhZ8YXn4rEKW2FapgMo28Li5AzRCjRpPNLetDPmOdTMfYC0ggxFJY+AA1wpCFDYTSoUPECwGZzjevrb/D69a9wffMt+pFIJvqxJ6SxNaXW1I0sfdsAqZ47FFBbdZr5S88+yccadNbitCzUQ2os8daQAAAgAElEQVQtFuFZl7rdebq5oJnpvyLHSynfdZ+zAFnoa0LMUipsKYjXzLwILbhPjJ88FM8d4+YaUGZVJ5YHc5lPmOcZfhZk9Yx+M0MpFO3sB2vZ8HOrntYF6EfryI6O/GkcHdGvDjFi5tLFaZoLACawUpYwZs2npUhC0/IK7atbcb4/Z1hjyPjlyp/+ZrfD4zRhv3kstfcYiXgDOaPfDOX7QMflBs01fWovtayuVQ0/p/rP2t7Eaaigv8Q0wMz7PTNAlLsZ5vmI04la0OiZi8CZWuTXDmc0eue47kzrN/uA2+2Eh+0eh3tH6WhB84P2bC41YS4RNB0PQD34ZD8bk5jnn8pnonduuDZaHIeUKMXPsqxiBFIidr7D4b4Y/tPxAfMyEZX4hUOAVADK8yR8FsfjfXGwp/0Jh7s9TvsTbr69weaKzrNhO8BwWYbaLZdSmojM00BrphjEWPEMBdvDtVyA2jNP+xP2H/fY3+9x//0dfvj+e7x//x3u799jno/o+Dnvu4FZ4i7b9z5GGGVXZ2YSg7xMWFiCN6WE0+kRfT/CuaGUFNuyRQYKfuuchbLcx0h8FTUorIBlAIW0R7hexOg/Pn7APB9xdfW6sLW+/vVrvP6j1/ij21foLlQnvBpH/PLmFt+/fsBvWGZ52AwwxmGeT7hL36PvRwzDppAc2W5TmG0161n42T95hqXmz/8p7biBQAC104ftK0B7adgOMNYWXNH+4x7vv/8d3r/7Dh8/vi3O6dBvEdOXVSm/aPh98ARUQAWkUN2qIZ0YutI/bZngQzZN9JFbzMRAUQuCGPjiTDS1WIlUjXHouoH594mNb7d7hdvbX+D6+g36vqdWEmNq/2vXyD4mch6ssxd7/gLga2v8SlHfbkHkoiGd4ddL/23giD+mtI6QyqFYpSe1NasaL7CO9AEU5qwQfAFACoMVAWBCyZoAlT7zkrHd3kBrt6pLZQZvxkRiStNhYsOzYDktMM4WgN2jm9BZC6OI191qovQFaA+dk51IV4T06s+BNKqPjZ5BWDwdFA371XJaipFoPWutGeTkLtekl2vbdGREdsOA19st3l9t0PWVizyGiOlAvf0iXy1c3cbqYuiFpEX2a/s68fwVVNO9kQqAMPjEYh+RDwwm7WH54Wk6lLbLyF0fX8MC+akRU4bRCs7Y4vDuhgE344jdzRbHh0NpOWolhItgDHeofI450EgmyYpRSEhMQ5xNQkpm9cyFQFFzTvXep5TgJ4/TaY/j4b5KyIYFEkmd88J/7SCsUEbXdSsysWUhTot5PhUcxc3DtwVncfWayMyk/ivnoJ+IXEmyFdXwc0Roa5ub7APBNQC09/d3ezy8f8D9+3v88MNv8fHjW3z8+BaHwx1yzri5+QYAoA2xRKoLn/sYIqDX9y1l1lGZ6x4T0HbnBqbwlSxBrgJSLYJdqLkbfEMlbhOw3gnzfGLOl5q9I4MZsPi5GP3j8QFaG2w2N3jz+o8AAG/+6A3e/Oo1fnlzg3i4jMdg2/d4vdvh9XaH3SvCh4xXI7puBHLC4XCPDx//gJ45Y7qhIzK7HeEBrNvAdRYzn0utE59TXp2lLuaSEaqCR2yzODAQlkClFPzssb874P3v3+Lt27/Fu/ff4fHxQ7EPw7ArmcUfG1+0hkuIxcBN3MeYc2auYJS0+vWba1hrcegdHj/uGx7xyqV8ONzjdHrENB1KetrariwCCfhURTHnemw2V9hubjBu6IHabK6x272CtT2gFR2mY4d+7AqPeumHLwpmVfzkkqEgBl36+HkzN44AsGbr0s3PzoP94gCkJk16BoIpWCezThGlGIoYDyBecixUveJQARUIeanhH8cdtLbIOWKZa1tYjIG7COYGhUqkNinUdja/eBzmuRj7zloi4chEXZwKaE+xY5k5wieDf5hnTN5jKexZsUSRAArYKzLbm2vUE1MKROU8dFD2MgKfk/eYvcfQdXDsOO6GAa+2W7y+2uHhZkv908dpJaoh96ofOkDrIhhCfbxMLsIpf4B71NlpbJ0+Mf5C2Us924F5IAKWhrRnmg5EaRrawzJBqcscXhKWSRhc3ffbvsfNZoNX2y0OtzvKOMxEk/wp4RmJbpRSCGpdbioObREmMdCaAMLJGNgMJBVrBiiuxX5SSFhOC477Aw77u9K3X9+bAgdnL4v6u75DvjLY3Gwx3FPUp7VGCORokPE/liyA978qLYYAGf5h25eyDqG3PRZuAy2gvZRrB4+UpBRpGRQuBxCT4N27e/zw/Vt8/PgHfPjwezw+fsDj4weEsBQeE4AAboZLB5eMsATAELtg0VTgzMrptMfptF+VX63tmnIdpeY7N1BAZpiqlmk72z1CPBuxdAxISzKVDiogXIz+PB9xPD3isL8joF1K2GyucXv7C7z+FTk93/xbb/Dr21u83m5xuFCrQMjEtn2Pa3bkdrc7jOMO1vU4nh7x8PAD+n6Dvt/A9R3RLbOxHXcjNkxxL8I8qw6fBjCZMzFiWmdL94+IcLWBAUD6G48fHvD9b97i7du/wQ8//Bb399/D+6Xc+67r0fcbdJsfz/J+9lSQjfnd736Hj4cDPhyPeDhSn2hKCYeF2lr6k4OzFiHMSGoBTASUR+Ye0n5nsH01IKVbdF2HzWbHNTK5yXN5T1oMSQFadN2ArhvRdUNp13CuR84R07RHr3r0yiBlamVLMxkAYS3SLiHezfDhhPl0WM3rS0Ne9/btW2z6HqNzpavhnE1rCQH7acJhmbGfZpyWBTPX1WdWyvLcjuMXOhwIEVy7DYivmb93WjBP0p8cEONS0l4+TAhhQooeIc5I0SOnAIUEramNKmfaKNZaGNNJ99yz5+6sQ84Z47grzgvR+GZa03mPw2FA90HD9gBMwDIfMXAfX3/oMY09jsOAq2EgEiROwdvGyJXaXc7Exse9+qdlwWlZiuGfGdHsJ4/pKFStR0ynGT5OCGleYUSy8khYEBnp/9z5f/e732HX99gOQ7nnSwh4PB4R9nt4f0SMM+bpgMP+gOU4w+wt+gee/6aHG8jQd2NHjGgssGGcqRkPZ0sHh1GasQ25lBDEofAzMfQtxxmnw0QR4McHPN4/4Hh8wDIfC3dBSh5oiIueO/ff/v732PY9rsZxNffD4YDl8QHLdEAIE0KcEeKMeTlyRF67VoCayhZiHunRL/353AlkODVMugWmyu/qpvTjCQW9TAv2d4+4e/cRDw9vsT98hPcnKCUBA2Wphn5A3w8XzX+ejwgxA7pmTeh5UlCgrqRpilx7jgjcVXDcU2fB5nrDKWBbWhvlfrYZEuGwlpJKyXBEVm3je3+4P+Ljh+/x8eMfcHf3PQ6He0wTnaNaGwz9iK7ry5rHNGNZ1GpOXzv3j9+/g9YdPr7/gAPPZ54PiHFBToHPogyA+D3m6RFao3SQhDCzJK+FtcTn34qKyRBOACIlmuGXhbOZC51xgu+IxAg7LwfM8wEhTtBao+8H7Ha3uLq6RX/FZbe8YHl8xP3797j78OGi+f/f332HDOCHjx/hJ+ZG6BKGYYO+H2CtxbIccX//Fl1HGgYJC3ygTMjNm2t0m76eb0gIKZRWzFYtNOdc+EBkX6ikkGGQMmeapoz5OGN/t8cPv/0Bb9/+Le7ufo/D4QMH1RodZyT7foAxpjBWfm7unzX8794RIcCf/+N//FWL9lPHp4LSZXnEhWWaz453797hj//4j7/qdQDw3/z5n//dXsDf8Rg3wLgZAAwAXv3oa58798f9bwDQvdlsJBLtAEQsyzu8f/8O798D+OuLL/9f63ju/P/bf8Pv/dcMIW/7+z73zUZhs/n2sz9/7vz/5//+n33y530PfPuL8+fsEfcPj7h/+Gv89rdffckXj3EExvEawLpla54JiPab37zFb35Tv//cuf9P//y/++TPjaG5P50/ADzieKQ68/H4d78IcgZtNk/n/f79v8Jf/ot/BQD4y3/x9HefO///8h/9ox993e3tCOJFSXh8/Fs8Pv4t/vpf8xloLfCrX30L4HzPz/jtb/8lfvvbfwng83NX+TNNltM04a/+6q/w7bffXpwy+jdpxBjx7t07/Mmf/AmG4cvMFn+f5v9znjvw857/z3nuwM97/j/nuQM/7/l/ae6fNfwv42W8jJfxMl7Gy/j7N14IfF7Gy3gZL+NlvIyf0Xgx/C/jZbyMl/EyXsbPaLwY/pfxMl7Gy3gZL+NnND6L6v/7BHQAXoAeP9e5Az/v+f+c5w78vOf/c5478POe/5fm/lnD/1d/9Vf4sz/7s/9XL+7/i/EXf/EX+NM//dMvvu7v4/x/znMHft7z/znPHfh5z//nPHfg5z3/z839s4b/22+pP3BZHJPpjBhHpi8cduj6Dbqug1IWzhEjmTYKxhJdrzD3iba8fE3/ohJ0FLL6Sl+pdKNFbwDgXNxGOM3XP3O9g+1tZdizBlAkHnTcP+B/+R/+eZnXl4a87j/8j/4zjJsd3NAVLuZ+0688Qrlu4S0v5BzNnJNQjWZUffaGrS+zgEWlp4zE3peYjAaVpjTHRFrZMRb2urB4HA6PeHz4Aadpz6+NhW50nt8+e+7/8X/6n2N3fYN+7ItAxOZ6g+3tFjc3V7gaemimK44iPRoCFp7/7IlbnRjnfLlm0vmudK4xEkNYSpV/nTi8n8p6ChNizvy1iKlMHtNxwumRRZ9mjwxau9P+Ef/n//GXz57/d999h5yBq6s3+OabfwAA+OUv/x18++tf4fqbawzbHkppuM6hHzu43sE0VJvGGhhbKayNMdBGwWoisBEqXIWG6ZFpjS0zRYqGN22SDB8CERt5j8UHnPZHPLwnPfb3373D2+/+AAA4HD6SkJKfL7r3r179uzCmQ99vsWHWzO3tFpvdCNs7uM6V57PhCSoMmUrp5jmVZ7RStbZEP/Q3ISeUZ6A8Q23TkTz75fmulLwpJBz3RLZyuD8ghYRucIhpwf/+v/2vz57/f/Ff/de4fvUG83HCcU97av9hj9PhRBzsMRXinZaYRv61xpZCanP7SMvCVHperTWgqgQzsfiplWgPvUeVPm41UYwzuPnmBr/89Td4vaPzudMax2XB//U3f4P/8Z/+02fP/Ztv/j0MwxV2VzdF4nzYDbh+fY3NzQYxxEJRHJg5M8VUdOaFaZIYU89Fp+p5bYyGdQ6mM3C9heuI4EpIroT6uiW8UqwPEUPE6eGIH37/Ae9+8w7vf0s9+B8//gEPDz/geHyE9ydst+bZ8//3/4P/BPN+wcPjD/ALEWF03YCu36DvBhhL9NuiEdP1HTp+/uV6rbPEyGl1Q+lOnyNbOgQ+G339t7D8BaK2B9CwJ5JQlXEW446keserEdevr/H6F7cAgF/d3iLnjN989x3+2T/5J5+d+2cNvxg2umALrS2co5RB12/hXM/sTA7OOeIiZ5UxYd4CUMQm9GoDyKG95r+XTS8CNkWXvlm0VrlKNa+Th4V4jekGaCuHbWUA+9oUjrxus73C9es3ZaEBcjCEU1lGTmSMP6U/LVzNQsubErM0xdjwjtNmro6AMBnK+9eDRWgfI4u2LJPHMi1QyjGXP/N7LyeklMvaPXfu42aH61evsbnaYLwmp+fmmxu8enOD19stNn1XVOsiGyUfIxZmLTwuC7EVzguW2RcHQOYaFnod6X4HpJiLEyB0lmXtQE5P6/QJx7koYfX9DGuIoXE6TMQX33C7P3f+MSY4N2K3e41vvvmHAIBf/4M/xrf/8FvsXl0VYZVhQ0IdrrNM0VoZ+eThJ00J5r5nPQejqkMsOg9akc6DiBtZbVb7LAijofeYvMdxmnF9+4ir2weMmx1Upr3/7vcWD48fCvvZc+cOaFjbo+tG9D3t+3G8Qj8MTCdKcyVlzlyeh6onXxUJRVsewEp4BpD7KzoWzf5uDEl9LZ6cD7LWKWVoRYZCZYfoI1zvENJ00fyvX73B9evXeMQj5j3risDBWUBnR3z0Rhcq2nIeNCx/IrzUMlTW80ruPVbqjUW0xxKNa1Xv5DVIZDCE6lgpBSSLbDp0V+Sg3Ww22MaIj8y0+ty5d92A7eYW/bDBsKEz/+b1Da7fXMMNjrQxvEaOBs4QtXDwYWWgRJxK6boGqnGUgHo+i3iV610xpsKBD5BugutcYXkUR2u+ndGPO2h0CMwoviwz5mnCPE+F7fS5889eIcQAQMN1dO6Nmyv0/QbGuEIF3o89urErz/8Th/8T90/uIQBYmxBtROpo/eRMlyCppQAnKnqi/6Xf7dG5EdvdNXY3t9jeEqnS1etXGLoOj9OPz/3LsrzawFpXqBcBibqbiNecC8sAqvGAFdSZMZSo7cxApsxaYrn8XB6S8tL0qejPIItYio+NN62g7FOu/OcM2zsMmx79Zig3diWfqxWLbjTRaisHueLfb4w+vy6ldaQgm7r+y78sDk8iZyqbDJ2BxHSn1hl0XYdh2GJmsRJRQszqMqqGnDNcZ+EGVw6AcTfiehyx6Ts4ziZopRBiRNIaIcUn7yGjZjPOsh05IycUh0bW6Pw9ckpIEOePJC3LumnSZZAIJUdSc8v+01KgXzO01hjHK1xfv8GrV78CANz+8hV2r67Qj0TJKWvjBlLd09aUDJdpHFeANB9ErfHHhog6KUWyv0ZXByHnjK7RJMg5I15vCq/7fKQIZZ4WLH6G96dPfsZzRht1yh6PIUHptQCJvAYQOWKiW40+rCK91ZvmXJ4dAE+en3PHNzYOROts5FyvQWuNrInzXP8E/HL0tJ77e3qe5sNUjBpdW0YOVZkQZwGOnIMZjdR2iew/HcDImVbEjiS6zw29ddE3yAAyc7g/YtjSMyoZo5Za/DlDKUvc+zkXxT3XkxojPZ8JkDMK7Lj7GrS0z67OGipFmk+qjiBAz2hSCsG3n92oUTaOn6gllrPdaHRjh+31Fre/uMH+I7EGPt7t4Loe1jq2Uc9/9qfpCL/MUEqRMA9QjL7WhumnSQVWshEriXW+z6sgMNX5rbJDuZ51SiuoJCqdQErCtU7ZJc3zFvGrEEieWzK+AOnp9M7BfsHZ+aLhJ5EFwypvIrvI/NrG8c/1SoUpaxLJkJFTTVXLBHPmScr+FwlINmzQCkqMxCcOblE6AgAdE6sRWSgdykNlrKke54UPQT+I91nFbqqxUZ+MxCVyWV2vqlKr7e+LTT5PFdLXn7+uVRZEKWhj4IYOg99iHsnzX/zMyleXidRoVgy01pRU/3Yc0FtL6erG+24f2NUhzpE7ytz58GoEV7LocbPRjyy5y5NbXRNlVrCOJDXLWDqL1DG/99jRId18znOHtT2url7h+vob3HxDqbSrV7ti6DUr8bnBUVr2E/K/4hQrliZOOUPlDA2FxM+EbtYsK5IvTikh8PdFEljkoAGSDO5yQkoWcegRd6TzvkyvAQD7uwOO+0fM8/6iuZd7l9YOmIjuRBWhgioReI55dahprYuTnnMGmi3Y7hX5rPb9V85yk+1JKZX3VEbDJLMqJcoB2maLFC7z+lMko396JAVKAPBLKM98zhm5yTgUY8U87ElTdgrWsD7B0+xluxbGmprN0OvygcxpbRSrwVimBceHI/Z3dK+3fY/d0F84c/m8CGNMEWFzLMoml6Q40+OXUBQHy/XK84+ayVUrh0juKZCW0EjRasQQEL1BaGyKN76Uj1elHk3Gf3O9xfUbovEdf78rmWgKVJ/P+e69h9IGvXWlzNV1Izlkhkp7nQhtGbMy+ACaYDWvvi9zb896Kd1I1qvs55xhWcQraQ4UdX0vyZiKJLeo4/oYMXv/xTPvy4Zfi+Fv0/L1a9M8dEppKJnoZ6Kac23ispFyAnRVXtPQvHGy1AZk5eghSE/rajJKxMWqbJZFUC4ZVGvqYWzzHlJ+lAMgkdpW9LFG8801tempcuPPIp1PlQjkQfuk49NECkrVOXdDj34iL7XrRpLQjRcavo6UxWznquHve/TOrpTkYkokO8z3MzUb/lNlmpTWymOiWtU6RuIkqCZbISlRWa/z9ZByD127g/VkDM0XJCo/N/p+xGZzg93uFba3lO6WzI+Ir8gaabNOY7bXRQI0fI3lGTpXcFz/TIaIFwFAThEpo5RyrDZwNqNLiVKPmx6bK7r3u1c7bN7u8Ph4mTpdiaRRs1IxxKLxrq1mw6PLPcoAVP68g11S2U1EK4dd+7mfPbRadTeWcs05k9P/ifOGDt/LnvsYEqYwYTrMBYuSUgIiZziaEoNksFonxEADujkjzjOe/FUpk0QOhpSCzqqESauSaUkbV8dAAo4W33K/PVwc7cvQ2sL1rtStTUlbf6KM2Zxp8j3+gv5JGVD1PpdgqQ0GTWIFO/s0E5ylNOgpi+bI0ZTskOssNjf0fF692mF4t0XX9QihxyWGXwSFqMxFz5O2hjMghD2wvSOcWilnqCc25lPYD8GoAVK6pPMhRQWYdZYDRcyH7n2MceVoppiK8JNnw39cSA3Vh7Ua5pM5PntVXsbLeBkv42W8jJfx/9vxxYhfvDKlKgJfKb3yytp0jkQ3q/fI+Qmyv/3d9nUqAQnkQessaVK6EhmlJsJpMXD6lzINVcoydZb0jc9qb88ZtrfQHOWcI5Elao9RdOibaL9Zn8QgnJVXnD4RsZ6liku0oNp0K6q325QMdNYc/dhal+oGhLD8eM3gR4bIyfabHuPA6F7n4Iwt8yHJ4YAlEKjQx4jAXmtIpLNNqN+mbJPWEdCTr5tUreBBfmwUL9oCrikfuM4SCtZeFvF33Yhh2GKzuyodHa5znJbVMFbXWl4iAE/r9cscRGNb0vZGKcqM8J42ZxFvmdMnMgirCAvg14A7agxcTxE+ATI3sD9cFvHrT6VmY0TwGo7T+EnxcyoYBF0j2bbOC6DMn2dZUvbnXTA8eUp9fmbbrnAiTbRd1sRoBr7h4tAmhoAcAL80uvCRMlUExKQoVM4AvmzIkaq1qpgW/v26ZzR0Oi8RpJIJka4mpRVk52ZV8RU51q4guS4/eSwnwndMhwmHvsPylXK0TwfNsRtqxN+eTSWl3dTrkRu8EmM+FCSdTRldbRTy2Z4W4DWd5TWLIlk/+j/XtG2EcbEAemU9lNYF27O53mIcd+i6EctywhcC388OYxy6boRloLjssba27xiQ6HpHKH63LoW3gMxSqmnS9TorJK2hGcwq2exoEnTUiLweYfGU/U4KWSuYBl+XAq2Nn7jGf5gxuO6LyIYvGn6A05XGQJsKbit/S+2l1qnaw69Fc8r/P/0Z/DO9dgRodvV1q1af9tqQSiryHDCX0tM0+teOFhVZDoCUChhNHv7V2zeblr6fC8q5XHc5JPnBj0+NoTI1pVfql4nSqwCVTY3UMlMs+uXOcRuM62BtV7SZnzu63lEbjdXoLN37FjQiBijy4R3YCZg9bcJl9vCTR1hC0SGPMT65F+f4gLatcXVIZAK94OzwoHS6hkFTV2Nch2aE7SXDOQJLDpuhgJy0UWWvrxHbT+uw51s95Vzq+Qm1JJIygTiN0ry+6xJA/f0zsCQ7Xu1yCr7FDQ7jbkTffZm45FOD0p2agbmM1o7V4KaUoXV+6lCX2t362gXnc54OPXeABbQmTu+q/i/fa8ChOSukEEubF0DlR607MhAeF40QItKSS/kOQC0zoIIXoRQBsSKXudjYBgAqpvJMCvBPgqNPDXHqNRJhPRKQpNTVtMwVp7gpF8YYS513OS2Y5uWpQ/XVQ8EYy51a1ZFRRgONw9G2arZnFADGfACRDa+xBprr4efPbvk/3+/AzrqUlXI0SDpx9w+dH7KGUkt3DLwetgOGYQPnehhjLzL8ShG2zRhdrkGutR8J0yMBgOF6vnEGls9IpVHmmrNg3p6WsRR/1go7ljKM5a+t2C+DzMEsUi2Hy1oF7pwCgOU04zh08F9w+r5o+KUfl8ASstgGWhkYY5/cePKGa4S9Qqr+WOgmaNcG1NGONtpukf0ZmXFDBkBEDAZGFizWnuBLkd2Ko4+YUvHs5ebFUA+F4o03bX2r68+qtBQCeDJHOrBbsGA1HvS7tS6kYloZFZ1o86ucAPZKAcC5Adae4P1X+XdP586958bZVc1QIn35murQCSFGhJhKlkjzIZdSggXV7nTUyCp9sh5O81UF3Sq1PRlJJeikYUwGLEp9Ta5DIiX57IKgvhTfYRycI2CncXUNV1gEjni11dXxLUDVM9AjMkKMJZJv637WGCg+IGLKyDnW95aokPeYOAwZueAr4v/D3pstOXIsS4LqawSAzKwi77m3p2f6/z+s50pL9yEPWZmJJSJ8mwdb3ANVxWKC/TLSdBFKLcwCEAEPNzM1VbXcJXFy/WEKiNP00LUbPvwpufkGadHsD+1vPrPD9d1LenXZ/c83a2AG7gz4vCulwJgBIeDkyVpK9E0r/bM4umfOOeT8IMWtEWqRc1bC3pjYjXyT8fOLqMXYxvptaPLpvCOS3B2y9xXqaTrLX/9+kPdqtW8MrDcoqagCAQCW64L59ljCR5/HKo9FzhKpfAXxkGezV7KclAM77o5F7c/lN553SeiQC6p3sJrQ9PO21kbIByM8JZXd9yAFKAAcnmbMTwd4H2HtY+ceQNw28qZx+jmp4vcs1exoR1dodQWGMRVmZNY3SVr3yazeAyY2Ax3plWWtRR33iCRYxiBvGeuyYVp7sbXeVpVUf2/9icA/ksgGeMqYnYRh9/MDBCSkq2+ZXXwz+7M9QApZzBioUQtA0r1ezXc4rTUD64reWDFGKGyK8MiirKo/+EBPQipn4aJNli/2nszTYSkoInKvMrhvDwjsB0CNbPT/iezD9GSpcPozkke893DW7aSXH1ldY+t7QB0SuForEgee3b8TUxW7b3d8F7sd1hhUxwoHoHvXLOlZDcNj1hJ8SDnPt5GEP0w4/+j6fUDwEWGKrBrpB1UphQh93inMLvK9mvdJUeW/V5KnMzDN7Mh91hglTAp8X1o//ACC9Uuj+yEBP9dCSekdEgaAWMfhwcOPP5tUPnI9wESTFcAAACAASURBVL5C6XAzw+1C5Bye9dYkCPa2zQAM7P5Mb8D0N2dhLMPkhciEJnXvh9aaVodAJ56ZChi//8yPLKqmsh7I90hO42dfqn4MPHoxbvKhBwmVe94VN98iNI73Sv587/WhypZS9cAHqOJPa0LFYxW/cwRnB/ZrkPcqpTAaw4nXUIHj7rwTFG5EfiSZuU8AqbjhYJ8LirVwvg6fx8IHN7QCSONORdn+vlnvEKeAKR7gfcAjy1pLaGn0iiyUXHZSTH3e+dkei0B6DbPb3D3IfyOOcCujDDFKJJv0b5qanskSf5acRc7HzwUTmn9U6P64x98aDKxCf8AQsG3PTOVGiGnJ/Vl7fwiPwR/YVwwj1GeM2fUrqKffH5b7bLnkohk6yZtWWG81eH907SCsEWYdH8LaA/f3Yow8CGNVOq7u5jccsBAGLzAeKrtMcXDxkmSh9+A8rPMPPwCS4TrnhqpzQDwYAg7eMysZuyQgSbUuD/UA4WtFJ9eeC1ettTOl72Bs+S7G6kGqBTdk5rJoP7pd++gjK4QJcZoR56hqgfGQlu+q5AoqTVl+M0KeDI+KtpwqEwfnjFbujBj3awTYEbG3Afq9J8lOLkWh/jLsAUmaffScsDwW+AXe37cuOntZ/o4q8b0OXX72W7/KffvW+hZsfG/iY5yFZcnwWDndJ3e6xx5sc5PZFMmlpIJ1zOwuhfhH1lvq7XIAGJ9rMaUZg183eBo8UIY+8O46vwGOlFzhglMzMFk1eFjW0gPAeltxO99gw6M9fnDC0j9nYT+GtCXkVDqqeVfkAPsWq+XNbQwb24S9IdXo2Jm3pMHPBaffb2GnUvq9hUkZJfegOr4nAHaWnLTl+eFr92QeZJ3d7TFrLCcp4GLy2+i0LE18B++Jb/2M+CDs2lps5gOgowmMatLfVeWKiJxSP+edr843r/FHN0EqeLqIPUQxOlZJ0PfhO3rmu8D/rUpMbqqsDiPd/dyo4b/T1rfa9IaZlLHdNljnNCP66KqZq/rhI7TGMD8Tbbq2t1/r/bXv7sO3Nkvl6xoqPIIw5T3bV79SwKxKHpSMVPpSnivW7VunyJ9cWt3fvf+o4S+1oll6KEZCmqA30tdsrcFV+tXsTFp6Fa3SviHo3+/h8cChz2P4oRl7/AT/+eDJPvWBNU1HzMcTwhy+Diylohh62Kw1aM2jln3iZyxZGVtr0QwjPqB71bDXmI9Jk0D8skZkQJNCw1K7xr1zDrwCyU7HCfNpVhnmR9cuMbfj99kDXCtVKx7L3/94A8bXkP3azwG+Nt4XavhVAcH/7wmC8p4j0jAmC0oyHCDYeh9R/+TKG7lh5gEtFAIXVYSkcQ9T0MDvnNv1hOX65O99DDv0TD7/iHJKkqDS12/cA7RuBFZSxu28EPrGcO9yvpHOfH4s4R2lg8prSHlnv51z5s9AQSptvSUi1Wi/Jw21Om0Fj4juKOu11sKDEpyaq5rSJM/EZe+ITJ1dr7jZv0P6647tkLuO/+PLD7bbOyK5MSilaktFrkGC+z3iNiI73wrEihJnMuNptbGk8c62Gvv4KftDpMTOWf3ueyz4wTX+2ZtRSkYdXNnuM/mxUv1WZS+/V+jvHt6qX9+cncPV3SLi277vXEtT2B2goL2tCdY73UQfXSPJTPMdZu9TBkaQjrEYuAffqT5Sg6sN4sj0Leb0+PMj0iAPoLy3ZIRaGWsi1LXsLjj4EGEehPopS7e4Z/R65+CsQW39/tBV7/Xo3lrU4OFHtzPu20q7SF6TqrOqCgmBtsgPnhEM57rRCfomhxMY0cA1rvxdRS0W8A1+eizwz/MJ02EiC+q7vVmGACQEo/v2jfjzmzC6eFmYWmFKfzhba1rZy16Th762tgv8ABEsg3Pk7jeQQ52zemBNhwmH5wOOn44PXbu8nxma8PauDSc/IwecGTJ36UErSmD6PXDOfQ11y/UZ6gtXOQ/knt5xSvgPqLVyO6vve2st2dqWbnv60bXeKOhLZQUQYXK0kxV7WalkyaJVP1pHH/n6yRNj7ylCHBr6t3INU+xmYfftIGPoOautYU0J18sN77+9o9aK90Tudet1xTXeMP/54323DKzuRT1LC5kTkZ/80FZ0ezY+/VrRWoUpFtVWtMbGavLcDsGQ4H0qrCqAWi37fBSt8sfcbdfmYJdMY2v3l3CGHf4cQngw8DPEL61cej+DktkKvHQuF51XXydnOm9hMGYaLgIAkLnKr7kgpYRSElLakNJKrqvMjKQzN8B5McwjN90YD5gOE1prmN7Yqvx8Qv7pSdut373GH92EWgtqzbv+g7VW/fmt46pv/P+c2Y03YSQ8favv1qSXb8zuQbm7V7ruqwtjDJwFV4p9A5pMPa/19nEjB2Do47N9Kn0W3nwciOS8G4NyP5s6bE2v1x2o4HqwkEBoDSUIY9DPqUsUKevOmiECHUISIxnt8QcP76My0j+6xp6m2MYGS0NkxEUuA7CNeu7eOUzoFrNyaI3fd8kOxmSt2unv2ISCXcDk0AaA6rr0KRgDY/xXsKp+Xmvh410yOATDj65pOmI+zR2y5fcQt8DRHCanAmP2B4AxBtUXlNINsOh5CWiu6jMh97e1PYwOkNRPWzfGwDsLxwZanl9TGLyCcADEbq6l4unT00PXTgGd+qryHLrgNbjK+wHSDvx2C48Sum/Aj3+AiumfLRQB6yZPGDggDcY0Qpt83/fG0tmxpR48PrrSlpA36mnHmQLIfJxx+nzC6eVEszs+nXB6OVIyEAI8IzkAkEvFmtNuRkXJgnj0exciKWdC9Jh8wOQ9phAw838Tf5+TD/DO7mZjnJcF/3x7w/8AcDsvuPLhn9aGtGyAe1DLBnxlw06wdz+7xkoX+JqTQN9PQuGEz1qLLGTcIq/p9Fwcibie74d634fOOZCZMD4GTSQElabXobkYxLZ/tMUpn78gScGoKpOGnBPW9YqUVpSSeoEyBFuyuY9sHezZ8n5/ZhHKmZHzhnW9YlmuWJYL1vWKdb1i2xa+l5W5NkQ2nucTpumIaTriuD7jmJ+VhHl4PuL06Qnh8Behfqr0qx4EALHFp+NEMN1AxKo8rOG+QpTA/73+vv6c4+ErjR6fmsu3/41wC0Y41Nn7s4T/HWlyt/XBwM+QiymDK5d38IaIU0EhIUp+0sp2ibXzDO6Z/o4zVfLY5+u/6/1ba3Yw2JjM5EyJQBlhfmO+InMZQxvQmMcCX+da9CAuLPMOOVPQmrxHdA6letXxrykp9L8KdJhr72nxoZ7Zb1o8p0c+hmuACV8HBbl/AFfW3G8VzwUZkJG2hDg9dgDM8wmHpwPiIcKpVKfDbdZbtjI1eq92n9Mauha+HiF5tdpQmIQE8N6xVdsngaeyeeswBa8zEbxzsIZY/+KfYJLB6hwWPgT1+uGp6ufhSh9dEsyd80pslAOfZLOO2PcWHercv0LX9DNRyXKlPyZ9ggqMlVwbYGbl6xTquesAE+bXyDNpHemhAcDZLht7lNSb1oTtlmEMDYkBgOPLAU+fyR72+ecXfPr0hM/HI57nGYcYd5bKS0q4rCuu24bbtuF8vWG9rhTozD65a8zt8c5iCgGHGPE0zzhNE06syjjGiMnTXsulYEkJzhgsacPr0xHzaVIPh21JJEe8PoZyWsca9YHcZwZ7YvXrl8/f9sUcTVqsqI3h69qHzQAYEjRuD0dqFVueOTIdph0/JUSPOIWu369tV4w1QV4hZ6vZvc9HV2t8Jm0J642Cb84bai1IacWyXHjy5cLoRN7FR2stvI+YJpIVhhDV658q9v7d11qRthW32xnn8++4Xt81+AvCXmvhQjgghIh1fcLp9AmlJLTa1T8AzVJ5+nzClP/4zP9xxc9wA1X5tAlpbCL1PaupymiUvveoNRxh/5H0Rwen6xJB1+FNeRBGyHsk+SgzdqxImV9geSqSvB9dQ3u4xw80hfStHq60QcMUNSstqSCt2/4+AD2gpaywhvSnfAyofPiH6GGj1SBWa+sM5jtiYpPe2gD5G2NQkoWPHfYShcGjrH5NNkpVeciSEnIp2qOWwO4sWSzLdDm6KB5T6RwW73H1VrWnADFSAbA/N+tRuVeoRiYcXOW+i2xOxnaO7Frvu/xIDFyMBezlses/vTxR4Gc/flkjbEtJgdsRcCShkWCVtqQIhvUOaA1hiijSkpGWAQd9qfYOMeI0Teqh4KxBLhXXbcOSNuRCjH6pMuW5ke/OGPxwWMf3VmNp6AhT1lpRN9lrmYIBP4s7VA+95z56lst9oWedkxnvBkKwUfSsGIO25f5vB5hZkkN5j6rM8/v24x8zm/9o5ZW8J2gEKiVP89OBR6EecThRYJ5jROShKOPwJc867lpJ5lpzIc7AlnurUH420F5OzwesJ/LBGPkeAHDj8bClNmw5Y8sZa044Lyv1v2M32zHWcN/4scA/n2aVxYXBwKbyvpcixTmL4h0sJ4LfaqsItyOlBeR/YWEn8RnxTEINu4mq0vaQa9+WjYoPnuZnDL+O7wnemHSPbdlHVt4yTF2xbRtutzcAQEobct6wLBdOAipKTqiN2xrGdha+87C2opTEhRdxDuI8IU6htwRbg70a1JoR0owQZoRACYYbeEnjXBxJMJzlgqNVbNsNtzPdv8uXI95/e0euf0xsfJz19ff6e/29/l5/r7/X3+v/d+tPWfaKfeHhwHO5nw5wwWkl2mpFYvcg6Xl0aYKwIi3LwqRv6FXnCoi+vUsVRC4njMecV369RjI1NWrhgQpMtvHRo03ynkCY4ldM248uwxWNwOhkjBIQWPIhvAKbnVYuAlGmNeF2vtEMa22ZUN95Ok67zN+x5lcY8MaYrhqQSq6I5G0g+CkkJASqnqX/1SVs++u26WvWgYzmxOCJKztn+zhQ7xymEHj+PLUIBKbdlq1XZpUkcRvrUWupcN4hHgjiEzvOMEdE/o/GBcfdkBzr+kjcxlW1cRbh/BjU//T5hOnI5C3X2wrWWf3+40wcCoHpc6H9CjAzfE2wF6O93u22sdZ2X/VZNp3x3DaRav8Qg/Z1c6W+8ZI2vC8r3m83vC8Lbsuq7oiyGreEHpUyOu5LSlV1v1oDWu6qlm7X3VE4sZkVKJ/McMh7wYvN7cHA+kZ/x1yKVqnH3lqD2TpTX5nmpTHPJcPa/qzI8+mC3yGCj6ycEmqpCL6jenHufg7rbcU/U8br9YowIDLKdUDDZV1xeaXpfst1weXLhchhrNuW5SOhCoenA6bjpDaw8RB138kQMJ1caYzebyVNDufctiaU+phj5/H5iNPnJxxfOjG0pIyyEsoiQ89UqRA8ttuqaK7dHJ/b1L+Wz5dzhkse1g3SM9sRPGnhAXTubGcaOiRn6aickL3ivN+pbgR1sP7x739bN7RqmWTHqGTJKIygiCW67DttXZgO9YcwYYoHxGnGfDwpcjgdJ/35UgrWy4LpOnG1H3E6vqCwC6uoEqRdQKqCgpw35Nwn8LXWsCVqSVzfr3j77R0p//GZ9yfG8gZM0xGn0wtOn4koNJ8mCghsZ5nZOSrnDSlR8M+JAkWpRQOTEBRimBDihFqjQqBC2JKLK6kgbSvW7aYsR/kC+rRAy6SJgGk7IG8J8RCRt6g3xDr3lYTmY4vlIkwqAaAkE2vNDtqVh0HsXOUz5C2Tm9J2U6ZmjAectif98iLPuxayT2OTilIoqdI567cV63VF2laU2tsw4lIlgRrYy78eWWJJXFLBbWFXsHXbTSLcsbuZSCe+/k8zj/B1DtH7LkGrJImRBzPx/VmvfRKakH3GyYCH56NK1AQeFHmV8xbBeSUWllpxm1dYa3C7vD90/bPA/OzNLdcZokc8ELM7Bo/o5T+H1rBri1xuixI3N2zc00u7vqhz3WtcVBMABfrblrCwBfJlXXFeFu0dL+uGbdmQGArdkyipzfAtxvGfWXGaMU1HDnbSatkfpqOaRHkPQztsbO3VAtZsk9GW4T1aUiYJ8B1HpbWGtKavDm+ByYsQaTmZ9qFDzbUKAcw/bGBUuG3ifCc3AmyOsxH5NLOBDbUCO99DVtoyluuC5UKT886/v+N2XpCWTZND56ySBI+fSEUyPrcjqbcT4SxPYjyqwqAN51BjbsS2PcZrOn464unzE47PBy040pb0THPekcyRizJjyS3Uc6sh85jYbdnQbtTjJ7h/3SW7MgtA2gHO9deVeweQSqFmmQthSKc/BcQ54vB0YAIufc85FW5R2R3h/EOrVZBzZiepO0dE6ZNzmOcTnAvc8pNzt/8sJaFOA718RlGESIKWU6Gk8HzD9Ns7wm8BaU0QC2DhAI1ERpET3i5EACT2f1Ei4OX9DeHXgPzXA7/HFClrmU9kA2mcRSuZ+jFsxVprhSuBeiFpw8ofJKVFCTbWOZ5rbGCdJ+tVXprNcyW7bTes6w232xkpLdqvGl/LOY8QJngfUWtGLgnbNiFy4LHWYD7NOz3lI8sO2a1cf60VZeXMlU0oNFBVaPW1LRv1v0pW0kZrDcFHlJL6yNifnxGmwBkhVYi1kqnF7e2Gt39Rr+l2O3MCIYQPhxBmWOshw5PGakISjUeWEDfzlrqe1ZLhRFoT912ZscwmNfNpwvZCyFD+VJSgNIcI7xwaGq7rBh/6+M3Env5iYiEVNsmc+uCZyA/7dJgQ5qDsXe8cJu9xnCI88xlSKYjcf3778vtD1++jQzzEXQ9SiETWWwTv4J3TKl3nGQzJxxTDLshba78pLRU1hvaGW8XCB/dlpaTry/WK1+sV65bUtIOIkXzAS4Mcvbf66JomYq5Px6lzZpywudmJUTX8Rrk1wqtR5IuTx8a675LyjtFvrUUYmPvW3flUiPf9jgCaFUlEI5Z12pw+nwAFxzA9ruigwUpi20qfd72utO8LaebX24q0DKzuwV9EkpKaK9K64fzlgi//+gWvb7/gen3HtlE1O88n/PTTf8Xnz/+Bp89PmuS2undtFNmsc7Qna+ljidXFkPeVYXfLUW/+kTUfZ8RDRJynYd8atcstzK/ImYbDlEIVqnAh2nFGWjcslxXWWmzLxiz1G4Cbkv2MMQhTxOnlqBLPVhq2ZcPtvODyyhK166I2vSMnQJAAADjao76mZae/x4s9sE9BJ5dP05EHdp1w+nxCiH6HSot3DEDPSeBzSrgSh6cDTp+6SgigGPH+2zvef3vXRDktCdbRfZHYICqxWiuwUnFLyW8iRKUWZC6ubzcD93tASn8x8BtD9oXT3D+IwBs2WiDuK4FaCd4BB34it9CGdKioNWjAijFi4mRCdJOFGeutNU4iVmzroq9BX4ZFQNTPQpCLBbDsAl3OBz2oHx3U0uHovZnGljetNHwMCDyoSBIAkYEIM1Yyw1IKUlqQ0grrPD6Vn+nn+AF4+ukZxhgslwV5y+TA5XoFkPOGbeuJkPehQ50uaPUhSx6yhxYnEZtJ8EEqO8o6JeinNWFbVmw8HYoMTwaU4ROx0+cQEaxFdn6nCqDPSIQ/OdysNYiHqFmyHt62P9Tkl02kQpEXCgseEHIVxcFHTWwikzcFWaBb0hm0pVVMxpD8KkZWNfTM/zRNpHgIPOeCvycfPcCELACYnw84xgmBK8uUM5pz8I4mYkpCcYgE9138ijNuevBLtTcGe5LR7Y2nPrJCCAhT3F07ADQOMjqBzYz/9X+vAan2Fs7G2njDQUR+DujyXNnLWZMbkcGKrHb/PrUV1ETIgFRI8tpxmCf/yLLMZJdgrraoArunTIiAcdzuzN0vn59fMbu5Xt/w6y//iV9+/U+8v/9Lz6nj8QU5J9Sasa6fmf1ttXUhe/f4fFDDIB8cpuOMEAOMpc9Fc9nFI79qUvjICnPQxHtUaIVM6O5yXbBeV6yXBRurmKy1qn4g9HJSMq6/OD7PV5LBbb1tO59mvPzbi0Lg623FduvtAbqgpueCHz6TmBjV0snUUjjey8E/uqS1LIT2w+EZp08nPP9EyVlrdNYJMiUGOgBg+cw6PM14+ukZp8/0755+esbzPCuiJwm9oKfOOZRSMB0mxENHw8+vF9zON6TLRoPP+L/u9UHkSQCqMEjpj8+8HwZ+7wP1H6bY3ZG8RRuhFZBMixiXwvLtJjWqA+a2weHwhOPTM44M3dLPGeRMgyZKLjArOQWWWpDyphmyXOxmb/AuwIeIECbOQL32Q+iGEgu41a8tPf/saq2ydrM/RMZSIuAYmoyHqNebGcYVGZ5AXl2beaNK3wXdzAD1+Z5/fsHP//EZ3jlcbgvCHDjzjgoFv/3rgMvbFdfrm0I960r3JoSZ+t2tQ04j/PrRlTMpFcYgbaxh60465ORBraV0yJG/ozIkAc4ShG2H+yiJlPQuxX/betd7+VPsMkonIzEDDlNEYA6BtxQcReMOALCV+Ad/4QCYDpM+hONnFia7t9TCeJo7qlGYxQ0AKUZctw2XdcVt23YzLMTpEqCDcg4B1hisKSHljBgCpuYxeYNDpPcXyZjot399e0dOGdvCozvv3BCBbzuG/Zklvf1xpLWMWAbwFYLWWmMIvMPL8nkUmdgSwdWl921lgqPsmciGJMtlwfX9io39N5bLgm3ZuKWYWEOdNbGttej5NB0ZUg1ekZqPLh8cLMjuWb362S3NF4c2NRyfD3DBo+SC5XzT5AugPb3eRPp1w/Xyisv1FdfrK97ff9P2Z2sVx+MLYjwg5wRrHXmnlAznA47HFwDAy8vPOH4i1PX06aTPGiqP/y2D0x0nWmMw+siSSnU6Tjv4Wvr002HS93LB6/vIPnHM1p9KVVntel2Rc8Ll8trbtrXg+PSMtCU4bzEfZ1hvv+YANTKnqpX2GNBd77q0t4cy4s88jng1VIgmXwo2Hz2hx95pIgsQKuqD0wIQAI4vRzz/9ITnn19weD4o3P80TzhOUc+oVDKmOeL4coT1HTGZTzOOzwdtB51+P+Py5Yy3f73h/EW8Gvp3OyZJlFx5jGZ731o/DPwxHhDjrBUYQOQ26+gLWm8rQxGNDwbZBNwPZxMDIQg+P/+Ml59eaBMf5x0Ul9akGs2aj/z+M2KccbudAUDNhEohuE8q39YaYpjg7ODxnKt+tkdXK2zmkTJK6Hps5x2CCyrlWpcViXuubwzfAMDrP7/g9bffcb2+caVOgdR7Dki8uaU//ul4pIDm/e5zx5kO/+efn3F9v+H6fsVyvuH6flPuQA/0e+Ljo9aVJVHv/b5/vF5X3N6vmom2UmG9w/H5SM5j/Fkz748zQ+LRJdxSwrolIrkNUJ2Qu6yzePp0wuf/+Iz5NGlfDKAe//H5iMPUdc7HGNXYhKbVMdSa+oPxqGNxmAKmAyFdvUI1CuM6a3EIAXOIOMQJnw4HWGO0J//lesWW887YxTnL/emms9ZLrpr933icqg8Okw+cUDAyEAKmEJRDUAqRBdcr8SMEcgbEtXFvuPORpf35O0Mh0sYzMuMkIaioLL3rk+z4wOb+fSllaEP0pE9mVLjgCAaV3n2phBIsPG502bDeFg0a8lmIUNqJiOMy5jv22H9i+RDQiujBe3Iuz+FY4d/ON1zfrvTr+xUAcD2/I6VV22954PaEMOl1WEttwG276dkmz3POGzw/u4fjC2Ik3sXx+IyXl3/g0z8+4dO/v2hRsDDacHunzyL37qMrBCLOzlPU6nRjiZ2gbY7nn4i1sXVup/n33jPZu1Jh5B227Ya3t39hXekeEbLz3+gsnaI+51e2oL3xvbzdKNjVWmEWcrGbjgdYR8iqXz1S7M87tWC2h3v8KVFhJlwygIcUpYLr61Wfz/npgOefn3F6OSEeenF2eDrg8HJAnAJK7lbKWy5wpvNW3pn/UUsBakOYybFxOk6Ynw/qBGuMUcOot1/f8P7bO+IUcHkLuJ2llcjeKeuNi+Q/jnk/DPwhUK9nOkxq/3l8PhKcsG4KoRsOXPNxZviXNl2tBSHQQxmmgKfPT5oF+eAVRiuZPLF9cIhzgGG4N21PmKcT1iNtglwSSk5IeVN2IxkZEDpQ6teBfjR4+Oiiw6zuDjUr4zWNUSOdbdnY7StTUH6nKnxjSMY5TzCeJESRdJsKWxV6jcu64hAjbikpBES9c0q6PD+Up09HbAvd5+Wy4PpG7GFKKrrTGgWpxw6AtGVsS9JeLQCkJeH8esbbL694++19Z8xye7vStV8Idno6P+H0+YTttuH2tFB1lDLefz/j8nbRqsFa6mlZT73CwG0l5z3Dm3TYCvQ6h4DItsHWEqmPhtX0ccGF9wDZ2z7W57UMcY9DMNDIi8F7h9oacq1IOWPZNkTnELzXuQa5UkD/cr1SgrZsCpFba7V/vd5W1ngn3M4LmWBZcUnrOnrDTG7p5y6XVQ94MbYR4tD8dNix7D987dZ+NXPDWgsTu72y9B1pGtwe0QAYoYmBLFSdRVoEFt4T4KRyo9u7Jz3uCFNuT56TFp+1Vl0Wge4zIpbJf2XRsz/8RSMnzfW64vXXV7z+8orrGyF5OecBcqW9TGqoJ9RaFN6vtWjwi/GAGCYOMp5UT8ag1oKcg5KkxdAlpRVvb7/in//87/j0y7/jH7/8N/z8X3/GNEfczvTe59cz1utN3+Ojqw3PjqBNQtJNx4l71jMVIG8XlFwR5qAVK0CVcNo6ElBzwbJccL2+KkI5zycsy0V9TrY1sfrhjLdf3/Dlyy8AgMv5CyoT7eb5CfN8Am5GNf1pTbgZes2OLpRdkvix6y9ozcGYMPwdTYIM0eP0csTn//ITfv6/fsLLv1ERezrMeq9E4XRZV7xdz8pVON89j+ttxftvb1gupIiQAU5Ecs6aZJZMCX7idmo8REzrRCZN3EoXxUFx5C0grYrvrT/h3EfZiHVGM7r5RAHL8ahE8QveFjoAEk+1kiVB1zqrkhXJ1pW1ujaFaAB28HIOIQLGnDDN9FCLZWJKK0pOyExwELZ/CBNi5CAp07D8vlf1kSX95x3czZl+q++kaChFB5aIMmHsK5PcjdoQIsXxPmCeT4PzX8HtfMP7+wXLTCz+6/uV0TT4ugAAIABJREFU+mjLqr3DbU1KJnSeqqTA3tLWGqQta5aqgybaYyQfCXglV+REr3F9u+Dt1ze8/euNPpM1sLYTXM6vFywsw1l+vmlQS+tRAz8dGNduo8xJnspTmJikvVUx/NkStRmeM67zRNV2XPEyHxA8Bf9VGPXbpgH44eBnrCY/Au0J3Cus7tuy4vfpgil4nKYJ3jq10H270fd5fiUZVyuV4OcpwE59P+Y1KYv5+nqlIGr6GFaFLHes/d7HHdsrsp+c96jH+njFq3uqD32yzahUTkZe55yVt3Av5xNnSx+DooMlFUJQ5LsRwhz3ZuU6ZG9LFR/nqM6Oslpr2NYJJRXmYnDrRHrhw1jVj66cElrxaHXVcbdhCsq/ef31lZPfVyQOMMFHTBMFvxACHMPcpRQ2XOmGRrcrkXVhLJwPiHHG8fgCawgdkt7yjrNRyThLpFzOeSzLBe+/BWyHSffoeluwrBek9NhzLwmfd1ZVMq01shP2HslTa+N2JtLxcllgz04T/sAsdJKwEmqzLGRxm9KmDPRluRDfaRNb48z7mgiR8vlT3si0xgVNfkKIPNSr6t4CwANrNuRMbZZHlhjy3J8bPnicPj/h8398xj/+n3/g+OmoSc3ltuDtjVDpxG2W5bLgdr5hvSyQwUUAhkmDBbd3uo/SMghTRDxEnL+cu1KBuV6CLum5nArPOTAIkQntznNB/JcDf8K6rvQl6tCEhmbkDX2fvOap6o8poxzH2cFVh13EA2lhBYYXjoDA5Ntt0y9UDrrxQHFgGNMH6vGVglKzEkxCmDXzPDwdaHrWYI/60SXMYhnIAHQWvxx+1hrSdEenbRBZ82lWff52m7RKcs4yc5NJf7liuSy4vF0R2bN+va4UDN46hLgtm04oE12r9GKlMpYdJha2P9J0fm+lhQ7aOEdN0NbriuWyoOS6c86qtsKhe8XLNW23FTeunL13KkfJ0u8FVYbjDAdRJtRQqZU0uCAuFyIW3U4zTSCLAZfTimOcYAzBaQBJ6cRz4FHIz0evSIxOv9Iev9n5hjtvtcebNZnlg29NQGuUhHIwHPdj2jLamnTSorgX6ujnQcYlQ6hEAZGHitkYoHKFW2slxKA9WvH3tpEOqeHerXUWPntlngvhE+hJlvNW+RmWoVtjjQan+6lnAB+ITHoUSZP0TUP0lIQxn0T2eJwnfZ4Eah39Qcx3hnz9aIldcElZA9p0mOg5Xqi9klMhiBsRzneJGV2/U3li5V53qQTpHw7rsNcrw/cvOJ0+UxLFULY82/R5SN5bB/jXeouJ0Y0RKRHv9x8d/t+/eNrDuVQkw+okUVeUgi+/veGf//2f+OX//QWvv7whbYmkyLynRWZYmLN1/v2M8/lVrW4l8JPtLXlQyHcYZ2opnz4/YVmI+Ox9VDldCBPLwuns88HvFEI5UUzZ1uXhit8Yx+85jCUexuaWXPD66yvef3unoo/PKynOEqughPEvsUKGOQmEXwqd+eLx4rzDfJwQbhHn38+aKMg5sFxI6SBIjrUy0KjzppxzqPXHSN8PA/+23bAuF6x84AIEUXSWM8PhpXtQj0YM9DOtT98CVytMEJMDNTEDdlv4gbJ9Mp8xw8AYZnZTdj+pR75ctBjjAETyCczKru1xdq+8h8jkBPY1xqjcixjEUXXluhGtxXSkL2Y6TDrXujXs7pFI5CioFu1xyj2S+yRad9mElm1t5eAfNZ8iEfkrVU8SmNv01wx8uI2mKoWRD2s7aU0slFupJPtxRLbclo0GlggyEWTkZVFzI/nuJcgBLHFyDsZxGylXeGZXv/rrriUh0idjyOb20SUPtNx/CbI+erRGwd36jRi5PDxJSVas926NzITiTIjX6fMTphi0LWE9JUHrbWV43CMb0rqP1yTvD7C+HFY11UDf/3RP/c4U66PL8B4upcDJNEnXWw0UeDzCZNVedYTER0Meuo+9fYHWMHKPpJ223lY4bympZNOqkaQoPXvTehVqnYEL9PzJcy/Vvg8O4UEdv3KNSreYJr5LYzY/yerGWQHjcyasfB89UBvqcULangc1TlclnU6f8PLybzicnvQeCYJh71oVwq2QoTXGGKzLhtvbrRcmrTEH6jFu07YmrJcFb9O5FxP8utf3K17/+QXnLxdc3wnRyynvkhR5VkopTNK8qNWtXLOsUjIyJ8hClBYzKtnLy/mmMeF+kqGYHMkzosqLv6BmkgE7fNEAOs9DSKaSGMMakmwyagFA7dmJ5F0V3ZYiQc9Mvj/rbUGrFc4H1FIx17ZX0hTyrSD1Rt6RWq31+l4AI0RtP/L7W+tvy96/19/r7/X3+nv9vf4PWj9Mh1PacL294/33M55+oh5GPJBjEunW+wQ5yY6I1dnJOvRzTkdzFoY/CELvDl45FYU5m3cAnGY+zvRpXiP0X2uFr16JX4Gd1gCo9Mt6C5cf7PHrjPg+mxlgfT73Y0hbOtjFDrIq64wOk6ksuRH71nEJlDNmhTQPvPHgCobLGdZqaZBulUqIijUEx4ZO7jPGICyPVT39GvqgHLQT+RSsWx8UVCuyy7DZajUMcNXI0LUMEsop6wRDkVoJVCyQqEgYPXNItKoaGLai+a6sEy/nhWA34Yg42y2cH6x8Rlb4OEBKeqD6d6kpTF+G9pXqibmvP59mHF4OOB1mOGtUgQAAZSI5mmWI3MemyM/IfSB0rV9na9D97ocWwsxcGqyPVT4yRbDmgjq0o6zbk2fp83XrZf2szgJbQq1MRhxmuI/on1aJA9HTMU+l3VUyeePqsHS0CwCcBXFNfH9GyMbZPaxqkM/USlVJ4c3f2NiHpGf9DGz6PY0IRYhen4HROtk5j+PxmX/OYp6f8PLzJzx9flKdPhG9himnDG3Lnu/yYUIFx3HcdG8cdoPsP7AKO+cR0bSjCHnLuLxdmIxmVGbcEZ/OyRFJc9oyxK6dFA4VnkeSSlWtLn/cDju8HPCSX/S5vzJxUZAXaa0q6mHvCaX+cdc+yHS9oJ4x9JeGCbT0uoqoOdtdHMW0jXlYsk/J+Io4SnZNuxZCWmnEbykJ1nqUcoT4G8j3X9gwiozS+Dlh63tjLGotGAexldJ+iHj8MCLktOJ6fcP76xc8/86WvcdJYUU9BJrMxzZqrkJ/TXCHw2AhK60BYHiwuZ8pk/lKpWlQzeo0PkAgP6hLljEOxsvEPK8QH0AkE7EDffTwzynDmbIjWVlLh7NYRcoGlfcZIfzO2vfcxyG29KhdBjrUfzsvcD6pyUlOJIMSyM1Yi7AFtfMd7yExn7uRhnqXXx4fy2vZivTIbnwSUG7nha2DkyoP5EDSJAEsqSxN2xHi+kUBsXtbWzbvmWYydZrmiDBHmG5Gp4HFDoG3bU3bIiPxS3z8/V9gtvvoviKI1dpgGhGRpKUlhx7JUO9mL3DrglpC5IVhDXm6yzYRsyHnifxV0A+XYotKCeGNmubcT6wUG1dtcx1IO9zODwZ+a75KZMcZHJLoAB2GH9n5o+HRaPLjvFO5nqzGY3tzykgbe9MP91tepw5TO/UccMQrCTH0eQrW8OyGxx07ya+CmPiGyapyv8Vj4vB0oLOGr6vkXsiItfCo3khLIh5CfUYpJ/2s89NBdd/HT90mWdoddI+ZdFeb9oyX861zYIY9Xtkmvf1A0vW9JVMlicfQORnbjYiOad3UZM06bieNaqKB+2OdTPLzeg8lSIn5mBCe6fdNvV+02IkBPmbdN7InAssrx/iwXIlI11rFo1NJATGp258dEtAVrvcOzvYW1xhiSqks0RTZalGeQi9ayc11Wa7IaYWxDutywbY9YVuelbMCQPkdo3TPDJ9T/FLIGnn7IbHzx+S+WrEsF9yub7i8Eqng8HzdGdeMUhwhnfSRuOK81T9YPyhbH2XICYEue/eaMnLTWd1UIoTuDmL85zvJjwz9+Svr/vPpNUgy4CyM6TIo2Yji4aw8CBABxXlHlaKa2HS+gw+Og1wn9YxL3KvcnVc+PWh9WIk8iI8qGuh6KLN/+omSPucdEevmCy5vVyVj8k3Z3zNxd7OdzSz3SHtkfD0wMnub7DhlzKjcCwCdbwCodajc45H4BADR8GxuTw54jyxrHbsu9u+zWSZ2cpIxst/pegGHfuCI57ZwGPKWkGJQtjTdtrb7FaBEcLSABaCuhXI/Zd/LACnxBgcIlYtTQEm3x659ICKNe0x+7Qzlsjvgre9+7o6TcSoCNpbs7uekj66aggiO3BGjev+eQLjmhvc0TDLtn72On/kHvc7vLceqJZLhMbmU958xPL+DtdzSa6f/L6Ome2KUlgSYTb+veOiHv/hfiMPb4emgs0VGm3DhPNVCMrn1QhrwcVaCIiDOw7uAXB4781ppxIxP+Su5MRFzM7Zl5fHZVr/jcZhWtx2vPEgrkOJq6pI/slsPQ+D8tgrHeUpqxTvG+W7kFdklT8miN9qbUzwgPbj3Rznpbg1oIsDnzkLk25H8ndaNA/oFrXWjoRAm7tEzByiR8uB2O6PkBOs8SRWBXdIiRVVrVcl7NHbcaiIgSV4tGSmtSqD83vrxdL5G09+2tGK50o28vl6IVLRl1ls7fQADM2o1qymV7G5VrtN2B9y3bjqRF6zeeDMkF862r/TCtVTUVlFwF3sYdr6fhPaRRYkNb8bhwJJDHADgZcOSnl0mSNFfemCiJGdbVqy3prPFR6/pyhWDMT27Eze7fEfmCypTtDoVTT4n3S/NgPjfPD6noDW6dgko8zRMx5uiknvAcO6IRMjhrPPBB9iwtY7CiOEHffaeHAL7ton8uZTy1V4SNEAHyjAiNU8RJTzu3hYZgRDHsMRkO1GRaMJluQLwXaomsHBhKJBIfuGrwTGtNQ4adfxLpGWf8E08LErY46qR5+dhHOwhFsBbeHvo2h2bl9TBF0P0+8Z3CH1k8Tvf0TZBOMT8qBbaH8ZW9n7YS05b7aS1OEdNelrtgb+xG9t4fvTP1mWfjlVD1j1O6gxzwHTo9r+yRkRHioqcCipb5o5tTyoMujJJvRmGxGYc5iIJhBAIa+2KChut7n2B91vtcy3odfk1nYN1HsE+Wuw0PnOSnlG1NqRlw3Ilc5y0ZVVXhDnsks4QPRG9LcHg6xRUZj1NByU4ApSYadLIZ1nm1oXec0YBRDEhaKH49jtvkcYWyxQRpxlhe8yqezyXehLgeqFpSH7dMrR9m1JS8iK5tN5oTs1wbhv06XoANDm43d5RSoazjlohziNOh4EAGrhdYNXvQQYfiWJOJ7Q2kQr+Rai/V65Zs4jr+43g6i1xdeYVdmneK8wFQCV60peUbJ5032UnEYQmCwUNCaU4+NphVEBkMt0RjD5bVYldGzgHIpGQCVD/u5bKN7Ch1sbXDw1yzg+blHtAactoN+rJrZw518FLvTUZBsS+1uxup/dv2EDzacbhaYYxESHYHceg3VVof2VQi94yazSgytCdyBXmeiWd/rb2CYKtJX1/OeSa6Qe43EM9tEtFtZwsNDZF8g6txV3Vq5k2JxHCYJV+gBkqTh88Jnb4M9NjB0CYA05Px44wgaHEO5hbepzyq1wjcVi6bTEN6OludGOCUFLZQdPCqB8DD6EfkZO/QM6GxwmTD/pZIsPdUyAb4PfYWdkfWS72JEY/Zy4owqy31IIzXM1Im032/XScEPm521YaPOKDQ2tWgzjQ4XPUBliefDgHbW0p1Cy8IGkTcAsx57w7HwBo2+evjOOmJKapCRbQn6f1turvxZRJEImxz43WVPIqvBYf3M7aF4ByQ/TvKw3AKbfu3yHtM5mPkYeCQCByTSZcgPcB+cHJjPqMSqsRfbz47XzTAV0AFGYPPFoYoFaIMYZH0QastxXigudc2KkPYGjGAtnUHnF4oZHvOWed/5FY3jz2xmUg1P345RF9fvTMF0RS7gEANCmwRJXCe7ikrEFfbOVzIl+ZlDbUmmFguXh1CCGqJ8q2dddVcaLdtgXrtmDaFswTVf/OWU20W2swySClBoACfi5JA7281v+GwN/nQcuF3S4TB++KcigIOXB14+BCgcndw59cvZI+wNK7EjKXfDkih7HeAZsc9gU546svkCCzbjRB2bW4KxWYYcOnmHaHwkcXPVh3G6k13N9X6uUC3vudPp9egwhZYv0rzobiASDLBad9PPGDlkAhDl5gyFggUV89w21O2x8j5F3/QptDiI0YzCfmGOAMTZEL3uM82MKKP7g+iJzg6TCN7yQhtTaYKrMeBM7M8EuiKkjIfQ06eazkrEgSvZVBmJweDj56TCHgEAK2Byv+4LwO2pEV56DIRk59Qtro7SCBPW95J62UloQgRTrOmW+XyLiIIEgHTBmekW1N8DHBB4ecAmqNMDAIjiyRnaXZBECfEPjoUn/+gYSaFEGysKbPIpd56mPVR+QkR+NcjSG9+7Sf5QBQL7TkqmQkCQbC1cmpm5ikLQEJWvkr6bZS4qjtQAOuBh9LegDid4QJOx8F/cxsZa3foySmw7Ay4XZIkkpSVW7DmT71zYvJESdO0l6povFeexLd0YCie2lEM+U5DFOEWwN+5Nf+vaVJSgwDkZKLrFzVQMZ5B8f8hjB1y1ryLnGakIkvg6yRryVB1FqL46cj/stPn7G+0GhaTXpbw+28aNtUWpp6P3LTny2F5ovIHJNHl3B5xqXIrzGwqKgQ0iV5Wog1enEFjd9f5i8455CzQylJ46iMKZZ2QOXKXUYCN3CxOxC20yaVfpf1yb8DwO+5MTHx+9f3p1hfZB+59tG4lQ6v5C2zSc2O0DU698lBJ3ailN1+HYh6L9+qNSddFMN3zMpvfLBLvwwAGgDrgNaMjqikL4pY5CUX4EGeh5rlmK8JgjItrNgBindG4VeAN+Lg/pa3TA9sEgtPr/fMnG9c+XG/MGWeWlfUHazyly2a1mmAvyRAyoaVhAUP5j303WWGqenvJOjXRk5eKRLRMAWv8Nu4xqSPyF1Wta3j0ofZWlZQFHaB/PaEtVbRjZ7AhCL2EgeoQpq8RwwB8cEg6DmgTq1hlUE91sCngrRsgEn9+2SvgrRuWJfuMyC+DrY2bHZj+Lzt/BV0H1ciB4pVcy1fV7I05pl6v5l9A2ocuAECjbeG0qr++aNrZNMLgqbJrzEIGNoM2t5yigwJ3OuqUxRAzIbG6lR+L0S4cSiQMOP12nJBQen8oNKVFNX09oFNRWd0PEzsDAHzicy4OurQq+u8JaTatIARG2Yh1h6fDwhsrANjYAv1p61vWhED3CJRzwUi9Eov/XZe1LhLSGXy/IhxDH2wbgMNCCvdo7XHDr1tIctYf+yTMYXbILyfxt9VnCNOn2lUrVi6T3OEsWR0BEbCvI+Y52c8nfqk1Wk6Aoas329MoPx8PBLZFf28td7Bx4saKckyprcFFZlYCHnctkWnAH503Ssn5O/u39w6g2CE4B41UK/rjVrjbFhESgYyIaLX3o9Nz3lDSuROGEIEDbrzMOjPgfVO9zSRN7naz5kcHYfnVcidf+RW/Sd7/BL85IN02EFHsA69KRkdCPBhxYNeXHAw295YQk0PuAome8cLBbgmxjzd9ECY0tLnF2hb+39DD1BaAK01rYQ+ujSDH/o99yQsyz1sMv1oO+ia+ptF4ceiFUDmZKFXOmIWI19yhzZLz+hKQl0LrCE+hfRSa/CwRlodfQBKSTS/4JGVOcDJOFWARtHaxlPvbJ8f/617JvD3rneZq1Y8I6lrlDFWWzWxE5YvAMBDq0tJ8MZAMk68c8EjssXow4e/MXDW0IhcRr4q8zlKdrDilc2mRGL0I/dK70c02oOle1D25k1qFNNRodZICWGr3QUJ/bmVWkE+eFycRSoFgRMyWc4aHejz0SX9aGP6CN0MUjIU1z//aFYlrSqgK0qoDSDTOftJJAE9TGE3ez7nzFakBdW5oXXjUILjXjq9hkL8sg92MO9fg3vjHBGGCYUAkEzWz5q3rAY/shdHE5uNuUUizQM60VlmuQNgXkDdnW/bsuHyesHl9YIbz/yopagsVDhUgnoaZ+GYP0KvmeGX6eGKNzGHYDpOyr2pkWyRraCy3N4RaXec405KSvLlop8vhIDD4YmfaZZ2WocpHiDE1/W6orSGn+YZ//dPPylhTQpCH72OIB5bYOPzTWPPE7ZtRXrQuZDaRxWA7YhiBoq1yjcTXoLsr2QMZC4YBfJVK340StRk7O4oZ3TWoVgP7xu3aCKm6Yinp084vZDkU2TJ6uaZAlX82aDdSTbv//y99ae8+o3Zv5gw13XzDRWLHoBChmI3OnmAxlGdEuwA6p3K6Nrb7TzoFe0dEzR0prwEe4ZZK8OGSsgIbTiUHmR2M3ERAKpc510gqdzjo6mBhTXrnRTTWuOD32lfiqDKBCHEC4RlYNHQky2Bc8ako9aKlOk+b7cNad5IScEOViMhJ29pRyL8yCql0OAMZhADwPKSaFALGg/GqUpEBHMs9tkntwmM0daBBH2pBMVidTpMBHVyb1SYzWPVh0Ztk8oQbGsNsH1EshBCnbfw1urAjEdWbk0H/0jlLFWk7L/WmrY4ZFiOLOsdVQXcw3z6fOI5F06td4FhHjdPNpTnSTy6RQ5meN/IyFLrOyegTAHLXa/TOYv8YMUv38Pu3ikcWTvaxaOUcyYpXuDnfrksO2VLtx3urQ8AiOx8psqMpWj1F6akwcR6UjMoD2howSkp8hv93YeTvugB53nwVofl6fum51Zc/cTbQWBmACi/03d2fD7i9Jl6tVIU3c5NHeYyt0LiHLF8fsJ8mlFywfXtiuvrZYceWc/P8nFS+Fx73B4IfK3pzgPgoyuvjEjmqhW/yJellVdrxTRHnF5ONLp6Cohzn07nvCW7b3Y1VWmrD6h1VHVYlQMu5xt+P5+1vSbDtTwjiaKGsNnsfCHunS1LSUjpccve1oisKSOSd+/DLZBRueYcXUPeeHptmDBNR3gX9CwHOjt/5GK01tViPkQcD894+fTv+Ok/fsbzzxT4A8vm5ZpLLkhJ0EKLNrQkyDPBq3zye+uHgb/Wog+89qW8V590xz1e6c2WUnY9ZTmAKsNieUsDQWXDyv2OdblgSysPclj4RnmEEDVIApSN1Vz0MBz90SUbHJdh7sB98vJnlzVURdahSrvvVUsVN/a4S+wVjwwyErtJCd7L9aabM20rGhrE5EIGccjAiC7VIRhIzDzSmrAum8pq7g2ViAD0eI8/8/AYqTwuP1MWLZPpNm5HtFq5cu2Dl8bvW5nKpekkL6kED08HzCea/+2kQoi9epFFbF7iNBhOpDz9D/Vs0D6no2lipVbkB41Mcin07wdJV94SWhtkqQPcPEKTQNfSH55mzE8HfPr3T/i3l2fMkSRMkkwsKeGyrjrdcbks1EfmUbtC8KrcCpBqs7WmxlLTIe603HEKaDFgHcYTf2SF6NGK/SphHpE15blsvQ+tAfe9e3bADGYMgHIk6JqaPj/rdeXhL/T8ix02AJW51dxneMjrS+Xfe/zDeOq/oOW+JweSYYvr9+GurShERgBoPMBq9DtZbyuubzcdVwsAKZHxVPAR25rw8o8XyBCkWptKyHJOaKlzSaZDo2FnrSnpLKO31GgPPpb0bCtNGiUyYreAlkJNRpHHQ8TxE/nyx0PUJMF5MqwZpYYh0nTSWqv2uAW2lj3/+usr/nOO2ErBy2HW8dbyGvI6rTW42pPwewQ5bQz1p8cCf61V0WZ57VIyyUgHXw5CXoB6nHk0sZzRVg2arBFTqajDvUSqLomAfMfTccLp8xOdF6dZORPi+0K8sKwDidSnwfS2txAoKWn5/jX+qcBf215C42PAdJo7o58H4cgSOFYu5vhyJDRAhqzwwUbZEGe+MmVPJ08FhBAxzyfEOMN7vgky31qIVcqgrnowKiNz0P4+vKxMCmwww0amgG7U0ztvGZtlUlfKGrjk/synCcYd2PFrxunTCW//esPlC02Qur6fsW43tFbVZ9sYgxDmnemDsx7OB3jfIcTCiZT4FYzmSH+lz2mM0ZGQ1zfqNV7ON5TWvRmkPZOGYCRLYOkrT6fLucB7h8PLkYcZCRmJfNbjHHnjWkQebyv9VIC4HONhIgeeMUQGE2kjAAT16S9Yt8eCX/f9778XZ8nCTHyRcFWewUBExS7hClPA/HTA6dMJx8OMOQY8TTOm0L36L+uKOQRc1hXv9YLKKhBCELImuduSCO0olOhSC4gH+6xJZbX6/Tn7MLE1TBEtE48Ed3LoxsjaGFNNKTqUCBjY58O9F5IbcSGYAyT3LndZlLiYTYd++OUt4/B80JbbyGa/v0ZBQ5y3QH0Q6bMWPhCsvg0GXUQiZRJXqXCJ+AUSgPpMh4rD6Uh8nTXplEvyfbCKUoQSUBIRsdTHgPe2j14T38YoX2u098TR03rxj+gtmY5KPXTpyjMBuivk6KQoKilxzpNhShL4Nm5Dvf3rTaf3AQRtE0Ih95MC6uXtHb//Lzrj0kaB7dN/fN4lnXGmCY+pbMoH6olNl9BuNyL2kXnOHxPcvrfGa5WzU39V5Qm1NqTozdwaAYCXf3tRAmTjxEyGD7nQCcjGGh30VpncPJ8mbXmOkkZ65sg4LkyReXDi5jgSJz2s/XF772+v/r/X3+vv9ff6e/29/g9aP7bszQnOZYxGHtZZnUY3zuGWDMnHPsEtzh0CWq8rzl/OuHy5qDHF2A8GyDikMgtymo6YpqOOYgS6G91XRhmlKuw65jPKLXiwzytsW9y1yVvrE5BqrWipKcvUbw45UtYVJu7FnWb1tj59YqeuTye8/vIKAHj714Tz72fcbmd6XUf9rxgPiDH2Gdneq4eBzCcgHXgnCd73tB818BEymmh4AeivdK0N63VVNy+xJJZtJTCu+H6ndVMnsjgF3A7cy507YuS9Q3EWmVnEoooAoOx5qfDUJY4Nk8aK1wqZkP97ZFWuLK3pjOmaiffQamWyJmX225JUiaHyQ9cNWwD6braccZ03HGLUiv+6bbguK86/v+Odnw/hysgIYwC3Z4zeAAAgAElEQVR6T6w12neW56ikzK23bnMKPM5tCVNA8w7uvOxks/LLfbvLWotiij4m4whl4eGsrEuXqh6gFtfGRCiAXed8QAgz/15ULwlTmdjroLvJyR4TBA7oZDAXPPAgsdUFp2ZCsueJrErfZ82FyKTeEgTfeIwv9z+macJ8moeJnUbHsoYYuhvlurGcjaYZzqd59wwLFwDGMIsbMLDIGxndhBjQHCl31D+iPm5YBoDbliSDDkOP3zoD7x22Oeq9cN6xXbLVM3m9rnj/7R3/+h+/4vf/9YVGyV5XlJJ6CwY8B+ZK5jUiSZbVGtQtdDpMjF71kdc7eBt1QMVWkvIlcUp85A6MssyuvFGTIbaIDjyDRWaZiKJD2mGCeNXStHoPsauUBKlMS1JpKrkdfu0JIe09AN28KEWKf9hz2Zzzfx3q37YF3keQ1lA008Q+DTNBPWJgQT0hgn1GTWdgy9LIs+rVDW8YrOFcQIwHlJJAPsseMU7wfiK/9Z0fPmC4dyjKAuEWCBtTv0JJMB70rb4n1QFQb2mAyUoVu2E0JdldsBLYzjlH1pPit17rzgedAltWdzjZaH7YLOLa963PKfyGEdon2PAxr35j+JBj0xKASFuOjWZkPrpAmSQpvBugxG6OYQ78HRE57f33s5JVlvMNTz89U48/OE1uxPMhD4OcZDAQAGVRky+72xldOCY9tdYeTvqCZ0LSALu1RkGt8vdN0tVNpZppSxogK8PX623F9f2K+Cs77k1xB/eWUjS5EkMkNUa6bV1OVytJm5hZ3fkrnWgI8OEjvecHMb35NKEmp/axwD7YS3Aae+tjwjkWCa01ZG77lFIh2uNxdS6LU3tXH70WDWqJPBz4TQJc2z/d6gliLdqDoKYE+HiIGvzSlnfkRBc8fJHhUyR5lQJlOs3KUbDOwsTuuij7erxPe2Z61UAjsujlsiihS39uULaM3B4pgh5dsrfzlvWcCzMHKzZukmRLYGzrnbYHzr+/47f/+Rtef3nF5csZ1/ebjuU1xqojXWsNOSdOaDaSPCfmSnE/GwDs0wHeO9RjxbbwiOw69N9zVbOf5bJiWS/IJcHDPYRp0zwN99U5qnJTVuiMbYAwRfg2JHMbfR/qx8IBfRykJeot8epoQ4tw9IQQNr8k/cbwPBzr4bxo+NNXn/UPr/FHN0E0huIsBIjLnNV50dVUGOEBMJlPiDHWD9PVpOfJzFXjusPdtsykB+cZ5kKMEcmerJ2krnXZDr5h5yk2pjAG7UHf6pKrDg8a+5fKqB34BK2yb3NCZxgXmjsv7mwyyznnog81AM0kvfcc9CWI83xuIdlMQVGPjpbwoAg+MOA60cMYAxcfIzjJBmq16YSybdl2uuyRya5s9btrioeIA382ShIqbu9X7YdubAUqc9SFOCo9RHWtksNMZzMYtS22bj/MSb+/Su5uj6yJTXGcNcowts50mWQWP4ZhMt8gV6U+NwXxK6NjQggCoAQvmcInqgdxbExLYjLhIGvyDm2OCCBuR94SJTzOIieLMPV7/1e4LdNxBorD9X3CcqUereyBbwUrY6B7FKCkR2ScouSwljzXSzqofE/sl8eJe9Zb3QMS+KUabK3tnjmp+q0d+R+MtniLmh/V8Tv9tbsIsludNTDBoTLRzAcH3Og5GOdjyDkoah7hO4UYNPAHHvgD9Fnvad00aR2HLonCx4fIlt3um0lXPxcfunTlDOVhxjwRa/uApVabJud++J4BYL2tuL1fVd2VcxrmyHdeF0CDeqyh4kJQQ0BMz/isr43mTswRcZ4oGR4QzrRu/SzZbljXW1cPPXD9MR74HvfgP+670WODkE9W1nDiJRwHIarKRNZtIWRrLNCEwC0Dgeg58ZimSVFzcQQ1lp4LTT7t3jAKoMTzz6A9f4LcVzWzFhiLGKeVTQy4ojaVNrrr1TlAB0BCUttaId8JLCLZT8kHMoxhf3qR5VXWwesmYAbwLvO/2+FyHnmpGp39Cqr/sytvGSVkdRijz1oAT/fGDilla0ArewVEshnmtjIcvGm1LCiFQHnjWMoIcBZYNfDuSHPDAUeqid5mkfGdALQSrnjMuU6yToGtAILxpsNEFWvpFQcANRGpwyHuuDISeErY/ZRQrXo/t9umbFnxodepdlL1uY4CyNASIdfoQ3NHxKq1YnsQ9jQc9J2xiIM+3VqL3IRB3QOgStIGwulmDNbbNiBdfZS1HKojVC1tm5wLSk678ZrOevgQtRrtNq8jk10qUtEZP3TpFHCKw3yaldg5qgnGaocMVoIie/L38RD1OsvTUI0Ouv1SipKb2gDbi0JjPNhoSA1X+IXlfByIrHX9QBxksw+3uTyZN0mAu/9/omIipK3CbvmrzytSQ/puvCY/cY56/oUp0O9HU6PW4FOBD04D//GFJM05ZyaBzcykF1Mf8leQ9SPL1h8tcaAUpG9n4lQbWzD3QW0SuAFo5S6tr9bK19JQyJ61iHFWhEfg7zAFbeUZS54hwTuEOWghIEZGMtYXwGCaU/An/em+WtNxQgx0xmkBV2tvq5XSfU2KhWM5qnwGQUFHBG/bbrjdqJUrk/PEsY/I7IQIS4srhIgY6Tv3PlKCxOcDEZn30wN135lvK8/u158y8JFVcjflqVwFIwic57QCa7VrDltb9SHJOascBdib4vjAF6ADLFgGVvfafJU/2O+z9XuPlXq/Y//voystGzYvjoXdQlJHsHpoai0fZ/xctRQs3COEIVnTOHmvTy8suyE3oh8VCD8PkinrDCVdtlv1ykTAUVFhGH1p5rHA72Tq2dBG2BbKWMdBI+M17xMBbgs56gPK4ZatBU1r6/Mc8pZ5yhYFgjzl3jPjexQPkSBTT8zob1X48pHIvpZlhw9W/OMSAx+ZGJg3q7CnDKqSe6CHgnqpiy9D0SEdotwAhnbUcO9ERkv3kTXvYUJD5X6xRVPI0HL11eWMj/b2ZR0PM2pz3Jqg9xF2dqscePl597znKCFkKSNLM+la+rx6WTp8h8+Fyu6QwvKnC+8yQG3nqQEWW/aWcb+NkwFleNFjaNd0nOBMBNidDmBtf2uKdIrPhAS4kvyuQNlWOsMEJree3OzIdroXEXnLOmZ3uazYlpWD2eB6ya6ALlPye3w+aI/ZWIPW8t3z2Id2fXRZ5g0JxwSg9pZjboPwqMixcZA7a+I1TGeVomWAn9UUqFZ4FzDNJzx9esbzT0944gmFx+cDTjwK/DTRwKkkEm5pqzJCvN02RaOW5fqXk575NCOGWdvRAPYxSJxFWwNYMSiSRPn9ttC9W5YrqwzIp+Z6fd97+g8clBAmVbEFHxEner15fsIUD3A+ADLno+7VWiIbNGqx/xe9+qk7zhfcuvZ0WxNKqfC10UPGciqRuOn41Fw4cFT1vR7lSKPLnOqiRQ+eezAciSsCjZCu9p7I14OBY9MPSjweq/pEtgV05y3xcS5MHqHDn6A9YwjGV0ldrd2uN3erXvJaHiefyZhFroha4YfXwi0egTFc7ykT9t4PFTHB/zKsRypkGd9a8JieVR5eOegBRntyhZlkmAUnANxnrm0/J56g/qaGF9Y7bI5sZ0dDijagJGOAIIiR4d5IhNLp2LPxVqrCxPeLZNAN6UEDI6nyAbLvBaBTCUVOSpB2wOY3JZ7JvaIhHERqynmQq+JeKuQAHq2phNHWJwBKT9RaC5s9Qmi7cajOOd4PofcPFfl46NLxcjjgWsGQu/TZDU/A5KE0A9IjULZUqMJDGBMiWdYa1TJD/D1Y1iSVpnyvEvikglp41kXmQTFjZaM6auYZOW/RHtTxn04H5EyoysQJ6HSIPGGQJ2TKjIIkg4KaVshkuXvT9kaae//av3tF5cZzT/rqNBK3JwRyj+W1puOE4/NRv5eaK6rdS9ekD/zI0sC/bEMwSzBTBJmCZRhDff/ZWUwxADEor+nwfsN0oM8pvv/WONTW7cVleU9eF6dPJxw/nXB4PjIiMuHIhkDRE1KYCvXKRz5NYpfYbeEEJS2DHPqhyyfiOhMwlTBq+7nWkZymZ3hJpZucXRestwXLcsb1+oZ1JY7DOvj3A1Cui5x3YjokwT9z8CbfFoqvrdmdfTIVdx6VfWpsIafAv+zVTxADjQuUD7yti5oJ1CnA22EmeWukyRcr3i1h4cxRsvkx8I/oj4yhJSc8/tk7IwU5MMWoIJhAE/AsYOq+3zF65otJwkeXWHICvUohm14LY2hamRCuJnbUEjib7pUMZCmoS0VKC3MmmMmplX2HfQDK2Bp7KIhhD12bY9/rEw6HJw0OcY7MEXDK6BYznPYg1O+CgzVuF4ilijXWwqLyCFTH32vRCg7oPSzxchfzDdknSpYcqkEx75kOE9uBkvkNAMxHMvmRXjI5WLEZDL+XBE4rMH/OyA+a2EwhwlvHBxf3ZB3BkNJ7l758nOkzj0nvtm1AWxTKCyGqP4VzQYlgrTXktHGyZxTlkcldYvEZ44HGm84RcQ7drZD13PJnufcAHp5H/+nAk9YOUedByNCWr1pPA+FS0AGZzuetYxc2p8mTvWvTbTljzZkMk0pFWrfeJ2V+QdEkkf//sg09d6t2tgAf3Kw6Mg8m/J9PJ5wzPcdyT6fDBGNpgAwMq5cm0pfX2nYBaL2RVzsATNeDJmaeOUeyCnsbyL3QoUO1KAseAOb5iOOnE6bjhOfPT5ifDqouSMwrsevIGTLaI/7okgRqnCRJA6ICo7lZLYSPzzS90juH48tR/13ekrY87avFdrOKZI6FgVpDW6Pox3JZcHm9dAviUpBrweX1qnFH9e+c/CmZusie8Dw05+NFTzyQTp78FfZmO8IjkfcpgzmZthuuF6zrDet6RUqkMrhd37BuC9CqVuWS0AMdTRaf/Xp3j7wLiJHO+JH3Zq2HMU05YTlbVA78f8mr3/sJIUT+UBzM0krEo9S/RCGxkMwJ2hO+vF3w+ssrzl/ecbudsSxnbOsNKW+7wC4XXwqNs6RqlcwOYuyziUOYMMUDQpyoImaIm8gcAGA6sXCAUB92r+Pe1b7H3oN0rY0gTe5NH16OrHSgn12uCy5sWWmMgbmQHCcZC2sLvuW1TD3ghJQ3FN44miEZgxgnzugjwkaJhkht7hUAFBAfq/hD9PA2UHU3SNJkzrwROC8XbNxrE4cp+VnpCRoN/mE3sx7oJJX/j703h7VsWdOEvpjWsPc+Q473vqHqNd0gYZSBUcLCwUACA6stKGFglNNuO5iYhYGQMBphIDVGCQcJqcEqCafUElIDQrQe3VJVV716dd+9N8cz7WENMWH88UfE2ifzZp59+4G68sTT0808uc/eO2JFxD99//dlJ8HoHNWszlf5QunWHTpjEGLEOM2JFIUuXUbZspKeEATss97DzScCPBikF0LuDGhSndHNTZWh8ggrouJdnfVYJ7GScX8O7zyxlq27FNmsKKppzaKllR1MN7sEirI43B2wv9kV5zE5FAyYbPs2tcvSmmpdqFrZtpyqUXHWdwgQuO5LFG9as8i+1R01dqT2vQzCS8jyOWXBmE66Jjbh94g+ZGwPUGq302HKjJFcL52GCdNAjgE5VCrjRxaSwEkpUM+nGf7zjp7nPMzQaf11k86CKCRlzGJH39tXhGIE2mKSpf1+i3CXAGmIuU2MwV2EbE+fo01q4+3Q9kQDu7lcY32xwepihc3FJtPX2tlCKDpveiyZsQ6A0CdiWxh8mtgUAWDaj9kB5CymVNSuyevOe60/73FhLxF8xLgfsbveJUM4YJ6GnDkWQqBtV7B2U9rVPoAF0EbnljY7zZiqTqK8f2ZmgvSpnU3C6AY+7B88/3bVQYkGE+YluM+DAkwBoiePFVlaqMt0IWMXiH22Rd9vsiPEKn70vr5iaSVEZtN2aJoeXUeljtXqHN2qv4enqLE97CdwoPajDX+hiNV5Ys4R6IMR2uAoXElASZi21PgBipqtnbDf32C7vcIw7HLKg/t5uSZB6W2K6AUEpNKZ+xgA1usLAIBp2pxq5pazCIryc8Sri5748Wb63OGthxOJjzuDuOjwR5X6NWeFNvF1tz0ZLHY+5mmF1VmP1e0B+7Mew27MojfHHQix2kyc/RjHA/b7u0xjHGOE1iYDPrh/nQB0JN9Y994qo4DPYHL60DBNQ5TJVR2VWQpjiDC9Sd8pgRGrVjQApZQRiN9a9JypkSk9XCg+a25+mdoez56eYXO5wSal/NZtCy0VJkcpskkK4usPEUITIIyjSpUYHiniP23+k7OZp5+fvRKErHVdQij7lPaWMvHwlyyTlALryw2evLzETy4vcble47zraB5H4kYxRozW4jDP2I0jduOI97sd3r69xu074nrY3+wx7IZ8yTEIShuVEeQ1lqX+Lg8drTY40xpN32ZUe9MZcugrkRTvPcQsMARCUg9JTU5ISUEAt2fNdtGmWHfIcJdECD45+3SGOQrkwUaWsUJCCAgtctcLlzlM2+SoX54o1LJqW5i2xXa1z45su2oLGEvTZzLym9raDC5fXtK6JNlml+a9vd5he7Utqe/KQZV6eWa7dYfV+QpnT89ymYGdRC41ZU2LMenDJ5lcIGVpVi2gTtv33gVIQSXXuo13db7OJTbvKLW9v9tDKoE5SS4DVObY3+6xu9nicLvH/m6L29t3uLt7h8Nhi8J/L9GYFl2/wWZzic3tE2yvLnDx4gIXzy9y8MjYCpLrthi2Q8E8cZbZcu07wJg23SUG/oSYp+kbKDQ5pQ8kJxfEFViDdEXKgAkpCsWuuECssCBc7qqxWfyetePLTjF3i/H1oBNPgkycAYQnKfaSQeC8HlRWtPghNfLPivi1LmlJgEh95nnAPEzwbkUAFh8hFbW2qHWb+4e5raI/63G5e4rtzRaHwy2machpb3qdTYaCo+kCcFNSLzxkmrBIoKrlxaaUyhFvXV88Fd3rrIOSpaOBhxASMtltK23uWODvxZdQ05F61dnTM0yHy0JveyQbytkD70p7k7eU9trd7DNAkCRyA5TSKbJpM0dAjbAGkNvjhO5xymj6Bm3bLbjVeU1iCFklLMaItm8XvPxA6nf3AUGUVD53CjAwkX5W6p3CCyhNiGFt9CJi9SEiRo/RzqQRkBxPynhQurdNzoRWCrNz9LoTKXu344SzVLbgXSYliYewQiBTRdcXeBYrOe/x9dMn+NdevMBPn1yib9oPRuA+lSR8CGiUSiIlJR3O69T2LSm27Qa66FOan0GGQh7hXU4tcgJojYFq2wUBV9M1mIYZmJdNMs6SYZ+GKX/n6TDhcLfH9voOu/0NpTqnAdM8YJ6LgAqrl9VRIJeyuLQBUNSzWp1nkBNTVjOIT5lCGmZaIldpjMZ0ouPTNw100yzoyNu+zV1G3KnC5FFt3+Ly5UW+/JXRWPcdtJQY5pkyVIneuc6aSCkW3QDM09EYja4x8Ol1+2HM56vWKQmBygPO+lwCa3siDwrxRFnahOnxooCKmUhK6VJqmwYi6vHWo+maXOLbXm3x/tv3uPr+PW7eX+Hm5g1ubt7g9vYNdrubjGr3zkIkKdrV6hyXly9xcfECz25/hunwdXa41hdrag1VMmV8poxul0kHhqV+IQS6bg3ucJlPMPzGaGjdIkbk0k2MQLTkYIEDlEilFo8AJTXWyUE+e3YGIYjat0vEbTI5rbUjyw4DZ/qIt4OUbGuQq0xt7TIDDUWVABZgtVwehB3w+CESg0fK3sfxOB7H43gcj+MLGp+M+JumhVKGItzKs7CW6mw+gSu895BBIgTyvrsV1aZk6jn1SWucW1a4jslpS+5ddm7CnNSVvLeLtBCAHA0Y01Upk5IJqGVc67TnqWQmDELjdqz8fpCISkEISr3YyWZv1HSmkiGlUgRRXtJahBCLylluEWHBF5cjbGd9TuH3Z31epzShLAJTlP8KzSiAXAdWJ9Y5iWK4g5wsPNeOq/YWKQVkAuzVRC3cvsREGzXNbvRUkuEyCQBgKtLBpiu1y8PdAc46DJXgkRAkBTuNc6K3DLne2rUNupTf0kriMBN5T02U9JCxPwzYTxNarXMJAaBWQdOQuEoNAKr7+QEqE90NA765eo/bwwFN+m7WLcs8PgbMzhPIzVrM3mdAk51dkSCVFMVooxBSlJvBUfzZTFSVep9PpW5tlYI2Bn3bLNLQ2uiiDFh3YniWaE6dPwmcx8Cm7e4a0zTcK/ER+jjxf0BkqkFue+Th7JQxRtxCpitwo0opVQCZGlcrhenEEp9WCsoYtI3JbaVcOmQSLuYbYKrds/M1zrpUk+86PF2vqRVNayghYLSGURJSyHvZmMk5jHbGYZoxpf0xe4/bA5VOlBTYKYkYp3sZA8Y4cGam3/Toz3qcKE5HqHF46NShBSRSmsmiXbU5k8gYAAZA8t10++4O16+ucrR/d/ced3fvsd1eYxr3ObvD6PsQHISgmnzT9CTPfphgWZJ400GoJpWdZVp7KrmFBDb0FT7CmIb62UVEWr4HDSFlJcRUOrqEI1l5EQKIlyG1jmemxdKp4hIR17ifMleDECJnQQEGcyZsVBK44tKCVCJn8Ch7rBGjSPefX2QO6mx2TLaY8CLtR+f4Gan+JtWUDfURghYXkcl1KqCbo/QQkzAAQJ+ASN7SQpjGoOkmzGvqVWW0LreJccpwOgyYU12bJsfUvkRykB9MQsFGHxIzYJNTx/zgamawhw4uOXD7Hf9MCAkNok10rrQb7a53iDHmVL+uWLqA0tPMfft17Zz/y+lATvcLUXqJRSLP4aFMUYoyLSGwM9vXqkW36qB3p9X6lJKEHtdqgcAXUmSKZN1oMDa1TnMC1K/Lzt08zjk1DnzYKWOyIq75MoDHNnSoTWoZDc7nlkmur7Zdg3XbomsSEDUCo50xbgcM+7KPHjKmw4hxmiEr0GDL8qQhwCX1NKUlvBW59sYIY+arv25uc90OwL2ediQAJFN3MskNDzbmpHnvinGPMTsDQiS8A5dUpCTDf6LhY06IVhfmRJUU76i9nrAbUST+eq0gYoT06bn2DUJYAVKg7dY4O3+Gcdznktlxy+bHHHMuMWaNc9PkWifjQmSSvjUVtkemuU/+NGCnlhKmadA3DabUVcKpZ253AxLRTwIWrtLrAWRnUUmJzhj0xkArRYqTMeb9xM+IdRsiyr+xAwgA42wxDxQs2dkVQhlRyKvYQdNGoe0bWHskq/iZg896zX8S05kUQqBdtdhcUDqd8EpFXRFIXU+auo+MbnJ/eowebdMvAN0AAdLatke/OqdnrJvs1AGASjLwECIBTAuIkxyP8n5dt4ZSBlrrk/v5OZCQUhIFN5BwTYld0BG3DDucURGZFFOLz8OcNSoY+yYl1ffrLrF8z9kR8zTmtnljWmjdoGkKmFIlZVNW9AOo64owX4WtL0TqBiGugB9h+Ekkp1sg65umg2lS61IC+gTnwctcE/NwPTfKkAldJNNYqkSCkwbzbtN7KJhkUgQKgxIxHC3BEUz+wQjfBWjKF4DRKUOmdi7v/QKISJ/RQetEG5mAS/ubXa55A9waUhwRQmTaBVkPUIwBXd7ItX4mwOA5kxCLzvoFOlF3Nh0Zfma+A8gQN0YjnFrrFSJ1CRTBiMzElnquTaJWDSEiNIm3XJfITEhkPvd4j3OBe2RF7kvPMs+JhlalVkn+Prw+6a+ZAvrsbI2L1SrX+LfDgLv9gLv3d9hebU+a/v72QNoEWub3NUpDgNbDMdlQQwbZJ2SyPOogmYe5ZI1Sf3o8es09ACpBdjOOgtee+Q6EROaoYEPI6woUfIv+IWjvD4zZOegQ4ePyuxL9dMzOtIoRwohC2CIrg6gV+k2f9sxz0qF39x3wGg3NBF+cFVhgepTJrI1KU31dapkArYXpje+fabYYxtPq3AAZ5UZTJinPP+1bdtIyrXiMsN7DMrtpUNhPE8lCO4tbQS2NIVI/Oi9pk57P7D3GuUT7MUYCek7ktE4zcb0z6p3vBM621XwX7AydCmoFkDU2+N6NkYIzBrEyT8P+Zp8xP94X/pRu3SH4c2o/NhQ8dt0awbtcj2dOFqU0mqbHanWG9foSq9U5YUsYA8TzTH/u1h3tL6OygdWp82wlz2FMA2M6eH+aw99vOqzP1tQymnALYxxozU3ikXAOgM7nL1RdKSxVzs8ohCojxwR0ODrzopBV0V9rErgIIQNsiJnQLrNbJjZTO83p9yRC8Bk799Hn+6lFWK3OoRUp5cnkfVOrSU8EGemgZiIeppjldEZCP1J6m+YWXMgpiUz0wwjYyZLIQdXbChEgRelP5gUTohC4MCK8BvTRa0v73SlDNxrRi9RXmw61qxe1g1IR1kpMh4kiO7WETjR92cQi0gGlvt+iNU9MVElfnZ2AzEMuAO6lRurrbEwWSWLSnqZvM3EGADTJaJyqTgekqL8vniOjeal3lb5v2zYQPbUYyrloMwBJ10BZameU1OcvRDH2ADOtFeeFkerc98xgRSBlRkQo7YutwfpshednZ3i22eQI6Z1z2F5tcf365mTDv7vZYX+7J6a+htZACoE2pex9TGjcBCDkPckODaUmBaIAYqwobqVEQDGoQgoIiEUKkEtEvN/5dbpqBaJLJ6XJmbHQlF55H8LJhv/mcICWEuNsF2AyTvEzyCz4oiG/YBKTErKRULFq/Qt9boPiLckgJtbEqDNhnAbmuZIDHugMJKEUzjbUoikAOfyzLV0GDx27aYQdW8zOZca4zITZGqjA2ZbEoZCcDgbjzc5DgPbElICbrPvgK9a1SRangTtIXAiwzmGwFpN1i88O3A3C+yiE1NWgIFWXX8tre8rgO6wmo4rJ6ABUSsCmh64YHYMrlLaRs5aJvC0DF02byrelZ53PjDFd5iZhThJU83DWZy4Qte5IDbCiBueuLw66TGsw29MCnm7dZ4pkjviJGdClzFrM2Y9FpirbHeT5SSlS2SidwyMH37sAEwzatl8EebUNqTu9QghE2JXonNt+GdUrZcDtfD80Pmn4N5sLaN1RP55rOqUAACAASURBVL/mWpdJi1vkYsVCOOU+dSgLdpaaORu2wtxHkUT52Yfa8OrFrv+dCEz0Ij3F7yujPPkQNG0DN9Hn5vqlnYlcJzkUnAmZBpmFgRj5yrK8TddCCGTkPm+g45YuPxZ2Q++pL1hU4idSURmFGbwY0c9iIt1ZT0xaae2d95hOJLCBoLR927c5mspEKqzXECJ0ImfJz4qzAzZF7EaTemGMEIGyM9FHSFFlhVTKAiWsAht9tVAyS04CEqmOpLTjy/NzvDg7w3nfYzeSl7+fJly/vsbV9+9x+/bmpOkPdwP2t3u0fYtpnVJ+3qNrTDH+bMhS6rXm9DaNyY481wN5/x7va+7hrclwgELpyq8pfbuVqA2LMumy7/hSPBXX/2a7RQNgnOaqdDFn0hSOaPJ5TEYw9+fnEmA5pyx2RXOhz6l1C1i7wtmZOCySUifNU+fuotzuK8v+UlrnrIN3HtMwYR4t9jcP7+MGgFc3t9De424/ZFlegHrKOdvWNJS+l0Kg1RqtMdBVCj+C2kmlEOQkeoGGFR+r1/lQ2rHqTh+gMEaiAWxCsBNFbymPUearyc5HCDGpRJ409UwM5K2HTd+F5YNjCGi0Rm8MGq0LWVXKagHIjI0qdRjpxuaedOpZL/c7/1cldL9ShecjVraBnGFkYq8Ygf3tHt4HmNZgxRgoF7A679F0DcbhtAUQSiw6RgDcv6cTRTHvfeaTAQqPQvAkZcyZ2ONzTyJTPjnRzGxbkP4FK+Tg5sJFw4Fft+kpo1aVF7XWyUn8YdP+ScN/dvYUTZPYmdigsAyqYUIWWfUZUv+hyoZfQASqf3KL3zEYarHoghWGPGRqnBRSZvayWq6QW+CoBajIh/IIqR6Nz/CAPja6TQdviuoTQO0S03SASpzrbbsCtyGKfQLdNcXwhzXrKGtoIRZc95wJIICOhWuphmcnC5k81lrmmFt1Vmc9msSNztzkbd9iveph0mXhA2VDhuG0lBfXDU1XAINscMfDVNijUg08GJ0u8pKaZ155ZyvNhFAMfnpZBqnxhc5RzfEeYbpX/m6XqzVenJ/hcr1GqzXuBnpG2+0e16+u8fabt7i6en3S/Mf9iGF7wGHdoU8RwLRy6JsGndFQUsDVdXVFDIZ1z3XtAOsk/NJoBSXKPvaJIIj/G0KAdR7OJdKSkUtMvgjZeJHFUniQGh7ya5WSJxv+1zc36HzA7nqXqUhtRb7D4NOl3obKBp0wO6UtNfPZH7G3+cB8CA4+4Whq2d7C785YIyJQYn0EZVQWveGoys0u4YQm7O5OM/zfXV2jGwijUchhApFKXazQtQ02HRFKMRMh1fQLYyID+EIkRslG6/x/fh2LqthUzx9BQL+QcABNlbGpwZOc8aC695IKl6PUU7Oc3bqDHWJWCgSozry6WBOo1jk8Wa/QNw2VLRyVaLjnP7jStminuXL2YqJvL+vJnAZkFCkl7J3P4FCAnJoYIpQuAc90mDIGyHQGMfb5e64vNug3HZrxtGxX9Ew6dt+ehFSCjbG6v7JoGBt+nZ1gZlDkLGZ9nzHPg5CuooX32VnO+Ddnc+ZL6wZCNLnEyUq3nHHXmojt9I81/KuLNdZnRJqjcw2J9KcNE4g0JtdclNbJ+0npTqMhcw0vZu+m9vbSH1JPtIZQAcGV+hWnMxcPJ6XCObVaX4BskAiARAs8n4jsXp2vEB0xeNk5kVkMgroaZpJZpOi/z6krVcnomrYh9i+W+eSe+5brcvxzA78qWtRMScpASUYWk9Ffodv0aPumKJFpib5rsWoKkcbsCFQ37E4z/ABSnZ96ksuPRAKZLJ0pnepNvGE586OMhrIF2MdambkuLUskDCA5agHSCzg4LBxFTQQvUkq02uBitcLlitDTPgQMqXH37mqL99+/x7u33+L29s1JU3fWkr73fsTEPNxnxLXfag0lJRwD8thgjwW4yBgMneanpIRW9L1NihQBKhnEpCvgQ4DzHlp5TIqBVclRssiaF5RBEOl7+ry/2PNvBBuW00z/+9fX6LcOw27IUfOwGzIPhZ0dpmGElAqNbxbgRfrOhZTEjjOsJa2CeSbujg/18df1fDrXGlKmEpcyBPBL6U3u627SBdh0TXYS5nHG9mqLw/aA3d1pZZ7btzeYVn5h+DnTohJJlpISq7ZBqw1m5xZaCwzs8yEAIcBz5kII4mioXDJZOYCTs4k4KqbOjpRldA7WuhxhM0snZ1hrRDiQ6HaH02D968s1tvMB2+1NuUumHu26w8XzC4z7EeLiAhcrSq97F6jf/Zb2yZw4/lnkZ54m2HmC9w7TPGYyMk77k1PMGg8t9ETiQ7ZyuADkDAKEKEZTEHFOrUuwebLB+bNzjIfTqMrtTAh7DqqARKs9O8wTUdA76/I+BJCpxoHENAhXSK5qAaMqK8aOMKvf1kJtzrl8RqydgRihNHXX6Uaj3/TYXKzRbXr6vZrUy7SZFvhj45OGf32xxtOXTxfIdL70uZ7CbVZsEDhq44lS8CYWspvH6m48MmI21qmNUg/mFFDwEQE+E2Cwl1QP/hxvHebDaSCf82fnEIFSWozaHKc9xP4W07QvEosgT5ZpN7NCoC6MasFHaKuzU1ALB3H6kteSCB9CFgSpdbm7TY9u3eXLj0RaFJGupPQ+AMzWLRS2Hjp4rZVSuU1OCJGR4vM45+hbUthO6b+GPr/pmtL65VnOtShdFflesVAgDAkVbFMaOwPWTJHrVFpi3ba46Huc9z06Y7AdBlzv6fK5fnWN99+9w9u332C7e4/+BA4jwmEQSyVHvftpwmQd1m2EUYR6n51DoxUmrWFhc4TuJgvf+dx2R5e0xOw8TFWXp7Uuhj9wnXemqIcPNXGkBxx3BXBKfRrmInOtFYncnBj1vfvuHVYr6rxh2lwygjZHW/M85mejTaGwBciZU0oh6pgyQTFfZMc01UCKfBmsKJmu26BJBD5dv8HqjERc+k2fiYu6dYd+k84C180PE+7e3+H27S0Ou7uT5n/z9hZd5xaaAErJDJiTSmLTdjBKU9Sfzl1dplFSQEmFEEsZbPZEb13mTdkBFwKmVNN3PmC0NmcBAGBOgjSIMZMXcRlxHmdYvywRHG732G9Py3acPTnDeOcwvtkRXTgA0xCF8eZyg6c/eYrwk4izrkOjFXbTiF3X5s93qSPnQ+p0LJsLlHY+rU2pXadOgBjaIrqkCP/TrloKOlNGNISY78VCHa9x+fIST75+gu3NaRH/dJgwHqZlm1wkPBYRqt3BeYvozxCZrTMUNsaMzUEq7SZhIU715/J2MvrkUMZCS2xHEnRLIEjnSB/CyDZH+quLFS5eXqJbd0nfgO74btWh7zefnONnGf7Llxe5Rg1g8SUzqCW1OAgZcgqHh5ACCmTUmlAQshkAAlRlBLVA9wJHCMcQ4B0gYkgGPy2kK2noosud0miJ//yUcfnyEkrS5uJIdhz32G6vsd/fwM5jrmFSGwal/3PbUuXEuI3NwJV7AJy4BD3RBUoeZ9s3aNcUcXcrovNk7XMhBOlUaw2ToozZlZrssBuKLPAJI6QLpWVWMF28aK4v62TwQyQxm9jeR60Why0AOBIkciQrGlIHBjOxUVuWyOlyw8AXIbBqWlysitFXUmL2Hm/u6KJ/9+07vHnz17i6foV5PqDvzx88d05nj/sxZ02mw4TDesbatpTqbQwGa6EllblijJnty1kPPUwJp1Ac3brez4PToNzWx3VvN9vscGZUv5Q5wuaedpuin5xFmS2UUdlwPHS8/eu3WG8mms9UAE6BNRlsidyU1HCWMk2qKl2hTcp7s8U0zMS1YFckw8siJ8GlLpmyHlISZ4hMqoMAZbrWlxuszleLVtnVeZ+xRuw4HrYHXL++xvtXb7DfX580/+vvr6HUYbFPlZIU7c0lNXuxWmHdtpQBEgKTK1GqD0mcSJKzHBJfw/EziYhwPuQI33mfQYVZICwFTLyXTGvQJZpcO80ZgwMAdmtxd7XF4e40YOPTnzzF4WZGRMR2R+tHYGyH/tcrfPWLl9iOI0KM2LQdnq43uN5si3wxl1ycJx6HYbcQreHgKMaQReCInVXnEp/pqvJmaksmkSQBN1CKn9vuuPsJAFZnPZ797BlePL/EPJ82/+31FhJUPuJSh+Ms137EYSAWSu8sNuEJ+rM+a1HQvMpdnvUEOFgNIXOWMPU7OwTOOVqfJNi1cCKVIQDkusPZkw0uX1zi8uUlulWHeZyxT9mW1fkKXbfOpfGPjU8T+PQNNpdn2DzZ5HRzSMQN8zhnIEmuuVgPp1w+sISyLwBA5mpnruvQLJXv2PvxzucSAaf1eZDBS69NdUTZVjgDTrUKAklQreq0VP/li0usNueZNwCglOfNDaWPp2mAD0SjW+hEJzimpfQ2yYt62HlNGZIEWFug/9Oln+eY0tmaKTyZArin+o7hXvlUM9ZS5T5hTg8SZ/YOu9vdSXNnBDZQ9Oj7psnpaIDSlI0mqlwfw0KFTcklHgOgSNBqe0/sJWcAYoRPKdXgPV3+7Awp6mM1jcZZ1+GiX2HTddCKDNzVbod3370HALz61Su8ffsNdrvrRWT9kMGRvmlNdhyH3YD9+YQn61VCaTdo9QxrNMycuihSypVSnqFgF0ThcWD56nr+ABbATibqqFH1UsvM2cBGNgRLdKpGl9SjEFCzy07gQ8ebb15jvR4XHRVFOTOkdD3JS2vdYh5ndLbLz7sWIQreo0s01YxcrmWrMxdHiItMIJf5gKrEte5yOx3R6SaFQx+yc3b3/g7vX73B69d/hcP+NGDn7nYHN2xTx1ABT5pGY3ezS5oJloIZrXDRrzLgEwCGeYZ1FOVpSYQ9MVJmjMF+AEX7IRacB2fTaicIQG5fpNY1jb5pcocAR708/2F7wN37u5OBjU9/8hSH6wndn60zrmm3u8Y0D1itzvD2N1/j6m/f4vaSNChenp/jer/H3SU53XdnK7R3B+jblP0RpZQnpYIxIv/cmAZaU9dY163p/6ue0uhdIe4iWmPuLKGgrukM/T1E8FXa9i2ePDnHs80Z3janqRMebg+QkSLrKXE3HLaHlOWaMY77pDdjM7cAayoAxZblALgK6BgMSC9INO3eZp4Y72zmOSjAQommaTP1+5Ovn+L5z5/j+bNLNFpjaGfsnlCU33TEm9A0pTT7ofFI2fs4HsfjeByP43F8QeOTEb8xJDRw+fQcT9ar/PPDNGM7jpgHqgMe9AH7211mcJpzvUMACYSS1edCzOxjGQglJYQI1BYEQBi1iA5C5TEJKQCPRKrDYj4m17s5NSyEKDKnJ5JZPNms8fyr5+jWbfbA9ncHXL9/hvfvv4XzFrMdqb7fb4hV0DRFiCIB/kIgcJs7c7kdiMg/SqsafWcUmt9GF2DXUQQAMMGIQqtNqW9ai31C8e9u99he7zDcnQjuq2pSGbmeQGptQjOHSLVuozU43uFyOgPg+PuzdOykp0xiBCD3+3Lkm2tkAYi1wI8iBsPzrsfFiqL9Pn2P3TjiN1dX+P4vvwcAfP/tr3B19R2GYYvV6uyk6XsXMOwGNH2TEcv7mx12Zz3m8/Mcza3aBj5GTNZh3+jSVpWQzpzaA0o7Z53FYnxKxkKkdlcWAMrZLinQdk0SoqJftKPNtMRNXwR1hCRsizsx0/X+/XcYxwGNaTN/B1DxaISAmKRkx3EPoxviMsjMjBSRSVXLz1btf3k9QhKZcQu2Qh58lpuOiKlMQ/VgJIbQEAJ1DTmfVQyvX13j6v13aQ53OD//OIPZx4adZhz2Y6YY5jlIKdE0PYbtgOBJnKZtG/ytF89xuVpn5kgAuf+/XjvNpZhQpaFCALSGYO4SpdDEUMCBaUQV4YKHURpN6ucXCNBSYYzIaen9bYr4TyxzfP30EruvRqw3lxlNvt2+xzjusVqd48Vf/Qwvf/clXjy/xMVqhVXT4KdPnuD2a0qtH7bUAnm4O6AfzyCEzKUuBqoBpLCqE7Nf267QNj2alrqWaiIyzmK56hzpRBU+D3PuagJSWcAYKCkWHREPGeNhRHQkCMR3FCtjjiPhusZxj2k6UBm0XeXWagDE0OdCzmACpRupbtf0gknaRLYRECJz1vCQUkFrArWePzvHs58+w9dfPcOL8zMoIXGYZ9xcbvK6CCFSxP/x8vYnDT8jz9ct9UsDQGcazM5hN4642u9xezhktOI00KXOLSWFTUtlbep8oVXp7RrRGyUgApMZ3e/lj4kwKGajXyhGVdXu52yhij1mSvvccdZ3+OriAk/Xa3SJHeqwPeDq+yu8fXOZjMsu1fWpXzWEVdZcJi7yAliKIST9dJkR+TTvst7cISFTX3it9bzATggBJQro0YeA/UTAJgC4fUPkNcPutFoXRNIQmB32E22iTBoiiMimTk2yoef151YnvrzqNC51YrDhd4W0JRTjIGQB9AEJ2LjqcNb3WLdUYzdaY5hnfHdzjV//5hW+/4vvAADv3v0Gu901vHefbG356PST4zjuBuy5fpmAlteX53i22UA1DTrTIETq8T9sVtlJYP14pqbmdDZjW9ghYBY4pitG/vPS6dONTil0MnhucOm8ET7Ce58vSb5o5xMNP60dyeTyaJqOuDyEgElngdL+Y+JX7zBv6PM4/axN6uxIKna5gyNTwcZC4JNYQOs23YwZSL8fY8wEYCGIRCUbsb894P23VOZ5//oV3l99j6ur7xGCw/n5ywfPX4BIu5yz2KdyweGwRYwBjelgLZWA1hcbuvQVtWgy0r1rGqhE3HP8vvpILtmA9o5KTjW3B6oFuIwciZpOl4cLSxzK7maHYb/L++eh46vzC1z/ZMbF8ws0TeqP9x7TdI13736Db//6L3Dxz87x/GfP8JOLS3TG4Ol6jd95/gxAaoPdDZj2IzFZbgmoqZTJARFA+6lpeqL1bdrcLdGn/vQu4Zr0UeAYQ2kBJFxLJfFtNEZrMc42lzwfOoa7Aw5+zjgjAKlD5DbhuiZYO2XwXWNaGNNgfUlcBTqVJUogU85QMvnpbyqXtqVQULV+CUqQZ0yL1VmPftPh/Nk5Ll9e4sX5GZ5tzsjBGSf0G1qrdkX0yG27hvc/wvADNIHeGJx1tAnOug4hRjxJIhRMZxoj6VQPu8InbEcLhJg9d58Zv9ig5Q9ZfqgUEPyzBRCMEKNutvnCNMakNjkmmUmfPbvEAlj4kR86Wk2butU6I9vv/o0d3n7zFq+/+xpv3/0Gh8OWPMF5zHz+fGCklJjnAeN4gDFUAzWtzxdivqhT/ZojKtrQPtc/sy40O0yIiz7hwEb/6g7Xr8nTv359g7t3d9ifCPLhNRx2A66T8Z2cy1GLUVTf5KESMQlH+To5gy7VL4FC4CSkgDJMBqQgVQF8MXgti++kC6A/X+FitcJZ1+G877FqGljn8O31Nf781Wt89y++w+tvKOK/u3uHeZ6ojviJetfHBndfzKPNAEmdtBGuru9wvdnknux125IR9KWuzm1vdrRJ1pT603PtnmuByehlxP6CIrfoXhhpEmUuZSLmYc4ZBboYAybWdJBEwjKfoksK4HC4g7WFbAUA1usL9P0GWjcLh8C5GcO4gzYN2l1ijWxNctqQsSosOat06WhgAFS9LrzHyUFYMpix5kfwPuMPvPW4eXuD96/eAQCurl/h9vYtdrvre9wenzt0a9CYDuOww27Hhv8ut+yG6NF1a6zO+uzQcgYMANYtodxr48MZAyVEJq9iwR4tJVyq4XtdhK/yGgePOWWKbAL/Ufufw/72gO3VFjdv6Htu399BCIG+X58098vVCi++foYXv/MClxcvAABt22Mc97i5eYNvv/1zrFYXePaz5/jq6+fYdB3WbYunG/q8r14+LQ5pArCZgdoxnZvzvNp2lbvCGAfT9A02TzbYPNlU+vYVaVtVP+c7nei+C3nT7jCQSNd0GqB7f7vHOE6JHr5Ihg/DFof9HcZpD+9J834Ytri9ewshFax9CgBYn5EzKBVlbGIIWXqZ8D1M3gQoqLJHnYAQLrc4GpPuvU2P9eUGZ0/PsLlc4+nZBperNS5Xq4TrQg5K1+drbM4vsFqdY7u9+ugcP2n42cMGgCYZ+HVLD4TTvNS6kP5XGSkgRd2ThayQyXNqb/CuCN+EsIzsGdVPl2HRMWYeZNJyllBKJ5a3pBE9FsII5g2wsz1dsEEKrJoGZ32fQWtXP/0a7/7N30nkMN9hHHe4vn6FaZ5JX1o3kJLpjTsISDhHwgmqSn3q6nBrAAhVZqBKfXOfJ60fXZDaUHTtI63P5Bzubna4fn2D23QB3L2/w/52nyPQhw4GEA67oZQS+gmskNWv6MBrpaAT5aggBFtaO5lFSwjTUl1k2uaIxFkHPbus7sdlASZCYsN/ebbG0zVteO7bf3N3hz9/9Qq//vPf4PWvXuH9u28BAPs9ZT3IG394qhcgIKWIBlLJHDnvrrdQWmJzucH7J+eZxIXFWNZti0vub36WyD5CimZDgB9cyXbVxCb0hxzVsLCH1CpfgN2qSzwJDsNuzIAjNijeh2wMQ4gI6xZRnBb1WDsRQ2W1R6VMbYkQ8JxFiSERjARIqTJ1KoEPdQKnlna2WgCFR6btVar0NtcZECReAOsXDlTw1GI17EZcfXeFu9u39Ix215imQ/pOpyE7BcigOG9zG+I8D5jnEcOwxTjsIIVC0/RUtjOlxAgAX11cLAIidggkZ//ymhLWK8SC3GcWTCFEBtHyv9OfKbtnvcf+MGB7tcXtu1vcJIbKeZwzpfopQ0mJl+fn+OoXL/H1T/4OAOD1m19jGLYYhi3evvlrtO0K5//nEzz76TOs2xY/f/o0A4CfnZ3B/jy1QBqFftNhf0e6Fz7x/QMUna7OVmS0RVEb7Tf9InXOXT5c4gWKAJaQAq1pc9zIQO65r3hDHjimacDhsEtdB6l0EyOmecRsx5zml0LCxojd7jbzENRDJzp17mZiXRUeHORxJ46cBKwVkJJaHHn+Tddk499tqJPpvKdyZ4wRQ9PgIvUrs9NEhv/jc/yo4WeDvLu7w/W7t3hz1uAsHUKX6pshEsnENAwQhwE4UMvGOOyysbFzkiEMEY5blCy1wyzaelzV4hViNvrcUjWn+pWdLLyfISAhlYaQxE432wi/I4lIDieEEBj3JAM8z/vFvD41+HWvX73GbzZneLrZlFrzMKBdKzz56QZPv/kJdrtrWDtgv7+F9zOcnxBCco6CSyhNlhyW8N7CuER6NCVmN0OteMpoaKegrIZ1Cs4PcH6EdZTGm+cDurFDd2gxNU2+UA7DiNu3N3j/3RWu31DEf/PuGvvdXVJqevjcD4cdGfOtwPamSiEnRbhu3aM/66ClQt80qa+31PW1klDJiZnnGW6a4KYJ4zTlnnQAyQn0Ge0upYQPEioqQDboUtAqB4OgNLbO4XB7i3Ga8Ov37/EXf/0tvv3z7/Dm2++wS3VNgYCu69F3a3RtB2B+8PwhHISSiPCYUoveYecx2wFCeUA5THe3OGs7NEajURqHecaQaIPDOMLHCQHEzhWFg8cM51Pq35foBekyj4hsdSC0htSATJmRgBnDfsY8Tdhd7XDYDZnzwBiN4aAwW8rurM/XWM9rSO2Xc/rMuYfgSuYhnSdrR0yjAkBIZNO0qWtlhrMRMfqsKKZMgI8TZntAf+hJV6I1mUddLjpailKZ9+l+mF1WYKTXRMzTDG+575nuC+88DncHXL9/g7stGf7D4QZAgNY6f5+Hzn8Y95jnOZ0dTq8D3tPPnJvw9l2HtusQMMOHCfO4x7Cn23b7/BJ920Lm364mCwEl6jR+6pypxbzSz+qMgfO0PnMKBGbrsLvZ4eb1Na7f3OSWLusnSO3hw2nP/tWrV5iNQcCM9RO6x54+/Rr7/TWurr7HbAe8f/cNfvUv/inO/7cVfJiw+8XPsEoB4eQs3DwD0qFZSfQXDQIsorAIXmS2QtMpNCsBoZi9M0Ioj9keEAeLJnL2SsBa6orxjnAzd+/uME8zdMVnAFBp2doB07jD3dXVSfOfZ/r92U65pBVjTC2sA6yl56+UgVESIcwYx23OOFOa3yVeC5m7GiL0Iu1fgroI5x1cKDL0ATFn2wI0AiZYO2AcdthfX+NaSvjdDiFGbMcR4y3hW5wbII1HY35433/U8L99S4foT/7H//6zFu1flfH27Vv84he/+KzXAcB//vf//idfKyXw1VfPADz7wL/uMAw7DKe30v9LGw+d+z/6h//tb/sr/dbGi5dPADxJfyPP4aHz/9//8f/8W/p2/9+Ph879w4C4GT7MOBw+DBobRuDu7lf0l1+f+k1//Og6oOuW5/Gh8/9n//f/eu/f1muJ9foSL15c5p9dXf0Zrq7+DP/8//nx3/u3NR4697/3h3/4wX+/vOxxefm3899v7/4Sf/onf4k//ZN/Od/ztzUeOv9vv/2/8s8+dG+vVgKrFUOYA4AJ8/wG80wt3tenYSp/K+NjcxfxGDmXxjiO+OUvf4kXL17co8v9V3F47/H27Vv83u/9Hrru0zXfv0nz/5LnDnzZ8/+S5w582fP/kucOfNnz/9TcP2r4H8fjeByP43E8jsfxN288Evg8jsfxOB7H43gcX9B4NPyP43E8jsfxOB7HFzQeDf/jeByP43E8jsfxBY2Povr/JgEdgEegx5c6d+DLnv+XPHfgy57/lzx34Mue/6fm/lHD/8tf/hJ/8Ad/8Fv9cv9/jD/+4z/G7//+73/ydX8T5/8lzx34suf/Jc8d+LLn/yXPHfiy5/+xuX/U8L94QVSN/+U/+Ad48fIl9tOIv3pHXNjvXl9hd00kPVJWtJpJKpeZlgDWbJeZiU0yy5Vc0nEiJOa/yPzdJMvqE4mPnYl5z1kHNznYifQApoHkgYftgOAC+rMez35K/bvPf/4c64sVBASu3rzF//Bf/1d5Xp8a/Lp/9z/8j9G0PXFPJ2nWcTfCWQelFZpETNJteqzOVmj7BqrRmaSCxRmEJOIKlmMNMVEW87k3owAAIABJREFUJ4ISn9idog+w1hGNq/PEFz57+EQD610gyuLJIjBvtZJougabyw0unp9nzmjTkqb0sNviH/4Xf/Tguf+df/3fwdkl9SyzrHEIAaYx9AxFkRauteCPhzyiIoZcCrXk1wkJocS93+XBGtfMiCINzbtpGyK9qeQvnXWYDiMOdwNu3r/Dn/3zP33w/P+D/+g/hRINbt/eYTyM+TvQ96J9zQxzTD6lEjUnrQkye51pDLHWGZXXLBPkREAosWCzYxlf1ienNyzfMfpABDfOk074MCH4gPU5PfvLry7RrTvcvH2H/+m/+28ePPe/+4d/D22/gZtd1nXf3e6wv9lj2o+IiFCa6Iu7TYcmyQQzgyZ97/S10/Ou5XbrwUI9QMwiRsEFzNOEeSjEXc7SOaiZPE2rYdoG3aotEr2IpIMgBZwd8U/+9B89eP7/3t/9T/DkxQu42WbK69u3t9jd7GCTVKuq5qoNMRTWMyOqHpLY5vORJcWrc1P/N7N1+kDys5rpXRWEElnnhCXJp2HG7bsb3N29xzQd0ntJrNdnUKrBN9/8kwfP/d/6t/99vPjpT3D+/BxPvqLzr1sDbz2mw0iEaOOMcT/SPTxaOO/ASmqstXCsj8L6DEx0o7SGMqRZohsFbYjYqdb04LVhuuYQ6P6bxwnjfoKbLLFXIpEgNQamofdxfsT/8Y//lwfP/z/7oz/C7/z859iPI371/WsAwG/+7De4fnWN3c0Oh9sD9vsbDAPJnZMwW5HC1Tqd9UYlSmKTzoZC07WZvY/ZOQFUWhUxE9uxzfOWaOqZzttZh2F/wOFwh93uGuO4X/D6N02Ptu3x6tU//ejcP2r4OdVx+fQpnn/1FbC9Q5848Fc7CzcBWndZnKdW31JaZupQVqIzDVPrKujG3KPSZIUyZuUKLNbhAuxss+Fhnv55spiHGcZMmM0MJVoM2wNENBBJJ87oHpvzJzCdgU8G9nNTOPy61eaMaDmjgYiJzSEQ5ahpSbOZtMKZZjI9ZBaN0DLzNPPcWEuAecqBcvnZ2WYGsxiI2cnOxHTIr6PDNmOeLFHCRsB0Bt1qg359jrPLCwBAt2phkqLZKXPv1xu03ZoY9SK9h1QSbU+CGpnKtaKXBVDU5ICsPb3QWE/sf7UwEaqLkdYmltem1/nEZc8XCHPAt6sWptFZ8ZHXadgNaJp9Xu+HP/tzhFlAyhFG81wFGXO9fK9Q0cvyHJSic8A89VKrpEEg82VGc0U28sfdtSJpFgBYiNzUZ8M7j7mji5jFoZRosTm7yEJAD517uyLaz1lNcInxWWEG/Ait+7wObd+ibYmNj/n56buq4rjkyYg8WVkZvlqBMDN2OodpmKF1EjxS5AQ4EMNZFImnHRoSBlI0mV5VCCCoCKUkxvwsHjb/8ydPcfnsGemxp73vhohgJWY1Z0POAUxWiKv2OYsRqSOHeGHU2Ck40iSoRcsA5OCJFRxF0qZ3Zw5tu4YxPW5viEBmmkcIYcod9MC5by4ucf70Kc4uz7C5SIbfKEzDDD8LGCPTfwVE1IBXEKIIodXOi6jOtDg641LLLL7EYlwqaZbIOjhI+ya0NHdnLIyx0HLEIEZEXzRSlNDQiu+n5bw+d/6/8/Of42/97u/i+5sbvL4lZ6pt1jB6hFEOUloIoaF1B6U02qYnkaF09pquRdMaNF2LfkNKg1KpbAOZSpnvg1qV81i7AiDVRTs7RE8stc46rPYj2ts1pDSI8W1WUdS6AyAyHfrH5v5Jrn4XAoZ5wn6asqc7jXPm72exBO9YhU4A0JAqcWwrCQFkL50Og0dMogzHqnP5wFSbQ8fyNY/5/L11CEahaQ3c3CCEkCPz/d0eq/MVTKOzsXjocNZBiiKgApAzY7qGLr1Vi9X5KhkfMvq8men7q+XlBywu72zUXLqggsqCRUGQuI2UElGnSDMujYMVAsGzSEvMDgMPjhBOGXxx1/zSbdrEnLlgERltdL6M/OI9xOISFKLMv778pVwqtvEUZcU3rqWg/STF4oJlo1FzkyuhKBruLUxXpFIfMoILmIaloI5UCkrJxXMlmukA4bG46JRSmWM8kHwfnTjHSoXIv0/zjXlN8/6oRHLICbqvVikEPQM1ybz3D9sD+k2XZUUfPPckmDOPxIlO7znk94uxzD2EIiUs2Pnx/DzT+T6K/vhZCSkXIly0P5bGsx5ZvjhRm7oZJdLky46dDyXvrdXnDhYUkrMrhkuV7CUbfG00pKa7jIVieBxnt46Nef26+n6KPubzsTgjLFetU5AlyQlenfXw7iIL2Gx310lM6DRVSjrPlKHNtNpAju6nw4RhNxB9etJWcJPNZ3Zxp/uQn6NUdFcxba0QQBAi7RUydNGToA20glL5gADg7F+iDFcyPY8kVJYcXAt2JjWiOO3Z6+SEER09nSc3kx5FSMEYgCQp3GTZahauMg1RU3frDu26yw5x7SgCWMiyh/SzEAJlEkOAV8Xh1w0JNLnZwc42qaSSsqW1Iw5Jm4TuUp0dgY/O8VOL4EKA8wGz8+T9AkVlLOmo29mVB6oVRAiFg9/LnNbiEUNAQBJf4LTn4lAsU8N80Ph3+fDrJsCklFgIEY3zcEkACADmYcZ4GNGtu5NleRcGP10osm+yR9et6QFzRMcea5krlTDqKJV/XuZLhoBTeCJd8CKl0lV1ebFyk5QiX3DOpfU6kvAVSf/ZjqdJs/LHKiWLzKpWi8ibIz2kzxdSQEHlz//YENWFKCXNM6+NLIdr+UsCQlWORNoj5DFHVIJxtG9SZN60pxl+O1nEQNErOxdSqfxd6z2rlILQy+NE0styYcDYUNbrw9ktHhISkGmPHFdPFkYjLpwy0zY5PTjuBmyvd3A/IM35g3O3DjqlUadk+N1c9tEimk+OWgiBbl6aKD2v6jnR76U5s5EWJeuRZxXS78oSCXNppV5Puog9nAPiYcrv0XQRUop7hvghw3QNlKZMZS11XH8P01CZgdeidvrqDGgd8R47AeXn5bPrFHB2InPWEOAaSlQqCVsB67DO2UMfHKbpcLrTY0zOXMrqfmajY9MdS1ryqSwbSvmFRy0+VWd5lCqOgQoR0dD96IUHtCIHGViI7JTAgZx+/jdldMro+sXnBh8QcJrTyxLnk3NZnMuxlDbbAmUABUilYXRDypRJTKvfkMFv+xZt36DpmpTlZmc3zYkDwBCW2U9wwMVrr6BT1oODJJWUHIMPmO2TLCRF+8F9MsvxScPPd/doba63cRTJilludqX2qSTVp6p7m729vOlDBASXB+oFB4C42OBCpEiHFYyUgpQ+LwyrfXnv0RxFdt6SJOq4H09WqBOySKNmFSUlYRqDbk0RP0W7fKGFdHEvD3jtxZPAUVhEOlzf55SPkMkAAIiipLcR+BBISBURo8qOAW94Tm0HHyERcnT04LmLYvSbtKm983DpYBZ9+NoI4l6dnr4bGzKR66DlYlpmBYIPiLXB4NeJpdGv35ucplr5iiSi6bufZvi987S+SgJJlph1wNlJ48idLyRO1x1/3+W6ftghqgWQhBAIHN1Xz7c2pCIpwLBqGdcRAcrKba+2iOI0p89bD6fokufLj4W0PjQi13SPHJVjo5fPQ+CsVsl65d+RAiKIvL4ApZrd/GEMSQiEhbmfFge0Oi3qJTySIuc6G1+ZFdMYzyC1vDdHANUlL3Im7IcMMZd76H2Q09tFhlaUoIK/jyQhK58UUtnw2nFGCAEuCXs9dAglEnamGCs7WxJPclSG9Al/xU6rlIKMNpYRPwU+yPfewvF15edlrUQOWI6dZRFKiYuNoE6KfhzxO+vT3SgRw4lZ3mT0h3kuJdaEO6HvKaFNAyU1VFLR01oXNUFVZQMXuAdWL+X9Ukrki1E5jajWjjIdCk5KADParoFbOXSHdZbwdW4L58K9M3U8Hvv4H8fjeByP43E8ji9ofJY7PCfvh1PoIdWRucbAOur1yOmMD9boBEQGqZR6D/17XTcsWsU82Jvn78JpN6qrq0UK1s4O3jmSfbU/XPP42DBNkzxKAlUAFA0oQ9GA0rr67lWavfLqckoaWHhyEHUasKTnOUPFUb//gPvGkQTV/wt40KWaG38fiqZP8+8EBHQCMDJYk+u92iiYxiw7M/J3k7wgZZ4AIJfpz2VUVDzke2Anft8PRM88z+BDiojKz2IgjIU6MeVL5ZeE0zguV6V5xBApw8HRvSrp0RqYSKltAXjCbQgpIWLx6Ln2x+ltquFROa2uedfrl9csAX5iel+A8AnD9gAfT8t0OevgTSBgUdXRERPmhJ9RXWutnyN/3xzBhWVkU2e6ljX9cgfQGjLWh56j9jqnlmO0i4wIlw+CD7kEIk7M9oujjB2QOjQMpVi10QRolfKj+5LmVOZbv9cxELS+/+oM0gdHVUYxjU4RINCt6eJo1x2mcUQI80lz12meQsqcQnepnBtD6phI51RIAREFQvXMFntTxlyePJ5rXd7jLJf8QLaQX8Py1ZFLB1IiSHoWPt1PdddNPPHe895jthaTs4tyg1SUgdLapGxDAexKtcRC0esJtCiqPVJnvYKnO56zQRnQnECcPBQAj5DfU2oJYQlnZLjk3K0AAOO4h3f2gzajHp+u8fuQ0vwTnCspZELe+oykrjcq11x58N+FCIicsj52FBIIENUFEWPMxnaBFOdfX6R+efMUe8MtEXaac3r6oUNqAvC4aEkfHljU/jSj249APLxh6HCUOq7Sy7QXp/IyEjZfXgxionXmdaAUV1oDKSEREKMsqeYYF50CStwHF3723I1C0zUwVY2c02sMVuFLnf8NQElPei7/VA7OEXo5veu9mmhdv8/PXgpgAYw8TvnHnEaTSmQ98uMD+blDMGL7WEP7yICx3vYxspvXRAgBVa0PI71F5SDw5cG/y2n/um5a76963iFdxt6HbKSpTBHh42kOL7cNzcNcdaFUKcl0SStF54M7drilrgYvCiEIt3FkHGieZc4ZMBrDvWfL66u1Qmh0fg7OzggxGSdHc1eWLkQ6EicmNT+QledLWQc6g3WbXlqSfPf41Gp53NVCe7TaQ4pR3chlHogl2G/x+yFmYCk7XTq9L5e02lWLduhh3WlOH7+vEKKk0GeLebTUViwEdGMSxiLken824pUjHENAtFWau5qLkuV+D0ZDpfkJSQvJODE28kIIRCGyM8hrCRSwaAg6rZEEcNq5n73H5FxuH+XvwEMZlUs8/BxkDeiucCjsmAohcgCS7/yQ/h8jnbHaST7COEkpluen2h/a6Jzq10rDe5fbGz82Pmn4Q6p3OFtqHN77vLEzIE1wTSoipsgTAOQkECN7Y0f1aHE0OU21cRHKhckAEp6wdyXbELPXjKOoKCFRrYcdLeZ2hptOjPhTH3bwIT986tml/1O9l4wTPxwABYUvJa3bB8CFjMIHUk3Vsld93xGoLw5yeNP6cFAtywWUgZXW0QV4YkeDSm1KtRfOm1clh4jn7KxLEWEB5eSoOBY0/4cGHfblHLnFy9ced0yOzlHES9G+zJEFvQcyGOdUYKdUEiJSlwVHkzJ/R+QWRq1VdhLYYPNgp5QdM5GAOcRlsbwo8twrp7HGj+QOh/RdnBPwjvZdCDH1NxcArlRiEbE8ZHjn4YRLWbMqgxQikHk7OHNDDoDSugDqOBrkyy9EyvKFpQORHTiO+BIgctHOheLcOH5d4grxXgKeHV2X535qhq8e1I0RFpfwMSC5drQX0Rx3OcRYeBtSfbp23ESge1BKSXvr2PHlGjHKfXKMBakzYgDdT23XYB7NSfPmzCnNI927CXvkHbedle+jG73IbtUgR289pPL3njOA3B3DOK267bE+s0KWaD9/p8oBr5HyWiuEkDqh4mn33uQsDvOceuervZoy0EbUXBUChlt2u9RGnjg76jX8EL6D77mQ7jkhBKIjZ6gOdEXKaEslMxYsZ0bTWeHgRJuWAp74w+f+k4Z/DgHCktefvbNk8MORUQo+AsIvoqJ5TC1e1hFRg1FQxud2GTZSQgpqh2JDyouS0KJ1avAYwUkb6v6m8dbBzUz8cVrErxvqh3WqXCTHUSmXJYIvlxpnB6JMjlFKaxHSu4BgckrRFy/QezZYS1QrgFwuYIKje0YtpbjTH/PanjI4qufLCKBLReaNWKJ9JrMRqEh3qt9bpPOPoz6tUstlMZrBeTjrEaOrXo9FyjgIv8g4GFEOAF8yfvKL/fKQEWOk1K4PkMwaxN+ZI9DUysjjY8mVfInnIEZAmOKsslGnfXC/rFDeX0DokiJ1qrwHt9UCnJYXcPZ0cB8Cgft4/WLVmsWOfp5bPPqelYGOMSIKdmjiPWdEqri4vBnAVqPbOW2qfelciTFCKU1dQrG6IwLtHTs7CH2a46OqNeZR32vs7Hv4pXNXvZZfh1BQ6nxm62yPkJRFi1FRRlAK+BxBlDX1MSKqCOnE4g4Jge7XfPfwWrWnGf46c+cqB4pT0BQE+pzm5ud8nKHh/9YAyPrfmNCN7xN2GGKMC0KrDIiu7/70mR9C7tNr/ckItmGaYaYpt8byd1BGQ8yOiJlS1kM3Opd+mS9FCA46PKRV8KbYxGXJg+4rmVpafcqix6MuCc5YEteLxzzZTGYEJDsC7pjQ0LqB+EQr4ycN/2QtMOv8YfQFSl2NDHWJqoQX2cAByJcRpzaVossyxhaNqOu85YB6S1GGsy6T2fDg1C/XcGPyqDkKF8pW9Tlqwzj22h8ypKSUppRigRItxEVc200Xr/XJMPNDoyiIDEViqTKaUtEuwMVyMcdIGYDjaGWR9kwRVqiwAEzsAiQny5eLpk5FPnTolhjnRPWclJaQQeTLWUiQsU9OHWrjm1LV9dpzj74Qpd/aNDqTXBAaN8I7BzHa/Pd6jWqMCdcajz1qTtHTup0W8RPDYsw8CTwkO3FmiSm574AWQg7+dwDp+QXItJ9CCICrarsoTm5tbGoH2DQGXEL7UG2V1+rUPv55mqC1XFz87Lx+KA0tBJJTX4irctRXdQMcG8hM1hUEhExOfNouwYfa7lF9Naf5AcQIN6dMSxSIkTkGKm6BH+HwA4SVqLED2dAeZTsLNqPOPMaMYQg+5DZoABBVVievlRSAA2K1xh9y7J0QkD5mg5odL3bCNZXo9HhaR8MilZ4G3wHMV+CqOebALywzQ0Itzwatp79/GqvP4TNV31v0GbSnYoyAjmDT5b2HrwjOcqY5SMgTnT4XI4Z5xnSYFsEuBZIOzrkU1csFMVft9PNZ5dZF3rd1yr8u+SotMU+W7Gm6U4qDUNaaMy/ZDoZl4GtMAykVYvxhh/8zavwefqa+zdqg1ICEBWNTrv/X9UyVwD+Ffeq4v1lqmS5zem9nEy3vZO/VOfny53Qw95LmWmkFGAGQX3PKoEgqeWC+XCw8b6FSZJzAWMykV34fC8+YxzHYSRkNVbWM0PcvG4M3oHACLhKBQ1QKQgT63XxIkB0HN9vsZZ4yiHiCwSulpzTKZZpJ6rqNTixq4t77tCYxPydKYauMHWBmO92YXFaYJwulU0aI1y0ZMuaPYA85r5W8Dw6iyPP05hWfcCL87JhdjHELdMmlz1JicYmFVCfPkXFyerz3kLPLaXGT6G5zXT9RVLPRKoa/pIn5efAF86FU4o8pcwQX4YK7xzHAg1uLUDnZdZRS7zs+F/z8aV9iMbealbAEEx8mn+KzIyRFYcSa6FG/NEetJ5Y6IOj82GnO/AUuGRg7zfAuLM4zM1nyvIjTA4tMTowR0ou0VoUATfhSDoFclgSODSd9FsrcGBdRbXveH8fskp89dVGAatnhT06fbvSipMd3MJ9voDx7EX128gMAMCCcybDSn7VOz1Bxbz59du101s6QVCrfCQQ6LJmJeZwzXkacGuwJKlW7uXDC2Nllamxeo3E/Yh5twrhUfA9a5bKjTkFN0zfoVl3OEgJVJO8DfMJN1ZleLrG52ea7juam0j2f1loraE2bom1XkELBfqKV85OG3+dDW4hsCDRngfShPAFObwAlxooO8MIVox/CAg2bf9d6QBfec8RSP6vTGqWGGxPojQAYPnHbc80xPcFchzl1EJaANgE/COiYGZZ0Q1z9ptHk4VbUo0Bi/qtS46XHNELIZSZDKQmvBICCTufLkA+Dtz7Vswu1Kae9jg0Ag70+dHF/zjBtc0QcguK4SVEAXYx1yAx6leGvDFMd+XOaDAC01jlVxhelaRu4zi48X+8CnHOZx5yySXQgY4jpfZZbmohcTot8KKIkLz/XH5vUxSEK/oQBfcfdLQy4Y+PJzjMzO3JU2VgqfSGlz/mC8dYtInri8k5ZHH0/2+D9UUksaT2cMnzwiN4t6o0iO+vVOfREKc3ZKpMvNZ0jWb60QuKA4HnREqd+7MSuWTvIyzsnlLugmidH2QRwLWvBjvmnQE4fG8EFuEBp1anSC2CjX9f2pZIQbKOYXlmI8kxB2R++H7JDmNaPnARZrW/l/MSyTsdrIgQleEv2rUTq4kfceexMcEobSNmeFOHy+pNOBGXFnC2ZSueW9L2cEblf4qF7sempc0rKJdHTMbMnBBGGscM8D8RXQDoVBdfk+fueaPi1EIXDIQ2f8FfkYAY4ZzENAlISxkc3JpOc9Zsu341Nl2jdV13ifKmyYkrxkU/zTLgSv8SOjIcR04H0Edzs0u8kkGdyTNg+NLGnszT+yFS/CwEipRUyfaOliAvHqcVQMgH367kxX4CFslXAVd5PrudV9b4Yk2hBlS4VVbRDi1GM8sJLDoV571Tj55yDEorqWr54pBSx0kMljv5S6xWiINq5E6Ju++Eo2B+lTEMi+WGgxzGjHYCFh00dCza3fLG4EQ8mMPLhtDqvaU2uw+ULKAFcWHOByStUovg8vnTcbBcdCkCqnesaDKMzy56s6lkAspHn36vT3yQaMmEaKCVXlySEQI5OTsU4xJhEoayDEPRdvSNDJ2Rt+FVO/9ffndO7vAfsSBEEU7zy/L3zmBJwx1sP5+i5BU+UuXXnxzEuQqY2ViEoKqlbbtkxPmV46xGiXERyFGilSCvVlO2UwH2GIp6QLj/TRsRI9U822N4T2Zez5TwznkfPJXsgc5Bwv4MiG/TIUX86I9XxjpH3yOn4FjtbBCGJojZFeXZegr0AoC4r1s9HpXQ7O8Xee0LC+0J8RvPnNtQlCBQAZBA5c1q3/8VA3VFsULOeAzuItpQUTh3soHIGTUgi6+J7X2m6e2pQ6WFLvPbzONEcvYV3NgUmlWhbzshqGGOwvtxkkiylSXiL6c8BVNnAwtwYA1GzxxiJQrjC9ngfqDNEnnbna60rG8PicEkMaxowTQd4byEl3X00jwbObdK8RAZ/N12T5qNTgFjmpXSh/671S3wFogeApm8wr8joT8OE/e1+eScelbRMoxE+Qdf8Wal+peLCS609Th6Rczr137E0VLyYSin4o15F2sAiRz3sDWqjyfvm902Xo3ekWDQdUpthiDnCL2n9mOshp3rA3noE2EVvcO3BdesOTdegbxu0WsNoDSkEfDp4rooC2YsfrMUwTojRITIaMzkF3JLDGyKGsHCwYnnisLPDuB9zR4C3JRMDILEqWrgfYfgZsMIjxgiVtBGIx4CMXrsiFsNjsZEC0osL8SGpSOQIQO4OMF0DLSWpDuY694fTzDFE2GnOl5+dbd4DQNX76/2PMPwx77f82QIlsuaohoU3+JJnBrV0IbLaXE4Rp+/PF4A2OteLsyqlozSqt75kW6r15igjtwhV5wLgZ+9ye99DR/ABCH6RKo8+wMEhTkyTTayQQggYYyil2ZHh5yjOJGORo2TvF9+TsyJzVS9mLMyCsldTtOw5i5KcLp96yo8d++hZxe5Eh392CBCYxzlTXmcAbow5uhVSZbwHYVVS1HfWo9/0VGs3Opfr2OjXbZc1ToDb2ZiB01XrVHQSliyJVF6TUOmzM47mxBIf7yXCYzFgrUT+yui8JvM4kYM5W0wDpZeH4Q7zPGKaDrCWziinogUEmNu+aXp03TpHy8YXavRu3aI/W+W17FYd2sZASQnrHIb/l703W5LkSLLFjtviW3hEZNYCoHtmeDmkUPjA//8XUoR3ZninL5ZCVWXG5outfFBVc49Cdxcq+nFgIhDMoKsywz3czVSPnmVecHu90WE8rkQ8IqMSEqAeHHM1xiBUFR+yVMxcPl9w+vwRr6cPuN1OTChN/Ow3aJoew/DM9+RN4ZbZxqLNvC998Yxqq9G1DYzW0NvvqiakfRZibspUdIFGoO2uxfXlgvEylWZrCxtUm0Lrb63fdfBDCHLSSasKgFQo1V3FQRXiZh4FRbr/tMLAlMq0zvTuljzU/LOIoboWCYGJX9GHMn8rL33MSHkzG8oZwdf38P83ruAC2H6mHFRt3/ImR/8MXYtd08BqmfPlIvsyWkFXdJjR50+IOcFbmQOtY4lyzzaaUEllkkVSFf4d/Pvc5MrBKpIb+bPL7BDjg0YeLFER7gUAHtVs7DdZxgiBbK2G5jmF0ZpgQDEB4Q0ipXynY5dDNPiAWK1w35fcjBXeXeekSldoGXER/oD8WSkCf28615cr8Kxd0AQAcNnxvVlHPAX54AJGMi1ySqRqCanYRodAhdnipiI/k+uytoU1NRMTaW7Y7pqy+ba7ltK+2qawiQX1mm8zlF6/5xQinJsQHtRypxBRQfF9lu4UyD7D+wUhOHjvyjVI9yN6Ymsb1G1DsaRcEMkcXIJdZJFiaIXvBf0TVAMAKq/uDk0/u/IdS/cvn1NIUDFEPMhrpcbCU3FZ9j32Gsk+F16PYtvwgv7x4UvfVYd21xQOR/msPB6R37OSBkn2SyM6h/m2oFroO3UpQ+mVIyNM8q1kTEZnddcgxvQwqVfWl4VXUzWl4xaU8b7544LXLViWEdN0hXMzck6oKoWcE5TSsJbuUd8FWFtDEkvvvPn1JpWSDaJqo2G1Qc3vs2sczc75XgOg+PRpQc4JITyGemit4TJlnIznGwDg/PobCOITAAAgAElEQVSCl9df8Pr6AdN4RogBMQakFGBti64bcLudAAC32yvm6Tss43sopVA3lH3gFY2kVw9+Qj4NQ/4VKmi1WoKXe8EIMmWj1GvEN6fcSvELUNEno7e/t/6w7P1j/bH+WH+sP9Yf67/Q+nrHHyJS5M56S7TxW91sKtnLMhcSnanMPoW4sO2UtiEQADaQdiohHQB1TlvrSEqHIn2+dMCFXRw2Rg/sH0CSmgdhLyalSRoXgJLKVXc1amvQWgurCZVYQiAlhJARZa6fEnyMcCHcdb7S8RttikHDKoUkcl5YfOkQBPpf576463xiWO9VSgnTZUJMj3V9Yl5ElrD03xJ3Jm7xhdNhalPgNvl+AYZ7awNdKbS1BRogtA0io0eCgiwTzenjhslMfIX7GXXxQZAwEIZFDZvo5M1Mcvv3pBP61uWmBdNlWTktcl8aSuLqDx2avqVxT1eXscg2FtrUFtN1omdjok5omW84Xz7DuYmuI2cYY9H3RwzDEcPwBv2hx/55QLfvSzeze9pheBru5pzzbcbl5YqcM9zsUG2SGMVZ7ZFVaVVsp2NkWDpGxBgQgiNb0LSSnXIey7gCALS2MMaiaTpY26Jp+gJ9b9U8AOvDmZ8QI0PoFY0z3BfywJQyJcR5X5jT2+e/fF5GwJR9HOmLjpClgjpUtM8IB6GqUCSomom+8qyRlWq97hNtja4lVFBVFTy/o7IfzLe57GvL7OCXDaSP1Zq8oAyMCspMG9goJNhx89Hnnq51dUIFNoTdhUiqy40IZ7IvEarKZF1bo3IE+8cY4NxUnhdjaux2RwBA1w3QmlQyYmbmZg9tZkaG6B6NlxF1Y3EdukKiRtqQhatVzUPQ9yp3e2TpqoL3AW5eRwjLMmKeb3Bugg+OR4gKdd2hrltobSHGcc5NGKcLzLmG+ZlQmRgT+n1H4znxcDCazjEmUAtAk3NGzKnsj7pSgBaCLfkitLu23Pe4PfO44//ae//VJ2O+TUD0mK5rwp2bHNwm/lZeeLOxeJURg5D4NKcX1a1lUxz2ut8SOHgWFnO+h8Cre+tIconLbJHKMImiQ9MnV4qOFOml8ouHso+TXQqZZgOdVVUFpAznA25qQYgRISVKdNqQCYOjdDPx+pf5rUheZKbf7nJ5INycKFL4NmO8jEyiWcluRb7IumZhD5dDU0iYQhaLj6V0CZwZwjYdsKJ5NcdzynUAm82Jr6Nua/T7HsPzgOp5j76uyyaaUsLo2GUuRNxOI6brVJwLZbRUMZMXICtSSm6Twi6SasQHGNa1i+XDqjB4XNY0XWcs4/2YxDYW3dCiP/bYHXbYPe2wO+zQ7ho6wGIqc9nD2wOub664vlzx2rzAzQ7X6wuut1eceFYI0Isum+Bh/wa7Q4/3//wOT98/Y/9mj91hBwDojz0sF1nzbYFfXBmFKMVzfyGYaoUYQzm0v3Vpo5H8fZqa/Dw5nLU20NrQIZtotOAd7RFzunFBU6NpevT9HsA74oN0NSq1SXLjjxiCh/cL8wa4iZiFCLU2CVueSwgr5C+LCgJ5Bx/k9oQIgLknG9lp3dVlfCGE1q1/hxTtdL+oKBI+Q2ct9l2Hztq70d9pHPExJbiZtOPX0w3TZSQ1QRmvqrJ3yr5XVnVfmG6Z8o8s7wP9uGqVJwbmq1xfbzh9POH08YT5MlFjyMqduqb7lPORCzEqFOn7UUDwd0VfVekig3YzPcsSpT5y0QxQIdO0NZpdC8uNhak3jpb6tyNjGkM+SGzNmQrqz5dy8Bd5nqnRtoBSGm27Q98f0PcHqEqVZ1prTWM72yCGhOlKBX5wHg1zw+i6DJbaAKqC06T+Mkx+1Eqh5u/YqLrEBAtnjPbRFjklTNemOHbaJqLuaizzPzjjny4zwgJcXy6YrnSA0MYzF30ryXEsgBpKR1Q6lBegqlZHNmOpU7bc6YvtK8CVsyJ716y4G6woCAFtLvr2nFcHqS+18cF5modsDobI1bRuHiN6eOdRJWZf+3VW7Xn2pi1V6SMTtMRmVg6e6TJivEzwiytdsfzvtjYYnvcA6JBody2qCoVUMl0nzNfpbh5aOBX83+qGiDFVVRUZmBBvQggwxjzc8W9frrLkIJA5fEpwnIdQchvUWn0PTzv6HFajfjLojIFRCj7G0vWMpxs+//QJrx9OcLMrHYvM7qRYqKWLamsiSzESIkTPLzXr0hF9KfH7vWu6TIixuuue+kNfDmP6virmAmho/v1SaNRPNZEeOS56vEw4ff6MFCOm6YrL5XP5XfS9BjRNj+N3T3jzp7d4/v4J/XFXCsIUIs7nEaePZ9zOtzuSa0okC1MbJ7+qUg93/DnlcpityF5ACHTwa0XaYa3FjjtBa1O6PucmeL8gRg/nJlhbI8ZQvg+5nzkmuE3xGGO4O/yNeP8zc1opU7r8lCO8dzRDxr15UvUPTjGXaYFhTb181t1xh91xh27oipKnqir4haK/aU9kPbl3rPn3qNsaOSZ0bYNj32NoW1i+rtk5TI6KF7943M4jri/XghIJP8W2mtnxtI+KmU1OQFIUg74qWiraF5hv8K2rkFk3371fPM6fLzh9eMXHHz/h/OsJ03Qr+QjW1uWeZyRobdE2O1So0HV7hLBwIRTRNETaa9sdtLZFMhvjFVoTr6XpmuI8qLVC3TXoZleKYGDzfuutRE6hqhRCeDyk6PV6xeUz7duy91pbY7d7QtP0iDEUhG6/f4P9056bXVFAKOa10Hdh2xpKK7iZ9is5pMWrIcVcZMyGlQD7vsOxo/vUc0HlU8J1nvF6u2HecE+2xTk14Bvr7L+xvt7xjzPiQl2+kOb84uCYXEMdJz1kdfdbGE8OaOmU2qErh/2WgBDc1pqVRgVyoAFAd1g9yL3zcKytFY0/kd2IjCTkNiL/BUzXCTY+Vvkv4wz6Tmw57KWLV2LGwAedrQ0qVHDOY+Yqb7pS5TjfZpw/nXH5fCkV4PA04Pv/lT6r5MZrvoYU+TD7wugEoE76SztL0exP1wkLJ+gRc9wgpMc6fvnZRCBZxyc55wJhRiauUTHii3QNWEmW2hp0Q4dhv8NRa3R1jdl7YuIDOH084ad/+xm//I+fsSwjum7A83dv8ObPb7B/HkrHZZsapuh91wNdQozuoD1VIWd9h0B861rGGcZ00Ia6VADo9x3aXQulK7h5IZQipVKUmNoWGK7fd9BWY/9mD/ABMV0nnE6/QmtLUicAi5thTE2bKFfzohaJIeLy6QIA5fkZLyPB+hXp30VhYWtToHGAOo9HZazeeYQlU5fPn1MOWaU2QUSbA1gOdoCMRLQ2FKKTAryn7rXuavT7rnynZU9xAXax8N4gBMeHTkTcvLcxaqhKM7Lj4dxcCIZKGdRMGqvUGpryqIHRMi4IHK61kisbDE877N/s0e+6dYTH5M9lWuBmRkVnh8CeG+2uRc4Zw/MejTF42u3QWvqZrzcijy23GdfXK8bLWKSSwDqmarqmyF63qgcA0HFthGTRaPKxjj94DzcvaHfNilz6SJ/xdMPt9YZ5HguBLwR3x9a3piaERhu03Z6L2liQIWPou5dDE+Ak1RjKYVkxmgmQvK7pqBggtG1Xnu2cXSGLA+D935fRwiPryu+11grdQO9yjHtyxEtkDy2EvqYn9cHwtCsqBCH80t+L5bOO5xuur7ey/5MBEDXRUiiIBNAd3DpmUQpD22IwBlYpXOcZ42nE9eWCZVownm4lNTXHBFMbNPy5/9b6+ozfBaRAM4pi5FFVLGlr0O470j1uLCa3FrLU1WuaiR536Pc9TG0QfSimBAC/aGztKyzYpqtR9w2api6V1zzOGE9rtyOVcsVKQG1XZyTZbGOISA8mNU2XGdYANqYylmhmj7qNRYtNzlOpQPjzdSoykGVcCvP6/PGMH//yb3h5+QUA8Kc//e/Yv6UHP8XE8bcGOQPjeSxFixiHABS5+dQ+oRu6NbQlZ6pOGXIsHtNVhRq4O4y/Za1GKms8cqWAKlVlxCDs6eACPPwdYzmw3DDnjKZrcHh7QPfmDd4f9rgtC/7jJ9pUXj+c8J//77/jP//z/0YIDsPwDO//Fbalw9Sx9EkO13bo2Ep4RXd+G+hTATw7fHQ5Rwc/IVbrpuoWmcNSAUpVvS1qD5FqKVWhO/Todx2U0fCLx/Xlgo8/vSNonyVOcBNi9EgpUmdxHGiE8DTQ4SXo0XVC3dVlkw8hEKzY1FBG3bGiE/Ni5ND+1iWSsJwjYlqfH5plrtyeEKYy88/IkJht5ITgHZZlRIgeFRRiCqgbi+F5j5pREL/4Ik0NLmCaaEQAPizU5vdK9x+C42I/IUaPEDyqar3OpumglOaR32PP/nSd0LR072Uct6IVtihWUs7FS2IrK5vHpSgPpusMVBWevnuCUgqHtkXfUJEyLgt8jFgmaqaQ89oYpVT+b82jUSn0bFOvSW753jtl9Q14DOmifXltOoDVq77pGuzf7OlzXDooZTDPVzi3lK7T+QUqeljboq5btO1uZarnDFvTte/f7NEfeyAR/2W+mVI4t7sW/YEOUilsBWHJKUM16u48kpX4YA7RF5XBty7PI8j+uNvwJwCcCfWi949S8PxcAcfdneRarsFwemvTNWQylBLG84TbKxV784X29t24oD/uCqdDG42Xn1/wcnwp9+nwvMeh6+BjxG2a4ReH8+cLzh9PuLxcy8Hf9A2Gp+GraM/XD34fgMQ+w7z5t0MHbTR3NKZYcJI+m+aCXuCMGKG0Rq9oVtvtWhitMfLLfvpIc86XX14xnm6AqrB/GtAfdxieB+zTigIANM91iy+a2Mgugvda61XLnVNGQoJfHoM83eKQA0nPCtTviPjx8kvA558/IzGhTmbtW09u0ROnmDBeb/j06Ud8+OX/o5uvLd7+8j0A4M//258LjFdVKIfE66+nu0jkpmvhZ4/Du8Om6s2l6vMulINSSEiPLsXeCkQaXO+v5gQ1t3jcTje8/PJCSMZ5xDLPCDxXXpaRrvHyA/p9j+gDGmuxbztUWMlY15cLfv31P/HTT/8dyzLieHzPjlg1xtOtkPkk56EbOvSHDu3QlU4IYKRInP9Y6gLuih9ZIUR4P8MvttzHy8u1FFkpRDQ9FbTd0KHfd2h2LfZPZOTR7jsMfYehbRHaFm5yOLw9YH98wrB7wm73BIA2q67bo20H7N/s8e6f3+H9n97iz8/PeOp7hD/Ts3T+PyacxhGXecZ4HnH6eIKbHIzVcIvH+dMZdmJvBGPoYEyPHXx0P+nfogm2TJQLgWRMYoVcVQq2bmFMXaRay3yD8wtSTgjBwfkZ3tOhWLc1drypi1xyGRfMW3lnJnmecBR84RswegCSGQpBskJFSAGAum7vfs4j6/pyBQ668IVkSVDMEjz9Th9WAvFmJu5nh+umE1umBf2hx8ufv0fYjF8i84KW24zpPGIeFyhNEjDJIAFQJM3BheICWDfkdClw8XY9ivQAwHieiv+EHCBN12B4HmBqgzc/vMH1RNwVv7xDiokPbib0+VhGAE3Xot/3qCpCyKqqQn8kuH7/Zo+mo/l0YKtzsqWt7sjUlVZrzLSPmK4zSRm1QnAet9OI25karTt594OujRVL8LqhW90IPaERwcdiNy7eI/s3e7S7FgO/98f3Rzx//4S3wx5DS8/iv//yAW5yeP3lBZfPZwCUh7G7ElqqGZ4XwqTw6YAVQWh3bZGNpphw+XzByy+vOH08wXu69/vjE3aHHWzzDxr4UEoX7roJYw2UUVjGBSnN3GkG1k/iTvctHZttLN788IzGWsRE5LXr6xU//zt1vz/99x9xubxCKY3j8xt89798x2QKCkGQFyBFzh73q22kHPTSFa9oA2ldxQzjkeUmh8wudcKWHy8jxvOtzONobh/Q9h12Tzs0fbMSsg49mrbGWI3wfsE0XXC5UiX3evqAz79+AACcP/0zbucRKWXMHA4RI7lTXa+vZVObphpudridbpA41hgilmkuf0bmrE3TAGgeNrARIhGA1RKTUY3gJyzjgtdfX/H5x894+fQr6VfnW9msc87o2gH97ng3tgBwZ6UafMQ83zCO51Is3G6vOJ8/EVrA3VGMCc5NUMqg6chEaf92j+O7Iw5vD6i7ep0J85iC5l2Ps5udWxBPEW6mg6oEsagKbd9ieN5zIdKjbqTrp89rjEFjDLSq4EOGbS2O7454/y/vcX7913Io9/0eXbfHu3f/RF3Qvsc/vXmDf33/HoeuLdHLt2XB5BZ8vt7weX/Dp2OP8TIhxURksNuMmzz7VvPM/bHCL6cMUxPSJu9yCJ5g3aoqnVzb0n0xtS1OhABwe70V6FdVCm27I4Z/Q7yHTro5TzPP6TKi6YgIOM+3AtM6JguSZlq6LzY3KsSxCpXSd52fUqR1z9VjBf90GWEtj3U2AViBDWOij5BYXFHpiLc/QKjQ668vuFw+I+cENy/Yv9njP//br3g7DHg70CHx0+srPn74jE8/fcbrryf42dFs+9DTOMTK9agVSc0ZCxt3SSQu3Zd15hvZvOuRJcZoy7gURUk3tAVtiiFi97LDuz+/pRFl2+B6uuLT//wEALidCM6OIcJYg25ooa2BmxxijOVnHt4e0HQNTh9PCIvHeKFOWFz5hC+htCoGXbatUVXFQBA5E0JxO/HIZFzK6Dd/JZr2by2tNdqdRWTYHKBmV8ZrwzMV6FRc52JedjjSd/rd4YA/Pz/j++MRjTE4jSN8CHj55QUxJLiFVT/jCeCfB7BNuWeb6HEpTXGOqRCtc6bEUGU0puuE6XbFOF7KnmtMDdtaWEbU/tb6eizv7KBVU6RbAODiAglbyCyruLxeMI6nwuSt2cgjpYT+0POsVaPWGmOMWKYF549nfP6JHpaPv/5PXK4vNBcMC8lhmJkqLEiAOQMsfxCDlZwSm1mwc5VU3bWku6WH/fpzWmdnwiuYrxOurzeM1wvmmeZdIXh03YDj9L48FABJsKpKFdiVmMszYgy4Xl8xTTS/vb5cMJ5uHNNYYXfc4fjuWB7k2+2VrskvGMfzxgpTMeS6dkFKrYSUf3SJSU8xHNkgK54TG5VWsLaGte1dkmJVKXT9AYe39M/+sMOOD/GJgycAerHruoExFssChs0Uattgtx/KiwHQhiwGOcEFjKeRCkQ2iVE9cx9SBbXhADyyUlqDqYTJLgYk3a5HpSrczje42RWnsX6jfml3LUII2O96vh8E/b/90xucP/2AxD+zbXegsJIa0dN45DrPOE/U4S+M9sze4bYsOE8zbtNMnRLbpooBzl2gTHy8288pQTeqQPLAKhutbYumJ2c64fbUbY26sQVtMkYX6R8A9P0Bw+GI/rA62gFA1GR+03QNXOdoljv3mPMNarNxK6VR2wZKG8TosSwjUqpgzDr6kCLHGIu6Y1MnPLb5A0KQrAoPIYTA7PPVxTOGSE3AiZqA6wu9z5fPF5xOH3A5f+afpfDyyzv8+p+/4v/ZteU9+Pj5hF//8hE3RgfGy0iEwdnz/JgOSZILtsUdM3LB5BcHbdaAGGC1bpaMgW9dbqaxwzItpZhq+xZaKcREqhXhDyhFpGLLlrr0+z2ip3yWpmuwOw70LjAyImqDft8Vu9qUcrHDnaYr6ropXXw3dGV8gdPtNxyyFGLZE9yyIAQhhz50+TRu7nrUbV1GiRK21fQN+sMOtV1RxtZadHWNgV0rh6bFoWvRWgsXPK7LjNdxXPk53J2LEVaUPBe2gU8xwvUN2p7PUEbNZYz88ulXLMsI52bE6OG9Q1OLvE9R8zH8fV7T1zv+GIEY7tz36MOQtWiK5G4Vo4cxDTkRmaYwm0XONRx36A892tpi8iQFHC8jrqcrAOB6e8U4nqGUhlYal5cn7J8HLG/25WWT3wuwQ2DO5VA31qCqfyvpEIlMTA9uANWaNS+zW9LgJ1jbogJpOeUwGJ6H4rAGsD1xpLm780uRQFG3kgpsScQguk5xBCRme4OXX/Y4fSQugMwQpQPIZT5qEQLNtb5kcusHyW0xBCjEO+JQVa/uY0pV5TrrtsZhfqZxAz/YFRSGpwPe/OkNnn94xg/HI4aWiE7zJideW006726P4B3qmnTfu+EJz98/4fDuQNesye1wGRfmdZC9qzimbccaObNneYilYPvWJchJCL441Mm/m9Aw/4VIT+dPZ9jGYnfocXhPOuXjuyO01ujZ2bHa0XPqZ4/Ly3VVX0RPXW7wGC8TPv34Ef8xtLgtSyGQ0TVlzKwO8TzuIka0x3xjiV/hV5DPQvWgnE0zkSyGVL57a6k4q9tVm27YqlYbXWSmgLi+aWhtUdcdmqbjXAv6rsSC1FcV+n3P5F4U17F6bGljE45CVTELnN6dpuko08DPJRypbRll6we0rKbwD2q5BS2qVFV4SONp5JAUHjuyo6nwam7nG8YLjR6u1zPGkdC9nEjz/frLK15+ecHwPODMB7UQNpdxgZscptsV3tO/236HYaSiN/pIElK/K42QBOOY+l5NI57ugj5865rHCdNlxHQZcWPnurqryyEvCYXLuGDhImE608EGEKFZabIf7489ju+P6IaWfFdCLKTcuqsROKVSXAhTilgWOvDtwi6Q/CwqpcqIV8YCZNEbiqY/8Vy/qTvgQbTH+4C218WREdjYInPxc7tOlN0QEqqK3BKfmKj456cKx9DjOs+4LQt+fHnFr7/SOHQ8jaUYFqtfy94WxhqgR0m+lDHLzAqPGBMHmWmcXoBxPGOarny9Mt5KMFZjOO6+vKy79dWDnyRzVI2I3lbpClkr5GwQPEn36q65I/YJIebw9oDn71iP3DRYfMA40Yzy+nItnew0XnC9vhb5StvtcTgd8cSw9zbK0NYGuWvKfDvne4OLbUUoL+10e+wlQF5zuLfGNLunoUBr0rEQ7HUfMFFVFebP1J1l9jWXzZi0yusIwy1UKdumI5vHhqroduhw5MOEvPlXq9fgV8vWLTwG0GGstUL1INIdQ0LTkDRIOoriAZ88TE1zSHlIl4mQmuBWc4n+uKOO/90Bu4a+syVG+A0DvO1b7HZP6Lo93DJBKc3ddiBCEVe+TVcTcvOGrls8IipFDOa6re/Mj0Tz+ijkaYxGzurOCIc8KxQWhuu2KXkSn7lKT8mP32iN1loMbQsfA9I/J4yX8S7C9no+EWEoRFw+X/Dxx0/ImRCjL2OVpaD1M3lGuMnheroWtj9AY5GMXLTF37qqqirFZUrcSW88I+ReAJrHPzyLl98fxMecNrWu3XOxQNHLwheSTqffb7o6/l3LVJfnOedEqg5jkDPZvErxR5uyRdPRc9L2LSzLPvFgSJFzvoQ/bd83c1sVSSLfharK4T9eaCO+cSOzLCMyH2bLQiPC1w+vpfARHgA1TxGq0ojR43ZbShIlQB3nPM6YLlN5zosMjAtgtSEwB7ZBfmSN4xmff34pDHNZelN4S0MlyXHTdSoFtq1XFv7uwCqIfcdBZ7HwJmxr2RZ3JDKcqQuGT0XjmtS59SWQ2OrgApRSWCYgJEE8E3NOmjtPhW9Z15crmrqHaVYiqzZkuz5dRtxON+I0cShRO3R4+u4JA7P6Javltix4HW/4+XSiQ/98u5vdG01+BNpqeEfwPtnU6zuvBlNbNjgjFKNuaxye3tDvbi93Ekmy816VRX9r/WHZ+8f6Y/2x/lh/rD/Wf6H11V5QGw0Fvc7KweS5ioJoFLlcAKCuVVKqWiFwvNlj/2aP7tAj5YyX6w2nD694/cBMcIYqaLZzLhXW7XbE5fMFt9MNx/fHAnvY1mw0mxxjG1KxCb7LgmdJy6NQN10rSzSMLlVUiatlZEMgT6q8V/4BIBneZC4TE7FdjaXOtanb0vFXFbNhfSwBDBXrSJWqSoewDC0cx7vGsFb1+Qv1A4ASGQz9WOUPYNWjboJ/6BfmwppWWq2aYq1g+D6Z2pIU5d0BfdcixIjzNJEL1SaUw1iDrhuwH57h/QJrCRnwnjLp5flSWhfLTnFyLIl1HE8qnyOljBweZzYDQLcbEF2GUrqML4hYFhGCI0MZ0DOiFclQhXkL0LtjLBkWaf5HVRbHXY93//yudLMxRNhfabbX9E2R/o2XsYS7yJI5aooJ03UuEb7Sha3mTRQIlB/UMicOiskby1p5vyRAZ+uPIARamfHL829tQ5BmzznhrLQRrkgM7DqnKMSn7VuEw4abIGZcSZXRDqo1272qdqSG2eShk3sovaMxPfbuh0AsddpfVo5RsOYua17+7ReP6TJhmhnunq5sRBRADnVCks1szUvXeH25kklXoHjepm2LLwIRB5nfMc5kYb14WEbypLOLgSJjtRazHVey2x9Z83zDeDvh+tLhzBwDcQ6U/ZaClgi6F4J1zeTOml32hqcBh7cH7J52pEDgwDFBg3dNg6UjyXK/79F0LaqzZndIsmUGJLpYlSz7FC2qaSlokakN5pEt4hfNbpENVP2gouP1irrpixoBWNVk44nGH/OVlA9CgLaNLUY7Rmv4GDF7j9M44fR6KV4uWqsSZCWoWvSR1TmmEOMpkGr9/kjmneE4iM3WFsfnt+j7A2LwRSJ5fHfE8Dzg2Hd/9xq/evBXiqALrRUyQ36iHwVQNLd0WFWcKNaWGcPuaSiElMWTgcnrhxPOH8+Yx2n1AU8RyzLRwWjq4o08nkeMp1shOljWuVdKsYGORaUE9lW/SeHbel0/ssQla5sUJpCzUoptiIlJiZSLlXHxy2dzGcohX5gR3dHcs92VDUEOLNIKa9TWwIEOzxgSjJfkqwBtNrn2QHGQE996OZxtS5thzI+NOUSiVCn1G01s2njplwO8NisczJ/r8O6Ip/dP6KzFEgLSOCIj4zrPd5Gx1rbo+j0Gv9AG2NBL510oxB3NM1fD9skrHL0eQlsZk/i3bxP+vmUNTwPCzJtuSaijsY9zE1KK6NKeDhmtUbd0z7beFG5akHKG0Rp9Tdabu6bB/nnAxOMbSf/zi2dFSF++02VcSsEtUqJtqqV4PYznEeN5C1GJDXMAACAASURBVPVzQfigpAmbFFFRyYiaRxtWyoS42QdQbJzlc+WcoRWNBjQnSYJHc7KpudkV2DbFCG1UyfLw1sALiW5ZLaKFSFZrKrolwnuNOdbrz3iQ3yjzZgCIgZ5n70LhUGhNKaMkGSb4exwvWBbJXyBuhGjZ+36PpumLFr2QMNl+HOA0UmvQqQG1WABvosylsJXCi5zrxGVxHUm42ZHh2oMjrhAc5uUGN2286se52ITT9bFzKu/DDX8HAO0D4nDZH3s0TQ2jNbIll8HWrge/1fRn+32H4bjDfHvGbTyV4hpAkazS6CyWEYtSCqpWd7Jlv1jkTMVHrh67/vk6Y+ompEAjObn/AHFnlFLs3EjjxcPbA3bHHQz/mSV4xJEsdj+cz7i93o/hRPIqP6fpmhL17tn8TUYp9H18EUPvAhU8lvNujC5Kiff/8h5v/+ktdl+h9vyu6e+6AaxdHTSPYzhMhzp9uhG7JzLqAVCMDCI70YnU48sggZQixJCDpF0XTNOFCSZjQRBoY6hXT3LWaRcP+w3BKLMZBrmaPeZipVmzWYlDEABtNnM+yQLYFB1KV6hksG4MlJ7KtWptinMVzbI3ITR6NQQyWiOyWsHUBnWq+T6x/37yxTQIAMe0KsSuKS+8Zovk6kGCU+ZDDhtzEK0Vb3gVB+nQn5GqVzSuAOVor1bEFQWSxIiUM9yG/a/Z5KJtd5jnG7Q2qFkXTvdJlX/nRMTCqqoA/dunW777LZP/0cOv7VtUO5pJr5GvqeSMa22QUkQbdmj6DiHEOyZ0v+/osBB/ba1htEKfahzaDjcujkk6NWM8T1REWFPIsdvOTcyQKPQjkkvjuDBfhvK5Rdcegy+qgUeWUqpES291mJoDkWDuXQG3rpv05zZcG35+tKHDSzzsAcAzehWcJyTLr0iObWwpEESilkJC0sQMt8yBkTn/l9p12q8eIzd+yRwHOKpYiVVuRhXX4ouKkrR6Hpi6WNK27Q673RNF9H5hgy2S48JVYI+MurF3e45kA8h+po1mkyOOKQ8rKkf30pXMiG9dMUY4t8AtK1l0vs3FiVD8BVJKbMWu2S2R3lMKsKLD3NaUS+BjQE4ZtV1jdSWGVhCCdt+h3w/IIE6NqtZ3IKUEY+eCOm0bDKVVmecr1RA3oavh/fjQ9V9ezkA2dw6pwjFQUnxxIVspQnfn24zP/LWOzsEohZASLpcbZduwk2NK272UrkHkgORNUBVypnx/8vcdy+WrarVqb9jWXA7+5x+ecRh2yOwI+bfW7zr4VyOPNVVIXjQVSdNsa0tMWq5gtnCnYvObhd2sxJxBYO7190jOeyphIAIrrbBoQtOTPWQJyfD054hcdd/xiUQixMcYbpoPdEI51oJCrj+GhBgot8A2dfnfRNOs+MGmEKIGWtvVa5yLHQAlaW5L5DJaIxqDXKe7zWexGnkir+etYYXSmqGn1bNbW42UHw0qIeOhlFbPbm0N6o0xijLMHt/kTheb0X5lbwNkPWlYltLalQCpuXsxpmbtuYLRFnXbcKY5oT3doSsuiXJ9AIf1SJDMhlj3jy76/P3dRg1gI6NZEPyCpRnRugE5PhHBlTfAcogpBV1V5dqN0qU7ANged5N2STe2KmS3IifzgboB7n4ll/52HnG7nDHPq3c6ckbKqRRP37qokKXOXu6td55Gaox0GT6wJBGyqqpCbEyR7X5TgFaEBInywm8SBEUhA6A4XEZGQOquvgtaybUUuba8U5LbQYX//TWkmJHj488BZR3ENSBMKWjD7+HGr0RCykTCLH83MIGVwlz26PY96q658zuoWyoa5moqTcs2Y0Lepa1Wv4wYUuLQsooNj1bp2aMSVv4lCIFcF8V6nAosgqq3LnraaJiuuTvQDBcoqCgrXgifprawZi18QiICrja6ZFo0fQO/dFiWsfhcVEHIs9Tp1mzbHfj3K7Xad9PIgX4Obo8VvrfTCCQhCzPZ3DU0YuHxHR36NAKYLhOZujG50bFvgZwRUigCa9AVQI1fYAdakSQDgG09zGQB0L0vTSP/TEHV212L4XnA7rAqPbqho/THfzSdL/oEqFQqDUBgJ4psFEa9rUk7K5t9CVZp1xhCYv+TRE3kGcJwpxcoFmMOCQMRaFUOM81zHmM0zQWNhp8VAIcYFVTOEM6iSI2it1jmx6B+eaDEwhRAqfSQSPImtplkNKTRDl0x8Ak+4PzpXP4edWyar3XtfDJvJMGR372qWvRtC6s1LizZo/sknRX47+X7z4QvDIy0xmOAF5ByKkFHK2JB978VC1E25dheXzH7YcVB6wOM1jj2fYG7q6oq4TUUYdpwB03ff8qppIzJwb877KCtXu2a0xpyQUXUfQdaVZSgKIfRt66mr9F2HbbOhcFHOLdf40ZThGN4V7o2mWEK4tQwgiMyRtHnC/QnHW9wntnaicx/+gZqqdaDkiU9y22m7p6Z/fM8wm8sU/nDQFUaD6ZRsy6e9OKlgDF0/ZUKhPgY2ge0pX+nzXNCUrsFwTuoRlIbV+7LVhIno7FiiMP5G4p14ABLOWPcjBVN+Yw5ZyQA1WYcJQ573j/29G/n9/LO0Vy7Kr+TJFi6oHvGGejZ8vU7rJHFGRVU6dppNCShRPSOVKqCnx2c4wwGY0riJIDfylUT4VgSQbv97gUR0w9++YlT9eblVoLZ/tq4x9QGTVszb2P1ShGjH4jyIZPra83vQs2IaUwJIUUOLKMOlsxpFJQ3a3OhVlRkO/KQ69Z6TeCsmAtg7Jpl8K1rvs0wmtzxVmSZuTzc9EogTwwJ3o2orvfqoXbXcCO4+irEELmhjXxdoSgj3OwKX2z/NJTRH0DJiNGHzTNHiIZ0+ltekdyHr+14v8Oy1wOKoie3D15V3WdRKyFYVRQYI5uFjWun2DFMNV2ZNBMDHNt4erb0DMFBKw3nCLak2d9qSZnZNAiKHgLVqrvKKlQV8MXmQgXK45V/CBGVVnd8AaXFDhiwjSkBRHVbc4IXHVbjZeKQG3KdC8FxalkoEgyAs87ZlhIpw3AwQ8sRntt5OM26DGyTSvG13ai2sLyxGpV7bAPIIRWtcJmTJypwtNZAQ79D3APXmf/aeXgXUGmFY9fhu8MBrbXwMSCkiI55G8OR4m2bpodSirPW5eVYTWT6I7njiYSx6NlDKE6OdzCqViVL4JHV7FrUDXnvr4VsXQhrUr2TLa1n3/hYurOmp+6uqiqEGLGEABcCZu8x3qbynQqPwi0kB2x3LUd4NsXKE0DhNcj9luuylvLA682msurfHzewoZ+TSscLoLzvyRqa+XK3VVVVsVYGOKdhvsH7BUrTn5WxhHAiAPb73yAe8k8KETAoP9OqCims44UUEhLo+a+0usuIAFD2o/yVzudvrbLJmrU7D+ISatfnsulXy+hlXMq9ijGWHAGZVVcVcwOUKjtv4SM0NZE6b0sx56rrBsExua6qSj4HPQOpzLpla1MbpE8xJP/ICt4BUHDLOjpqoySAemDJMLUlNIiLjJTWFEMydct3o9amttg1DYzWcExYDCnCsetrLmhFLIW6ENZEJi3LsG8EcXwIcd4mE2IzInlk+dkhduQvUApZJl7bzRhZ4ulzIqlpZridPAwatNbSOG4iueN0meCWuSAZxtAIfDpPOJkTUkw4vDug7cnmeCxuhA2jrgzzdzUa9nmxbNu8ZqlQQ/61b/6rB38KGclkdmLa6ncVKpVLRZx9hK+YdKXu40Dli095NdSZrhOWZSr+3aRpt+Q2xge59wu8IyMKYbdKOpxs8EopjmKsyj/bVV6KB6tfpSoEF5E2aVhiB1px8SGBEsPTDsN+h6e+h+WN7S8xF2a8W2Y4NxWDiq2dqptIi1/Y8zIyMAa7pkHY3x9e03WCsRoxNNyJpPKyfdmd2wcta1PK3MWt8KK47VVWTJOIbCmdW6V8uekyj97verwZBjz1VOjElJAyCtxNwRI7DIcjdq9PuFw+03e/+LvnyNYWx65HbjMuln3BQwRSLp3kNrVQ1tZN8FuWMYZJadvZtS7jCK0NcorIIBSnqoiYtjvSde4OPbnbGQMfI6IUp4n8xq+vpGiROFdhhgtcKXNuOfzqxiL0TSEN5pSQN2S/LawqnWMIj0UypxihKmHUS9G3xjEDa3CLvH9bkys59FeCXMT19YaUcnH7k/sJrMx4NznubnimLt9jysyhqRhW3nzWkAoTGmAIPkTkvxYr/Y1LikdZUphXuirkKkmpdOyiSLcqlMLB2gbG0sEuXKfyPrEFtpCE1UVhiZwuF32JPS4EWumWYyy8pqJuEoXPhlPxyHJ+BiqFlLfXvZ6lldgHf3Fv5fcRahXLqFAbjba2q/kNe2DMiythbeSZcB8DXRBmVlRJXoHWa+rmX+N2aEMEtPQgt2l1FNUrklBV7GDqGWVkm+xxgbYGh7fm7pk0/E5KZDOddyN8cIV7k5qE4AKuLxcyRJoouvjwZs/+JatKRRIb02afL9ed7rNUjNJI+u9/9189EYQYtYVNyow1MktX0Xy68kBubHkhAKpO2qZGX9dwMWJkAoutScJ1PL7nmx1gbYN5ukJpjWF4RtcNsHWzdpjAHRFGum4ohl7+ykteVVT9yzz5kVVgLomJlDl6RbOW3YE61jf7AT88PWHXNLjOfDDxjNYvrigVlmWEUgbezxhHGgNcTq84/brH8EyJbBL1u2ua0v0DQLOB2jxDQJ4PDQkDKu6GqrpLSvzWJdW0xv3ml1KG5v9dDiWlNqgLQ145kXTn7TCgr2ssgR7eyZH17G3iAJPZQWmNw9sD3lx/KO54MXIASpnp0/VbbUqeOcnI4vqybtQRlVbAJlXyW5euCXas+D7Iz7eNRb0w2VRpJCZ11TU9CwJPp5ThpgWf9BVGazTGYAkBnz6+4tOPH/H5Z8psuL5ccXulhC1BKOQ6dk9D+T5TpAOiP/a0AbKZk2KzjxgiAndG9UJkonh9TNGR0wplFwizbDisbkjik1/RIRRSiVpOMZCpTtOhbUlyl1KCmxaSp/LBbxnOJqZ7uNvEcwawTUcDCrFUDh36bjMTTdeCs4zlHnz2c87Ml7Ab5LAqCINYitddjaZrCNm6Vnc2yda20EpjtzsSydkaRibW4li4GkKUA/i5TxHYoAWysYvCSAeFoFThNQnJVj6n+isH8+9dMa4hO5K2KO+64RGf8CtKA5Zzee5JfUNoo+zRjkN4Fu8xjWtwUQypqBCUUhieB2RO6xMkpT+QI2rTNXf8Hs38jrTJkxDkz80e8/hYHLn3M4+01v2NIp5pzDalXLg1fvFoOVJ9vlFBY2pq7HLK+PzTJ5x/PeHycsb5/AnjeIaEW/ng2KiIyMzN5w631ysO7484vjuWM9S2FvmUCtzvZlfSGoUsatJ9Ifm17/53tYKp+PSvEHpWqXR0SnNsKxP5NM+5AWDY93je7dDVNXwIlF/9T+8AUOzh048kafr1x/e4nD9jmq9MiBnw9PQdju8OGDbQucCn5NCHAvMUluW269/83492/NR9WRprqJWr0HRUkPR7kq087Xr88PSEPz8/Q1cVXkeOXuRuLgQiOtE9o9s+zzecz5RVUNsGWpFWU1L28OeMcBjQWlsQBKs1cECJsvQuQE9EmNzC7dv1KNxpmMUKtWE2b7utSsh1qTj6ucmVF654GsSIXy8X5DOnUoWAT5dL4T6cPrxiZGvQtu2x2z3xfW+KMx5Ae6WuVIEMF++xNAtcawv0qb+odOWzPbKatoFWlr3Q2c+gjWg5JIqgRvKkF+mWtrqwccfLiM8/v+DSXGGZ9DjfZrz8/IJPP37Eyy/kWvny4TOm6YIKFbPeO7x+oGtexqUEbkix23RNKWbk4Le1uVO0LBPB6rg+dOk8zyTXvm3XvbXulg1GaY2KrUulI21aGt10Azm3dUNHs1ee5fasD6dgEnov2l0LbRQpfkIss375XQIBbw/irCrkkCExxPfXoB5+7+l5opm+wP1SCAsTu9936PZ9GcXQGETIeIZDjHbo+wPlBnCHDuB+HDb78g/dUwWtNHOBVhSDis6af1ZExX8+JU/Y7hev+aPjzZzjXREBcLPDxDoZP0lRlZmzI42RzKH74w790BUPC1lymDa7Fra2FOZT89x6T3Pr6TqV764bSB3U7ztIRowsSslMxZ54jjO8i3dyuG9d00QOe9EHQDIlmGTuXShOqdONgtf6fk/ZECI9jZTfMF4mfPgfH/Dzv/+In37+N3z+/CPm+bY5k+jQrusOu90BfX+Ec6uS4s0P5M4nBZCQaFPM7ONgiq/JlzLmr1nUf/XgN7VBlRSTNGTH92STCiBXiV8OU+Z+VbUazuy7Dseuo4OfX8wQCT4mL/r1QJ+u37Gud9VK9scdukNfCol216JluVjKGaj+ysMtskNDm5ZIMB5dMsPcvgTdoSfThn2Pfd/i2Pc49j32bYPbssB9MRe1TY2+P2K/f4O6btkApi0GK8sy4Xp7hf2Zq2ZN2sz9fofaaHS1BBDRweuGCKgKpo4F+lrjidcuT+bHjyxhrwo5BwAUUpk1b38HwIdsCAV2F+jv0/WK13GECzSS8IvD9fWGTz9S0fPyyyuZOV0nhBDQ93tYS3Muka4BNHsbnUNX11AVjQqE6BmtRoobAx8mdwW3KkK++fprTbKsXVsg7AKz8UbsPXUhxpCZSkGhIPbKsbBwtVZws8N4GWleyO/IcBy4S40l5yLnjNt5RM4UXwoQ5FdV9DzKe0OBKabMXLcplG5y/xC3BRCPct7QEHmGuJWecefNz4gU6KI3l+5l/zwQq5uJv7L550QdjOKNzNQGmgmjhpVAdJ0s64sJ0CupLzNCkjZchC259VH/Dsm82NaRQt4V3XWZsWoFbSOTVGlvGgZqaOg57mDZ/jUxMiLWrfN14vnvyH4RqYRrUTd4X8gSt0qIdkSkLdbB/EelYArLY8TGrT21FD2kWydlhrFmRVjzap+9XYqbo0PXobEGXd3wyCtg2XCw+rqGVgqf317xy+mM88czbmeyxJX31jaW2esUjCVdrXhJeOcB6iGwcJzwfJvv1CPfsqbpQiFQ8an8N4p/D+X9nW5XTNOVbMW1xngZC0onwXGvH0745S8/4uef/g2vpw/FW985boy0RtftESOdd8bUmOcr/HIsplxyn5RkvkwzlNLF88E29m5/l1Gi/8p486sHv7UWyAaSAgeID7d4GGtUqUJSCfcw3RdGKsgIMSKkBOdo7jHflpXly1pMYutWxSBne6gANPdDIwzyVGJ6t1XgHbnNGOQ6Qz3I6peOUzPbFEDZyJuuwa5r0ddE5GiMgQsRl3mBZ8iv6Rsc3h7w/l/eI7j/C8PwhHE8I8WIumkLwa/vj5RetifHqG6gzWJoW7wd9iXNK8SI66yLBnacF4Y+qSNx1To/pOwEfZdu+C3L1LoQiUrXXQnCIoZJCrlaoaWqqsqhtH9D/uzjbcLEqXVuWkh+9norSWarX7mHMQb9fleyD1JKRVJ0Pd1opqwUH/5kWGQaitDcHno5sX/6tMA9GFYC0PfXh750FMVLnu9rYD8K+Ry2sQWWnS4TFjWj2bXEFWDW7uGtxvC0K4qRFBNOH0+4vlwLsU3mnVtYe2skVbU0M04hrh0/B2YBVBA86lVO92/t+Iz98vCpyuciR8pQJEmCTkiUthwUzY4KuUIAEw96yVlnBz8yarHQQeHuW9sYBVUpFxRKxg4xbrpUMDS90ZZ/6yKPDDITkw3d8hxepFSWczlyzkWi3B/WBmXLfq9FuWIUQlhdKwHOH2lqdENeteI8Qlm18TVLF9cxjHBPCqFNxlxx7U4fu/ZI3JWcCronAVR115T5OkDPrhZVxuZeZzaymluPAzd/jaV7FfgdkoLAhwCjFVzgMLN5uSPzASix3m3XlOsUQqfklMh/W8aFUdPHDn7hYW3/vjRAlIq4cDpkKoFiWuvyuWKIuJ1uOH86Y7yd4HlcY4xF1w1lz2/qFv3uiK4bqCkcnlE3LeedhBLLKw0CNUEezs0sqbUU4MOIC0AI4OID3FfULH949f+x/lh/rD/WH+uP9V9ofb3jby20aph1zAlIG2kdkfCrlTyXxdSC/owLAedpQp4yTuOEl5czPv7lIz7+5WNxGwNA/sfjjfW8NC9te/Kan9mSFGC4Uzp5JpNJIlpxmuNVMd8AwJ2RzLcsVRGZyFhTNJ0i5aFuv0ZtDFpLP3/2/g7qN8ZgeB7wL//nv+DND28wnv+VYL3bUkxP5Lq6ocPuaYfjuyPe/tNbfP/9G7zf7/F+v0dXrxVwV9eF3LZKoH772VOINFNNj3X8SrGSYYMa0ViBcxE2kiwhJ3VDh/5AFe3z98/Y9x1uy4Lr6Ybb6UaZ5a+UWz7fhJlP3U/bt+gPHdqh4zEDdRS3E333pl67oedhB9EqC9QPrMhQziupcut5/S0rRVZW1NStAYBbPLTWyH2DlpUMIYRiG6qNLhBlCAQDthUZOu2OO+yOO+wPpPyQ7zSmhF9OJ3z48WMh+t3OI6KndMKS9MgmSU0nMj9T0AXqDtf5p/BepMP+5msvGuoNgZP5Hdt0PUFa3OJXR0UwNMyjv+k60Shq9qi7+g6+9stKkgJQpH5/zQ+fkeXthywwd9rK2kRerH/L+P69a+WVrMoCI14lW5jfkDmZPHcdO5YSB6CnLjiTBNayG5/o9OWzNjxK2somy/pipHa3v22IrPhizktw+OORxKKskhGL+JMQD0MXFCT4yARFVxAsuc7xPJI3vdawmkiw25hoFyJcGNnTfsTnC+0L509njOcJcaPkcrNbCZV2TeGstLqTOnsXSm7FoyO+lCKcm7GwUyaAMlqiOTuhqk3TYRiOGJ73ePruWEZyjq11TW3Q9Qe8QcYwPNO9sTW6jv7cMDyhG+hZEjdAgJ0gOR0SQFGQyXcTo8eyjLAj845iKsoxMVKq7d8/774ey6sVmpZsYOUZlHjFL9dfkxdcpxmjI8/n6+sNrx9e8PEvH/H64YTr6YqFmd3zTDOTlCLNVbsBSilM16lk0wOkEtBGAV1T9LtiMJM28KT8u7gsPQj56ZpgOJJxrVIxYylqtbUWnbWouRBZvC9QFsAkKa2wf7PH8f2RNa4EUU3Xey03eQD06PYkDexryjiwWheov2bnq9l71IYMMRbjET37d1sDKRGCIvZxrh47+FMi+E6njZRRr/HHSlckr0y5mK10Q4fv/vQWAPD98QijFD5drxi7sdh8iiRFltKqeNRLtoPIZ8LG//36coVExVaqoox7/m41B1ysJMTEG3J+mNVPEC6RlsRtzU4WoQql2DV2LUAB3pxLmJVdsyueBjx994Tvno74p+dn/OnpqRSLPkZ8ul7wH8OAHz/T+2FqC7+4VUoH2QDYMpUtmoVUKcXXHfFOVQ+PeYTHoK25e3dyyndEVzn0/eIL3A+sChjvyF1Q7LrF8nW7opd5+ur1LlJZGTMIkTClVIJhVka3kAzX300hPeY3M/Lfu5SybIm7cgdyphhcW1t24tRsqhRL5kDxnNj36A89qRfY+6I4DTIxFyCVkpspoEXChgpsG2OR6AUfirQ2BpI8SwNWfEziSgQkou9jRU9dt7C2YeLqeqjWrS0FnYRiudmRc2ZeHTuF2xNcwPV0QwgRr9cbsfBTLrwQXZGtrXBTSPY243ai6Fs5b3Kme3H5fGEb3QpKr881KYk2OQXzwqZQD3J7dM0GXa7cU9OSTbzsg9ZS/sL+zYGDyI4YngYAwPnTGVVFo6HhcCTOEvMU9s8Dhmc6+PtDX/Y6NzuMZ7rul19e4C/+bnS5vVaAQu3UdXUxjceNBbpSSF+htP2ug9+2Fm1oywNZgmhCLIfAlkkv+dQAcDtdEXzEyDOPzz+/4PXDK6bLCLcsWNggIoTVtCUjkyWqW+AmyiIWqcR0GUsmeNl0ecYhxJftAyMbdD0+1vHbxqyVupgkVPTQ1oasV0WPHlJEYGlJOYAidcLChpWZH+lu1y9WmJlSrDTGUjjSpojZrpgSO1+l4m0gBYn8bgN6WXP12LUHH1H1ayIaIMxlXcg9Oa/66bqrcXx/wA9PRIo5tC1izmiYBVw+V23Q7rt1Hsxs5f5ALGnLSVQpZiyqKlaYy7jgqq/saa2hjtTxFzXHF+FRd1rXB1biStrUdt1YJldY6EopVHZ181qljGJXTcXQ8DxgeNrhedjhzY66/b6u0fDB3+QMFwKOfY/JOfj3ZI07XWcmCN5vAKWwVQpVlcqMPIVU3jty/9Poh7+f0vU3r92T/Epm1+W/h8Te6IL20IGT2SFR1WtBUAqSmBCYcLWd5wOcuNhampWbXLIpxBZ4W7jkTP74hdTHUs6UUvG8B8C24RScpefHDn5CF9hFMq3vcuG38HeeYi5ErBTWOfdWVmpaW/5/gJDSUkxsHArFr72qSB45XedVm59WRKMkcn7BvC99V4x8GD/27HfdHsY00EwoA5jXpVTJihcNvXwOYpiv+0zmgijeKFPCz6ts0WwQLPngYgbnF/cbt1BBR/xCATZbcug2mRKgPcL/A4c+8NcD3QzbFCtNJDxrFWXSHHv0hw5NVxdOgF8cKkZyxI9jeNrh+N0Tnt4/4fl72h93Q4/WWoSUcB0nvH54hbGmXKe892RoFmBMU/ILvHcAzkiJ0MbyPqkKWlVwX/nqv37wsySmbuu7Lm2VJFQrc35zOMkD42bqCKbrjCtDvfN1IptR74pmWymFriOrQlVpKJbFSOqUjBnc5Ippwhb6EubjHRTG0hiV1Z2r2LesuqHcgS2BAgBiXqVFSimknBFTho8RLq6sSpFf5Ezad201rDUUadzUqDefi0gZrGFPCSlnKoJyhmNdbYgRLngEPvhdYKmgGOuoNSWruN7lB9m9XLQobe4gL6Wr1TQGschMOkNER7tx0UoxIrLrlOIO9EvCoS6bvynf4WrLu36fKaWSUEWOfRG63hJA82risnHYklHCt19/XAtHLlLENUz004oJqZo/N/kZoPzZbujQ3PD6ogAAIABJREFUs0e7FIguBIzOIXCh60LE5BwaYzC0LcZ9Xzo2+vzra1rS0bjgE6tfcVj0Gw8Fbdd47G++dr4IiX+V5RZfyKSoKlQVsfm3xS7wW1QwgZMqv/hObU0EVNTbayPyHkkKuYu0sfgJKKUQ2BxGCG4SEgaguJoZYx4m9wHUjFizCWiSa9w8b6XbX3wZTcn1BxdKTon4XCS25S4W3PznJfFQ4oR99nejipJFElJxZxNIXukKIaxjHnpuHr5s9N0BldJQypQD1M2OskFCKHa5yGLsxijIF+NUCQuarnNBN2NYx5tb1KyknNYWTd8WtQf/QRhDRlqeA2ykCVvEHIfJfZ5HTvTXHpVwr7kIUvTJHtB0NfxCTbAoVLQxxU0UoOJDxl5135CLH7u6trsW/Y7eyV3ToLEGISbM3q1WvHsad24RG+cykS3Z5j5xmi0AdHHYeM1YGKW/Su77eiyvyHZUs3GlWh3iVjMdYv9K91MiIhkdcPPC1ZjfmK2shx45n22kQtrCWsuxu6r8brFOFKivuFNVESqpAv+Vz8/Ofl8+lL93SfTm9uhIkSQsLkToyqPWumSuA4BWVZnfuq6l0Qe/1F3boLM0q9ZKlaQqABiVWHtWhCYoRRyDCoh8/T4Ta9OHQCEXKZZDUu691CeK2cJ1/vvSjr957ZtuOm8qSllbJ0eBln2MmPy6QZeZs1HlsHeLRwppE6NqyvyW5vK+zLm2ML08X9vNXCDe7cYKrN2PMHEfWX6mDqWxX4ROqar4b1NYjQFqcDRvU66rGzrsjj36ww77tkXD46DROcScC5oTuIDzMcLwWGfuHcvcVkhXvgNtyN5Xku384ojnsjUrqiqW4j723BfDGN6M5PcCWNnjJCW528jvRx65NAXAvQnYHQM8iwEPdcIxfHHQYtvVrgecoDxGSWode100NfFBzP3c+1uWMTVydsjYHKiMboCVF1VVlc9Lv2cd84jSQHgsBb7nNM87tI+RqqW25NneUjiRd6s0VnTp4k0vqokip9xYU8cYGVJ/7OCrm46uJefSmE3XCfM40z3m7zVHcisEwAoG+Z43gUITOa+Kg53fpAbmlGFrA9vWdzJQ8QAQRFCKBXGEdczwzyljHmdMl6nc58Dnyz+C9Impznb/qqoKTUcpgiTXI3TWu0Ca/fOtfIbxMpXE1pxpJE0jCPoOrxz163xAbQ1iJh6TICNFGcXX4J1FdSYrfBq/Gih2VqwqzslpN7JLrRG/cv1fd+7LmUMRVlkBVZ4aSjGBjYNztDFlI1wldRpZpzLjGp4GhjP4BspGJfKgjcmF2syKHM/EXFvzmIFeQEIk1od/a2ICPhQsE6IeWSWGEesm4he2He5c+TNGa2hFUaatrctBXVzJYkTDkj9CNegfQQ7kexLoWleqdIgprx1YiJG83tn9TnK3c8rILF+q1BrY0NQW9kGSjzZUNG2foZxJLrU9hKqqYtvciNt1xMuG9AcQetE0NY7vjnfzYCFLaquL/GcZF0IJShwryuZWKuZ9V/wcrNbwDMd+KSctWt8HZ/zzuJAWeugK3E3zZ9Lqe+d5hk2kNcpo6DA8UUDT4T25b+lKYfYeSwg4TRMqLuTk87oQ4JwvI40UWdbERMLt7FTGCcu4YOGoVJn53n93xEnxj0azipxwUzSTRbRI5+jPFY/wSuROEti0Ou/lLwhqla7urkkpSiGswr3NLHWSG5RNZI4bpzaJg5XOUb4j6ZwfNfBpOgppkbEjwKl3MfE9SFCGun8JKqLPSJ+LOmSZycdyr4i3ElZ3SybmClpkm7pA/si5dLLTdYKbKJ7Wsp6eJ0qlESljHiEWP0js3A/P8MFz2uMayztdp+JHIE6R4lNRVVW59qrKBa0Qsh1FR18x3cZyP6mIsajrGsPzHm7x6LgzNhI9DPAelNkjI/GeR6Pm+TphvIyFM+EWykdAzg+TG62lwLBtE0njDDpH/Ow5RZJ+/+31VsJ2AHL+E6mfREmDi5yz1gUZWGOlUWSJfqHnhpoIZmtVFWJXU8FTOn8NlSsYQ86RhVTaNdCq+scP/jVJSJXZjKktYkgFnhKGt8z+bLOJZgXNBWsmftFNo01tazBSKnqu6PziSwdQbeDTwtQO4a+OHgSCkhsmMynTPPYStH0DY5pyuAIr3LQEXw576dDF4rhkTlcVfAyYrjNepxPcvJTNE8Cdv7ZsUmQM1OH1eY/LPOM0TYXcpyoQS36eMc4LltkhxVx09bLhAUBryQfgr8TW/66VMzh1bSXjpSpBmdXQSDbelMg5cLquTFhJ4cs5k+a+tqVT2XpLK62QVIIKkeBpDu9JKcOFjRkRz0L7ww7tjgxBANn4AsPj64u6mnw81vGTB4CnrouLS0qepHucY8J8W5Bz5ghZZnYzvC5OhuN5xOUzMZWD8/Tss/sXwNAgEz63ZDRtNd788KbMBPsjKRnEAGm+TvCOZnzElt9EOltNEGR60Kv/r3TburYwtUeaV79wpBV1IcbxmtQoTUDR/OcM26zmLgDr0/kdFTi15tAaYDXumc4TEsPqkktRqQoVCAYWVQXAaY+NBdTjrP7dcYfkgcVNK18nkFIk+AATTDmgam5Gtkzs8TJuOrYAiTcW50PhjGw7Sgk82r7D5XnekJeB1cypEB7jipSkRGmmTbvGBH/LOrx9Qpjv0RU3L5jOI418ea+KG7Rjiy7QmUDE7E//8xM+/fgJ19czbuOpmNf8/+19WY8cWXbeF3Fjj9wqq4pFsjk9jZENwYD+kQBB8B/zswD71fCr9CCNgIFhwJJGs3U3m0uxttxij7uEH849NyLZYrMrRzIEdV2gQTYrqypvZMQ927fwnsMwhpT2ebEqhtkiQzpLENtzgK2XjTaOLeVb9kxd1C4p4s/oj5pzgMCNpJo4Skjz/RXFJNTFn2HXSDRlg7rao6xIiVPKjqr2JEfbzpG3VswpiWD04PRLgihEEJIDKQPIozRyYGUGNVeHyhW7CsqNnX0/QBiSXHBmA38aU9HZ9j+sXfL5it/OKlh3GYB1yxtbS84RyKeHMEoizJZU9WRxBOH56JRC3ZIMorIHNMmewv0M2dM8n2Vf26rD4eGAvukmc1zjHJw4U3Zx3mqze5PWIvuHfwyO+7ErXWSI4xz1oXJzORZZkb1C55HxRCBo9qIHuug849/XNbY3O2xvtig2BepD7VrY/P6BEdWf5AnSeepm2F3T4T4+TOhFoz46Z520V+HwA5k9NLMoxiyJTzapkV1PN5tU4+x8sCIrGA8lX/jwtU83p/y+mJJrQ9v37kB3nCBwJWV92IMohNf2zu98KjfhWVZB4FOHRWpNiaA9XLlTwG1vQpSfFvjrA0l3cksfsMAxq2IG2AOxJJ/y+lATVdGa7yRZgq7pUG5L3L+9w/39exTFBmW5RVXt0DT0OjoofGfKwkIfs9kZrq6+wuXlKwDAfL1wojDcMh7MKHULBC74Rwm1uwfvVFNmOMwAX1MRHFvDYrBUWm4/Z7F7nqM0Rr4kn/AwDp1dNxn0jNcviaOxy2Xb9jwyK9oW+x0dkpzwdLbaU1qRxPFkzHBkBR4GUP0nKHI/Yp1dncEzAXYPI0jOmMFp60e28o2SCLmX0z29Hyv0w8MBVXFw/hxK9RMHx1GDn8elgQghghBBECEIRtofm/Rk2Rx5viIXv0kXjoMzq9nRtaDPP1ud1uVcXi6gex9aK+clojXhtOKcAjcF/rHlTVr243sSgU9jEHvvRHEMYIk4zhDYPflCIE4TMrKyDnyhDaxRGh0xRzg2DIMdKwgBrdT3zpwp1ms4Ed+XJBkG4zlmEECfE5tSRWlE3cmKwLd1tUfdFKhs4C/LHfq+RRTGyPIlVqtnkPIKXdMhsY6bAKH+RRi4DiaJOCXIFgJd3TlJc8duyciQDfbeCGy3hE3iALgR8+cozJ9355u03XkmOQxUYdFhO6ERWEehJCcJWwCY26yzaFsCq0WhA64BOKraZUc2h8xnHAxV3NOYHSWk1S9CCnTcBgJGoN1o0WhbgcHYrXjsCiOS5TXGwLMVGtHS6BAQQsAMAzqpEAqyXL3bHyjjB7X8ik2BtmxglLZ8/eRIvhRghDa1Ov3AR5xE7mtTNy/VSes5HdiRiz6i2MVhiMRKfiYhWWH27WlVH82Q5ZFmuhACUijizlvwXxAGVtPBOHthwFaylorjmAD+6KjFs2t3+FkVNC0VdWlCga4Zu07s3qZ6SQwKo1H3xPjorczrOD+kCrpre/TdafsvdxXqfY3Feu7a3dRWS5GWqVXaI2pN3zfw9wLxJsf2ZuuulTEGXdOirvdo2xJtU6KqdigOG+fSyNbUw2AgBGm8B0GErmvw8PDeGaYcDkuLfYndPR4lRK9ipb/AqiYmeYI4T04O/MNgXFdOz6cqcxMwpTUrUUqP2uH2OmXzDMvLJRbnC+SrHKt5jnVODo2LNP2eVzwHfg8eOmtdfLPfo7WgXiGES/rYbtdgHDWQ6ifjMIjmxHigU9bycolBC3Rtj64mEJXWVJRwUs4t7yAUzi2PQciDNiiKDbbbD9jv79FZcy6ljz8PrRXTjxDYwD81bpnP1/b6vEKS5AgRue4In5NGGQuEtc9oGCBdZMhXJ1b8F0t4JqTi6saeO4o6VH3bI4xCGE/TfH+SbHBAE0K49nOSJzh7vqZn1s7qx9FR4Lqb2TJ3Jjx907uEGaCZeVeT7kkYBa4bpCedSJcACh/BENB478RW/3y5RFcrSNW7tjyPvnh8JHi8HQoI68CYpgt7raQF35Ebn5QdqmqP5IH8K1i5j55z4YyesiX5WoRRAD2RdSZ8QQwRavRNPyaFAekaZIsMuS20he+jlT3a8ocNin5U/3uwHFsGbxhjLLqR2qBMdQtsBsMzbQBuFr3ZkCSpbCm4m0lLEBjR11NeMC82pgBgUdKpbXV5rg0EpWE8z4HxeBEdKUQcHktA/tgVRAJJbqtmvsns4aM6CSl8DFFIladWaGV/JB7BwT64WFILRwikswT5aoZFniKYABw5IdLGQPg+kjB0nQTuIBRti6pu0FmZSq2My45FIBAJAcFcUyEI4XnSzkchiumc0iVQiT2swfKiPoLQAyBcVegJGoOkixRZmjizDqYiOmaIvZGV1ugl6WEHUUi+72ZAYyV7ldXK7poesQX4NGVjjZDofuH3ybraspNHXOTHrO3NBpsPGywvl4htQE2zBFqTS1e9zFDtSpQ78tXudUuHRU+vjcIEYRQjThPMV3M8G76g1n9dou8b18buOjL7UKqD7wfIsoVtN/o20FuTHvgYLMyURUTCIYIvPCcGxcEvWxDeQMrmpL0DcAwKrto8j5/F3gFKGbxLz6J0ByUBNOn7y22BfZ7gQxYjTmOESYRgwvzQxrh7gO8PqUfZU8BW0LuKAFA9j/ksfsByykdw2ThaOLXVf3Z1Bm8I0RxqZw4kJSWdfdND5WrEQdgup1GjS6TsFaQkoFYUJmi7aqLRHjhgszHaSWK7hJgpfnGGxYI0MVarZ0jzmZMLjmxhIC1lktrtls6YxmRslp8G7Lx4eYEwSu0+6NkptgeHbYrT2HUcjdKUXLf9+IzMyEzHB0iMKwpdBUpAtBEsysJG6Sx1ui1M1+R7qS5qGosNAwYrXiUgLJ+fdFKcxorvYxADAu90j5L5xQJi26M87Eeb67ZHjtzhScKI3rMQ1rBNXLkkpClqlOUeRbFB05RQqh9Hj0ajtxT2YSBfBm2lh3nOD8B5VwA0XoyzGOpQu896GAZnCT9b0egTAJq+x8Nmj2JT/OAenyR7n9bTelpP62k9rZ/Q+vyM31AlrpRCENlKJg4BM0yyfduKsxmr7HoUturcV4TmZBvSYkNSreznPc3IuY0yRYyzwMdsOXP/FkQEsGJAkbPLHQDPeC77GwaL8BUC/YkzfkbZGjMcicMEVtGMW4mN5Zm2HxlDOICMFfEJ48gZXdBrR+U+rvQDK3GZRsTz14bAcQCgowhdL9Ho4QhzAACB/V7uIsQhzQqlOa3lpW02z218ADB6Aj6zc2UCfo4IfTbpyVKisLEXPQAHfgTg5Dt9z0Ovicvu9A8sIpqV74DRnMRo42xMm7JB3/YjfsDuVXbSgX5OxTgc9ve4e3OH8xfnrpWWZ9RtShcZ8uUM2bJCvCtRNwdobVHrHrunURtufj7H8oI82RmMNaW+Oc2CSSavFWX/fdO7z5mtQgdLe9RSI7R0V1hFNAZG8tywbU9r9woRQKkOXROis1VPbkahFu5qOTqh+j6F6p5nvwPbp2oHkvQnFT//6QuyYk5nNOP1fN/hSLhTyGMv3/fhAxh8PitGm1h2CJ0KDT12zZY5jPRxuD+gsW1Trcdrz//Jrscw0IjKE76rZlfPVoizGBfVpaPveZ7nZtnOnXAg1TnVSbDlLONVpmY4LJzFfHdWkSOxHmOFZehn5ssMi4sF/PC0bsfqcon52doZ3gCwQDaiZPd579D9Smp42hx1mxj/xcwO2fXoLfWU9wxY9z/L4ef2dt/25MJZNKgsVqZve3f+DIaAtKFVd/StQydT/8JewVd2HORnJ+1/ebFEGPRODhqgsaXqlTNRYi2SdJ7CFz6yeeZGGLKTKHclKms+9rFL57SSD2wcYzdRBxg2xt1L/Bw4zQdBXfU4TpEtcizOF268e6hqbN4/OIOfT60fgeqnAyhKI3gzekgZnBF3sbtJA6u2JluJLujGllcrnS57by0TGdk8DdpE/xjndSTgIDGYDlpGR3S8IBQONMQHKS+efwCAJzwkcYQkDNGeGPgD34fw/KPfP1UsZIpR13TUpreKU1NRmb6hVhhbo/O8W6uRlmQsP1hYlDBp3pOQQxgFCMX4UU2dyPh7HdfV85yOfygEOSKeGPjcITRBIavec9QxPvAcKjugBOBIi11rlF3r5v/KtmmnDxUfDowl4DbxYCYiRBhBQ0qS3DHTnXjc5AvfHRDs2KX608FtbVdhd/+AzYcNVs8ImZstMqJJWmtWdmkUIkTft9BKwgT2gbWAszgl3fbF+cIKGPm2NT1ep6n2hJYkdVpsCtLuP1Tu82BhEwbG0uE6OLpRtrQgn0Xm3OROWSIQtvVYo7V+GvpCI8kS99yN/HWMoN2J2I02pKCmtYSUPbqucWMNXnwGCBEiCmOk2QKzfIVsTtLNDuBlA5xDtQWA5zHdSzj5XPe79fE8+bFrPZsBA13PZEPJEyeYrLxnlEbfSscqOQLbeh7mZzOsLleupR1YunOYhKPIlvAdUJOSPOkSBaVGW2KSxjVHIGumyvEzFbDN89kcq2dnUPq0Mc96NsPzi3N4nufmzF3TYXe7c1Tb0IpWyUnhMtUcIAAqPY91UaMpGvRN54SNALqfWX/fceZtQVntKjRV7V5H50CMYRi9YnwhEEbDUYLECbEIBYLTcl4szxeIE0rG2SOmbzrItncCXqFNVigORYjSyCVeyo53F+cLKlJsYiwCH3GWuCIiTmMnu10XNeoDMRSm4D8AttDprRCUj8gW4Ok8Q77KKcGXlJzvbra4f/+A/d0fGfhlxzf0iBLkOS/7lDMYC4AFcYRH4i6UCceYrWbuwOSHiLMfpkjxfI4fBtUriFA4ihRLW0ZJhDgK0SsF04/VEv1OO4u3/x+H4WdNCz61ePbmC88Ffw68/PD2rhIjhgEBgEbULkAfXtd0zoyD5+eMZeDZNClXxcgWpNefr2bIl7lDbQb2QB6pO8ZWXRLSBnnWSemVIuChOi34cVKlpT4KoHxjDgOgJVGQCLMRwfNHBSul6CHs6o48x0uLTbAHwL9EVfI8eqCjhLojLGHK7wceyUG3qoG2Wv4ARsU/e136xs6hT+TwAzR7L4stNtcbXLy6AAAsn60wzzOoPEGSxzbwExJ7GAYoIyEsYlv2Hdq6Q2y5xlEauYPC972JKNU4L9aWycHz7OpQoXigeV3XkGUpzwyFoGvECRTPdgHqtoRCoBSn0VhFKCih7RtX8cpOkp5HQshrbfXjtcX4iGG0Zzaedgmi5/mIohRpOrfYhv4oWSdAboAojBEnOdKcNO6DUByBtzzPo36RpXax7S5rK/Ai1o05osg+dq1nM8gwcghzviaykw5VDcCB6tisZkpT9bwQUeK7riExLcii98ivwFbO9b6COYxJ9rQjKj5SR2WhKzpDelcwAMBsNcPiYgHTnMjjTxK8PDvDIk0dN73a13RvWkncIGJpaA1oQE9A1oMh6qIviHLXVi3auiWwsBzPrsGqGDoDnnj0KQiiAEFnmTN9P3Z5At/peoQWTModHoCqaGNBg0qfVuzNzxdYXcZHplOyk/QsZ7GVsY9sYhm5LgCfNQxO5M9PBAHijDplYTLBONizLQjhBHsYMzA1xVOW1UDMAjJ4EmFApl8Let63N8QoeP+Ha1z/4RqbD5sf3OPnK37L1VRylNoUIUmrcqUPjPx5pmVx9sM0NYAODKMNVmY5ytjaJIINHLRi6oaG7G3gF75r9waR1bwXvqUBwV6cSfAfxvc5DAOE7zmK22OX53lOWjWcCkpgsNKJBkMNp01A4kS9y9S1HrXshRBATM19zwKieLH8KI8GlM3mAwuGcT/PHoaOxmOFO8j9rIeMYyce1CkJMwDliX70YpJF963N7H0PgW23akme736vrKbD+OADgCfJOasuatT7GtW+dPrzUwUvoyhIsI9BnMX28BgQYVTC40SL6YVd09uWWAyIY4aI1sS55gr0lNV1DYpyi82He2yuKfBf/fwKYp5jnqWoZymSzAKtLNJeSuUq2q6rEVSRdZTcU8LX9McVDix7QimnP983vQMtltsS+y09xEwL8z0fIgiRJLl7r0EUYnGxwPoZuYDlsUVIn6hhQPTcAEDvKq96X2G+niMIqIvBNExjCJU+bUfyszgMiTsDvhfw7GKRLKKvUXXM5wEffnwm0Gc6Vv5Tdcnpohb8cJRgPGalYYgEdM85RcBodNSD1RYZzED+HLYjMe3c0HPjobeqdSxLzTLE7jrZUVrXkNBN3/bWHMi4Kpr1EGBV/rTRjl0AkHYBC0flywyzLMWpAjZZHGOVZVhlGR5eUp9ye7OF7CS2N1tKdHrp4oCWCkpqhC4pIcVCz/ddUcLn29TohkcUYRwSi8ka8AB0JnTsCql8CEGmVCIYxeKilCir/HOAsXMYJRH8E5P+xWqGbLmiQs0mYfu7Hap9RYweW4TyPQtYcal+onJYtRZYrN39H2cxMpO5e8SNo0IacbOsr5IaTVGP8ttW9InHmQyIzFc50lmCtmrxcE1nxLvfvcOH796grP7IwD9Y/XM9ydQ8z0OYhjBaO3chnr1py29nhCnNYchONPjIQY0V/wB6ALgl7nkepEeCFlEcuo3y7x6M1bCfcMuN1cRXcqQYspqR8HzEwWkVv7SJSBAGLpmZJhwDyC60b2gGycIhU2MPeKOtL1fRfdsfdQZYg5udCINQuBaSP5l19jaRYMlWrSiB0DHNYpu0R9Oz9C+dj1X7w9SOTy3PYiiGSaufM+/BDFC+co5iQSgspUfAY1Mgra2WvLKfyyj7G0yCwBAQVYvFn+I0thKsdNiNbVHPtnEHOzfsnTQrd5ymQiKjqtxpVR9xmH0cDvcOJduUDbxnaySW5sn01ThNIA4huq5GN3FcBKzNJ8t17isKgL7v7lM+XAB6yFkgqm8IWVxX1LbruhraaEoyRADhj49vvspx9fMrvDyjwN9JiaIlOdNTlggChCFpDLQtjRoODwcsLpeYLWeI0shSN1l+26NWP1e8TE211KcojWhMEAXufuHX0cZH/AyzSPiw4y8PxmDoSARHWInYUf1sYhWsxk7QqYGfGQae503u/TH55KDMI06H/5nggHzfQ99KQsCbHvWhGmm5E5U93q8bl0wT4Yk7IeC5JInFqhj3EicRssXYQk7CEDI4reKfSkd/eU6sgndfXKA5NJBdb0W66LVRGkE5TNU4uyfxPOUU9pj94HkegknYIQEbGtsGk6JmKmYVBIETtQkiOof5fGQKoIspUQijB5KwPdGWd53niPMMm/loMV7uSlIvLBp3PrFnCRdBrkK3eyYcDBv3WIzDMDg6oi98K7UbwYusDkUUwOskmnJSFJrBjUn5WsZWtMcPBKp9jfu39wCAD2/e4u7uDaT64ef+84Efg2ujTdtYYRRgSGNkc20DgRWRMPpIkY959axHPbUONcYAE9oeZ/QcXLzQKoYFI3iMLjZl2/SBw8loyq53SmoAcZx5hniqgE/fS2QDJRSeFUcIfB9JFLqbv7K8fqaP8RyI3i8DvUYQi+eTm9JUiYtb3CxhSsCdwVW3I01NOi4xu5z5gUDErb+ux05QhcYZYtGcdvgzoAgYFdRkK48oR2xK41s/BQacAbAGNiGiZAQgkcJdbvfMLnMTwyeWHbaf25SeyfrlLJlJD0B8JNs8rTj5z6m/wGOWkj2a+oCmLR2lsKtaDBgQBwGyhEYy+WqG+XqOulxAyg5SsnRnTwBGb3xvfdMjSsIj+9UpJoTFmUj4yOonMO1RBPCFVeuKEkQx0aqCOMTqcoWfPb/EOqdre1cUKJsWxfaHaT2fWlESwRsGBH0wBv4tUeryRe6u+2CFmejZHhzYjis5Filhn4XAtjunyRwvAm6SBzx3GhmYye1ODpie7zstDAfm5eTYdsumQfuxq1eK8DFKTTTYK8Rp4ubZzgWP9z5JMpieDBh3f7P2CbfpgbHb46iBtqgQtlAYufG+rbDp6yyOw3gjTkABuFn/qaDWfVWhlRKrLMMyo+p2vVpgd7FAW7dOfTCwrWpqc4/mO9Mz7ghzxWJtDoPlO6oaPw9aaQjrO8IjFs/3rJyxBXXbrzGtcYpBYtCsZ2POKStw96Y4wuHw2GIwxp3xXMhJC87kffpWxtlYgStOBABABJbWGZKJ12DgkgfPg5XhPrZk5g4w4yJiK4KlpUa5Kx3tdbe7wd39W3gwSH8A2/ijtPp5Bstzfq00QhFAZL5TZevbHn03BpiSxm5/AAAb1ElEQVSjTPsIiDa4G/1j2VYAoxSnZx2oPko6hmG8caQFCrJ7X100qA+1+8DTWeLa9CfeA+iq1nkdy5j2HwcBAp9mysLzJ/gGUjoLo1GymO1FWaQhyROa84aBQ/ADODL5kVqjkxJN3aIuGrSVdIdPW7doDrXtqvju5wehoBapEE5vAKCkaHO3O2nvcUJzSe/gj1xmq8HACFfHnfYo0E/BVEFIcy0GY5KhychDnQI7eTFqmn0XWKoToAeiqzvL21dI8oSqCN/qOejjuTHNwQMIceKYx/fQyw51XaDeU/Criwa9oopoliSYn8+dj3Z9WKHvOzTN1Ixm9K1gVO80GNLvGQWYWByLgaNGzdzP6vuGHvwwRpLkrlLKlznWz89wlufufhqGAcWmwPbD9qS9p/MEvmdQF75zAZOyx+52hfXzM+SrHGESjhU6t+D52jkA6KiuOa3Cf2j2rrV2c1IHbuO5qTZ2nn3cGSKhJMt+sNoTvvAdOO2xq+17NPbAZTlYKTvEaeICHh9rXLF/rBvA4xvXiZDa2TofGeroY9U91gVgADMAZ0XLWv9d29tWtEScEANiagXM3hCnrNvigJv9HoskcaDiPCZ76XJbOFwBB3dK6kY1wSk2J2ZhL6WtrPDYludEfzB0zxN2wzr+eSPzg6t8xkmQUl2OfJlDBIIwVlwYdda7ZBio+3rC2tc15mnqurR8TWUvUe1K1Os5skXutGS+x9JRGuEAJxnft+Nn3rf9aE6mB1eUssiZ51MHsLG4IADkaFs30FrC8wQSkbiEUPYUGxoLQqxrUhD0PCDNFp/c42cDf1d3aKMWURI5J6ZhGEgcJgyhBwr8TVG7g1dBYejHeS/P3/nisBALV/fAqIVMF5kRm8GRJSh/jQ9HgBCUhD4mpae6qN0sri4a10Zl6thjV7WrMFu2pCJnD648jiF8n8xVNO+NPoSmaFDp6sipKs4I1W0mlCeTmCP/aoBc2qRS6NjOsmjQFDWKbenQpQySU72igJrGSG07ilvpLrMWPtqy+ayYw6dWkseIE0pU/GA8qDn4shgLd10AOMQrAKtqJUZMiJ1b/pCoClvxagCDVk45ECCkfluOTlz+PHVzYSEE1GTWx9l4GIeQ8jRKV5rO0bYNmqZAWVK7vS5qNFYJcZYkOFvM0Jw3xFwpCbnMJ6BUPcIgQhhGiBJK+ubrObJFZq+THV/5NvjbuX/f9m6mKyxNFGA7WokgCBHZpCxbZrj44hzLiyWE76Ozzohl22J7s8X9u/uT9n52tUbh13j4cOukhbVW2N2foTpcYfls5cCubvZY1CMWxQL/pO0OkZsm4Hmta/ny9w7DANgkjw5Bc4R0B+D8OTyPukaxBd3xIcoVEUDJMVfIjT08H7saKdEc+FmzJj2KgLm+oIpTBAKwI0mp9ZHCJeM1uNPFNFRfUKvbdbmEbw1lJvNby8aYShtzh0Rb9kDfUHeTTHtCh8MCrKy51qg/o9f+qXX75g5fv7hFbmf9AEmvJxmpQRLIcXw9O6AeAxtt8SaEUyVlWjGf3dwtGCZdrTFejAWBL0itL7aqp7NljtmKAM9GM2vABsldBdlJzNcLpIvTEv6bmwcoNjqbnGWy61EVB0RJhLOrlZPKNnqAjMYut7HMLl+PSpPAGLdc9zmAVSrs3DUZDFEoD5vCVfFNVUIpCSEChGFoKe1Eo+b2v3adTu4e/jCV87OBvz7UiMIMYdwimVlpUU1KW8ssQ2zR8vWhQnWo6UGVE31rfVzlEZKTKnRjxjasATDIyfw/oIw9AI5mXdwSmtKICARVYH+3Pwr8URJRdXhiywsAim2BdHZAOktHqciBHiypNaqGgGps08rAjinQLAgDpLME6SKjhyeLLS1nDJJ+II4Q9F3duYqn2JZobau57yS6poUxCkKQvgG1iajiEoF/JANc7ka7yMeuZJYSxWqWuiCleknGQIpm0ZGlGYVRYN/DiKxXUruKhTtHDoSpRyT/tC0PwI2E+FpwwknsAHKwYvUwGgcQPZKBkABcd0VLhWJ/4qxv/QL7/QaD0WgaSp6qXYV6X0FdaiyzDOezOborsp2WPalO8r6aprSHG1UO3NZMZ4lt1Y24Fd6/1jS+kUmELiK0MFeMvicgxIAwTFzlc/bsDOcvL3CWk4EP4ztuH3a4/voaN2+uT9r75c8uAL2FMWbEGPQNZrMVDg8HXHxxgdkyJyU2O5/1xdjWrYuaWAB61Dt39L9J4jfYKo8DH48E+Z6WNpFxSoVhgiixCoAOTa2tjr/1ZG8ltPDg9QrNifd+pxTqoiE6ZUWffV0fIESA5bByNM4ojeg+tQUId8bGCp72Ax/WOdNDMHEcnJ5lg0V1s5EVV7oAdZ+UJDZQU1JB0LYVhAgd2p73b7RG10vs96cl/N/903dYrNbI4xi/ePYMABAKGm0x8p4qdxuvjYEeRmdG7uZywuPYKm3vrK557260OXktuy9yVevb4imdZ1heLLG4XCJf5EijCHVLWJjCyvseHg5uFOOd2Om7f/eAMCT8jjtLPQ9d12C/v0MQRHj21RWeffkM2SKHMQZtNbGOt9bAxprq8JqeeQB1SNECyleO4dDZgq/cF2hbq2PQt/A8H0mSw/dpXEbsAg99S/gzHvOk6Rzz+Rpa/5EmPTzXiNLoiNMJwGWEge+jKVsyodlX36NQHaFufRZ+GSkN/Bp/QgmZtsiTPEHCdL5J+2sM+iX293tsb7Zoy2aULV3m6KqWRGFODP7b2x3iZAbZSiQZVzhkk9tKegiJZkVI7PpAfExleZVmsFKqQTy279gu0/PGqs/OdjjgMV6BdOBbSPvzBqMhLV2MNZ9ZDIS17p2wjzZUhbanZf4XywW8PIOSarzmytgsU0JKj+hlUiNKKcnq2pHR0JaNA38yj/8InzBZ43jHw8fVII+YurojwyZ/DPKURFFHIYjGrgTLSbPU5ynrxYs/QRgkOBQbN+fe3myx+bBF+eoKqzzHKstIYvaVtjiTUW7aGO2od5yV87iDqxpezNF3lrxWBlV2Izd8gEEQEPgzTiOk85Ra/M9WWGUZhOfh0FKCePP6Bu9++xYPD29P2vvzZ+foK2M9A6iaOhQbJEmO3e1XKHclzl+uHd2NgrY/iuhYnQY25GIMkMP42ANQa0N69WAp24kTpxrllo0xCAS199NZMoKFhwGmM0cUV2J00L3zOc3yT61OSoey3+1uaf+He7qX5UuiTq6o3cu/u296aEzGl76PwRtcQB/PQO9o1OECvzajPbm1F9eMA+mpE9QcGtT7ysk+h2GCsI+sSIztgkqN/d0eN9/cnLT3b3/9B8QR6UCwp8oyyxAHVHkPA/mUMHiYu4FHTqOt7/590MaJvbHeCABns+sLAf8jp9UgGIHfIiDE+9nVGc5erJHNU8QBeb7URY3Dw8GNtMrDHnk+J6tkcVqnb3e3w8WL51TRT5IPrSXqeo/dLsTDu+e4+vkVVudLpFGEIgzcOcOUx9F6GUeYLsYj8PXi4rfvqGNcljs0TeHOfN+noB9FpBuyvFxisZ4TrdQnF0zWBjg7u6LrUD7AmE+PeJ8ke5/W03paT+tpPa2f0Ppsxe95LBIjXSXXlC2qroPv+85pq2hb7G532N/t3esAOMEKzpyAsZIjqdcR4DTlRfrWdCOZpcjm5M8M2IpfBOiVovn3psDm+gF3b+6weyDHM66Ez8ozyry0xnBixX/z7Q18RFg/Xzv1Ns8DooCyYQb/8J9N0aBpSlclcYuSudcE5vBHzMEEzT1adyr0vVU56ztoc9yq9n2BMKTug5TUjdC5sbS/YfSm1iO24pT1YrVCtFjA6MHNisMkRHUoIFVP0rJ6gC4kVJ/CD8QRICtOIie9qqW2s8nOSXBOUbeM6/AF3RM8F2Q5S94PAOtklSG1dLrAquMxgJCuu0a1KzEMA4ITDZpe/ekrRFEGcfOts1Hd329x+90t7v/zSzxbLJBmGdZ5TgZDVx3qQzVS/w5svtOja3xU+wpC+FBKj8hnsODJaC9N3Z4W1b5CU7bO1AMAwjB0HbB8mePsxRovz86wzDK0UuKbNx8AAN/9+jvc3b1FVe1xysf/6vwcVTdgcbaAZyWg63qPu7s3uLl+iYsPF3j+FVU8QU40zDCOjsBQnueRqU5HKoye78G3pk6M/jdmxP6QYQ0cT1/p0ZUyCAJk8xmWl0tSs0xYBGkUseJKqm9GudW2qR6/eQB1T6JTbdmgLKmafHh4B8/z8PzwFTzPo89gkVvrYk3W3R9hCX2fJJxZm4CXu6ct/oGvBTQw+AZDwDN9OzZTCm3RoNyXaJoKfd9Ays6do2mfuBl/uS1RbA5485s3J+393bvfAiB5YT63n39xedSNYzU656wZCkftdPQ92+VgaubH1Er+vsCCoUUgvjfqAOh5n6/nWF4skM5SJGEEqRSKQ4XN9YbOfgtg7vsG8+UKs2XuquDHLh4ZsyoeQHFHiBBS9jgc7vHw/gGb6w3OX6xxMZ8jCgI3p48OJLylrBaNVtwN7B0jghcz3gDqFJTlDsWkwwiMlsyryzPM13NcvrrE7GyOpmxIzGcucHZFNN62egUhiOmy3X664v9s4Gdd5MEM6Oy8rNwWKHYl2sseyZJaHWXb4sN6jt3t7ujwZ5oHo5gZCQrPc7aMwJRzTQ57QUDSljyrzRIKdKG1wa2bFsW2xP27B3z45gb3t+R13nU1ZjPLZa6/oDbrYOB9BuzwqXVz8w3NVcLAzZoDnziunZJ4sJrV7AWvlISxMqUA0HcNtCHKIzorPWo4OMmxhT9Qy5PoYD2kbAkfYYM+c7Z9IRDHmbPrnHJYAZovTWdoPA86ZV0uFkiWSxyaBvP1HAA5bxWb0CU2tA/CG0ApdE3rrlOSJfAPvpWtpBa47Dv0sj2iPtE4hBKiwFpchnHk/BgcYM/6NqSLDPOzGbJ5hiSjgL9YzLDOczR2JszAmMGMIlGPXV/+ly+xXK8R/WOEu5t3AIBettjf7XC422OzPqME1feRxRFmc9LNPntO919TNuj73nmyD9vBgpGaI79x2UsHeuT5pOwk6rJC25ZgIZYoSh2FLbZ+9xfnK1zO5xC+j+vdDu9+R+/z+g/X2G5v0DQFsuzxkf9ysUDhezj/4gKzGSW8Sklstx/w/v0fcP71F3j5i5e4eHWB82yGRZpiG0cfMTQG/gskqL048t0/FvrhRIAwDmawbpCWi54kGfJlhswKqMQpjZD6tiMwmwW9AfQMkNqbdoyExy7ZErCwbWvUNWEc9vt7aK1wefklXpWvKOAuUswDejbKXTEm3TYh4UBGDBhL75oUIVP2yqgBQDogxh9BW33TozpUaKrSBX2tCeyotYJWxiWcbdVic73B9evvTtr7w8M7KKWQ5ys3Nm2rFsvLpdPX94WgpLQhBgXZxo7JCgkoUULPgE36nKcMBg9GjQBnBuOy9a7z/FhkZFebRPA8cqArdyX2tzvcvyU/jaJ4oN+tNdJZSnoTi9POvZu3b3D+/IqeZRtQF+cLzGYLxFGKyibAD++ucPXVFeTFGmkUIUutBX1isSeWFaLZ16FXo86FXUMg4A8E6pWyt6qWCkIECCyjIssWmC3n9v2ssHq2hAgDyLaHn/nIVzmWl/SMOnXQNwLb7e8+ucfPBv5skQOGKFM8Oy7sTL38skVsPd+rrsPybI7d2cyim0cxg87rRpR1IigDtjP8MLGBf6LAxQE/SiMkcYQ8jpHYG1CbAbuqspU+ZXs3129wf/8W+/2dzYAD97uN0lDaQJjTAv/9/TsIESGbzR3ALhCCsA3Cx83igHJbUrfC+oRro1xF7/kCMNoFfGMUpOzR960LCADbc2o311Syh7YJgvAFYqvSliS5BYxZiVqbYAxW5GE6E2dazal65XkcY5amWGYEqgGAs+dnqHYlpGydTzx1IEiQR1mFNQBOjEn1Em1b26DfOVoa+2Ub+6fv+QjCGHGcIpbkW02V5EhpihISrmCd6vlqhov5HOezGZIwxPudpa/ZZDNKQojqtMD/p//pSxy+IsEN7/9Y06nNBk1JSlmv8wTFRYuzPEMSRpgnCcr1HBcvSfSk5Wp3Z9C0RLPpuhphGCEIRsS2cbNvru56Otz7DtpoCHsAhGHinpEoIaW2szxHHAZ4v93h9etrJ+Sx3d6gbSvL5X68kEsWRXixXOHy1SXW65cACDi0291gs3mP6/e/x/vfX+HyZ5c4n82xSBN4HhzzpWssrYqT0Kp1hx4J0HDFOzhA1+D0IAwEQhcIAEo403l2RHMbhgFeZ7sFyjgQK187M4w6AI9dfdc7PELfj0qMUra4vv49Xr35BS5/dokgCHC5XCAMBKp95RQuu6aDrz2XzLlq1vehMGHgCB/+MMAY0kwhhgM9E8oY9/PaurVBvyV2A6wZlE2aqqKEfj3O2LcPN7i5eX3S3qnibJBlC5ectlWLF3/ywoKcSYsBe1K3bJoSogqxWJy5PREzQzob6amdLK8gIJT6dOZPxSCh9xnXNVvliC2OpClbNGWD3e0Oh/s9Nh82OOy2zvI4yxZUqWcJkvDx9z0A3N6+Qf7rNebrOWZnRKedr+c4/+IC9/fPUJRbbLcfcPfmjqh2UmKZpchjSjRYgI2xVwTI7mzFr4/kpX0rC+wov0GENF1gGAxia428WK6RLTIkeUJ+H3kC1SvEeYL52QzPzs/cz0tnCfkk6Bb4zaf3+Nkrk85TqOYYdd2WDQ53ezwUJeq+xyJNcZbnuJjPsT1foClbBwDsd9UR1YbBCL4gVT7mWpJKG12QMA5Jqz6JEQchwglIo5Ud9tsC92/vcfvdLR5ubvHw8A73929RVTsIEeLigg4qqp40WinhnUht6boa2+0NVg/PXBVZdR0CIbDOZ3i+XKKYHUjG1wwYYKBkD6VH9TbhC5jBtuINBeyuo+DnOgM9BVI+/DkYCl8gilPMZiv6PNI5wjBGIEIrXDNSo/g68mLjiO5Eze7AjnIuZjMsVvQALC+W2J3tSJJSShjbkQiiEOksOQJZTVubfh9gALERjFbjIQc4cJfxfHi+cIcEtwIZABmnMfJFRt4F8wxn50v8bL3GejbDIk3Q9BJqAixlx7bhxKTvq4sLqCSBkuP9q/6ROMaHhwOCUBC98MU5LpcLav9mieuOrF+cW6ZLB6l6myz10FrC91p4zUTxbBjsNbFmRnbsw5Qnvk4spMSqZcMw4Hq3x+/evMfN6xt3jyrVw/d9hGEE4PHqdaEQWCUJVlcrPHv+CgBwvn6BpinQdTXu7t/i9W++oY7A2QxZFCHwhevMtbPUaj4M1nshcuZLxgzw/FEFlMc5jObnLg9LOANAZhO9zAb/ILSeFVZDpC5qq7QIN17xfQosp6z60Fjt9BDBRPVTa4X9/h63797h+fVzXH11BX+1xMV8jvqrK3efbD9sXOudjXimzAVu73qGKHzeMDj/Bk5keitUBTCdUUIp6TpAVPF76Drae1DQSMsYhYeH99hsrrE4gdJGnccD7u7eILCGU1rTZ3f+xQWdN6yuqBSZU+nSfX+WLRzYEwbQRkHJ3qqc0pkFkCS0sDoMQWQFzizzJUpiNzYCCLCoJHHbD3d77O72KB4OODwcoFTvjGsWqzXSWfo9s7DHrM3mGlGUYvXPa8xt4M+XM5y/PMf6zXNsNu/RdTXu76+xud6g6jpoMyCy5x1LCSdZ4lwZATseMco9jp7nQcrOghnJ7yMIlsgs/56dNufnc+SLDNkisz4fxHJYr5d4dXaGq+VyTBxsJ2Zze/uDe/xkROBM2QyS+OB16364Uj42d8Dr32U4830U52t48KCrCnro4AUaxro2SdmgqRW6NkDcxYjaGJE1rdCmhx6s2lQYwBMkk2kQQhwUvCZE45FdLbdwy6LC9naH62+ucfvmA7bb9yiKB/R9TRxGGDfTlKpBWexwfx1D1tXRvj63+HVhGKJtC2y317j+lhDSv//1GWbGYJmm6MsSvWxgIKFMByU7KN27mbAQlrPLGT7I3CcMQ2gdYBgIj+B5Y1uTOyJChMQBjxIkCd2AUWS1z6MYURQjCAJo00GZAF0jUJeTgOuTEExdl0d7+rF7//CB5sXF4YBmZ2doskaQAPFMwJQSUDTD67oKfkgc5yCxCl4xYRm8QENrAa0jaC1d58M5uVlXxiAIEUWUrHgeAKHgBSFEZL0XkgFeYGDQY/B6ZEoh6Dp0xuBmt8O+rnF7TxVvsduiLvdom9LNeR+7/+ZwQAwg8DRC2y7PliGMGdA1Fe6ve+w3G+zuH/DwbIkkjiAnjmpKNfBDAy8gBTc+sIcB0IMCJtXQdGRjzKhv4cF3yaGULWTfQKkYbVti9/CA3wmDTkrcfPMBH17f4GB1/YdBUzWFGcxwePTe37x7B6QJlG6RndHhvz5/gbo5WKZJjZubb/D1/10gSID2FwfkcYzWPqfFvkBTVej7Dkq3gK/gBRqeMTCttPsH9KAxeBrwDCBg+2QafhjCDz2Eqb2XUg9+YCBlAymtW6BUKHYFtjcPOOx2aJqD3YPCMBgEwQBhRw+P3f/Dh1vIxkCpFnFMZ9RstrQjLoNDcYe3v/8W+TqGkS3OsgxC9c4RbvAUetm4inzgX+9b3JSyn7XlXbu5ryG/E2n1/Z3Cn2xtQkP3hlKdHQv6bnzGnSGtJIpyg64rAJw/eu9pmqFtW3Rtgd2O6KC+T8nk7uEB2SIjam11QNcVUKqDkj2KQrv3GscZuZBa1T3WK4DnO6VFeJrOLj1gkAJeq+H5Gkq1aOoRExFEAcIwIBT/vsb2dovdzQ5d0zpsSBiyHwygTYdiv0FXDEf7+rH7b5oDisM9bq/fIv1He++/XMMYgzj3kecLDING31f48N1bvP7nBfqXByfAVrUNlGpghg5+OCDKPSgt0DUepBxgbKEzwNIW/QBeFMAPNSVCPnULUjvqiDIffjhAyga7zT2M1+NyvcLa8xD2HbyJMqtoG+ihg1Lt0Z4+Xp8M/Hd3dwCAv/mff/XpK/U/ftT1/DdfUQRcXCwAUKZUVRSgf/W3b/Grvz1+7d3dHX7+859/9mfy/tOUxBD2h6/xy7/+GgDwy7/+7/8q7zsMgTCkmyXPZz/wyg5KcTChf6kqYPtIUbbH7v3P//zPH/cL/p2vx+7/v/7lX/4bv6PHr3fvAPzT47/vsXv/y7/4i3/x6/N5gPl8AWBAUXyLX/3dt/jV3z3+/fz/Xo/d///6q//2va+9eHHp/r7Z/Babv/8t/vff/+u9xz92Tc/4LPOQZTRyeuzeX7x4PvlXKmCK4jWK4jXw9b/a2/03WV9/DeCj+/Gx+7+6ugDQ4vXrX+L1619+8vVdd4P7v/k1/v5v/p0Ewn9hfWrv3vAJGbW2bfEP//APuLy8PLll8u9paa1xd3eHP/uzP0OSfN6o+T/S/n/Kewd+2vv/Ke8d+Gnv/6e8d+Cnvf/P7f2Tgf9pPa2n9bSe1tN6Wv/x1pOAz9N6Wk/raT2tp/UTWk+B/2k9raf1tJ7W0/oJrafA/7Se1tN6Wk/raf2E1lPgf1pP62k9raf1tH5C6/8BaGx1x/2TL1AAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(3, 8, figsize=(9, 4),\n", + " subplot_kw = {'xticks':[], 'yticks':[]},\n", + " gridspec_kw = dict(hspace=0.1, wspace=0.1))\n", + "\n", + "for i, ax in enumerate(axes.flat):\n", + " ax.imshow(pca.components_[i].reshape(62,47), cmap='bone')" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n", + "plt.xlabel('number of components')\n", + "plt.ylabel('cumulative explained variance')\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 150 components account for just over 90% of the variance.\n", + "- using these 150 components, we would recover most of the essential characteristics of the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the coponents and projected faces\n", + "pca = PCA(150, svd_solver='randomized').fit(faces.data)\n", + "components = pca.transform(faces.data)\n", + "projected = pca.inverse_transform(components)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0,0.5,'150-dim\\nreconstruction')" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2, 10, figsize=(10, 2.5),\n", + " subplot_kw = {'xticks':[], 'yticks':[]},\n", + " gridspec_kw = dict(hspace=0.1, wspace=0.1))\n", + "\n", + "for i in range(10):\n", + " ax[0, i].imshow(faces.data[i].reshape(62, 47), cmap='binary_r')\n", + " ax[1, i].imshow( projected[i].reshape(62, 47), cmap='binary_r')\n", + " \n", + "ax[0, 0].set_ylabel('full-dim\\ninput')\n", + "ax[1, 0].set_ylabel('150-dim\\nreconstruction')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- our classification algorith needs to be trained on 150-dimentional data rather than 3,000-dimensional data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Principal Component Analysis Summary\n", + "\n", + "- PCA's main weakness is that it tends to be highly affected by outliers in the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ref" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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\n", + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo('DUJ2vwjRQag')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- [PCA 차원 축소 알고리즘 및 파이썬 구현 (주성분 분석)](https://github.com/minsuk-heo/python_tutorial/blob/master/data_science/pca/PCA.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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\n", + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo('jNwf-JUGWgg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- [공돌이의 수학정리노트 > 4. 선형대수학 > [9] 주성분분석 (PCA)](https://wikidocs.net/7646)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/05.09-Principal-Component-Analysis.ipynb b/notebooks/05.09-Principal-Component-Analysis.ipynb index 01f49a277..8ba55ca1c 100644 --- a/notebooks/05.09-Principal-Component-Analysis.ipynb +++ b/notebooks/05.09-Principal-Component-Analysis.ipynb @@ -2,10 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "\n", "\n", @@ -16,10 +13,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "\n", "< [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) | [Contents](Index.ipynb) | [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) >" @@ -34,10 +28,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Up until now, we have been looking in depth at supervised learning estimators: those estimators that predict labels based on labeled training data.\n", "Here we begin looking at several unsupervised estimators, which can highlight interesting aspects of the data without reference to any known labels.\n", @@ -51,12 +42,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -67,10 +54,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Introducing Principal Component Analysis\n", "\n", @@ -81,18 +65,14 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 14, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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v0D/63nP4nOwAGE4gVXEwqRtmtHVtpEQTsBNxY1eS3JqEIsULlwFSMbYk7iPmEsGgV8Oo10CtupqRUjN7LF7b2YpdB8+io1uAH/2BXOoQi8OtbWg5bYvY1sOt7RDc3mDmUJvNmdD+hbth1swei7m3jkRJgXFIXwcKl9UkJ2AHhMvSycUCYxy5k+JcrUPSv/U9MBVy5HgbNGpVxCPmpEb+A88AjYcwoJxt3fyb4PX58PJ/f44Dn30t+zU6ugU890ZTcF5dSmd3H/7ybguOnbEFvw0Y9Oqov4VI9T3cKDyaujZSc+nRfBNL1LcFpdCsWbNmTbobAQBOZ+QiQpkqP9+Q1e2PVab2W61S4ZbRJfi604kvL/YgEPt6XV6cOt+NXsGDW0aXiP6uVqNG++U+nDrfPeTazEkjMOb6Qly2u9AreER+u19JgRG3fXM4LnQ44JWahwHg6HWjo7sPf37nc5y+GNsBEJGOlzbqNfjiQg96rxyb1yt44fFK/9a1JaYh2TNA/3mngb4LLg+KC4yYfsu1qJk9FuowZQSA/s8036SDVhN6Q/3be8ex6+DZkLYN/vuE+3tMv+VaTB4X+i3tmzcWoVfwxNROOTLx33x+vvg3Eo7cSZEEt1dyPjrS4lq4CoQatRqLZ44BtBps3dmCphMdwedUjCnG3FtHobjAiMt2Ae9LLHQGdPYI2HMofD3zVPu6oxfXW/PRJ3hg6xFE+67R6+B1ueMaCUczlx5NRchI3xZyCYM7KVI8i2uRAoRBp4HVakHd926CMGto7rXX5wtbQTFZKseV4suLPeiy9wflm8qG4Z9hin2J8QM41+bArMkjMH9qmXjfS/PjPmoumr9PLAGbBcYY3ClLRLvjMBGpeHIChNhztuw+kfIRebHFgJX/q//w6sDnBAAtZ2xR72QFgKaTnaiePS5po95Y/j4M2NFhtgxlNK/Ph9d3teKpjfvxHy/ux1Mb9+P1Xa0Ra7GEy5pJ5uKaU/Bgb1P46Zhkve+bH5yEVqPC8KK8YM2WirGlMb3e4EwUOaKp5ZOuv08u4cidMlo8Ow7jOb0n1tokf93ZmpB8+HAqx5Xis9O2kOyVPpc35HMJZKIcOd4/r61C/5RL4H8jiSZ1MNYt/zxdKbkY3CljxbvjMJa52kiBKhD0LYWmkHZetgswGbQ4dsYWVR8NWjWgQsQTiQLUKmDZ3HFY99oh0dTEwOfy5gcnQ26K/kH/G0k0o+dYb8Bc/EwuBnfKWInacSg2Vys1MpcKVD6/H2qVKhj0rUUmTBxdDBWAxivFwQrNenTZo0uTEzzRjfKvt5rh9fnDfi5tXb0x7UINMOo1WDTjG7Kem4gt/5xLTw4Gd8pYiaxPEhBuZO7x+qUPwfj0YshI+ZKtN6SIGICoA3s0AlUYf72iEn6/KuznAr908JfD5fbC7nTLOgmJW/4zF4M7ZaxE7jgMjNTf/eSrkEyWgVMIc6eMlAxUidiZGosiswHfGGHB8u/dhGFXbmZenw95Rp1ocJ9cXgprUV5M9d6D7xnFjTMZN2BKDAZ3Srtwi5fxLroNHKmHC3aHW9ux8I4b4wqKyWCzC7C1CiguMAbnr7fsPiFac2bUcHNwbSCWeu8B0dw4ueU/c8Uc3H0+H9asWYOWlhbo9XrU19fjhhtuCF7funUr/va3v0Gr1eKRRx7BrFmzEtJgUg45WRbxLrrJLW9r6+lDr+CRDFSJqisTq8D8df9/i08dOfs88Hj90KiH3hSHmQ3IN+ng7HMHd55OGldy5UCRjriyVZj1kpliDu67du2Cy+XCli1b0NjYiHXr1uGFF14AALS1teHVV1/Fm2++CUEQUFtbi+nTp0Ov1yes4ZT9osmyiHbRTXB70WZzyl5YLMzXo9BsCAlUnd19KDTrMXlcKdRq1ZADOGJRmK9DT68bURyZCiA071zOHLfUTVHsW1LVnfEdScesl8wUc3BvaGjAjBkzAACTJk1Cc3Nz8FpTUxMmT54MvV4PvV6PsrIyHDt2DBUVFZKvV1SUB602e/9BWK2WdDchLWLtd5/LI1n7pelkB36y2ASjfug/zz6XB7ZuAUUFBtHrXq8PL23/FPubL0S1qFheVgSNXodhBQY8Wj0ZL23/FAeaL6Kjuw9HTnbgtpuvxfe/8w0caL6Atq4++R0d5Okf3Y49DV/hv/d+EdXvlQ4zYcyN/cW0rEUmXLL1Sj5n8OcyctDzBv8s9Vg4Un/3aF8nG2XL/9djDu52ux1mszn4s0ajgcfjgVarhd1uh8Vy9QPIz8+H3S5elzrAZnPG2pS0s1otcdfayEbx9Lu/tvjQAAUAbbZenPyyI2SkLmcKx+vz4ZlXDkrWQA+n9SsbfvK7XaIHZHR2C/j7P0/DbNJCH+eIVOv34d47bsDXHT345PN22b9XMaYEPZd7g/8tNnUUeE6y/yXm6r93IDP7LnWziTm4m81mOByO4M8+nw9arVb0msPhCAn2ROGyLFQq4O8HzmD598rh8fr7s1z+dSbkODmxKZzXdx2PKbADgO3KOaQd3YLkgqq91wOIlMONxmW7AEueHmajvClKo16D71RcFzJ/zTlukiPm4F5ZWYk9e/ZgwYIFaGxsRHn51TnSiooKPPfccxAEAS6XCydPngy5ThQuy8LnBz5oPI+GlkvQa9Ww9bggVYp74EJjY6v8kTDQnzsOFaKe/46LShW2HPFgeQZtf5ndAdv4OcdNcsQc3OfNm4d9+/Zh6dKl8Pv9WLt2LTZt2oSysjLMmTMHdXV1qK2thd/vx6pVq2AwMN+VQtXMHguv14cPGs+LlsYdeGiE1GlDAxcau6IodGXJ0+GniyZi3euHo2pzPAw6NazDTFEd5dfZI6Czuw/XleSLvB53dpK0mIO7Wq3GM888E/LYmDFjgv9dXV2N6urq2FtGiqdRqzF/alnEQy3CCWyU8fp8UKtVYU8+GsjR64ZWq0ppzfXbJlwTrEETTT79roNfoXr2ONFReqwFzkj5uImJ0qrQbIipJktAYKPMK//TKjuwA/03Bb1Om7LAbjZp0fxFBz5quhBctJUb3P959Gs0newIWUhecudovPH+qagrMVLuYHCntDLoNDCbdLKDe+BA6OKC/kXEJXeOxqa/f4aPmqI7cWhyeSmsw0wotujR2SP/xmLUq+Fy++CH9FTRqOFmOPvcwWJi+SYdzrVdTTAILNr2P88DW08fCvMNsElMK/W5vMENVIGF5JYzXSGLx9GUQqbcwOBOaSW4vXD0yg+uI0rz8ciiiSguMEKrUeE3mz7B2QGBU45A1UODToPKm4bL3qY/c9IIVM0ag687nKj/vw2iz1GrgH9bOgl6nSY4BfPMK5+IPtfZ58HT99+KXsETfJ7c0fy5NvGsILmVGEn5+P2N4hbpBB6x64LbiwsdDmz+++ew2d2y3+tsmwN7Dp+DQafB6ztbow7swNWqh0D/ou7cW0eipMAItao/8EtpPtWB7R99AYfgkayL7vMDZy/Zg4udvYIn7I7SXsGD4UV5sOTpJU8mknofqdeM9gQlUiaO3ElSpMU6r7f/CLzB876LZoyG3emCOU+P7R+FzgtPGld6pZ5Je8wFugJFvg7FWLN8YLXCwWmF5jwdtn/0BQ63tqOjO3QnamDqw+X2QK0WT6FUq4CRw69u7jMZtBhmFp9yGVw1UawejFPwiNa0kVoIZiVGCmBwpyHkHpv257ePitaG2dt0HoLLB4NeHXLkXEe3kJD6LLaePpy9ZMdlh/wR/0CTxpUMuVkNTCusnVuOhXfciDV//kQ0KP/r8zaMGm7B6YtDdypebzXDkqcP+Qyl5tIHV00Uy18ffKLSwPcR27DFSowUwOBOQ8gp6CW4vdjffEH09wMBPVlniRZZjBg53Bz1YmiAnASZXsEjmTff5/LihhEF8Pn8ONdmh88fepgGEL4aZZHZgElhdpQOvNFI7Ua9mi3DXaokjsFdARKZ6yz32LTLdgFtXeK1YZJtcnkp9DoNxt9QjI+bo8uSAfpL3Fbd6Q37WRWaDSgKc/M49kUnfvPgVLjcXpy9ZMfI4f0jdiD8Z6hCf432phPt0KhVEVMXw+1G5S5VCofBPYvFeup8OHKPTSs0G2AdJl6dMJm+Pd4Kv9+Ppzbuj3nOXs7xb4YIN4/2rt7ga0y4sTjkWrjPMPCtIdrURandqNylSlKYLZPFAl/9O7oF+HE1YGzZfUL2awzOZAkU9BIzcLEukJ+eaoda2/Bew7m4TkuSu+hYO2+cZPZM6TCT5GuE+wwHO9zaLpllRBQPBvcsFWn6JFLA8Pr6M12e2rgf//Hifjy1cT9e39UKrUYlmZI3cLFOcHvR45Q/360CUFJgxJwp12P2lOtRUmCECleKd0XBm4BpfLmLjnkGHb5TcZ3otWkTr5N8jUBRNDmYukjJwmmZLBXvqfPhFk3llJS9bBfQflneoRUlBQY8tqQC1qK8Iaf/DD6wOllUKqA4hkVHqc/iwYU3o7NTOsc+5ESnnj6owNRFSi0G9ywVz6nzchZNIy3WFZoNKC00yjqVaHK5FSOHi9fz7y9nqwoeYB3I31ZBXlaLHMUWA35R/S1Yh5miXnSUWtDUaMJ/6R38e4Pr0QcwdZGShcE9S0V76vzAjBq5o/5wi3UGnQYVY6147+BXkm0sMhswZbw1ZKQstgg8aVwpJo4pxpHjHeiyuzBMpB5LPCpvsmKk1Rz5iWHEunAZ+L3aeeXQaNRMXaSUYXDPYnKmT8SCacWYkphH/QP9eNFE7Gs6L7qDsiBPj1/VVaKk0BTyuNh00OCNTV12F7rsLowabr6y2BvdRHthvg49TndGBVAesEGpxuCexeQEDLFguufweYwabhYN7oNH/eFy6PNMenyn4jrRbw/dThfWvXYoJDUz3HSQGGefB7d98xp8eER8s5QUtQqYdvO1qJ03DnmG1Gf0hMPURUoVBvcUSeahClIBI1wwdfS6MavyejSd6BAd9cvNoR/47UGqFgvQn8sdzQlEANDZ3Sf7OLqBbHY3Pm6+iDyjluVvKWcxuCdZMjYayRUumHbZBcz/9ihUzxoretORU4IAuPrtYeEdN+I///wv0brsgUXacIvAYnQ6dcyHeAx8X4NOwxOLKOcwuCeZ3CCZDHIyagaP+gW3F202p6wSBAP1Ch5clgjEAxdppRaBxbiinGsXe9/O7j7sOXyOJxZRzmFwTyK5dVqSJZqMmoHfMMKNrKVy6OWmZg7O/5Y6zSgRiixG7Go4G5JHzxOLKFdw6JJEclIOk23wYRQlBUbMvXXkkAySgaUMwpHKpgm3K3PgjSQwjVO/8jb85sGpKLboY+xZZBVjitF0ol30Grf9k9Jx5J5E8Ww0ShQ5GTXRZLGE23Sz5M7RaDnTNaQM7pI7Rw95rkGnwUirWfKYO+OgWvBSCvP1uHW89coBIKGLw7MmX4/3RTYOAfJ28RJlMwb3JIp2o1Gy2yIVyCJlscjduv/G+6dCDpDw+YGvLtnxxvunJKdApHL13V4vPjgcOQXykXtvxjCLAYVmA6ruDF0cFtzetN9cidIlpuDe19eH1atXo6OjA/n5+Xj22WdRXBxa9vThhx9GV1cXdDodDAYDXn755YQ0ONvI2WiUbuG+Ycjduh/r+oLUN4sLHQ5Zwf2/tjVCcPtRIrJQmkk3V6JUiym4//Wvf0V5eTl+9rOf4Z133sGGDRvw1FNPhTznzJkzeOedd6BSRVn2Lw2SmSaXDTsTwwXBSFv3A5+dy+OLq5DZ4G8WxQVGlMhImxTc/SuyUgul2XBzJUqGmIJ7Q0MDfvSjHwEAvvvd72LDhg0h19vb29Hd3Y2HH34Y3d3d+PGPf4xZs2bF39oES2UOeqbvTIw2CHp9Pmzc/in2HTmHzm4BRRY9DHqNaCmCWKZAwt1wwhn8LSEbbq5EyRAxuG/btg2bN28OeaykpAQWS3+Vv/z8fPT0hB4U7Ha78eCDD2LFihW4fPkyli1bhoqKCpSUlEi+T1FRHrTa1P6fbuP2T0Vz0PNMeqxcdEtUr2W1ilc9zCaPLZuCPpcHtm4BRQUGGPXaIT8HbNz+KXZ8dCr4c7izTKd/awRGjhgWdXserZ6MPJMe+5svoK2rV1bapK2nDxq9DtbS/CHXRkbdgvCU8DePFfue+SIG96qqKlQTAoV7AAAJlElEQVRVVYU89uijj8Lh6K/Y53A4UFBQEHK9tLQUS5cuhVarRUlJCSZMmIAvvvgibHC32ZyxtD9mgtuLfUfE64jvO3Ied08dJXuEZ7Va0NbWM+TxTNwVKadNWgBdNofktxqP1y/52Rn1GuQbtbD1CMHR/8Lby0Q/HzkWTb8Rd08dhbauXjy3tTHigdhFFiO8LnfM7yeX1N88F7DvmdV3qZtNTNMylZWV+OCDD1BRUYEPP/wQU6ZMCbn+8ccf47XXXsNLL70Eh8OB48ePY/Tooelw6RTvYRfhpLPkQKLaFG5n7dwpIyU/O5fbi18tr4Rep0nYTS1S2uRAXCgl6hdTcF+2bBn+/d//HcuWLYNOp8P69esBAL///e9x1113YebMmdi7dy+qq6uhVqvx+OOPD8mmSbdk5qCns+RAtG3q7fNg+fybhlSCDJf5svCOG8N+dgNPXEqkwesC+sCRfy4vigu4UEo0UEzB3WQy4Y9//OOQx5988sngf//617+OvVUpkKw0uXSXHIi2TfuaL+Lz052ovGl4cBQf6VtNr+BJS4qh2OIogIyb+iLKBDm9iSkZaXLJnO4ZTO6cfqRNSp09rpBvFnK+1dTMHos8kx77jpxPeYrh4MyjTM5CIkqXnA7uyUiTS0XJgWjnz+WW2h34zULOyHzloltw99RRHDkTZSAWDsPVkWCiFv/kFNCKx8AiX35cnT/fsvtE1G0aaGAxM7kFxxL52RFR4uT0yD1ZkrkrMtY5/attki7pO/CbBTf/EGU3BvckSGZgjHVOf2CbXn23BR83XxzyHLFvFpm+s5aIxDG4J1EyAmO8c/oGnQYPLBiPPKOW9VaIFIzBPcskIoWTUy5EysfgnoWGzukbML6sCItmRLcLmFMuRMrF4J6FAiPvRTO+gdd3Hsex0534uPkijp2xpb3MARFlBgb3LLb9oy9CFkYzocwBEWUGDu+yVKSUSB7+TJTbGNyzlJyUSCLKXYoI7oLbi0s2Z8aMVlPRnkBKpBge/kxEWT3nnml101N9bB8PfyYiKVkd3DOtbnqq28PDn4lIStYG90yrm56O9nAzEhFJydo590xbUExne1iZkYgGy9rgnmkLipnWHiLKbVkb3FNRNz2b20NEuS1r59yBzFtQzLT2EFHuUvn9fn+6GwEAbW09Mf+u3LNEk8VqtYS0P93tSZXB/c4l7Dv7nimsVovo41k9cg/ItOqGmdYeIso9WTvnTkRE0hjciYgUKK7gvnPnTjzxxBOi17Zu3Yof/OAHqK6uxp49e+J5GyIiilLMc+719fXYu3cvJkyYMORaW1sbXn31Vbz55psQBAG1tbWYPn069Hp9XI0lIiJ5Yg7ulZWVmDt3LrZs2TLkWlNTEyZPngy9Xg+9Xo+ysjIcO3YMFRUVkq9XVJQHrTZ7M0ukVqyVLlf7DbDvuSpb+h4xuG/btg2bN28OeWzt2rVYsGABDhw4IPo7drsdFsvVDyA/Px92uz3s+9hsTjntzUiZmB6VCrnab4B9Z98zR8ypkFVVVaiqqorqzcxmMxwOR/Bnh8MREuyJiCi5kpItU1FRgYaGBgiCgJ6eHpw8eRLl5TzTk4goVRK6iWnTpk0oKyvDnDlzUFdXh9raWvj9fqxatQoGAwtnERGliiLKD6RbJs7DpUKu9htg39n3zCE1585NTERECsTgTkSkQAzuREQKxOBORKRADO5ERArE4E5EpEAM7kRECsTgTkSkQAzuREQKxOBORKRADO5ERArE4E5EpEAM7kRECsTgTkSkQAzuREQKxOBORKRADO5ERArE4E5EpEAM7kRECsTgTkSkQAzuREQKxOBORKRADO5ERAqkjeeXd+7ciX/84x9Yv379kGv19fU4dOgQ8vPzAQAbNmyAxWKJ5+2IiEimmIN7fX099u7diwkTJoheP3r0KF5++WUUFxfH3DgiIopNzNMylZWVWLNmjeg1n8+H06dP4+mnn8bSpUvxxhtvxPo2REQUg4gj923btmHz5s0hj61duxYLFizAgQMHRH/H6XRi+fLleOCBB+D1erFixQpMnDgR48ePl3yfoqI8aLWaKJufOazW3JxyytV+A+x7rsqWvkcM7lVVVaiqqorqRU0mE1asWAGTyQQAmDZtGo4dOxY2uNtszqjeI5NYrRa0tfWkuxkpl6v9Bth39j1zSN1skpIt8+WXX6K2thZerxdutxuHDh3CzTffnIy3IiIiEXFlywy2adMmlJWVYc6cOVi4cCGqq6uh0+lw7733Yty4cYl8KyIiCkPl9/v96W4EgIz7qhONTPyqlgq52m+AfWffM0dKp2WIiCi9GNyJiBSIwZ2ISIEY3ImIFIjBnYhIgRjciYgUiMGdiEiBMibPnYiIEocjdyIiBWJwJyJSIAZ3IiIFYnAnIlIgBnciIgVicCciUiAGdyIiBWJwj1NPTw8efvhhLF++HDU1NTh8+HC6m5RyO3fuxBNPPJHuZqSEz+fD008/jZqaGtTV1eH06dPpblLKHTlyBHV1deluRkq53W6sXr0atbW1WLJkCd577710NymihJ7ElIs2bdqEadOm4f7778epU6fwxBNP4K233kp3s1Kmvr4ee/fuxYQJE9LdlJTYtWsXXC4XtmzZgsbGRqxbtw4vvPBCupuVMhs3bsSOHTuC5yPnih07dmDYsGH4wx/+AJvNhvvuuw9z5sxJd7PC4sg9Tvfffz+WLl0KAPB6vTAYDGluUWpVVlZizZo16W5GyjQ0NGDGjBkAgEmTJqG5uTnNLUqtsrIyPP/88+luRsrdddddeOyxx4I/azSaNLZGHo7co7Bt2zZs3rw55LG1a9eioqICbW1tWL16NX71q1+lqXXJJdX3BQsW4MCBA2lqVerZ7XaYzebgzxqNBh6PB1ptbvxfaf78+Th79my6m5Fy+fn5APr//j//+c/xi1/8Is0tiiw3/kUmSFVVFaqqqoY83tLSgscffxxPPvkkpk6dmoaWJZ9U33ON2WyGw+EI/uzz+XImsOe6Cxcu4Kc//Slqa2uxcOHCdDcnIk7LxOnEiRN47LHHsH79esycOTPdzaEkq6ysxIcffggAaGxsRHl5eZpbRKnQ3t6OBx98EKtXr8aSJUvS3RxZOOSI0/r16+FyufDb3/4WQP/ILpcW2HLNvHnzsG/fPixduhR+vx9r165Nd5MoBf70pz+hu7sbGzZswIYNGwD0Ly4bjcY0t0waS/4SESkQp2WIiBSIwZ2ISIEY3ImIFIjBnYhIgRjciYgUiMGdiEiBGNyJiBTo/wN5fpLCgroijwAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -108,10 +88,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "By eye, it is clear that there is a nearly linear relationship between the x and y variables.\n", "This is reminiscent of the linear regression data we explored in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb), but the problem setting here is slightly different: rather than attempting to *predict* the y values from the x values, the unsupervised learning problem attempts to learn about the *relationship* between the x and y values.\n", @@ -122,20 +99,17 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "PCA(copy=True, n_components=2, whiten=False)" + "PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,\n", + " svd_solver='auto', tol=0.0, whiten=False)" ] }, - "execution_count": 3, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -148,29 +122,22 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The fit learns some quantities from the data, most importantly the \"components\" and \"explained variance\":" ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 16, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[ 0.94446029 0.32862557]\n", - " [ 0.32862557 -0.94446029]]\n" + "[[-0.94446029 -0.32862557]\n", + " [-0.32862557 0.94446029]]\n" ] } ], @@ -180,18 +147,14 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 17, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[ 0.75871884 0.01838551]\n" + "[0.7625315 0.0184779]\n" ] } ], @@ -201,28 +164,21 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "To see what these numbers mean, let's visualize them as vectors over the input data, using the \"components\" to define the direction of the vector, and the \"explained variance\" to define the squared-length of the vector:" ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 18, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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OR+h135AGgIceeghGoxEAsHjxYhw/flxRUGdlmYb9DLGeRoJ1pcxEqacTJ9qg1xdCrw+8\nbm9vw9y5w8999np9OH2681I4+3H99XmDBvxo6qqx0Q+9vrcLWq32Dnk+WZbx2WefYdu2N/CXv/wF\nzc3NoWNXXnklli27G8uWlWH27EJYLFOEGhg2Uf6bEkFUQb1gwQJ88MEHWLlyJb788ktYLJbQMbvd\njtWrV2P37t3Q6/X45JNPUFpaqui8/Rdxp4GyskysJ4VYV8pMpHpqbnZClnsbER0dTmRmDv/bAstg\nTr30SoO2tqaIA7ZGW1fd3Z1wOnv/7BoMnbBa1QM+d+rUydBc57Nne/d1LiwsCu3rrNFMD5W5qQmD\nljkeJtJ/U2NJ6c1MVEG9fPlyHDx4EOvWrQMAbNq0Cbt27YLT6URZWRmeeOIJbNiwATqdDjfccANu\nueWWaC5DRDQi0T5fHq8BZEN1qzc01IfmOh8/3jvuJyMjC8XFJVi7tgwLF34rNNf52DHbuJSZ4k8l\nj+eadcPgHdjweKeqHOtKmYlUT72raY3sGXV4ixqDTslKT0/Gp582xWxbypaWFrz1ViW2bduKzz47\nFHrfaDTh5puLsXRpGebPvwVGY8eA8igtczxMpP+mxtKYtqiJiEQU7WpaSgeQnT7dOeptKTs7O/DO\nO7tQUbEVH37YO9dZpzPgO99ZhXnzbseUKVfjsssKkJ9vhCSpI7aWucnF5MGgJqJJT2nAR9tF3t3d\njT173kVFRTnef/9vYXOdb711BRYuvAM33FAGq7UHLtdUnD/fCLc7DY2NHSgqSo3Yhc8lPicPBjUR\nTWojmdIVCExNv9eR9fT0hM11djjsAAJznW+++RYUF5di9eq7kJ6ecakb2wi3O/DcuaDAAElqhcfT\nDYOhh63lSY5BTUST2kiW6LRYpqCtrWnQ7ma/34+PPz6Iiopy7Nq1PWyu87XXLsSiRXfgxhvvQn5+\n1oDFTmpqWqHX2+H3A/n5UyBJahgMfraaiUFNRJNL/xZ0d7cKfTaNitidHfxOcnLgefKVV6aEQlaW\nZRw9+gUqKsqxffs2nD9/LvS9yy+fg5KSMqxZsxY9PelDLnZisaRfCmwbXC4bnztTCIOaiCaV/i3o\nlpZa5OT0LrUZqTs7+B29PgVOpwY1Na0AWlBRsRWVleUD5jqvWbMWxcWlsFjm4OzZLnR1SWho6MT0\n6WmhgI90Q8DnzhQJg5qIRiUWy3aO5vwjvX7/gMzKmgKDYejR08HvnDtXj+3b/4j9+99ETc3xPufI\nxt13F6O4uBTXXfft0FznvlOo/H41mppsKCwMBHEs1xCniY1BTUSjMpJnvGNx/pFev/+iKCkpGPLz\nLS0tePvtP+O993bj+PFPQu+npk7B6tV3obi4FDfdtAgazcA/p31vCvLzjWhuboJKpWK3No0Ig5qI\nRmWsV/Xqez6v14fqantY63ek11cy/9hm67y0r3P4XGe93oCbbroVDzywDrfeumLYfZ373hRIkhoW\nSzIsFgY0jQyDmohGZSy3hex//uZmG2Q5HbKcGmo96/UY0fUHew7sdDpDc5337n0vNNdZo9Fg2bLl\nKCkpwwMP3AunU/lijmZzKqqrraipcSG4daXP5xNq8wwSH4OaSDBj/cw31oItVLsduHixE9nZaaiu\nbo9Zufu2gFUqO/Ly8kLHXC4JV16ZMqIVugKt8nbU1jrh9frQ3v4ZDh16d8Bc55tuWhSa65yREeha\nNxqNcDqVL40pSRIkSY2CggIAgMcT+0cDNPExqIkEM9bPfEdjsJsIiyUd1dXtkKSZAGJb7r4t4EDr\nune3Kb3eN+KR0mfOdOCddz7DP/7xHr74YjscjtbQsWuvXRDa13natOmh94O/u7HRj+7uzhHdhIzX\nhh80cTGoiQQzXn/Y+4dPUVEK6usdYS3j5GQ5LJSGuokYj3JHu75137nO5eXluHjxfOjYtGmX47bb\n7sL3vrcOspwJl0tCV5cP2dm+Ab+77/QspTcHY/1oQKlE66mhXgxqIsHE6g/7cH+Y+4fPgQO1yMmZ\niaamdrhcs+ByBdaZVhrGYx1I0QTN6dPVobnOtbU1ofenTs3HwoXrcN11ZbjssnzMneuFLGNMbkLG\nYvOMaOpC5J4aGhqDmkgwsfrDPtwf5v5h09WlQ04O4HYH3vd4Bi7MMVQYj/VuTsHf4/P5cfKkHdXV\nzbBYjANCqrHxG1RWbkNlZTm+/vpY6P20tCwsXlyCxYvXYurUTPh8OgBqzJrlgdmchqoqR9j1YnUT\nMhaLmEQTuuyCT1wMaiLBxOoP+3B/mPuHj8nkBgDodD64XIBW6wt9LmioMFZS7kgtQVmGotZhsPyN\njXa43WlQqQCnM1Ce9HQvdu6sRGVlOQ4dCp/rfMcdd+Kaa27H/Pm3Q5ICf/JUqg7Mmxd+I6HkJkSt\n9sJg6Iz7HOhoQleULngaOQY10QQ13B/m/uGzZMl01NW1Ij8fsFrPIDs7DQZD64AwnjkzNRSsp061\nQ61WweNJUtQFG6kl6PP5cfq0Dh6PBK1WDb+/A3PmTB309wRb+j5fO957byc++OANfPnlR/D5Ar/P\nYDBgxYpVKC4uxbJly6HT6S6tEDb0rldKbkKyskywWtUDvjveogld7l+duFSyLCufFDjGrFbl0x4m\nq8AfCtaTEoleV6Md/OPz+S5t8DD090daT32XxWxoaIcsSygqCvzRNxjCu2D7/oakpB5UVzvgcmWg\no6MT6ekmJCd3wu+X4XbPDH3HYKjHypW5A347ABw/3oLy8nfw6afv4auvPkBPT6AXQKPRYOnSZSgu\nLsXKlbfDaDRFVRfDEeW/qVj9nrEiSj2JLivLNPyHwBY1kbBGO/hnrDZ46NvNGnieLQ04Fgzo6upu\nyLIReXkm1NU5cP68A263Hz09M+B2d2HGjHScP9+A9LBi+sN+e1dXD/70px04dOhd7N79Nuz2QACo\nVCpcc831WL/+Htx9dzEyMqb2uTGwhQVYsC6Cx6uqHMIF3EhuzLh5x+TCoCYSlKiDf/p2u+p0PvTt\nkwt2wQaD1uXSQ5ZNaGpqhcejRXr6FLS02KFSaaFWdyE/PwuybIRa3Qq3W4JO54PZbEB3twpfffUR\nPvhgK/7+90rYbG2ha1xzzbUoLi7DmjXhc537Xhfovbnp21V//nwbMjOLIEmScCOfOSqbBsOgJhKU\nqIN/zOZUnDrVgtpaJ/x+GTqdH35/D1JSEOqiDt5UaLU+uN2BlrdW64NKBRQVpcDlMkKn80KS1Lj8\n8hSo1X44nUB9/VG88ca7qKjYhpaW3n2dZ8yYhXvvvRfFxWsxc+asQcsW6eYmGIBerw+nTnWjuvoc\n8vMNANTw+VwAYreK2miIemNG8cegJhKUqIN/JEmCRiOhoKAIQKDL1mqthyRloKbGBrM5NXSTkZ9v\nRGNjB9TqdsyalQxZluFySbBaz2DKFCM+/7wOdnsbvv76LRw9ujdsrnNubj4WL74LK1feidtu+07E\n3an6i3RzEwy8wDrhaejpARoaAMCDyy5LhtOZLkTrVdQbM4o/BjWRoER7Dtn3GWpDgx3Tp6dBkiQ0\nN9vgducgJycl1GXb9yZjzhwfzObp/eY62/HCC/8XH330Phobe+c6Z2ZmXdrXuQzXXfctqNUjG2Ed\n6eampsYGpzPQqs/JSUZHRzMcDi20Whfy8qYBEKP1KuqNGcUfg5qIhtR3YJjXmwxARmOjHk1NzfjW\nt/JC3dpBLpcU8Sbj4sWLobnOn376ceh9vT4V11xzF771raV4+uliRS3nwUS6bjAA9Xo7/H5g9uxc\nNDbaoVJJoZsHEVqvot2YkTgY1ERjYCKtq9x3YFhzswqABzk52bBam9Dc3ITU1B5kZs4Ifb5v6HV1\n2fDWWzvx+ut/wWeffQS/P7iIih7z5t2C+fMfxpVX3oakJD2mTDkzIKRjUY/BAAy2rl0uGyyWHsiy\njJ6eDrZeSXgMaqIxIMII3ljdLPQdGOb16iDLGqjVEoqKUlBYqLq0zWR76DrTpyfhrbe2h/Z1drsD\nc50lSYNvf/s23HbbSjz88D3QanXYv78ZXV1WmExuLFkyfcC1ldZj/98a3GCk/2/vHQEeWKDFYkmJ\nyQ3URLoxI/EwqInGgAgjeIcKub7BMn16D9LT1YMGS9+BYefOnYfH44NG44LfDzQ0OKHX+1BYaMDB\ng39HRUX5gLnO8+Zdj6VL12PRorsxZUomVKqO0IIkK1bMGPI3KK3H/r81uMFI/98+VjdQItyY0cTF\noCYaAyKM4B0q5PoGS3d3CtraGsLmG/dtFfYd5LR4cRJkWYOaGge83iloa6vH889vxUcfVaKjo3eu\ns8UyHytW3ImHH14Hp9MUuhYwsrpQWo+DbTDS//hY3UCJcGNGExeDmmgMiDCCd6iQG2q+MRDeKuw7\nyEmWZXz11VHs3v1n7N+/C1ZrU+gcs2dbcMstq3HjjQ/A681GU5Mbb79tRUGBB15vJyQpCbNm6WE2\npyn+DUrrcbANRvr/9rG6gRLhxowmrqiCWpZlPPfcczh16hS0Wi02btyIgoKC0PF9+/Zhy5Yt0Gg0\nWLt2LcrKymJWYKJEMNYjeJU8Ex0q5IaabxzU9/XJkyfxhz+8jj17dqKxsTb0fnZ2AZYsKcWqVctx\n++034auvulBXp0Zjoxoez1Q0NanQ05OMGTN6UFCQDrW6FbIcWC98uOe5I3nu2/+3BjcY6f/bx+oG\nSoQbM5q4ogrqvXv3wuPx4M0338TRo0exadMmbNmyBQDg9Xrx/PPPo6KiAjqdDuvXr8eyZcuQkZER\n04ITTUaR1tB2OqWIz0SHulnoGyzJyW7k5fXONw7q7PwG//3fe1FRsTVsX2ejcSq+851bUVy8FhbL\n9UhOlmE2p0KlUkGv98HjSUJPjwoAoFLJ6OlRh/a4Hqrl3t9InvtG+q0Wi1bR52KBU6toLEUV1EeO\nHMGiRYsAAPPnz8fXX38dOlZTU4OioiIYjUYAwMKFC3H48GHcdtttMSgu0eTSt1Wp0XjQ0GCH3Z6D\n8+e9yMpKQ1NTBwoL00f8TLRvsAR3OjKbU3Ho0EmUl+/EJ5/swunTn4c+r9ebYLGswpw5GzB79rXI\ny0vCvHkuWCzh3dhmcyrq6r6BVqtFT48TanUPLl7sQkqKEzNnpsBoHLrlPtT7fO5Lk1VUQW2322Ey\n9W7PpdFo4Pf7oVarBxxLSUlBVxe3O6PEFq/pN31blTU17airMyA31wS/X4WWFgemTYvNgh1WqxXL\nly/FuXONkGU/AECr1ePGG7+LxYvXIDf3VtTVJQOYAp+vC1qtN2JwSpKEZcsKUFDQjv37G6FWpwBw\nw+3WoqWlFvPnzxjQch+s7HzuSxQQVVAbjUY4HI7Q62BIB4/Z7fbQMYfDgdRUZc9rlO7NOdmxnpSL\nVV2dONEGvb4Qen3gdXt7G+bOVT4oKlqNjX7o9SkAgAsXvDAa/UhNNSAlRYsLF1qQne1GXp4bFkve\nqG4cPv/8JJqbGwAAV1+9EosXr0Fp6XokJ3tx4UIHrFYjWlqq4XBMQ3KyA/PmTUNOjjxo/ebmpsFq\nVWHmzBmh9wyGOuTmpiEry4Tq6s7QTc9gZc/ISFb0ufHG//+UYT3FTlRBvWDBAnzwwQdYuXIlvvzy\nS1gsltAxs9mM+vp62Gw26PV6HD58GI8++qii83Kj8eFxQ3blYllXzc1OyHLvzWlHhxOZmbH/99C/\n5e73++F2B/43dbu7kJ6uhsfTCLdbQl7eRdx0UwEkSUJbW/ew5xqsFyAry4Tc3HnIzi5AS8s3WLr0\nf2POnIXo6ZFQXV2PzMwZaGpqgt+fA5vtAgyGLHz8cQ1yc6fCatUP+ltsNhecfZrEPT2u0L+PzMyk\nS++qI5Y9SOnnxgv//1OG9aSM0puZqIJ6+fLlOHjwINatWwcA2LRpE3bt2gWn04mysjI8/fTTeOSR\nRyDLMsrKypCdnR3NZYiEEW03rNKwHGyQmFbbAoMhMOhr1qweqFQqeDxJ0Ot7YDYHQnqwa/Td3rG+\n3obq6vOwWJIHrNqVkZEMg8GP5cvX489/fgFffPEarrnmMhgMLmRlTYHfL8PjATo6uqHXFyA7OwOS\nNAV1dVZcddXgv9nv96KtrRHp6UZotTIkyYNjx2xcuYtohFSy3Hfb9/jiHdjweKeqXCzryufzXVon\nOjCoS610gryIAAAYv0lEQVQOBubQoVNd3R622IfBEHnkcvBzp087IMsm6PWtKCxMh8/XCqMRQwb9\nYNc4diywrWNDQztcrqlQqbowe3YKLlzoXbULAPLy3DCZZLz++of42c+KkZKSioqKjyFJU3D+fBu6\nu6fgm290+OorF2Q5HZmZLkyfLuOyy9qxenXeoL/F5/OhqckGlcoOrbYHmZlFobIPVg+i4/9/yrCe\nlFHaoh7ZHnJEk1RwlPS8eanQaCR0d09FXZ0ax47p8f7738DnU7Zi1nAjnIO7UAWnM1282Amncypk\nOQ1O51TU1NgUXyPY6nc6VTh3zo7z5+2or7ehs1M74PP19Q4sWLAcM2deBYfDhl27PoUspyEzswgX\nLrQgJ8eP6dNbkZlpg1rdioICL2bNitztHby+JEkoLExHYaEJubkZYTcYHMFNpByDmmiEXC4JjY12\nuN1pkGUTbLZM1NTY4PX6UF3djmPHbKiubofP5xvQRT7UCGcgsJ62TtcBvb4dBkMrsrPDB6xFCji9\n3gefz4/6ehtOn3bg/Pk2+HyB1rfB0IqOjmaoVH5kZWXD7U6D3d424PvB8y5dGlic6OOPdwAIhG1e\nXjIuvzwVd901CzfeCNxwgwpXXOHD7NmRB9NF+s2DlZGIhsegJhqhwKIevYGp0/nCFvLo2/oNhqVK\n1QGDoXXQFauKigJd0mfPnoNefxErVuTAYklHcnL4k6lIQW82p+LixTp4PIBO50FmZhGqqztCXfW5\nuckoKHBDkrqg17fi6qtzw8pksUwJnXfJklIAwLFjf4PT6bh0fgMMhlZoNF24/HI/Vq3KDS0tGkmk\n3xypjJF6B4hoIK71TTRCwUU9bDYfdDof8vJSodd3ROyCVrJildfrw4EDzWhvT0dHRyd8vnTs39+M\nZcsKFC1NKUkScnMzkJPTe6ymxhVa1lelkqFWS5g9O3C8//PhvhtvTJs2BVdeuQBVVZ/jk0+24vbb\n74TZPHgoRzLYb+5fRnZ/EynDFjXRCMkyUFBghF7fDpXKjuTkNpjNqYq7ufurrbWhvT0TVVVu1NRM\nR1VVNzo6At3pSod6DryWP/RPeXmpUKvbh2zVyzLg8/nR0NCF+fNXAgA++2zXkC3nSF39IynjSEbO\nj+Q6RBMNg5pohGprbfB4slFQUISCggKo1epQq1RJN3dQMIC++sqF6morurtTIcvJcDrT0N5uH7Q7\nPZKB1zaEjgVauEbMm5c6aPDW1tpw+rQOTmcRrr76e1Cp1Ni37320t7cN+Gzf7ygp2+BlTA2rh8GC\neKTXIZpo2PVNNEL9u2zt9r67QQFXXpkybFex1+vDnj11OHVKj9rai3A4psDjqUVOzmwYDJ1ITzdB\nr/coHjXev7s5MJ1M+W5OLpcUeu6empqDyy+/CSdPfoi3334LDzzwkKJ6GK4re7Au8eE23+Ca3zTZ\nsUVNNEL9u2wvXOjEyZNJqK5OQlWVhD17vhm2m7a21oYzZ/Q4d84Ih+NyWK0SPB4dTKZTuOoqA9LS\nrKPqTu87nWyo7uu+5w1ODQOA66+/CwBQWVk+5HeiKVt/wwVxrK5DlKgY1EQKBbtou7tVuHChFn5/\nKwyGVng86tBUrcZGDU6fNgzbTetySVCpVGhv74EsT8HUqfmYOjUFmZk6XHONH8uWFUTVnT5c2Qe7\ngTCbU2GxuGEw1MNgOIvS0uXQarX46KO/48KF8xHPGauyDRfEsboOUaJi1zdNaEqW8BzsM5HX3c6C\nSgXk5GSERk9XV/duQtPTo0ZSUu8IsMG6afV6H/Lz9aiqssHrdUGvd2DWrBQkJQVGjwendsVqn+Ph\nupclScKcOVMxZ07vd5YtW4Hdu3dhx44KfP/7PxxwzliVbbiR7dzrmSY7BjUlnJFsOdk/oKqrrZAk\nddh3Bwux/u9/8803uDTjCUBvCBcVafGPf9TC4dDB4WjFlVfmhj4zWDet2ZwKn68d58934dy5Gkyd\nakBSkg85OdmXWuPAqVMt0GikmGyt6XIFbjyam21wuyXo9fZhz1dSUordu3ehsrI8FNRjsd0ng5ho\naAxqSjjDtQ776t+i7Tu/OPjdwZ6RDmwN+8NeBUM4KUmDadNS4PFI0GgM0OvboFJphhzEJUkS5s7N\nhMWSHlqYpKGhE9On9/6O2lonCgqKFP3O4ej1gY05XK5Avfn9QE2NbcjzLV++EikpRhw58hnq6s5i\nxozLRlT3RBQbDGpKOCMZBdx/1yuvtwcNDe1wuyXodD7k5wMpKZF3xur/XbPZAFluQW2tEz4foNV6\n4XAATU3dyM/vXctapQLmzVP2HLVvazJwvb6/JXwIyWhGO5vNqaiuPg+VSgut1of8fCNcrqGnOSUn\nJ2PlytuxbdtfsX37NvzkJ//MEdhEccDBZBQT47koxUhGAfcfiBRY1zowJ9flmgqrtXPQwUr937dY\n0qHRSCgoKIJKlQ67fTYaG9Xw+9PR1BRY67uhoR0NDV1D1kGwrr74ogN/+1sdvviiHdXV7ZgxIyXs\nev03vRjNaOfADUEyZs9OQVFRKiRJreh8JSWBJUUrKrZGLENSUg8XIyEaY2xRU0yMZ5eokmU1g/qv\n7JWVlQ6PpwMejwSt1ofs7LRBn5FGej/YggzOOXa7JcycaURzcxPOnbNBltORl5cHp1MdVgd9n+2e\nP9+GzMwZlzb2mAW3uxXTp6di//565OZmhP2mkcyFHs5I6i1o8eLvIj09HSdPnsDx41W4/PI5Yefw\n+WR2hRONMQY1xcR4domOZPBRba0NXV1plwZRJaGrqw7z518FSQp0JhkMrSO6drA7XKv1we0ObMgh\nSWpYLMlwuSTIct/Vtux9As0HjycbAGCzqeFy2cPCPlC+HOTkpIQFXixDL5pBW1qtFqtXr8Frr/0B\nlZXleOaZfws7x7Fj4d3n7Aonij12fVNMxGpRilh3obtcgRAMdnenpMzAxYt1Uc/JDXaHFxZ6MWXK\nGeTn+0Pn6fubm5tt8PvT+8yn7n3YrdP5Qi364Gu3WwpbcESkwAt2f1dWboPL5cIvf/k8Tp06CYCL\nkRCNB7aoKSai6VaNJNZd6Hq9D253Up/XgV2clA726i+8VRq+H3PfOlCp7MjLywMQ2OyiqakbLpcD\nWq0PWVkGnDxZDaMxA3Z7HS67LBdtbe3IzJwRVm5RfOc7NyA3dxoaGuqwefN/4YUX/g+amhrxm9/8\nd8z+vRPR4BjUFBOxmgsb6y703i0ppdBoZ72+fVTnHMzAEdyBDqvGRjtycrIgSR643RJOnqzu0/2e\nD4OhFddeW4CamnbhAu+f//kn+PDD/Vi0aDG2bn0Te/f+DQCQnp4BgHOgicYDg5qE0n9K1GhblpIk\nYdmygtBcZb2+XXEIKl2xLNKiH31bmmp1J/Lz80OfOXNmaugZOaB83+p48HjcOHu2Fm1tgWf5VVVf\nAQAKC4viWSyiSYVBTUIZi67UaENQ6Ypl/bvnBwa5ITRwDABMJnfYdUTq5u7vhRd+g4sXrdi7929Q\nq9VwuVwAGNRE44lBTeNuqBapSC1LpSuW9X/dP8h1Oiu0WitqalwA/JgxwwCNxgqPJ0mobu5I9Ho9\n/vCHP+N73/tfePfdt0PvFxUxqInGC0d907gLBtlwO0yNFaUjywcb0TzcSOf+we3xJEGS1CgoKEBB\nQRF8vmlQq9WYNy8VM2emoqbGJvSCITqdDr///R+xZMmy0Hv5+YVxLBHR5MIWNY270Q4YG+3GEEpH\nlkfqhvd6A7toffPNNwD8MJsNMJvDvxvpOftgvzlR1s5OSkrC669vRVnZGphMRuh0ungXiWjSYFBP\nYGOx01EsjHbA2GjDTemNQqRu+OrqdrjdWaFdtCSpNVSnwfp2OACrtRbZ2WlITpYvBb4t4m9OpLWz\nNRoNKit3xbsYRJMOu74nsHh3MQ9msLW1lRptuI1mkY6hrh2sb7V6KnJyZiI5WYbFkg5Jkgb9zUrK\nMp7rqBOReNiinsBEba2NdMBY/54BrdYPd5+B0yNtkY9mZPlQvQH969duD7TAlU7jGqwsidI9TkRj\ng0E9gcV6TnK89A8qrbYFBkP0U7hGM7J8qGDtW99erw/HjjXBZDJfWmglFTU17QOuq6Qsot5wEdH4\nYFBPYBNlecf+wdTTo8XcufH5LUMFq9mcilOnAvtVNzc74HZnIjk5BW63Go2NHZgxI7qAnSg3XEQU\nnaiC2u1246c//SlaW1thNBrx/PPPIz09/I/Xxo0b8fnnnyMlJQUAsGXLFhiNxtGXmBQTaU7yaMQy\nqMZygJ0kSaH9ql0uG5qb/bhwoRvTphnh8UjQ63uiOu9EueEiouhEFdRvvPEGLBYLfvSjH+Gdd97B\nli1b8Mwzz4R9pqqqCr///e+RlpY2yFmIlIllUI31895g61+n8yEnJw0tLeegUnmRmnoRZnNBVOec\nKDdcRBSdqIL6yJEj+N73vgcAuOWWW7Bly5aw47Iso76+Hs8++yysVitKS0uxdu3a0ZeWJqVYBtVY\nP+8Ntv7z8lLR1NSByy7zwWLpgdlcMGAal2jT5ohITMMGdXl5OV599dWw9zIzM0Pd2CkpKbDb7WHH\nu7u7sWHDBjz88MPwer148MEHcfXVV8NiscSw6EQjN9bPe/u2/i+/3A+zOReyjD6bgvjg8/ng8WQD\n4ChuIhresEFdWlqK0tLSsPd+/OMfw+FwAAAcDgdMJlPYcYPBgA0bNkCn00Gn0+H666/HyZMnhw3q\nrCzTkMcpgPU0PK/XhxMn2kLhaLFMgSRJyMhIRnV1Z5/382Lems3NDX/cc+JEG/T6Quj1gdd1dXWY\nMSMldFyt9sb932m8r59IWFfKsJ5iJ6qu7wULFuDAgQO4+uqrceDAAVx33XVhx8+ePYvHH38cO3bs\ngNfrxZEjR1BSUjLsea3WrmiKkxBi1d2ZlWWa0PUUK9XV7dDrC9He7gCgQVtbU6jVmpmZdOlTarS1\ndY95WZqbnZBlR+i1zea6VK4Ag6ETVmv81h7if1PKsa6UYT0po/RmJqqgXr9+PX72s5/hvvvug1ar\nxa9+9SsAwCuvvIKioiIsXboUa9asQVlZGZKSklBcXAyz2RzNpSYMLloxvgI3ROGv46V/d/usWXqo\n1RzFTUTKqGRZluNdiKCJfAd27JgNstzbJapSdWDevJH/geadasBwPRThLWrAYIjfjZHP5wt7Ri3a\n4DH+N6Uc60oZ1pMyY9qippHjohWxNVwPhdmcivb2NnR0OOPeauX0KiIaDQb1OBmvRSsSaerPaMo6\n3DQrSZIwd24aMjN5V09EiY1BPU7Gq1WVSM/CR1NW9lAQ0WTBoJ5gEmkDh75l8/n8qK7uVty65rKa\nRDRZMKgnmERqafYta2OjHSqV8dLe2cO3rvncl4gmi/hN3qQxYTanwmBohUrVAYOhdUxbml6vD9XV\n7Th2zIbq6nb4fCPfFzpYVrW6HXl5vWUVuSeAiGg8sUU9wYxnS3O0z8P7ljXQuu4NZ5F7AoiIxhNb\n1BS1WD4PH8+eACKiRMIWNUUtls/D+cyZiCgytqgpamwFExGNPbaoKWpsBRMRjT22qImIiATGoCYi\nIhIYu74pJhJpjXEiokTCFjXFRHBOdWBlsamoqbHFu0hERBMCg5piIpHWGCciSiQMaoqJ/nOoubIY\nEVFsMKgpJjinmohobHAwGcUE51QTEY0NtqiJiIgExhb1IBJpulEilZWIiEaGLepBJNJ0o0QqKxER\njQyDehCJNN0okcpKREQjw6AeRCJNN0qkshIR0cgwqAeRSNONEqmsREQ0MhxMNohEmm6USGUlIqKR\nYYuaiIhIYAxqIiIigTGoiYiIBDaqoN6zZw+efPLJiMf++te/Yu3atVi3bh32798/mssQERFNWlEP\nJtu4cSMOHjyIuXPnDjh28eJFvPbaa6isrITL5cL69etx0003ISkpaVSFJSIimmyiblEvWLAAzz33\nXMRjx44dw8KFC6HRaGA0GjFjxgycOnUq2ksRERFNWsO2qMvLy/Hqq6+Gvbdp0yasWrUKhw4divgd\nu90Ok8kUep2cnIyurq5RFpWIiGjyGTaoS0tLUVpaOqKTGo1G2O320GuHw4HU1OEX4cjKMg37GWI9\njQTrShnWk3KsK2VYT7EzJguezJs3D//5n/8Jj8cDt9uN2tpazJ49e9jvWa1sdQ8nK8vEelKIdaUM\n60k51pUyrCdllN7MxDSoX3nlFRQVFWHp0qXYsGED7rvvPsiyjCeeeAJarTaWlyIiIpoUVLIsy/Eu\nRBDvwIbHO1XlWFfKsJ6UY10pw3pSRmmLmgueEBERCYxBTUREJDAGNRERkcAY1ERERAJjUBMREQmM\nQU1ERCQwBjUREZHAGNREREQCY1ATEREJjEFNREQkMAY1ERGRwBjUREREAmNQExERCYxBTUREJDAG\nNRERkcAY1ERERAJjUBMREQmMQU1ERCQwBjUREZHAGNREREQCY1ATEREJjEFNREQkMAY1ERGRwBjU\nREREAmNQExERCYxBTUREJDAGNRERkcAY1ERERAJjUBMREQlMM5ov79mzB++++y5+9atfDTi2ceNG\nfP7550hJSQEAbNmyBUajcTSXIyIimnSiDuqNGzfi4MGDmDt3bsTjVVVV+P3vf4+0tLSoC0dERDTZ\nRd31vWDBAjz33HMRj8myjPr6ejz77LNYv349tm3bFu1liIiIJrVhW9Tl5eV49dVXw97btGkTVq1a\nhUOHDkX8Tnd3NzZs2ICHH34YXq8XDz74IK6++mpYLJbYlJqIiGiSGDaoS0tLUVpaOqKTGgwGbNiw\nATqdDjqdDtdffz1OnjzJoCYiIhqhUQ0mG8zZs2fx+OOPY8eOHfB6vThy5AhKSkqG/V5WlmksijPh\nsJ6UY10pw3pSjnWlDOspdmIa1K+88gqKioqwdOlSrFmzBmVlZUhKSkJxcTHMZvOw37dau2JZnAkp\nK8vEelKIdaUM60k51pUyrCdllN7MqGRZlse4LIrxX+zw+D+AcqwrZVhPyrGulGE9KaM0qLngCRER\nkcAY1ERERAJjUBMREQmMQU1ERCQwBjUREZHAGNREREQCY1ATEREJjEFNREQkMAY1ERGRwBjURERE\nAmNQExERCYxBTUREJDAGNRERkcAY1ERERAJjUBMREQmMQU1ERCQwBjUREZHAGNREREQCY1ATEREJ\njEFNREQkMJUsy3K8C0FERESRsUVNREQkMAY1ERGRwBjUREREAmNQExERCYxBTUREJDAGNRERkcCE\nCWqn04kf/vCHeOCBB/DII4+gpaUl3kUSkt1uxw9+8ANs2LAB69atw5dffhnvIglvz549ePLJJ+Nd\nDOHIsox/+7d/w7p16/Dggw/im2++iXeRhHb06FFs2LAh3sUQmtfrxVNPPYX7778f99xzD/bt2xfv\nIgnJ7/fj5z//OdavX4/7778fZ86cGfLzwgT1X//6V1x11VX405/+hDvvvBO/+93v4l0kIf3hD3/A\njTfeiNdeew2bNm3CL37xi3gXSWgbN27Eb37zm3gXQ0h79+6Fx+PBm2++iSeffBKbNm2Kd5GE9fLL\nL+Nf//Vf0dPTE++iCG3nzp1IT0/Hn//8Z/zud7/Df/zHf8S7SELat28fVCoV3njjDTz22GP49a9/\nPeTnNeNUrmE99NBDCK690tzcjClTpsS5RGJ6+OGHodVqAQTuXnU6XZxLJLYFCxZg+fLl+Mtf/hLv\nogjnyJEjWLRoEQBg/vz5+Prrr+NcInEVFRVh8+bNeOqpp+JdFKGtWrUKK1euBBBoNWo0wkSMUG69\n9VZ897vfBQA0NTUNm3dxqcXy8nK8+uqrYe9t2rQJV111FR566CGcPn0a//M//xOPogllqHqyWq14\n6qmn8Mwzz8SpdGIZrK5WrVqFQ4cOxalUYrPb7TCZTKHXGo0Gfr8farUwHW3CWL58OZqamuJdDOEZ\nDAYAgf+2HnvsMTz++ONxLpG41Go1/uVf/gV79+7Fb3/726E/LAuopqZGvvXWW+NdDGGdPHlSXr16\ntfzhhx/GuygJ4dNPP5WfeOKJeBdDOJs2bZJ3794der148eL4FSYBNDY2yvfee2+8iyG85uZmuaSk\nRK6oqIh3URLCxYsX5aVLl8pOp3PQzwhz6/zSSy9hx44dAIDk5GRIkhTnEonpzJkz+MlPfoJf/vKX\nuPnmm+NdHEpgCxYswIEDBwAAX375JSwWS5xLJD6ZWyMM6eLFi3j00Ufx05/+FMXFxfEujrB27NiB\nl156CQCg0+mgVquH7MkS5gHC2rVr8bOf/Qzl5eWQZZkDWwbx61//Gh6PBxs3boQsy0hNTcXmzZvj\nXSxKQMuXL8fBgwexbt06AOD/cwqoVKp4F0FoL774Imw2G7Zs2YLNmzdDpVLh5ZdfDo2roYAVK1bg\n6aefxgMPPACv14tnnnlmyDri7llEREQCE6brm4iIiAZiUBMREQmMQU1ERCQwBjUREZHAGNREREQC\nY1ATEREJjEFNREQkMAY1ERGRwP4/bV+C7ucCrxYAAAAASUVORK5CYII=\n", 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knEYKBBixN8vAKpT64mmpcig1Uk/JJB2bUu3kJKVgSWsCFYaEQZSu6WwZ/I1gYG8aw1SgqFXfHLpuy1QYpmJFV4oVXanGIR0zeZTeYiczd9G0pnvuZ7EY9U/RWrNx43qefPKxKb3OwBmy1ppsvsKHPUX2Hizy4YEi+3pGrxMf7/Ug2uBTqvi1oKjQOkqZ9BWqg35vaE34wBr0MAyJOyZxxyAIQ2KW2ahGWdaZGFaKWK9/r59KNNChkkroy1fIl6KTmw70VSiUXAw1uBzTUIqYbRKzTQnsM0iCu2g6WmsOZEsT3mgzmt27dzf+2XVdbrjhOh555OHaTSMYdtMY6WZSP0wZFT02MBCbhoFpKcqeP6n0zMAZt9aaYm22XX/ONNWwFAiMXBPe2RJjb0+JV3/fw+/e62XH3gIGik9+tINjV7SwtD2JaRiHbgIa/CAg1KPXy9dvGr25MhXXpy0dIxmzot8PoDdfJRWf3UqXmTrQ+3AmaRnRdCbbLGs0O3fuaPxzOp2hUMhz443/i//9w//icxecR66/TCpu0Z6OkS24FCuHFjDTcRvQFGrlf33FKmhN2Q0HpGd0o7f5ZNrZDtycFITRrlGtFIGvSSUslIKK6w9KgYwUiEOteXNHH4EOWb4kFbXXNRXokPf25Dn+qDYwo/db0hrnYH+F7v4yvQWXUqlKV2uCo5eNXGPd2Rpn14F87SYQkk7adLUnaEk4KAOWTGEheSKVRjO9cH04k+Aumsp4J9tPJIDWA8Tv330fgNa2dvr7slxy2ZU89PPNvPnGK1z4+QvIFavs6SlQdQNijkkqbtOeiaMU7O7OoxR0tCTAiDbl9PSV6M9XaE3HUUZ0aEV7Jh695zjH1g0NbPW671yxSl++ihdo4rYJShO3LRzLPNSDXelBlSf16+svVnl7VxbLNojbUT91gFzJZ9/BXmxTYVr1WTvEYyZHLU3TkkmSy0e9oHr6KyPeMMMQ2lJxzBaG7Xb1fT3hI/oGjnciAXumbuzNQIK7aCoDF/OGmui5n/UA0dO9D4DPnXcR92/+GVY8zdZHXmDpshUczFUa6ZVcyWNZ3Kbi+mTzFdoy0bZ5gPbaZh+lFEvakpSqAcs6k1imwcD7z8CUycBArhi9rHFpe5JQa5Z1JHG9gELZpzdfJQwqxByDY5a3cvTyDNaQRcr69QVhlC4yANcLyBWjw7BdL0CjCdE4tSqc3lyVJbWbg2UZjaA62g2znjpSSmENSQVNtmXARAP2TNzYm4nk3EVTmW4fkoFlfFduuJ6rN97IF9f/LwCefuIhOpcsjfLZpWgDThhGOymjskFFqepHNePhocoQrXWjjtyxotzzwPhTT5ko4EC2xPt7+nnvw37e39PPWzuzjc0+UC8njMoaw1q+vastgeeHUSomDGs7PhVxR9FXqA5LxdSvz7EMDOPQh1V2g8ZNyTAMnFrHyFBDoeITjpC+Hq1x11gNwyZzRN/A8Q5UD9gDc+ojNT4bb5zNTGbuoqmM1Cwr1BD4IZmUM6kj5r70p18DooD0kZXHsHvXDl568Xk+fdZq6nHCMKKSvvqJQY2a7lq1SK4YlSjmiy4VL8Q2wazlses7QOsz8f29JXYdyFP1QnQYLcQWylVScQfbNhsz92TMwg9CknGbINAoy8C2TbraEoRhtJ0/1BrNoRlr/dpCPaBM0TBoSztkc1Usy2jUtSsTOjJOI/CbhkLVTlAa+q1nrBvmRFsGjJVLn8w3MWkwNpgEd9F0GkGlFKUTyq5PwrFQBhzIMu4Rc0MDhFKKtRdfwf/5j//N449s5azPnkM9TigF7WkniuS1+nDLNEg4JrlilXJVUSj7eH6IZaja9v4oqMYdi+WdqUZ3xFd/343rh1Rqs+eYZbCvt4hjW5x0TDtm7Si6/b0l9vSE+L6mu79MImYRBodKEwEUUVD2fc2+nhJlL8q/ozR9xSodmTh9hSqWqQiCkL5iFcNQZOIWbekEKwcslCqlSCWtYZ/LeLPwyfS1GS2XPpmAPdkumM1O0jKi6dSDSiph05ZxOGppmq72BJZpjLsrdLR0woUXXQHAL594mEq1ihMzCcKQuGNx9PIWknFrUH34kUvSpBMOodaUyz4hYJoGLSkn2hCkDEpVnwPZMq/+vpu3/9DH/myZbN7F8wP8IMT1A0IdpXr6C1E+PFd08fwQ19fEHINM0qbq+hQqbmOsUX26hVKK/lKVsuc3dqtapoECduzrp1z1QMPyziQfP7KF41a08ImVHRy9vGVQukZrzVFdaTIJmyDUuEFAEOoJlzMaSo24OWkiu3Ynm97pakuQikfj9H09qXE2G5m5i6ZUz0fbQ45unMji2kjphFNOOomTT/4k27f/jg/efJHrNm7g7fcPUqx4hCG0pGKs6EzRkYljWQZBoMmXPNwgINtfJQBcz+dANsAyDJa2JugvugShpuIF2EYUdIsVF8e2SMZMKm6IaYDvhZSqPq06+lmlNXHbINRRXxeISi1dP8QyD515GoYhaIYtMLalY/x+dxbf01TcgKoX4NgGR3Sk8HVU2RJzTPQIZ5UuadN0dKToTU5vJjyZxc/JdIQc79vCYiLBXTSl6VTNjBYg1q1bz/btv+O+++7hS1+6juUdyRHzxXpAB8VcscKBvih1ogA3CPFDjfqw1l8l7aBDMC0DxzYplr1o841j0FeIKnJCDbsPFHC9gJhtkUrapBNWo7ywPRMnFbOJOxZuEIBWhBoSjkXrCMeL9uaquK4mnbQxDIXjGKChv+xhmArbNkglbDpbEsOCYzQLn/5O0sn8/UwlYEuDMUnLiMPEZHcczsTi2tB0wrp1VwHwyCMPUygURvwZqKUbyi5V12d/bwU3CPmwu8D+vgpKQTJmU3FDqDXeUkaUI+9siUV91HXIwf4KVTfqwtjVFqct4+A4JoGOOiimEzZKqUYljmEqPrIszUePiMofj13RwvLOVGOht05rTcn18cMQ0zTwQo0iukl4tXx/lB7xZ3XWO5W/n9HSO2JkEtzFgjbVVgIzVYo30FFHreTMM8+iXC6zdevInSKDMGTXgTxv/yFLf96lXPXRGlCKStWn6gYkYxYJ2ySTdBqLvaBpTcfobI2TiJkoDa2ZGEvbk7SkYji2hWUa0YYpW9Gacho9avb0FOnLV+npK6M41HzLUIpU3MbzwkYZox+EeJ5PKh4twuooc0MQasIgxLGiWf9USgcncwOejb8fMZikZcSCNp0dh9M5vWe08rx169bz4ou/4Wc/+xkXXnj5sN/bf7BMseTj+hrHMUmnHIIgJAwgETNpSdssaYt2pSZiFsWSRyZpk+t2yRaqKKIe525Ms7Qt2uwUc0xakhahVrQmHZa2pzjQV6ZU9jFMRToR7Ywd+LnUK1EKZTfqZ1ON0j22GTUVc0wTPwgoVzz8WuS3TIUyVNTYS028dHCqW/7ldKXZJcFdLFjT3XE4lVzteIHqssu/yD/8w9/yyCOPkM320t7e0bgRKANKrgeKRj7ZsQz6Kx6Fikuoo1x1f8xlRWeS1nQMzwvJFqqYpmJJW4KEbdKWcXj93R4StfNLG2WBCrRlsHxJCtcPaUvFoxr7xiUd+lx6ajdFyzToak/Qm4tSQOmEQ0s6xr6DJXpzFWzHIG4YmAosy8RQBn2FKh/pykx49jzVG7Asfs4uScuIBWumdhyOlKsdLYUwWnnegWyJA9kSBT/GGZ8+G8/z+K8772Z/76GU0Xsf9pPNRR0P610gARSqkSbBAGVE/dc/7C7gByGFkkvMMlnenqCzLYFtmXS0xKm4PgNjndYhXa0JlI6m1pY1MLAf+lw8Pxy0q1NrTakaYFsmZdenJRWjqz0RpWq0wqxvaEo6pJM2YRg1/pqIyewgnczfj5g+mbmLBWs2dhyONTPXMOo3hT90F2jPxDENxUWXfpEXf/MM9913L2ed+0U6W6PmYKY2qHg+Sccmk7DJFaqUyh7xmEkqnqQ9baNMgzCMGovFYxaEcCBbRpmwpCXBEV1pUnGLo5amaxuafAxMDBO6WhN8YmVbY7NUWN81OmD23mgFPKASpd4CAaWiHHutqdfRyzIEYcDyzhSObaHD6HCNMIhOWTInMPWbiV4+YnbIzF0sWDO56Fafqe/PlsmXqnh+gGHoQRtnRvumoLWmWPIbz33uvItwYjFef/UFdn+4p7FhRmvw/ZAd+/qpuAH9JZdCxcfzAmIxkyO6WjhhZQeaWo7biKpVQjRBAAdzFQqlKvuzJX6/u4+YY3FEV5pjj2zhM6cs58RjOqKzRwHXDfiwO8/eg0X29BTozVUIw+hzsS1j0E1xYEfG+glL9f/ZtkXcsRrfVAw1uRunbPlfuGTmLubdWL1FprvoNnCm7nsB297twfUDEo6FYRp0ZBxWLsuQL3t0tMZHDFRBqNGKRllhOtPCOWsu4PFHH+SJRx8g0/oVLNOgUHFRaFw/oFTb3KR1FMrTcRuNJtBQdUP8EJShyJVcqq6H59cWMffDso4UGljWbhO3LcIwJJurNvLX3X1lYo5BOmE3DuQoVjwSjsWxK1pQQ7bhHzpCzyMZsxuz/IRjohTT2qovW/4XrikH9zAM+e53v8vbb7+N4zjcfPPNHH300Y3n77rrLv77v/8by7L4i7/4C84999wZGbBoHhOpspjuotvAHPoHPQUKFQ/bNHADTdpW9BVcIM9HulrQISMGKkNBOm4Nym9fdsVVPP7ogzz71EN87uLro8XUikcybpEvenS0OMRjUS7ZDw99a0gnHcJQE7MMCkWPfMEl1Ao/DPD8kIP5qIpmSWuy0V994AIy1FNHBu2ZOG1p3eiXHuqorFEx/KaYSTpRn3fbwPejxd+PdGUYeKDIVKtVpOplYZpycH/88cdxXZc777yTbdu28b3vfY8f/vCHAHR3d/PTn/6Ue++9l2q1ysaNGzn77LNxHGfGBi4Of5OpspjsjsN6GiZXcrFMgzAMyRXcRvMt1w/QWBhK0Zt3ObIrxDTVoEAVBlHKpDXlkIo7lKqHgv4fffocnFic3R+8SffeD1h6xLFUqwH5YpUD2RJ9+Qp+GHWNtE2FZUDctgnDkI7WGP2FKvuzZUKixcy4rYg5YJvRwmIm5TTGCoMXkAfmuAf2S9fBoUMwRrspjvQtqWsCJxyNRapeFqYpB/eXX36Z1atXA3DaaafxxhtvNJ57/fXXOf3003EcB8dxWLlyJW+99RannnrqqK/X3p7EGtIH5HDS1TXycWPNbqrXHYaa3pJHRyI27LkgDOnsTGMMLQWp/V4QhpiGMeLzWmv2HCzSl6vi6ZBcOaA1Y5FKOcQSDm2WhesFBFqTSDiYhkHF9ejqaqGjI4VpGHR1ZdjbUySbrxBqjYGirSVGh1K8894OHti6mQe23ItbrQDw1MP/xZf/8v+jJw9VX4MCXysCXUvpAMowaW+L8+lTjuTjhSovvbmXfdmoLYHnaxzbiDovotCGSWdHis72Q70DgjBk+bIWAPJugGkMzx/Vf2akz2WmLdZ/3+HwufYpB/dCoUA6nW782TRNfN/HsiwKhQKZzKEPIJVKNbZrjyabLU11KPOuqytDd3d+vocx56Zz3Z4f0ttbwrIG52mDMFrUzDgmtjW4M+F4KRytNW/tzNKTO9RVMF/yKJcrOJZBuVTFtkwCP6Ds+hQMA2VoXDfgwIF+eroLKCNarHTs6ISlfNmnv7eXF557lOd/+RDbX/9tY4HXicVZceTRXPiFL5EvVamWPfL5Mv0lD9sMMGp9BSwTevvL2Ab09ZewlOLIjgSvAdUgIPBDygGgNaaCgtYc6M5RKVVrx/BFnQ0PHoz+G/KrHv0j5LgH/sxsWqz/vsPCvPbRbjZTDu7pdJpisdj4cxiGWJY14nPFYnFQsBdiYJWF1lHdd7HiN7bBp5M2yzuS0db4QHMwV6Zc9cdM4ezPlunJlQfNahWaPT3FaPIRaHKlctRHvSNJJuGQK1VpTdlYltU4rKInV6aQz/PrZ5/guV8+xPZtvyYMfAAs2+bsPzmP1RdczpKjTqPgGhiWQb7ogoIgmrjXWg6AYYDCIAhC4k50uIZlGzi2xUeWpnG9qESxXPWpVn0cxyBmG5imQaHsEYaalcsyg/LXkuMWEzHl4L5q1SqeeuopLrnkErZt28bxxx/feO7UU0/kxYUpAAAgAElEQVTl3/7t36hWq7iuy3vvvTfoeSEGVln0FaqUKj6FkkfZ9YlZJu/u7uODPTmWdiQIQ9jXWySTtGlLxwYttg5caOwvVKOSEzRBrU5bKUXVC0lgsLQtQU8OKlUf3w8JdUjMMWlNxtjTU8CtuLzy22f45eMP8OqLv8Rzq9H7GAbHnXwmf3TW5/ncuWv51CePpbfosmtPP9lysfYNQ+H5AZZl0N4SIwiiHi9oBUoTcyxa0tF/bvV2xEcsSbO/t0TFC6m6fpQrR3PMihbaUvGoQkfBkiHb+CXHLSZiysH9wgsv5LnnnuPaa69Fa80tt9zC7bffzsqVKzn//PPZtGkTGzduRGvNN77xDWKx4blVsbh1tSUIs5o/HChQKHlU/aipViZpkyt6lKse8ZhBSzKOIuqJAtRSFZGhO1Xz5SqVagBEdeflWqOujpY4R3SlOeaIVtAhZTfgmBUt/O6dbl584Vc88+SD/OZXj1Es5BqvdfTHP8lpn1nLqrMuIJ5qQxkK07Hx/ADbNIjbFjHLJJ20MZUinbDY31uk7IaYCpLxqM2vZUX9X1JxB7vW612H0J6JoRT0F1w8L8C2DKpeQLHsUamGKANilom/NMSxh69HSVtbMZYpB3fDMPjHf/zHQY997GMfa/zz1VdfzdVXXz31kYmmp5SisyXB0nYXrUu0mFFv81BDxYuOmsuXPFrTUf15dDi0T1taD9qUY5pR69vdBwr09FXIF10CrTEMAz8ISMZMjlySxDYVSoFG8fvtr3HHDx/ivvs205ftaYzpqGOO59QzL2Tpxz7LiSd8DKPWVrdY8WnPRDeZINCEQGvGoVR1aMtEZYZKKSqeT6wSkM27hGGIbZm0pBza0tGJUPUeNPXrac/EaUlFQb5UidoN2JbZuL6K59Obr7C0PTniLH2sPQJicZNNTGJeRTsYNcWyhxuE0fZ4rSlXfVIJOwrGWpOMWVRcv7HoatUCen2jzJ6DRfb3FukvVClVA7QGg4Cq7xMEJt39Fd59921+8/RDPPPkQ+zd84fGGJYfsZLV513K6vMuI952JOiQnfvyBL5Gm1EQti2TmGMQd0xijolbCdjTnSebd+kruJi1g647W2JkljlU/foYo94ttmlSqvrs3JdvLNrGHAPDMDANRTrhcDBXIRGzBy0Qp+IWu7sLFMoeDDgZaUlrnJ7+yqQ7MYrFQ4K7mFeGimbCFc/HMozaYmk0A7ZMRRBGeyMySQeto0VUHUKgdCPIfdhT4MmX/sD+bIlCxcNQ0QHVYagpZPfx1lu/4qcvPcGeP7zXeN+lS5ezbt1VfHr1RXzkmBMoVQN8T9Obq5BI2Hz8yBYUKjpUo9blMZOIZuk6hO6+Iq4XsKw9QbEa4PoB/XkX04zR1ZbCsaMeMiGawA+Jx6xBJYoxx6TqBjhO1O8llbBoSTrEnOhs1vquUq01pbJPWyreqCwqVjwO9leIx8wptUIWi4MEdzGvQh3VeLembPoLHkpBoexTLPvk8lWyxSq7DxToSMfJpC06WxIcvTyDZUU9Vv5nRy+vvdvNzn15qn5AEEJQOcgbbzzN2688yd5dbzXeK51p5TPnrOX8z3+B69dfjGPbHMiWKFY82jPgBRrTim44iVgcpRSFsofvh2g0Jx7dydL2BK4fkqt4hH6Uu0/ETOIxi7hlkIqbHLeyrXHTUgbs3JcfVnseHW9ncvTyTGPmHR1fR2PHKcCHPcVGH5g6jaK7v8xRS9ODXnOirZDF4iDBXUzbeHnfkZ4Ptcb3Qw5ky+zpLmFbFvFYQK7ooXVIOmFRKINtKPwgJFssk0qmScRM+gpRn5V9vUU+2NtPoexRKuV4e9vTvLPtKfbt+B3UmnPZToIzzjqX8z7/BS688AIS8ThhoFEqWqAcWFZooKKeM8ahRVutNbmiSyrmUKr6HOyvkIrbhDr6NpFJHqrMAYXrhrhuSDJuYVjR0XVjdU3UIY16/nr1kFXbmeoHIWGoSSfsQa0PwkATBjTSU0NfUzoxCpDgLsYwXtCuH4E3NO/b2RonDKMa74ND8sLpuE29n0lvf4Wy61OserSlHVrTCcrVAM8L6WyNAVXaUnatxa3CMM3G7LSjNWTPgV6efuLnPP/Lh3j3zRcbteiGaXHExz7Fx/7oc5z52fP445M/QiJuk4jHo1OGBnQrHFpWeKyRaYy5t79CxYv6n7elY4RaUyi70fmjjWirBrXGNUyiA6frzxrRzlFDm8N6rw/tmji0fh0FyZg1qDooeg+FYTKsNfFIrykWLwnuYpiJHpu2r7c0qDeM1vCHA3l2HcjTlorTV6yggI6WBKr2b9ru7jxKRTPjqh9gmVF6pb9QJeHYBEFUKaMLVSpVn/5a10LbMGhLhZQrFV54/hmefepBHnv0YSqVaDeqUgZHfvx0PnrK5/jIJz5DLJEhFTNJp+Ls6y2zvMNgj1cg4ZgjnjI0sKxwaXuSjtYQ3w/psOLkilX2HCyidVSlE3dMVnRl2FGqoAa0kawfpmEZxqDPMFd0qXg+qXh0HF59kXho18SR6tfrJypFW6NqP4emq3X4hiXpxCgGkuAuhplIQ69Qa3KF6qBgn81XqHoBQahpSQaNHaXZfIWOljhhre4cIJMMG+1wUVEQC3WZg31V8hWPzrSDYRnRTFsHvPm7F9n86pNs+82T5PP9jff82CdO5bjTzuOE09bgmxmCIEQDyZhBZ0uCFR1J3BC0iraMRsMd/3QgHYJpGOSK0QYrI+qNC0Cp7NPeEiOXS9DdXyYMGHyYxpDPcElbnL5ClULZwws0bSmH1nRs1B2lA280o+1GPXpZZtRqGSFAgntTmMla54meWxoEmqDWYyXUUX64UPYoV30KZQ/XC+gvuCTiNkGoactEue4o3aAbr5krebiuj6EMUnEL0zDwugMMAz58fzuvv/gYb778FPn+g42xHP+Jk9iwfgOf/dwlaLuVV9/pJpv3cMsehqGIOwbHLMtg2xYdbQl8PzpsOuYY0SJpxadLj73oaJrRztJiPbAPYJiKUjXkE0e3c5xuw3VDHMeIqn1G+AyVUrSl44RhlL9vTzm1A0IYt3RxrN2osktVjEWC+2FsqqfOj2Wix6aZpsJA0ZurUKr6+L5m1/48lqlIxm3ijkneNHBdn3ygCTui4+BUbeHRMg2SMZPubBnDUFT9gEzSJrt/J799Yiuvv/g4fT17G+/d0XUkZ51zMV/edB0nnnAyhYpH4Ids39FDxQtxLIOYHeXlW5I2FS/EDQPyJY9k3MapHUwx9DpGYyhFMmYRhnpQpQpENfeaKKjaloEVH/xhjfQZ1r/VWJaBYR46m3WipYuj7UaVXapiNBLcD2NTPXV+oKGz/okem2YohesHlKtRXbllajw/INQKOzBQtVrzihtQ8YPoeDc1+PSfTNLBsQ1279rJ808/zGsvPsaeXe823q+lfQlnfOZCzl5zCUceczIajW/H+bAnT3smTn/FBWWwclkLrhuQLVSpVD1M08CyTFLxaOOTYw1ezJzoouOyjiR7eorRGkDt5llf4DRrn9VIhn6GoYZSNfoGoBSDZvRSuihmiwT3w9RE0yejGWvWP5Fj00KtcSyDRMxif2+JQsmjVA+sZrSgmElG55/6fojnhWAbjdN/dvzhQx7/xQM89OAW3nnr9cb7pDOtnPGZCzj+9HP51BlnUXI1qUR0qIZtGeTKLrGYge+XKVWjHasKhW1HjcFKVQfX94nbFrZpYprR74WaWv5+4ouOpmE0juCrHx5df42WdAwjHOHAVYYfPVdPR2kj2nE66HOV0kUxSyS4H6ame+r8WLP+ibSUrfdXqc/AU3Eb0zSoeCGVik9vf5X21hhL25NkkjYrl7VQLPTz0IP3sWXLPTz33K8Ia8ExkUhy1p9cwGc/dzGnfepPQEUNuLRhEIY+KIVjmyRjFt19ZdDg+wEl16dYOxc0CDSObdKasgm0SVs6FgXmkkuu4OG6AbZtTHrRsf6z9ZOZdO2zWN6RpKdn9N7pg0500hqNJhmPuloOJKWLYrZIcD9MTefU+fFn/YlxF+tMU6EgWnA0DAwDEnEb0/BJxEwyaZsVnSmq5RLPPfUY//joVp588nE8zwPAtm0uuGAtV165gVV/vIYAm55clVLZJe6YLOtIkohZFMt+lC8HcsUqfm08xXIQjc0wqLoBsZiJF4aUKj6JuEVrysEyo3NGM0mHo5ZmsC1j0umP0RY0x1vTGPp7LSmbctWXQ6TFnJHgfpia7KnzA3PrE531j7VYZyhFKuEMWnBsSdrk0Hiuy0vP/5L/eP4XPP+rxyiXo1p0wzBYvXoNF6y9grPOuZBMug1VuymEOqQt1LSnHLSCwLejBl1+qXb4RdRcLJOM+q34oSYeszD9qOTStkyUhpLrs6wjQUdLovF5tCQdYiO0zJ3s5z2V1En995a1J0dNgwkxGyS4H8Ymkj4ZKbce5X1rRwUNMZk0wZFLUmyPWVS8AN8L+J83fsuvn36I555+hHzuUC36GWd8miuvXM8XvnAlyskMuyHVNzbVAzJAaCqqbkjMNjjQW6bs+pQqPumkQ9WNZsAxy0RrSMUVR3QkUYaiWvVZsSTV6OuyUAKoHLAh5poE98PYRALGSLn1ctXH9ULihhp31j9WDb1hKPr2vcN9993Lk48+QE/PgcZzx3z0E1x06Rf54hVXcvonT4gWFrXmg725Qemg+samaAG2tmNVKQzDwHEUR6bT6DDatdrdV25UnBhKo1TUTdGxDFIJC9fXGDE7KrOMR6mdkQ6Snk9SuijmigT3OTKbhyqMFjDGyq3btkEiZjda6A6d5Y5VTfPWW//Dli33sHXrZt5///3G6y4/4ig++7lLWHvxFZx26idRtYMuuvvKjcMmhqaDgiAkV3Apuz5BEDZKGNvSMcJAkyu5UfsCIyqjjAK8jefX8v5KUap6ZPPRwm46bWNbJlUv4GB/RdrfikVLgvssm42NRhM1Vm4drehsibO0PTHiTWfojP/D3bv4vw/fx+OPbOWd3x9qo7t06TK++MUr+cIXr6Jl6ceJ2eag6xpYmjnSInCu6FL1A0zDaJxAVKr4aA2eF1B2o+eiI+cMAh3tjHVshWUolKnwfEWpGrC03WxUowwtCZUTi8RiI8F9ls3ERqOpmkhFzdBZf6g1nh+SK7n0ZXt44pEHeOTh+3nj9VcaP9PW1sZll13BjTfewEknrcI0TTw/Or1opBtWfZHWtozB9d8ayq5P3DahtrEJov/fe7DAsvZEo297ruSxv+zWjt2LkXAsTjymnTBUfNhdwDCpHVc3uIbc90P6ClU5sUgsOhLcZ9F0NxpN12QqaurfMD7c380Tjz7Eww9uYftrLzZq0ePxBOec+3ku+PwVbFh3GalknK6uDN3deWDipZkDF4E9LyTwNcs6ktHRem7QaHWrNbSm45hlj709RXrzFVxfky+6hEBLwqK/6NKWSWDZ0RsP+5wN6M1XKVfn5+YqxHyS4D6LprvRaCZMpKKmWCxy95b7eWDrvfz62V/i+1EtumXZnPnZc7ns8itZveZCEskkQahJJGLD3mfgjQQGnyY08EYycBHY80MsSzUOpwh17SAKNGEQ/X4YhuzuLtS6SSqCMMQxovYC+3tLtGfig1oa1NXPH218axpAtv2LxUCC+yyazkajmTJaRY3rujz11BNs2XI3v/jFw5RKxcbPf+rMs1l93qWsOut8UplWjliSntDW/SWtcQ72V4a1wT16WWbYzxpKEbNNWpJO45uFocCwFEEIqWQ0My+UPEzTpC1lEmrQ2iYWs0ApCiWfqhc0WhoUKv6gG1hbOkahVJjXm6sQ80WC+yya7Eaj2R6LViHPP/csmzffzc9/fj99fX2N508+5XTWXnIFF6y9nCVdy9Ba01eoTmrrfk/t0OaPLM0QBrUukGh6xqhaGe2bRTJm8u6H/SgVvYZSBqYCyzKxLIP2dAzPDzlqWQan1kOmCwbdwEKt5/3mKsR8mVJwr1Qq3HTTTRw8eJBUKsX3v/99Ojo6Bv3MV7/6Vfr6+rBtm1gsxk9+8pMZGfDhZiJpkdmktebVV19my5Z7uO++zezfv6/x3IknnsS6deu54oorCWOdg9IXSqlJbd0fuL6gYMCMeOwUyKjfLLyA3QeKVDwf2zTxdUjMskBDNlfB9wOCMOTNDw7SlopjmGrYQulCurkKMdemFNx/9rOfcfzxx/NXf/VXPPjgg9x22238wz/8w6Cf2bVrFw8++OBhUZEwm2Vy87UzMapFv5stW+5lx44PGo+vXHkMV165nnXr1nPiiSc1Hj+QLY0YBMfbul//7EI9vfWFoVU7lmXQ0Rpv7ETtyVXozVXxau2Dtda0pBxcL6RQcWnPxEdcKJ3vm6sQ82VKwf3ll1/mz/7szwA455xzuO222wY939PTQy6X46tf/Sq5XI4///M/59xzz53+aGfYXNagz8XOxJ07d3DfffeyefM9/M//bG88Xq9FX7duPatWfWrEa5tsENRas/dgkZ17c43TlfoKVTpbEwx9+amkQAbOurvaE5iWQbnqE7MNEnETQxm0phyUUhQrPm1pPeJCqWz7F4vVuMH97rvv5o477hj0WGdnJ5lMtEiWSqXI5/ODnvc8j6985SvccMMN9Pf3c91113HqqafS2dk56vu0tyexrOk1d5qsvQeL2HGHJQOqP0KtCUyTFZ2pSb1WV9fwRcO5sG/fPu666y5+9rOf8Zvf/KbxeFtbG1dddRUbN25kzZo1mOb4n+3SpS2EoSYIQ0zDwDDUsD/X7T1YJF90WdKRPvQCpkkAdLUdmjmHWpNJOSyb5OcJsGRJmn29JXKFKolUDG0YZJIO7RmHD3tK2LXWAl4Q0pJJYlkGbhDQ0ZHCnoN/l+br73whkGtf+MYN7hs2bGDDhg2DHvva175GsRhVVxSLRVpaWgY9v2TJEq699losy6Kzs5MTTzyRDz74YMzgns2WpjL+KQu1ZueQPid1vdkiRhBMeIY3sN576HvMxmyxv7+PBx98gM2b7+HZZ59u1KInk0nWrr2Ydes2cO655xOLRTet3t5Dn+1ExzTWtxoN7NybY0lHmmxfsfE7ptYczFUgCECrxu+YcXPEz2ciLKAtYeH5BoWEhWVAoeBSLFQGHRySy0e7W4NQ05uc/Xz6aH/ni4Fc+8K69tFuNlNKy6xatYqnn36aU089lWeeeYYzzjhj0PPPP/88//mf/8mPfvQjisUi77zzDh/96Een8lazZjZr0Gcj3VMqlXj00YfZvPkennzyMVzXBaK+6BdeuJZ169azdu0lpFIjz5AnO6axdta2Z+LRZzeEUoq2VJyjlqYbR/bNRJAdWjZZP9+04vq1rpBWo4+NLJQKEZlScL/uuuv427/9W6677jps2+bWW28F4F/+5V+46KKLWLNmDc8++yxXX301hmHwzW9+c1g1zXybzRr0mWo54Louv/zlE2zefA+/+MVDg2rRV69ew7p167n00stpbx//sx1pTPmyh+9rli9JDusEOdbO2o7W+Jif3VQOxZiIgesCmVoveRSk4w5BqGWhVIgBlNZaz/cggHn5qjNahUgqbk8qCA/8qjZSW9u6INQcu6JlzMAXBAG//vVzbNlyDz//+f1ks9nGc2ec8alG6eKyZcsnPL6hY9IasvkKpapP4GuOXJqiJek0ZvH1PjHWCN9cfF9z9PIM2XwFO+7Q338o5TOVz24qBqaWgHlZKF2IX8/nilz7wrr2GU3LNIvZKJObSrpHa822ba+wefPd3H//Fvbt29t47oQTTmTduvV88YtXceyxg1NbE82fDx1TNl+h4vrRxiYzurcP/GYxkW81XW0JAtOkN1uc8xLDoZVHsstUiOEWdXCfjTK5yaR73n77LbZsuZvNm+8ZUot+NOvWRbXoJ5108rDXmWz+fOCYQg2lqj+oVNCsHdoxsIxwIpt/VnSmMIJASgyFWIAWdXCvm8ka9PEC4+4/7GrUor/55huN57u6ljZq0c8449NjLrxONqc/cExhEH2DwDzUXKv+XgO/WUz0W42cLCTEwiTBfRYMDYzZbDfPPvUQj/1iKy+99GLj51pb27jssi+wbt16zj579YRq0afaRrg+plzJJdAaVasyqR9uAYO/WcjmHyEObxLcZ4FSipjh8uhTW9my+R6ee+6ZcWvRJ2qqJZyHgnWiUUZoDDhfdLQyQpmZC3F4kuA+g8aqRb/ggs9z5ZUb+PznLyadTo/zSqObbgmnoRQrOlOj5uyFEM1Bgvs0ua7Lgw8+yO23/18efvjBQbXof/In57Bu3Xouu+wLE6pFn4iZ6HQoKRchmp8E9ykIgoDf/OZ5Nm++h5///L5BteirVp3RqEVfvnzFrLz/0Jw+SpN0bDpb45N6HUm5CNG8JLhP0KFa9Hu4//7Ng2rRTz75ZL7whStHrEWfDfWZd2dryP7eEqWqT6nqs2NfXg5/FkIAEtzHVa9F37LlXj744P3G4wNr0desOWtedq0d7K9Q9YLGGaQghz8LISIS3Eewa9fOadeiz7aplkQKIRYHCe41Bw4c4IEHtrB58z389rcvNB5vaWnlssu+wJVXbphwLfpcmM2ulkKIw19TBPfxeqz8/vdv853v/L/ceOP/w9q1Fzce7+/v46GHfs7mzXfzq18d6oueSCQatejnnXfBpGvRZ/PYvrrZ7GophDj8HdbBfSI9Vt5//12uuupy9u/fx8knf5LVq9fw2GO/YPPme3jiiUcbteiWZXHBBZ9v9EWfSi36XB/bJ4c/CyFGc1gH9/F6rOzcuYMrr4wC+0knncyuXTs5+eSPUywWgJmvRZ+pPu4TJYc/CyFGc9gG9/EWFEu5HVx6yQV0dx/ANE3efHM7b74ZHRo9G7Xo87HAKZuRhBCjOWyD+1gLioEfcu7qP6ZSKdd+NqC9vYMzz/xjrrtuE5dcctmcjme2FzhlM5IQYqhRluQWvrEWFFEaY8gMOpvt5ZFHHubLX97Ie++9M6fjkQVOIcRcO2xn7mMtKLal43zwwV4OHDjArl072LVrZ+N/sViMlSuPmdPxyAKnEGKuHbbBHcZeUFRKsWzZMpYtW8anP/3H8z4eIYSYS4d1cF9oC4oLbTxCiMXrsA7udQttQXGhjUcIsfgctguqQgghRifBXQghmtC0gvtjjz3Gt771rRGfu+uuu7jyyiu5+uqreeqpp6bzNkIIISZpyjn3m2++mWeffZYTTzxx2HPd3d389Kc/5d5776VarbJx40bOPvtsHMeZ1mCFEEJMzJSD+6pVq7jgggu48847hz33+uuvc/rpp+M4Do7jsHLlSt566y1OPfXUUV+vvT2JZS2MdrpT0dWVme8hzIvFet0g175YHS7XPm5wv/vuu7njjjsGPXbLLbdwySWX8MILL4z4O4VCgUzm0AeQSqUoFApjvk82W5rIeBekrq7MvJzENN8W63WDXLtc+8Ix2s1m3OC+YcMGNmzYMKk3S6fTFIvFxp+LxeKgYC+EEGJ2zUq1zKmnnsrLL79MtVoln8/z3nvvcfzxx8/GWwkhhBjBjG5iuv3221m5ciXnn38+mzZtYuPGjWit+cY3vjHp04yEEEJMndJa6/keBLDg8liTsRDzcHNhsV43yLXLtS8co+XcZROTEEI0IQnuQgjRhCS4CyFEE5LgLoQQTUiCuxBCNCEJ7kII0YQkuAshRBOS4C6EEE1IgrsQQjQhCe5CCNGEJLgLIUQTkuAuhBBNSIK7EEI0IQnuQgjRhCS4CyFEE5LgLoQQTUiCuxBCNCEJ7kII0YQkuAshRBOS4C6EEE1IgrsQQjQhCe5CCNGEJLgLIUQTsqbzy4899hi/+MUvuPXWW4c9d/PNN/PKK6+QSqUAuO2228hkMtN5OyGEEBM05eB+88038+yzz3LiiSeO+Pz27dv5yU9+QkdHx5QHJ4QQYmqmnJZZtWoV3/3ud0d8LgxDdu7cyXe+8x2uvfZa7rnnnqm+jRBCiCkYd+Z+9913c8cddwx67JZbbuGSSy7hhRdeGPF3SqUS119/PTfeeCNBEHDDDTdwyimncMIJJ4z6Pu3tSSzLnOTwF46ursWZclqs1w1y7YvV4XLt4wb3DRs2sGHDhkm9aCKR4IYbbiCRSABw1lln8dZbb40Z3LPZ0qTeYyHp6srQ3Z2f72HMucV63SDXLte+cIx2s5mVapkdO3awceNGgiDA8zxeeeUVTj755Nl4KyGEECOYVrXMULfffjsrV67k/PPP5/LLL+fqq6/Gtm2uuOIKjjvuuJl8KyGEEGNQWms934MAFtxXnclYiF/V5sJivW6Qa5drXzjmNC0jhBBifklwF0KIJiTBXQghmpAEdyGEaEIS3IUQoglJcBdCiCYkwV0IIZrQgqlzF0IIMXNk5i6EEE1IgrsQQjQhCe5CCNGEJLgLIUQTkuAuhBBNSIK7EEI0IQnuQgjRhCS4T1M+n+erX/0q119/Pddccw2vvvrqfA9pzj322GN861vfmu9hzIkwDPnOd77DNddcw6ZNm9i5c+d8D2nOvfbaa2zatGm+hzGnPM/jpptuYuPGjaxfv54nnnhivoc0rhk9iWkxuv322znrrLP48pe/zPvvv8+3vvUttmzZMt/DmjM333wzzz77LCeeeOJ8D2VOPP7447iuy5133sm2bdv43ve+xw9/+MP5Htac+fGPf8zWrVsb5yMvFlu3bqWtrY1//dd/JZvNsm7dOs4///z5HtaYZOY+TV/+8pe59tprAQiCgFgsNs8jmlurVq3iu9/97nwPY868/PLLrF69GoDTTjuNN954Y55HNLdWrlzJD37wg/kexpy76KKL+PrXv974s2ma8ziaiZGZ+yTcfffd3HHHHYMeu+WWWzj11FPp7u7mpptu4u///u/naXSza7Rrv+SSS3jhhRfmaVRzr1AokE6nG382TRPf97GsxfGf0tq1a9m9e/d8D2POpVIpIPr7/+u//mv+5m/+Zp5HNL7F8SRJikgAAAEMSURBVG/kDNmwYQMbNmwY9vjbb7/NN7/5Tb797W9z5plnzsPIZt9o177YpNNpisVi489hGC6awL7Y7d27l7/8y79k48aNXH755fM9nHFJWmaa3n33Xb7+9a9z6623smbNmvkejphlq1at4plnngFg27ZtHH/88fM8IjEXenp6+MpXvsJNN93E+vXr53s4EyJTjmm69dZbcV2Xf/qnfwKimd1iWmBbbC688EKee+45rr32WrTW3HLLLfM9JDEH/v3f/51cLsdtt93GbbfdBkSLy/F4fJ5HNjpp+SuEEE1I0jJCCNGEJLgLIUQTkuAuhBBNSIK7EEI0IQnuQgjRhCS4CyFEE5LgLoQQTej/B4YvEonU8yzuAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": {}, @@ -247,10 +203,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "These vectors represent the *principal axes* of the data, and the length of the vector is an indication of how \"important\" that axis is in describing the distribution of the data—more precisely, it is a measure of the variance of the data when projected onto that axis.\n", "The projection of each data point onto the principal axes are the \"principal components\" of the data.\n", @@ -260,10 +213,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "![](figures/05.09-PCA-rotation.png)\n", "[figure source in Appendix](06.00-Figure-Code.ipynb#Principal-Components-Rotation)" @@ -271,10 +221,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This transformation from data axes to principal axes is an *affine transformation*, which basically means it is composed of a translation, rotation, and uniform scaling.\n", "\n", @@ -283,10 +230,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### PCA as dimensionality reduction\n", "\n", @@ -297,12 +241,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 19, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -323,10 +263,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The transformed data has been reduced to a single dimension.\n", "To understand the effect of this dimensionality reduction, we can perform the inverse transform of this reduced data and plot it along with the original data:" @@ -334,18 +271,14 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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6XS5oaZVMpDEF9datWzn33HMBOO2003jjjTd6n6utraWiooJgMAjA6aefzpYt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\nme1er6+PUlpajGkmiMdNduy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YglpERMTBFNQiIiIOpqAWERFxMAW1iIiIgymoRUREHExB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\n", 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\n", 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" ] }, "metadata": {}, @@ -361,10 +294,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The light points are the original data, while the dark points are the projected version.\n", "This makes clear what a PCA dimensionality reduction means: the information along the least important principal axis or axes is removed, leaving only the component(s) of the data with the highest variance.\n", @@ -375,10 +305,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### PCA for visualization: Hand-written digits\n", "\n", @@ -390,12 +317,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "data": { @@ -403,7 +326,7 @@ "(1797, 64)" ] }, - "execution_count": 9, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -416,10 +339,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Recall that the data consists of 8×8 pixel images, meaning that they are 64-dimensional.\n", "To gain some intuition into the relationships between these points, we can use PCA to project them to a more manageable number of dimensions, say two:" @@ -427,12 +347,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -452,28 +368,21 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can now plot the first two principal components of each point to learn about the data:" ] }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 23, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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62XbkD8PoD6vbdkVXwcw4S3oO0tOxjxEjwtiyNogE8CslgkIQNSz0UDNhexK/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K5QJ0cLtaiwYOUp22UCWgAn6TLePsCtE58CoP3KRUeb0d1pTrw2BcDciQLSha6r61GFyifVu3jB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\nGGOMWeE/TqIe1RXjFs8AAAAASUVORK5CYII=\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -483,7 +392,7 @@ "source": [ "plt.scatter(projected[:, 0], projected[:, 1],\n", " c=digits.target, edgecolor='none', alpha=0.5,\n", - " cmap=plt.cm.get_cmap('spectral', 10))\n", + " cmap=plt.cm.get_cmap('prism', 10))\n", "plt.xlabel('component 1')\n", "plt.ylabel('component 2')\n", "plt.colorbar();" @@ -491,10 +400,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Recall what these components mean: the full data is a 64-dimensional point cloud, and these points are the projection of each data point along the directions with the largest variance.\n", "Essentially, we have found the optimal stretch and rotation in 64-dimensional space that allows us to see the layout of the digits in two dimensions, and have done this in an unsupervised manner—that is, without reference to the labels." @@ -502,10 +408,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### What do the components mean?\n", "\n", @@ -530,11 +433,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "![](figures/05.09-digits-pixel-components.png)\n", "[figure source in Appendix](06.00-Figure-Code.ipynb#Digits-Pixel-Components)" @@ -542,10 +441,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The upper row of panels shows the individual pixels, and the lower row shows the cumulative contribution of these pixels to the construction of the image.\n", "Using only eight of the pixel-basis components, we can only construct a small portion of the 64-pixel image.\n", @@ -554,10 +450,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "But the pixel-wise representation is not the only choice of basis. We can also use other basis functions, which each contain some pre-defined contribution from each pixel, and write something like\n", "\n", @@ -572,11 +465,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "![](figures/05.09-digits-pca-components.png)\n", "[figure source in Appendix](06.00-Figure-Code.ipynb#Digits-PCA-Components)" @@ -584,10 +473,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Unlike the pixel basis, the PCA basis allows us to recover the salient features of the input image with just a mean plus eight components!\n", "The amount of each pixel in each component is the corollary of the orientation of the vector in our two-dimensional example.\n", @@ -596,10 +482,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Choosing the number of components\n", "\n", @@ -609,18 +492,14 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 24, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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1PDrYVUoJ4UF+7i6FiIioxfDIYBdC4FJZTd3DXxR8+AsREVE9jwz2\nKr0JBqOVh+GJiIh+wyODnefXiYiIrs8jg51PdSMiIro+jwz2+qe68VY3IiKihpw6yboQAgsXLkR2\ndjbUajWWLFmCmJgY+/atW7di9erVUKlUiI+Px8KFCxu1Xx6KJyIiuj6njtjT0tJgMpmwfv16PPPM\nM1i6dKl9m9FoxMqVK7F27VqsW7cO1dXV2LlzZ6P2e6m0Blo/H2j9fJxVOhERkUdyarBnZGQgMTER\nANCzZ09kZWXZt6nVaqxfvx5qtRoAYLFYoNFoHO7TbLGhpNLAw/BERETX4dRg1+l0CAwMtC+rVCrY\nbDYAgCRJCA0NBQCsWbMGBoMBd999t8N9FlcYIAQPwxMREV2PU8+xa7Va6PV6+7LNZoNC8et7CSEE\n3njjDVy4cAHvvPNOo/ZpsNS9MejcPgQREYEOXi0/3tjz1di/9/bvzb0D7N/b+28KpwZ77969sXPn\nTowZMwaZmZmIj49vsP2VV16Br68v3nvvvUbvMzunFACg1ShRUlLdrPW2dBERgV7X89XYv/f27829\nA+yf/TftTY1Tg33kyJHYvXs3kpOTAQBLly7F1q1bYTAY0K1bN2zatAl9+vRBSkoKJEnC9OnTMWLE\niJvuk1fEExER3ZhTg12SJCxatKjBuo4dO9q/Pn78eJP3eamsBkqFhIhgPvyFiIjotzxugppLpTUI\nD/aDSulxpRMRETmdR6Vjpc4Ifa0FbXkYnoiI6Lo8KtgLSnQAeH6diIjoRjwr2IuvBDsnpyEiIrou\nzwp2jtiJiIhuyqOCPb+YwU5ERHQzHhXsBSU6+GtUCPTnw1+IiIiux6OC/eJlPdqE+UOSJHeXQkRE\n1CJ5VLBbbYKH4YmIiG7Co4Id4Pl1IiKim2GwExERyYjHBXtb3sNORER0Qx4V7NPv64qo8AB3l0FE\nRNRieVSwTx4ezyviiYiIbsKjgp2IiIhujsFOREQkIwx2IiIiGWGwExERyQiDnYiISEYY7ERERDLC\nYCciIpIRBjsREZGMMNiJiIhkhMFOREQkIwx2IiIiGWGwExERyQiDnYiISEYkIYRwdxFERETUPDhi\nJyIikhEGOxERkYww2ImIiGSEwU5ERCQjDHYiIiIZYbATERHJiMrdBTSGEAILFy5EdnY21Go1lixZ\ngpiYGHeX5RKHDx/G3/72N6xZswa5ubl44YUXoFAo0LlzZyxYsMDd5TmFxWLBSy+9hIKCApjNZsyc\nOROdOnXyit4BwGaz4eWXX0ZOTg4UCgUWLVoEtVrtNf3XKy0txcSJE/HRRx9BqVR6Vf8PPfQQtFot\nAKBdu3aYOXOmV/W/atUq7NixA2azGVOnTkW/fv28pv/U1FRs2rQJkiTBaDTi5MmT+PTTT/H66683\nvn/hAX788UfxwgsvCCGEyMzMFLNmzXJzRa7x/vvvi3HjxokpU6YIIYSYOXOmSE9PF0IIMX/+fLFt\n2zZ3luc0GzduFK+//roQQojKykoxZMgQr+ldCCG2bdsmXnrpJSGEEPv37xezZs3yqv6FEMJsNosn\nn3xSjB49Wpw7d86r+jcajSIpKanBOm/qf//+/WLmzJlCCCH0er14++23var/qy1atEh8/vnnTe7f\nIw7FZ2RkIDExEQDQs2dPZGVlubki14iNjcW7775rXz527Bj69u0LALj33nuxd+9ed5XmVGPHjsWc\nOXMAAFarFUqlEsePH/eK3gFgxIgReO211wAAhYWFCAoK8qr+AWD58uV45JFHEBkZCSGEV/V/8uRJ\n1NTUYMaMGfiv//ovHD582Kv6/+WXXxAfH4///u//xqxZszBkyBCv6r/e0aNHcebMGUyePLnJ//Z7\nRLDrdDoEBgbal1UqFWw2mxsrco2RI0dCqVTal8VVkwQGBASgurraHWU5nZ+fH/z9/aHT6TBnzhzM\nnTvXa3qvp1Ao8MILL2Dx4sUYN26cV/W/adMmhIWFYdCgQfa+r/7/Xe79+/r6YsaMGfjwww+xcOFC\n/PWvf/Wq3395eTmysrKwcuVKe//e9Puvt2rVKjz11FPXrG9M/x5xjl2r1UKv19uXbTYbFAqPeE/S\nrK7uWa/Xo1WrVm6sxrkuXryI2bNnY9q0abj//vvx5ptv2rfJvfd6y5YtQ2lpKSZNmgSj0WhfL/f+\n688v7t69G9nZ2Xj++edRXl5u3y73/jt06IDY2Fj718HBwTh+/Lh9u9z7Dw4ORlxcHFQqFTp27AiN\nRoOioiL7drn3DwDV1dU4f/48+vXrB6Dp//Z7RDr27t0bP/30EwAgMzMT8fHxbq7IPW6//Xakp6cD\nAH7++Wf06dPHzRU5x+XLlzFjxgw8++yzSEpKAgB07drVK3oHgK+++gqrVq0CAGg0GigUCnTv3h0H\nDhwAIP/+165dizVr1mDNmjXo0qUL3njjDSQmJnrN73/jxo1YtmwZAKCoqAg6nQ6DBg3ymt9/nz59\n8O9//xtAXf8GgwEDBw70mv4BID09HQMHDrQvN/XfP48YsY8cORK7d+9GcnIyAGDp0qVursg9nn/+\nebzyyiswm82Ii4vDmDFj3F2SU/zzn/9EVVUV3nvvPbz77ruQJAnz5s3D4sWLZd87AIwaNQovvvgi\npk2bBovFgpdffhm33XYbXn75Za/o/3q85e8+AEyaNAkvvvgipk6dCoVCgWXLliE4ONhrfv9DhgzB\nwYMHMWnSJPsdUdHR0V7TPwDk5OQ0uPOrqX//+XQ3IiIiGfGIQ/FERETUOAx2IiIiGWGwExERyQiD\nnYiISEYY7ERERDLCYCciIpIRBjtRC5aSkmKfmMJZdDodJk6ciKSkJFy4cMGpf5Y7vf3228jIyHB3\nGUROx2An8nInTpyAWq1GamqqfSpTOTpw4IBXPGOCiBPUEDWDAwcO4J///Cd8fX1x9uxZJCQk4K23\n3kJRURFSUlKwY8cOAMA777wDAJg9ezbuueceDB06FAcPHkRERASmTp2KNWvWoKioCMuWLUPfvn2R\nkpKCyMhI5OTkAABeeOEF9O/fHzU1NXj11Vdx+vRp2Gw2/PGPf8R9992H1NRUpKamoqKiAkOHDsXc\nuXPtNZaWlmLevHkoLCyESqXC3Llz0a1bNyQnJ+Py5csYOHAg3nvvPfvrTSYTFi1ahIyMDPj4+GDW\nrFm47777kJmZiddffx0mkwkhISF49dVXERMTg5SUFNx+++3Ys2cPTCYT5s2bhzVr1uDs2bN47LHH\n8Nhjj+Gdd95BTk4O8vLyUFlZiYcffhgzZsyAEAJLlizBvn37IEkSJkyYgD/+8Y83/LmqVCps3rwZ\nq1evhhAC3bp1w/z586FWq3HPPfdgzJgxyMjIgEqlwooVK5Ceno5FixYhMjIS77zzDn755Rds3rwZ\nSqUSd9xxBxYtWuTCvy1ETuakx8gSeZX9+/eLO++8UxQVFQkhhJg0aZLYuXOnyM/PF8OGDbO/7u23\n3xZvv/22EEKIhIQEsWPHDiGEECkpKeKZZ54RQgiRmpoqZs+eLYQQYtq0aeKVV14RQghx8uRJMXjw\nYGEymcTf/vY3sWbNGiGEENXV1WLcuHEiLy9PbNq0SYwaNUrYbLZrapwzZ4746KOPhBBC5Obminvu\nuUeUlpaK/fv3i5SUlGte/8EHH4i5c+cKIYQoKSkR48aNEyaTSQwdOlRkZWUJIYT47rvvxMSJE+21\nLl261N7nqFGjhNFoFAUFBaJfv3729RMmTBAGg0FUV1eLkSNHiuPHj4tPP/3U3rPBYBCTJk0Su3bt\navBztdls9p/r6dOnxdSpU4XRaBRCCPHWW2+Jf/zjH/af6/bt24UQQixbtkwsW7bMXl96erqwWCxi\n4MCBwmKxCJvNJhYuXGj/vRHJgUfMFU/kCeLj4xEZGQkAiIuLQ0VFhcPvSUxMBABER0fbH+wQFRWF\nyspK+2smTZoEAEhISEBoaCjOnj2LPXv2wGg04ssvvwQA1NbW4syZMwCAbt26QZKka/6sffv2YfHi\nxQCAmJgY9OrVC4cPH0ZAQMB1a0tPT8eUKVMAAOHh4diyZQtOnz6N4OBgdOvWDQAwZswYLFiwADqd\nDkDds6Lr++nZsyfUajWioqIaPGby/vvvh6+vLwBg+PDh2Lt3LzIzM+0P/PH19cX48eOxb98+DB06\n9Lo/14KCAly4cAFTpkyBEAIWi8VeEwDcc889AIDOnTvj4MGD9vVCCCiVSvTu3RsTJ07E8OHD8eij\nj9r3TyQHDHaiZqJWq+1f1werJEkNnqVtNpvh4+NjX1apVNf9+mpXrxdCwMfHBzabDW+++Sa6du0K\noO4we1BQELZs2QKNRnPd/YjfnHWz2WywWq037Oe39eTm5sJms12zHyGE/dz11b0plUqH+7Vardft\nuz6sgev/XK1WK8aOHYt58+YBAAwGg70XSZLs3/Pbn3+9d999F4cPH8bPP/+MGTNm4K233kLfvn2v\nWy+Rp+HFc0RO1KpVK1RVVaG8vBwmk8n+OMqm2LJlCwDg6NGj0Ov16NChAwYOHIh169YBAIqLizFh\nwgRcvHjxpvsZOHCgfYSfl5eH//znP+jVq9cNX9+3b1989913AOreOKSkpCA6OhqVlZXIysoCAHz7\n7beIiopy+Hzoq8N127ZtMJvNqKysxK5duzBo0CAMGDAAmzdvhs1mg8FgwJYtWzBgwIAb7q9///5I\nS0tDWVkZhBBYsGABPv7442v+rKupVCpYLBaUlZVh7NixiI+Px1NPPYVBgwYhOzv7pvUTeRKO2Imc\nSKvV4vHHH8fEiRMRFRWFnj172rdd73D5b0mSBL1ej6SkJCiVSrz11ltQKpV48sknsWjRIowfPx42\nmw3PPfccYmJiGhx2/q158+Zh/vz52Lh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+ "image/png": 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\n", 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" ] }, "metadata": {}, @@ -636,10 +515,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This curve quantifies how much of the total, 64-dimensional variance is contained within the first $N$ components.\n", "For example, we see that with the digits the first 10 components contain approximately 75% of the variance, while you need around 50 components to describe close to 100% of the variance.\n", @@ -649,10 +525,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## PCA as Noise Filtering\n", "\n", @@ -666,18 +539,14 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 25, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -698,28 +567,21 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now lets add some random noise to create a noisy dataset, and re-plot it:" ] }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 26, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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EO1mhTqHp06e7eagzV3UmlilTRtaoDrIoOnfu7ObNmzeXNaoLrWjR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\n", 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HAADEGpsdAAAQa2x2AABArLHZAQAAsZasQaBRW0jNzB599FE3D7XujR071s2feOIJWRO1xVwNIAu1ySnJaTG/88473fyqq66SNYcOHXJzr1X2l19+cW+7fft2ub5qMd+0aZObq8GaZrr1/NKlS7JGDXKLokePHpFrVq5c6eZ///vfI68VujyAGgzpUc9TaBiuahtW3nzzTXlMtc+Gzs9QO2wUqnXWTL8mqiU+9Hyp1umUMmvWLDcPnaOjRo1yc9W2HLrkx7fffuvm2bJlkzVei7mydOlSN7/11ltlTbVq1dz8m2++cfPQoFN1SYrQVVVOnTrl5moYpxoMrYaQmpl9/fXXbq6GB7dr106upc7R/v37y5qUcN9998ljXbt2dfN58+a5+Y4dO+RaqsV827ZtskYNPO7WrZub880OAACINTY7AAAg1tjsAACAWGOzAwAAYo3NDgAAiLVkDQJV3UBm+pfxzZs3d/PQsLbq1au7eWhI5vr16+WxlNK6dWs3V11My5cvl2upYYc5c+aUNcWLF3fzQoUKJcmOHTvm3jZHjhxy/bffftvN8+bN6+ZdunSRa82dO9fNb7rpJlmjRBkeuHv3bnnMe57MzG644QY3P3z4sFxLdfNNnTpV1qhBp15Xm+o4CA2pLFeunJurtUJDb5csWeLmdevWlTX169d383z58skaT6irrEqVKpHWKlu2rDy2YcMGN1dDWM10145nxowZbh4awFq0aFE3V11MX375pVxLnQ/Zs2eXNVGsWrXKzUOv9759+9xcdXyqDhszs06dOrl5qGv34MGDbh6lCy25GjZs6OahLmf1flOvrZn++/nvNnjwYDdXz7mZWeXKld1cDSI20wOP5e0j3RoAAOA/DJsdAAAQa2x2AABArLHZAQAAscZmBwAAxFqyZmP99ttv8ljjxo3dPMo8oL9s2bLFzdUvt8303JGWLVu6+fHjx91cdVCYmc2ePVse8/Tp00cey507t5vfdtttsiZKA90PP/zg5jVr1pQ1lSpVcvPrr7/ezc+fPy/Xuvnmm9385MmTsiY5s9cSUx1XZvr8VTOiQp0/qkMsGU2Orp07d7r5559/LmuKFSvm5qpLSs0vM9Ndc+p9Yxa960oJvc/V87Jx40Y3V92gZmYJCQluXrp0aVmjZvaUKFEiSdahQwf3tp988olc/6233nJz1RF5++23y7VU51O/fv1kzcSJE+WxxFR3YaiLsVmzZm6u/n6EqG7C0Kyp77//PvL9eNQ8PTOzRYsWufm5c+fc/J577pFrde7c2c1Dn71RqM/j0Gexen2bNGni5qH3s5qjtmfPHlkzcOBAeczDNzsAACDW2OwAAIBYY7MDAABijc0OAACINTY7AAAg1tjsAACAWAu2nquhbKGhet98842bv/HGG27eq1cvuZYahjl58mRZU6RIEXnMowZlPv7447JGtQGqQaBq8JuZHjYYaj2PMhBTPaYQ1WL+xRdfuPmvv/4q11L/vjVr1sia0JDJlKDOKzWUNtRGrl6L0PBX1ebZtGnTJNnDDz/s3nbFihVyfdUWrf7d6vIEZvpSDuqyEMnxzjvvuHnBggVljWpV/eOPPyLfv2pjD7XXhwbpXinv9f6LuryFGqb48ssvy7WefPJJN1cDPM30IF3vkg533HGHe9utW7fK9R977DE3D11SQVGXOhg/frysUe+fWrVqRbpvNTzYTD+Hbdq0cfPQ3xx1aQF16RIz/d5t27ZtkkxdaqVVq1Zy/fz580d6TL/88otcS52jQ4YMkTW0ngMAAPxf2OwAAIBYY7MDAABijc0OAACINTY7AAAg1oLdWKrTZO/evbKmatWqbv63v/3NzUO/JldD7y5cuCBroipatKibq84jM7N69eq5efHixd1cdVCYmR09etTNQ50EmzdvdnNv6Gbt2rXd23777bdyffXL/AkTJrh5+fLl5VpqKGVoSODZs2fdPEOGDEmyjh07uredPn26XH/Dhg2R8nTp0sm1lAULFshjQ4cOveJ1Ro4c6eZ9+/aVNVE7SlKn1v/nadGihZuHhmQeOnTIzfPkyRMpDw2rbd++vZurzq4QNXxyzJgxskZ1ZHouXrzo5mnT6o/fcuXKubl6P6uOKDPd5RM6r9Xng9eNpQaahrrN1GNS3byhDk31bw915kYdVquGd+bKlUvWzJs3L9J9h7rX1N+p0HvX67pSOnXq5OaZMmWSNeo5UY/pmmuukWu99tprbh4a2Lpw4UI3V+cD3+wAAIBYY7MDAABijc0OAACINTY7AAAg1tjsAACAWGOzAwAAYi3Vn4Eph2qIZKjdTrUhqlbcffv2ybVq1Kjh5mpoWEoKDRRUwxknTZrk5qH2vTJlyrj5V199JWumTJni5k888YSsSezNN9+Ux1T7p9f6bRZuQ1atwGoYnpl+fr1WWdWuuXTpUrl+79693Vyd17t27ZJrqfZd1b5vptuKQ89jYjNnzpTHVFv2tdde6+YHDhyQa6l/hxrEaabPk+uuu07WeHbs2CGPqfZSNQR1yZIlcq06depEelxmZv369XPzESNGRF4riquvvtrNz5w5E3mtrFmzymNqiGW2bNki308Us2bNcnN1TpvpS1X8TwhdnkA9V+rfGFpLXb4k9D78+OOP3bx58+ayJgp1OYDQZ6+iLj/hXU7lL6HLw3j4ZgcAAMQamx0AABBrbHYAAECssdkBAACxxmYHAADEWrAbCwAA4D8d3+wAAIBYY7MDAABijc0OAACINTY7AAAg1tKGDp47d87N1eXgzfQYgg4dOrj5kCFD5FqdO3d28/z588uanTt3unmRIkXcfOXKlW5es2ZNeR+KuoS/GqFhZvbZZ5+5+YkTJyLff0q58cYb3fzHH39081dffVWutWfPHjcfOXKkrEmJ1+T48ePy2IoVK9xcjalIlSqVXOu3335z81OnTsmaq666Sh5LLOr5nBzqsu9mepxJqVKlZE337t3/5cdkZvbee+/JY8uWLXPzJk2auPnhw4flWmp8x0033SRrpk2b5uaPPPJIkkyNB2jdurVcX2nZsqWbf/TRR7JG3X+rVq0i379HjTIIjUzp06ePm0+YMMHNQ58XOXPmdPMBAwbImuQ8954tW7bIY88++6ybq/Mt9JjU2ITFixfLmnr16rm593mmxvq0a9dOrq/+pi9fvtzNn376ablWpUqV3Dz02VuxYkV5zMM3OwAAINbY7AAAgFhjswMAAGKNzQ4AAIi1FL+CcrVq1dx81apVKXYfP//8szxWsmTJSGtt377dzU+ePClrypcvH+k+ihUrJo/t2LHDzUMvy5kzZ9w8c+bMSbJLly65t929e7dcXz1e9cO6jRs3yrUWLVrk5vXr15c1UWzatMnN8+TJI2uuvfZaN584caKbJyQkyLXGjh3r5uPHj5c1Xbp0kceuVOj1K1SokJu//PLLbv7UU0/JtaZPn+7m6seJyXHkyBE3V+e5mVmFChXcXP3Ic968eXIt9UPSHj16yJpbbrnFzdeuXStrolDn6MWLF91cPYdmZgMHDnTz0A/Ta9euHXh0/zrVhPD444+7eeXKleVa6rUN/YBV/Yhe/cB92LBhbh760e0rr7zi5qppQ51TZvrvUePGjWWN+nGv19yQnB/Rh3487GnQoIE8Nn/+fDdPly5dpPsI4ZsdAAAQa2x2AABArLHZAQAAscZmBwAAxBqbHQAAEGtsdgAAQKwFW8+//vprN8+SJYtcsGzZsm5+//33u7maJ2WmW9tCc25y5colj0WxevVqeUzNQ1LPV2j+l2od/PTTTwOP7t9LPe/qVAm1qapZaV6b/F9UO76an+IJzQpq0aKFmz/wwANurv4NZvpSBxkzZpQ1aibRoEGDZE0U69evd3PVitumTRu5lmoxP3r0qKzp2LFj4NFd+VrXXHONrFFz0h577DE3D82BUrPK1PvZzKx69eryWGL79+938wsXLsiatm3buvm3337r5qFLVajZfE2bNpU1p0+fdvOrr75a1iQWmu+nLlehLpsQ+lxXf49Cz8nmzZvdXM2gUr755ht5TF2y4t1333Xz0DnarVs3Nw9d9kP9LY5CfZaYRZ9NFboEy6hRo9w8pea3mfHNDgAAiDk2OwAAINbY7AAAgFhjswMAAGKNzQ4AAIi1tKGD+fLlc/Prrrsu8h2pris1KDLk999/l8fWrFnj5lE6eczMLl++LI+p4WhqcKHqrDDTnUEzZsyQNVGGMKoBrFWrVpU1atCqGpoa6ghQnQeh5yTK8LfnnnvOzatUqSJrli5d6ubqeQ0NvOvUqZObT506Vdb8u6nhr+o1f+aZZ+RaderUcfORI0fKGtVNV7RoUTefMmWKm+/atUveh3pNFixY4OahjrPvvvvOzQsUKCBrolDdXufPn5c1qutKnaOff/65XEt1XZ07d07WjB492s3V0FRPtmzZ5LHXX3/dzdXg2UaNGl3x/f5FDVM10wN8o3Zj3XrrrZGPqc/LUDfmTTfd5OYp0XFlZrZixQo337Jli6xRXcnqc2br1q1yrYceesjNQ91Yv/76q5ur9y3f7AAAgFhjswMAAGKNzQ4AAIg1NjsAACDW2OwAAIBYC3ZjqS6F0MyRqBo0aCCPqV9ih2bW3Hvvvf/yYzLTnVVmejZXpkyZ3Py9996Tax04cMDNQ91EUai5MWpejpnuwFm3bp2b582bV67Vt2/fwKPzhbpUEnvhhRcir6/m4hw7dszNS5cuLddSNf9uy5cvl8fUrDI1L6xnz55yLfX+bNiwoawpWLCgPObp16+fm7/xxhuyZvHixW6+bds2N587d65cS3WPTJgwQda8+OKL8lhiOXPmdHPVzRKiZubVqFFD1qiutrNnz8qaypUrX/Fj+uc//+nmoQ4xdS527drVzUOfoeqxbtiwQdbcfffd8phHdQCrDlUz/R4dM2aMm6dPn16utW/fPjf//vvvZU3hwoXdPGvWrEmyWrVqubdVuZn+t6tu3osXL8q11My1UDfYjTfeKI95+GYHAADEGpsdAAAQa2x2AABArLHZAQAAscZmBwAAxBqbHQAAEGvB1nM1PG/37t2yJleuXG7esmVLN//ggw/kWu3bt3fzlBp+ZqaHspUrV07WqPZPNSB17969cq0KFSq4eagdOAo1fO2uu+6SNUuWLHHz+vXru/m0adPkWvfdd5+bDxkyRNaULFlSHksJUYf9qbZPs3DrqaKGaEZp0z99+rQ8pgZxqssNqIF6ZnpAYKgVWA0iTZMmjZurS1moAZpmur115cqVbv7888/LtfLkyePmofbyZcuWubnX9q/On7Rpgx+/riNHjrh5r169ZI26jEVo0Onbb799xY9JDXEMXVZk1qxZbj5+/Hg3z5Ejh1xr1KhRbq7Ow+RYu3atm4eeQzXMWQ2rDQ0cvv/++908yuuUHJMmTZLH6tWrF2mthIQEeWzgwIFuri6jYaYvIaIuhcI3OwAAINbY7AAAgFhjswMAAGKNzQ4AAIg1NjsAACDWgu0AqnviwoULskYNcVSD30JdOWrIY6g7Jqo5c+a4ef/+/WVN69at3Xz//v1urjpazPRwzY8//ljW9OjRQx5LTA2DO3jwoKxRXVeKGmZqZtanTx83T506ZfbZasBjaHin6thRz9UNN9wg11q1apWbDx8+XNao7h+P6pQqVKiQrMmdO7ebq/Pm9ttvl2uVKVNGPzhBfW4o8+fPd/PQsFo1qFd1V6oOQzOzcePGubka+GsW7hJJLH/+/G6+cOFCWaOG8apz99y5c3ItNZAy1OEUheqaK168eOS1MmbM6OahDuCxY8dGvh81JLR8+fJuHhqIqWTOnNnN1b/lsccek2tNmTLFzdXfLzPdAR1FkyZN5DHVffzTTz+5eWgwrBqOqroPzcweffRRN589e7ab880OAACINTY7AAAg1tjsAACAWGOzAwAAYo3NDgAAiDU2OwAAINaiT6Izs+uvv14eO3TokJur1r13331XrrV06VI3v3z5sqxR7apNmzZ180GDBrl5aMDjli1b3Fy1A7///vtyLdX2G2ovV4M3H3zwwSSZaqVUA0LN9CBONaguNNDw7rvvdvPQ86sGQ3pCA02VmjVrurlq/Qz9+1QLf5T28uQ4deqUPKZaXpXQwEYl9BqwkHLtAAAL0UlEQVSFBhp66tat6+ahdmrVkv/aa6+5eWhgY+gyBSlh586dbn7HHXfIGnUZix9//NHNb7nlFrmWau2fPn26rFGDL737UZciSZcunVxfUcMdVfu8mW7TD4n6mqv7D7XXP/LII27etm1bN9+2bZtcS7VzZ8qUSdZEoYZ0ZsiQIfJaI0aMcPPs2bPLGtUmf/LkSVkTdQgq3+wAAIBYY7MDAABijc0OAACINTY7AAAg1tjsAACAWEv1Z5TWFwAAgP8wfLMDAABijc0OAACINTY7AAAg1tjsAACAWEvWuIiNGzfKY99//72b9+zZ082PHz8u1/qf+O30ihUr3LxgwYKyZtOmTW7+wAMPuPnkyZPlWjVq1HDzAwcOyJo0adK4uRoN4VGXnTczu/HGG928atWqbh66pLcapzBu3DhZ065dO3ksMTU6ZM+ePbKmSJEibr5hwwY3X7JkiVyrQoUKbq7GgKSUM2fOyGMjR450c/VYn3vuObnWd9995+YZM2aUNe+9956bq8vknz9/3s0//vhjeR/33HOPm+/du9fNK1WqJNcaPHiwm3ft2lXWRKHWX7BggaxRIxDmzp3r5jlz5pRrde/e3c3vvfdeWfPKK6+4ee/evWVNYqHXb/jw4W5+5513unmnTp3kWur8USMWzMwOHz7s5u3bt5c1UQ0ZMsTN1bilMWPGyLW+/fZbNw+N/SlRokTg0f3rbrvtNjdXf1NDY2zU6JTZs2dHf2AC3+wAAIBYY7MDAABijc0OAACINTY7AAAg1tjsAACAWAt2Yy1cuNDN1S/pzcyyZMkS6QF88cUX8lipUqXcXHXTmJl9/vnnke7/xIkTbp49e3ZZk5CQ4OaqK0l1oYTWuuWWW2RNSpgxY0bkmgIFCrh53bp1ZU3z5s3dPFu2bJHv39OtWzc3D3WzKaqDKtQxqKhuDzOztWvXunnDhg2veH3V8WSmu3/U61S7dm25lupuKlmypKypXr26POZJnz69m4c6TTJkyODm1113nZuHumwmTJjg5qFuLNVlmDdv3iTZE0884d72wQcflOuHPuOiUt1KTZs2lTVRuq4U9d43M3v00Ufd/KWXXnLzJk2ayLUqVqzo5pkyZZI1efLkkcc8qlPKe73/kipVKje///773TzU9dmrVy83D32Oq26wKFauXCmPqa6rv/3tb26uOq7MzH755Rc33717t6y5cOGCmxcvXtzN+WYHAADEGpsdAAAQa2x2AABArLHZAQAAscZmBwAAxBqbHQAAEGvB1vMqVaq4+UMPPSRrOnTo4OZqKNs111wj16pXr56b586dW9ZEFWq/VFRb+sSJE9182LBhci3VghlqQ4xCtU03a9ZM1qgBchcvXnTzbdu2ybVUe2CoNVudK14rqxow+8Ybb8j1FXXuqtZoMz3UL1euXLImSov5888/7+aqvdzMbObMmW6u2vFDLarJGSaohrMqashhly5dZE3RokXdfPHixW4e+sxSraqhQZahturE1CUp1q1bd8Vr/HdCQ5PV+bt8+XJZE2r1Tky93qdOnZI1apDtrFmz3Dx0KQ51+ZLQJTGiUq3fIWoAtHpeQvdx1113uXmonXv69Olu3rFjxySZav0+evSoXD9fvnxurj7bQ+fU9u3b3bxQoUKyJiq+2QEAALHGZgcAAMQamx0AABBrbHYAAECssdkBAACxFuzGUsPu1C/pQ1TXyoIFC2TNH3/84eahTpSPPvrIzVu0aOHmW7ZscfPffvtN3kfNmjXd/PXXX3dzNRjNTA8uXL16taypXLmyPJZY5syZ3Xzq1Kmy5quvvnJz9cv8I0eOyLXUr/xnz54ta6JQw2JDz5/qpDl27Jibv/vuu3Ktd955x81D7xHV1eYNR82aNatcR1FdPtWqVXPzG2+8Ua61Z88eN//yyy9lTaNGjfSDc1x99dVuHhp2qro31Hsw1Pn01ltvufmtt94qa6JQHZHqvWlmliNHDjdPzgDfli1bunmoG0t9XnvdealT+/9nDg37VQM0VQdR6LVQ3XShbrCMGTO6edq0wT+JSahB0mZmX3/9tZurAbNqQKiZWevWrd1cnSdm+u+UR3VWPfzww7JGDU5Wz22bNm3kWsWKFXPzX3/9VdacPn3azdW5yzc7AAAg1tjsAACAWGOzAwAAYo3NDgAAiDU2OwAAINaCPz1XnS4bN26UNeqX0Kqzom3btnKtxo0bu3n+/PllTZ8+feQxT6gTRUlISHDz2rVru/maNWvkWjlz5nTzULdSlG6sdOnSuXloPtOAAQPcXHU9PPnkk3KtUKdWSlBzf0JdBN5sGDOzffv2ufnnn38u17p06ZKbhzptxo8f7+bdu3dPkqnnNtQForrKpk2b5uahOWI9evRw87Fjx0a+f+Xmm29289B7U70Hn3rqKTcvV66cXGvv3r2BR+dTnaIFCxZMkqluMzX7z0w/h+p9u3nzZrnW0qVL3Tz0/CZnJlpioY7En3/+2c03bdrk5qHZWEWKFHHz0Lww1a2pPltXrVrl5uvXr5f3obrwlP79+8tj6j2iZsGZ6ZmMEyZMSJINHz7cva06d8zMdu/e7eZqnpXqzDXTnVWhOZihfYCHb3YAAECssdkBAACxxmYHAADEGpsdAAAQa2x2AABArLHZAQAAsRZt6tn/sXLlSnlMtRSWLVvWzdeuXRv5/gcNGiSPpUmTJvJ6nlDb5Pz58938p59+cvO6devKtdTgu549e8oa1VIYxfnz5+UxNcBPtQ6+/fbbci01LG7JkiWyJvR8JabaS9XwwxB1GQA16NBMn++hSweE2v6vlGrVNNMt5qr9esOGDXItrx3ezKxOnTqyJtSuGkXoeVeDQJs2bermpUuXlmupttuXXnpJ1kS5ZIVqjX7//fdljfq8VMN4Q5cPqFSpkpurdmYz/VnmXY5EfYaH2sXV5ThU6/mFCxfkWuocVZeYMDMbPXq0POapWrWqm2fPnl3W/PDDD26uzofDhw/Ltd588003f+CBB2SNGu7padCggZurYaZmZtWrV3dzdTkOdekSM30Jmnnz5smaVq1ayWMevtkBAACxxmYHAADEGpsdAAAQa2x2AABArLHZAQAAsZbqz9C0NCE0sGzEiBFuru5GDRs1M9u2bZubq197J4caZnb58mVZo7qSOnfu7Oah7in1i3b1i32z8IC7xLZs2eLmyRmAmipVqsg1qqtDDYw1M1uwYIGbN2rU6Irvd9euXfKY6uZTXUwhyXj7pIgPP/xQHlu4cKGbL1u2zM3Va2SmX/PJkyfLmooVK0bKlZkzZ8pjU6dOdfMvv/zSzTNmzCjX+v333yM9LjOzHTt2uHnRokWTZGpIZ6gbSj3vatjwuXPn5FqFCxd2czWc2UwPxfWGL6rPw6xZs8r11flbq1YtN584caJcSw3qDb3moWHWUYQ6gubMmePmqgM31H1YsmRJN1ddYlF16dLFzZs3by5rVLdZgQIF3FwNXzUze/XVV908JT9f+WYHAADEGpsdAAAQa2x2AABArLHZAQAAscZmBwAAxBqbHQAAEGvBQaCqpU8N2zMzy5Qpk5urVsqBAwfKtQYPHhx4dL5Vq1a5uWrRK1SoUOT7UG3h+/fvd/PQ0Mv77rvPzdXQRjPdVu21mKoW8379+sn1ly9fLo95XnvttUi3/+9EaTFXQv8G1WKeK1cuNw8Nyjx79qybh9peBwwY4OZDhw6VNYmp9k4zs0mTJrn5kCFD3Dx0SYFmzZq5eagl9eDBg/KYR7U5nzp1StaoFnN1WYFQm3dy5MiR44pvm5z7PnHihJurIb2hy4Hkzp078v17LeaKGvC4c+dOWdOnTx83V5ciWbdunVxL/fvUeZUcqgU6NPBXDSMeNWqUm48dO1autXXrVjcPtZ6rYaveYNgSJUpc8W3/ooYRt2vXzs0TEhLkWup8V5efMTM7cOCAm9esWdPN+WYHAADEGpsdAAAQa2x2AABArLHZAQAAscZmBwAAxFqyBoECAAD8p+CbHQAAEGtsdgAAQKyx2QEAALHGZgcAAMQamx0AABBrbHYAAECs/ReNA8FtHevHmgAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": {}, @@ -734,10 +596,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "It's clear by eye that the images are noisy, and contain spurious pixels.\n", "Let's train a PCA on the noisy data, requesting that the projection preserve 50% of the variance:" @@ -745,12 +604,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 27, + "metadata": {}, "outputs": [ { "data": { @@ -758,7 +613,7 @@ "12" ] }, - "execution_count": 15, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -770,10 +625,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Here 50% of the variance amounts to 12 principal components.\n", "Now we compute these components, and then use the inverse of the transform to reconstruct the filtered digits:" @@ -781,18 +633,14 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 28, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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eAACQPDY8AAAgeWEsvVixYm49iu+qKF4UFz333HPduhr6ZmZWtWpVt75u3TrZ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\nAEgeGx4AAJA8NjwAACB5bHg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\n", 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" ] }, "metadata": {}, @@ -807,20 +655,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This signal preserving/noise filtering property makes PCA a very useful feature selection routine—for example, rather than training a classifier on very high-dimensional data, you might instead train the classifier on the lower-dimensional representation, which will automatically serve to filter out random noise in the inputs." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Example: Eigenfaces\n", "\n", @@ -831,13 +673,10905 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 29, + "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading LFW metadata: https://ndownloader.figshare.com/files/5976012\n", + "Downloading LFW metadata: https://ndownloader.figshare.com/files/5976009\n", + "Downloading LFW metadata: https://ndownloader.figshare.com/files/5976006\n", + "Downloading LFW data (~200MB): https://ndownloader.figshare.com/files/5976015\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:184: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " img = imread(file_path)\n", + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py:193: DeprecationWarning: `imresize` is deprecated!\n", + "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``skimage.transform.resize`` instead.\n", + " face = imresize(face, resize)\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -857,10 +11591,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Let's take a look at the principal axes that span this dataset.\n", "Because this is a large dataset, we will use ``RandomizedPCA``—it contains a randomized method to approximate the first $N$ principal components much more quickly than the standard ``PCA`` estimator, and thus is very useful for high-dimensional data (here, a dimensionality of nearly 3,000).\n", @@ -869,21 +11600,25 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 30, + "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:58: DeprecationWarning: Class RandomizedPCA is deprecated; RandomizedPCA was deprecated in 0.18 and will be removed in 0.20. Use PCA(svd_solver='randomized') instead. The new implementation DOES NOT store whiten ``components_``. Apply transform to get them.\n", + " warnings.warn(msg, category=DeprecationWarning)\n" + ] + }, { "data": { "text/plain": [ - "RandomizedPCA(copy=True, iterated_power=3, n_components=150,\n", + "RandomizedPCA(copy=True, iterated_power=2, n_components=150,\n", " random_state=None, whiten=False)" ] }, - "execution_count": 18, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -896,10 +11631,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "In this case, it can be interesting to visualize the images associated with the first several principal components (these components are technically known as \"eigenvectors,\"\n", "so these types of images are often called \"eigenfaces\").\n", @@ -908,18 +11640,14 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 31, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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2nG544liMyEv71vue3z0380r1rnzpJgUQPC1V4s4GAc8Cc/x3+znwENr42U0B\nKH0NxW4G86TQpgscnvAPKoMQRfnDjQ5TMOO5Oo9KpdJ4S5sKlYpWRjEzJ0rOTG91/sxDtKJnzBpi\nbSILPcVLlb41d1nPK9a6gnn0MwE0tU9v0HgyjQitd6/4+bNx21PCDNkDeqe+C+MHVRfxhiHgMRP7\nUX81B+Ygzs4jOyoY9exJQTVDAhxN1jDh+Oyb0MUdw2o8tCo9Ug6K1u5pMu5aHeXpAUwGme6jpL+3\nOQxTTQA3oBitHsmAZvilFBEXrS7oFSaNV2AI90BApcKshFitvPr3Mj1+ovzlf0QQFj+iQKeslbPQ\nnRWJpsZCY08NarWilN07z1nTCIt/2Ua79fylgEqfBMBQKJbHbZ6oQyS62J06QpeQBIVhsFhMLL6j\nxKurVhqQ/5gbdivWY5d47YEyjn/4ZiySMNj8k1cQahhCtWlKzZwP7FS6AcUb63VgTdMgcoXz02dX\nqGIgNyMlb1b68yfYz4zR7x6/EYO0aMXt/Nh8BCKQpmJ2ZnCMI5zSO1KIyLGDE/ua+16q8hnC8Rj3\nbhAQ0fsq3Mk+m+4tRFciQ1l3NzTMIDBP+1WFMvd2g9f+H8JsVv6275sw5TWfmXUjvUIGenMPShbE\n1nhGAIyJfL8kNITqGIbDUIguxOjt92GEf6h1dDrC9aO2vSqVQFrSlyVko3tD1r+jMwnJcTL+rfTz\neM/31fx7PH9TKgRo+E29aZbUT0bVroHVO0zuF+l7YlVOq8V8rca+LU8Y8kz2viiQpj1Syr5LNlXv\njgLGFJGXBctpxfqw4vx4wnl/EEgbox4BMIx9Amk57I7S2t0xf9s7pvRHn5Ujmz70oXyPSpYO+x0Y\nRruhU8feB1PV127OoX12h8VtBnJmqZNyZTEcqnyKsdymXTAb6veMtCQntDOzpyw6esFHX1cQF62s\nCEKPVvwuHgjzliZrf+P8KeNtJCU5WgaBIz7RMw6kwJrJDHjaeu/BHfGUkiAo+sxRSZ9vPuuPJiIk\nKV1oQhxsRTp08/bhTfUm3aPKrg0RSkXZd+z7FaVsaL1M3v/w8GVixdsz754iobWhYH1SaDDHbYFs\nIqyJBSrDwM0YoyMXlvP6Hu/vdVzJlMoQjNGg7xDQKAB0LFvsLYxn2K8Pctd8fWtKQpBqaTGPDQcW\nNnys2p2wd1BorkBs83tjJNZ8WlghGfgBuvPhR6qiwd+zQgE8HEP6fheY8kQY6ub1nN92+bPNasZA\nj1KTgGMEWPjOAAAgAElEQVS0CR+IQpiKXthca3wtaMdIS0tiZs8rfl/sL0nxKHRwiAgxAWQksApD\nBWwPyhoezaIhsERojTa+UYsELVPMVPeG1rBjC5Hpz6xp0cwadgHZm8aZrUaOna9xdpklbfHeMUO7\nHkqbvO9ocWd5M2zx/My07l489Hz22BFbBOc+kAkVnla/j2goLcN7One0wN4a+qD8exePDDOnCMd6\nA7fG9k+GeGGykkHhVnFSGCgV3KWRijQO23B9vuLy7YLLtwuuL1fpBleqKP5mDXb0jE/tjQ1etmyq\nUiZ52QpMRsaYsSwnnE6PePj0gLI9CjOeROnkNaOdmtt/UQnBrK2fa63Y3+kCm+KfvfBeGxqRy6MI\nKSUcI0BRHROGVHFkAIHBgdG7eqdtyh6rwxns2pbX0Aq5VZUftk9Ah+JYco965tUbFiRYrkk0dMN7\nujqmlFC5IgSrkXJEaBEIiSI4RqTMSIsYeNXW2p5xamVfqxTIk9LYDk24oR5TQI5ZFL7WFLA+Ah7y\nmVMJdXbs+bvuIUBCVmXP2PeCUkUTpj+i/POSQAzth22xKAVsmF2BkQoc6SetZYDLhn2/Yt9fUMru\nit+su3lCzSCwFpI9VYSYkGKSVCFZWvTevaCKLHRzz8EOd+8AaUMGjiJAQgzITXK1w7vyXf0mBZaH\nQfSivMyiYtY65GHybhi6qZu0Ri4NTVuFWl+D2xrNUtBBmp1EjkNw6X+5J1dysSf03rS//DgIY46V\nPapCgiahee9wuD8aZCUEzNG4Z7TwDRpbyikdavyPgypraBtYDCcT7+RkPFP4zAykdDCyslqxWZtW\nXGfl3zu4J60/0afDf1TQ944ByY8YmxS2lPkNlrZHUkXt+418Fi/3Ozf2yXlBytoi9QaNOsZSh7HY\ne/f6GiKUqwiWJt+LBzQUsD23Kf93lTsNNJrPTPd09O7HsP0n+dwTIhYILTRBflJECw2xaUEkRQ0D\nB8QIBA5ovYonEwJaCKCm6ABmvggdntHkAzCIv2Z4ewfC++W//G01L00ru4F0vps2NpPOppdvF7x8\necG3377h+fdnXJ8v2PcNre2qiIbBFWMEUVQZNlJCS9lRynX6uqHW4uuXUsa6PqhRIEo+xoh8yjg9\nnQ58glkuASydWZs4Cvc9e5fslF5R645aM2paEFtCKYTWunQN7GLIxRSRc0JcEoL2oyDCcLSIZL+S\nrEOtFVWfc9831LpLeKQV1S2i5KMa3BHaMGtqhT2cRnF4ZB7l+skRYzGsiIDO7yzzS6pv5px/kuY+\nRNLm2fYua4igNW0GVKUXjjfB2wuwwZ2RgwGgKEDKGfmUkZYk3SNPGcu6IOeEkAdabZ819rbW39ml\niFfTz96ed7ycrnh53FF+UNTuh8pfKl0Rcm0jlYFHDKvV6rDWnBmwbRfsu72uvsAOT6s1ad6qlQ0V\nS1aE55JX5LwgxIzUMmotiHFT4Wls6LmLmZYI1SYYUvyGUVtD0JxLgN/l+ev6DAgUyilowwMKgYTo\nhdFVLCg8DxC4MdreUHbNlCjNhbTF0Wby4tyJLNFQfuIlMGLrSD1NxYqmEEgf1rrNt8yLvDOnkRJ1\nz+jMrpQpADGIYFlTwilnnLRyWI4CRyVLM6Kb+YGhuoPF7vNrChpDkJPBnWAQBA2Kk7A0wzOnhLpY\nMSAxpkJtCKV5GdlZSbynqx+RsmRJG+PoLQ9PMPj+S1Eb+UzlfKUk8hvd/XL2RiF5yUhLRsqmCAdr\n2+KrBwRJDXCvuaHeRFW0rZYyhdjMCDRFrOTAu56dDn9HgbTNLXwuu+XQ2lIewhFHTWtGgGXupAnG\nDCki9QgkQc9ClKyMHvqrSx+IezzuTRAEuGF5yLjpmrL4Hs+/aC+EMFLm3MvSImL7dcf2vOHy9YLL\n1xdcn6+4vlxxuXxV731XhMiQ0uhloHtvzvkQz9wcpeskLwXKttTOFDN6E8QJzKP2ReuKMIzqi2Z4\nw+Hh15UZv/vsrQljvBFqLShlQwhSO0LaRQfEPSEmKWudckbLCbGksa5T8S0L+QEQT7hUlH3Dtl+w\nbVeUclHnsPm5FyN5RSaSqpiwjIbkhpOdi9Y6YmT0nh3xMgSykeqc9r70ZnT2e1XLE3lJyOsiXR5P\nGXmRkr92Vuw8lk1b3r9suH67YHvZfA7KXnV/CXpkDkpMActpwenxhPOnMx6ezlgfT1jOC3JKmsXF\nKEWMiplnEvcqBnaR/dJqw3bd8Px8wdfHC14eHvDpdHrzWX/s+Z8WKS4QSWL+Zu0UI7vIQZCCBvZA\nmHI+2S0yR80dqqiH7837T70hKywIIkQ1FkLYYaVTrSvaHDsVKSRwXQgdKRlUb3DbbUzyvuHENUC7\nzsnhkjgWDT5CmMhOhzazABS1kM1RpAyxeWUxIiYc+q1TICRKaNRgbANu/XCQZjfeHRsDHXzeO7Q2\nnv7tO9IcNcwDsBo1US3ygKTe/5IS1pyQQ3SB41D8fC2aOBEmoNWjJYPr+JgtYe0r53g229MwO9Kw\npOQwr81xKxW9EZpWfGGG9kC/+/EPkPYoR006l0GEU16Q8wnLsiLndTTySVPRnzgZqEFisxajTTmN\nrnHafU2IXfFATLWuek2hRfEoBpRYyxR7vm7YLpt6kN3jgjTtj3uf3deNyBXVnLY1X5LVOwfB63bI\nGg/kxeZDSqbq8y8ZvIrRlxLQ/L26rz3zQGs6GHjkkGn0tF+KitB5ZgL7/PV3lPUGRjc3kUNS04K7\nIAKtDWV7DOd1NbwrukHaqvyTyp/R/ClDUkhFMQkyWj00JGE2ICaB/Jf1jLycFDGSbnvyd10bq9VD\n8bUYAk7qsOy1esXVnw1BK2z9gyrbK3qvsLDPbNCmvCCnjJjVWzVjVg3YsV9kH9RWsJcrtu2C6/UZ\n2/aCfb/C0r9zWlSGp3EPKSOlFWkqXQwMOefhkZCAOEKuXKEhgPsPvp0la1dvJeRTltbOp4cVy2nF\nac3IKSHFqIgUo9aOrRRcrjsuLxdcHlZcvr4gP2dsLzsuzxdxmsvoiGvVA9eHFZ/+/ISnPz/h06dH\nPD2ecV5Xye/vjK1WPF+veIGcL6oAFBEOIcjh0d+10rC9bPj27QW/PZz/mPIPMaiHb9W91CuAwGvS\n8MeEjsb6/YDSFONszpRsTZSfQP/mYYyYtVnKury+wK02NJIDYl6UbYBbuDNqD2uB2VR9qrAK7/D8\n7XqW1wpmF4YzeYmU8BI8rj7FS8P43o2dqoWNAjlxJiICcXhJIGhbYJsf+OKOilFHToJ5ovPcieEB\nFSzl7oPgnhKRs7gNxjMB7AVEppbHfv0DLEtOVLSfHVOPWOO3qsA1pFP6IBlayInA7tXY/ck1RSin\nJaGWhFY7Qm3ooo2cVXzviDEdYHJraxpCxLKsOK1POJ0ecXo4Y304Yzkt4tVbLrZ67mM6eJwLVSx5\nzchrUu9f2dynBXlNiCkp1GyGdkXZK+pW1GBUAWIx1LXI9bK2X33pqoQkBHQsj/zjEUJAZ+tMqYrY\nnsHIRHpufRlCGOlWE1ox6nzAe8WnlJBWeVawkKpCDOAQvPSr/L3ETGfinHn7QclQMrkBHDXWPBHb\nZL+pvHrH2gNwprUVfBkkNXlGz3G30IUaIqIUxbGhJnUuiCTMczo94nR+wHJakZcMClK4pew7Ls8r\n0sszYswo5SrOFBhJlf+6PmBZVsSUPXQpsnnKNqjNy2obORqAn6F7hnANSPdBVHkdXYlL62WrfJcQ\ngiIAacGyrFjWE5Z1kU6IOXqoBo5kddRasG0XXC7fcL0+o5QrmBk5L2IAmGM0OXsmy4GjE2nhTUNQ\nAyIiC19HGlod4/Y/G/u1YLvu6kkTKJOT7gypy0tCStrx1ZHPAc3X1nDZdnz7fMHXr894/vKCy9eL\nEJJLxeVrx3bZdY4DmB+xnhY8/fkJf/d3f8JfPn/C5/MZaxY0Y6sFXy9XXLYN3DrKVrBddim6V4+c\nEmYxLvbLhudvL/jyeMbXx9cd/YB78vzV+p6tShNKZZ9IL5crahkkjln5z8Q+iVPvEA8q+QEdrU1l\nY1nM1Lz8uRWwWMsKc7TZEhTPP2tcVQR4d9blews+zJC7oEHsscUDdK1z4obBGwiDISKGaEh8VAgx\nUCs/EIEsb5cA7iTsYKv8R/aCe8iGqozPHfX+iSbCk/M17hMCXZEGu7fZ4JmLGjHbvbBW5RtzZ/tH\ncuC7z5XX7HcDQ+NZRiziUd/fTJ+5pXJLjNo6ShjZA7137Dm5VynWddAWvzrf5e6l1zK5w3OQV1Ih\n/oSHh894eHzE+ekBp6cT1vOCfBKvbHT/gkP3XZVYaw1gqPJPKkwyllPGcl6wnFcsa0ZQxdatpfZ1\nB7BJ6Kc2dEvpUSKrpYSRQoTugfYm6bB3en6AQumHczWa/RyUqD6fIQNO/qUOtKmTn3vMFb0xaqzI\nLatnG5GM1KofaQSmpoz6Wo1xLlZnTBHxJswQ7UYjjqE9UqPlHcq/taaKSxR8M9nXJcxhBCxDFllh\neIfW2VKOF+V3rDg/PODx8yc8fnrE+nhCXiV82VvHft3x8uUFz1+/4fL8jO0qcX/m5q2jrVOkrG+/\nMawUlWAlPTI7WTEBSK2h3in3WtOeKCFgpJDZGRqZWq1VEO2OwKaUUcoJtVb0/ohV95GhWUTyrPFq\niEXBvl9wuXxFKZvvlZQWMEZfFynkFieCqTlR8Myo3glx6ikgTbQqWpPMhbtJzoBnbvTWkZbkSFVe\nJKxBQZCloo19QpDW5ktMyElCokSEUhu+PW34/eGM3x6+4bf1K1qt+Pr7N+zbjuevX1S+dTz++QkU\nAk6PJ/zl8yf808+f8el0Qo4RrXc8bxta63jOGc+KwDUlGbZSXa8dKpkSYbvsuFw3vGzbm8/649r+\nzVInZhhfYVolGpRNqv9JvHEfHhoFpJSdzdm5I/BI77NUn+HxD2vPYj7LckKMSb06g9ajCmZRNG1i\npHo6yhSjZTBCCljb4gSOe8ctI12dGv9dCAGh99fv61N6R2P/GwqjHGZlTedpADh6PNrYnwJ9MXiK\nA6cc0XJEqwl1r95cRhpwzHF240FM5WKJ7lT7Y+2ZCdFKSprRA7MwMQkCGmkYIQxjYRI4tUvVO/CU\nn23Yrl7dFL9BWSIAhgfTehfyZO8o1GSJdbPXlJBzQlmSxOta9linGVrvgX4NRbF9JVB/xrKccTo9\n4HR+wPogcbnltGB9OOH0KEZAXJKm5MANmxmaZ03bNKWdloTlvOL86YzT4wmndZE4H8s6lFJGMRcN\nI1Eg1BKcC+CG4WxolBFOa+6t3/Ps+s1s2HXWvTwZmxNXZV5vIgLPuobVGNEc9qCcmbok4foAnv4E\niEdbdyEPS1XR0UpYlL/W7UgjFaq37kaBIJZTnYT3Of0+pzTJEIO7LAXLIO1Wq5KuCsqusXqCOi4Z\n6+mEh6dHPP3yCZ//7jOe/vwJ56cT0ppBRA7Rvnx5wfPvT7g8X7BfN+ybkvu6QeYAITgLXGqXWKho\nsMPd2JlQS+cA3DHcU+YROhletl135JUTEVobZNIYE1pdwSx7OKmBC0j2WN0LXl7EoxVS+MV5BTmv\nBx1jVE7bt0Fr7Mse6egtDPJr6+jdcvIbZk7Oe/geVR1a7uwGnu2zELV7bJPsGiICa0w+p4jzsuCU\nMyIF1NR8zhszrntBzAmtNLx8+4rffvsPaLWg9YKnP31CLRUhBKwTlyrqBlxSwsO64lMpuJ6FaFq2\nIuX3mxDtbZAEUpE6Cw9or98l/f2ksY/GmaNBX/2QmlJVuBBBYMoAj4eRKvqUusazzO2yGKoI9VaL\np33EmJDzSZV/8raq4sFUSI/0FTlnEEX0VtH2i1/fSYS9oXNTK1QKrpTT4kzJe8dcIxt4DZt6a1tm\noDU0lrSOshWU6yiHLMJEn15zOEFwY4RCGJkEU16xweHUrH53Ql7l4Ml1M3pe0FpFremgbN1TvzHe\n7ib+VGFot2iQn1wzEiEp5GkGxeHFR8UNwD0R86A6j3SeeV7NwBLGcnDBRSTpX3spaDQIgmZkRI39\nn7KkPKFbvQmJiQdqqO9UAIaimH1CJMS+JZ+Q8zqE8AThnx5PWB9WLKdFvf+BftS9YnvZADUEvGhS\nUEUTCXnNeHg44fG0SmU59bhrW7GfCtbTgu1hxfVl83zyslUnk5oxXUvDcsqo++pnwIqn3DMO6+Gx\nbPUwbeZta03jlmXvRLSpIlqvDVAhakQVm0OLbxqcXXZ7tmP/j0KEEMsgTq4L8in7ulmcz1uevuPM\n24IZ2ueGrML6gPQ8GfMkZzbmKOu+iEzIy4LT+QGPnx/x9OdP+Px3n/D5L5/xyy+fcHpcpf00M/a9\n4OXliuevL7h8eRFHaq8eF661eT2BXhtiTmpsrkIQezqPfbdkD8HZ2hj7/95zb0rXcyh4hGO9Qmtv\nbsATSTZCDMnDwhZyTYsUJFrOq4RRckLdCr59XVUp9ynTK2o4Nzkx1RwpU+4xYRTGiQEhGukvaLod\nIfSBOH0Phf3RMCOdmRFzPBidNkjn1EjPa5JXVqe0tIZrKdiUa8HK+SpbwfXbFV9++wf84z/+P5Ld\nUXc8ffoFz7//Fdtlx3Xb1dOX+h2tS/p2Ve9e9ntGShHXyakwDooY5gF97VhOi+yfP6L8hzVOI7ZY\nhLneFIoLIWA9n7BCFP9+3bFvV9RalMgnjNFtu2BTRS1w3wLjDxjZhWikbAgv4AWtNWzbC3qvyHlV\nFrOQq0TpFRdu0re8o1FDbRUpSWggxoTlcsJ+3VHL/d6fdcRKWmxm3lSH/aBKtdbuaR67pjzWvXjF\nLhGYBk0FdIWQAJNXrE1xfAXk8hq2MNhz5IbTBLsllHJDiJmuIopfytTeM8RLntpKatgiKdFviREp\nkJfsndseA/B0yPEUABO5J9kajzj0JJyNXDgOrjyJFSvZa0Gx6mk82M2rEaDYlEdH2QtiLAp5Mt6j\nA+YQihmsg6y1uOJxlu7TWQTdafHDOXK5WRtzNOxbQC3s56e3DooBy77I+0JAjmmkkep917ZgOy3Y\nns64XjfvtVE07LZdNlxfBN7La0PZNJNgz6/2xc8G9+E9jj4dfdoLkuHCoY+4t8Gxmn7UlWV/IAnK\nxI668HH2XsWY4toOVoUQvliuxZb10F2xA0BIDZnzzTPeoHHvWHyHmacsk0iEnhNqE1JpXq84PQqR\n6vS44nP5RXuekBuCD09nPH5+wKc/PeHznz7hl89P+LSuWLQgz14rXvYd365XfHu+Yr9uUmPAvX3Z\nJ/t1lzh07Z4lEi2v3pjiDyvWZZDQBnk4YFHj+Y8Ndrkq7P99qkEQnbiIhTQ8IfF+q0a4nBas51Xm\nE8ByXpHXOQV2RUoZ5/MnPDx8xun0hNPpAetyRk7LFDbW0F3D4NNMqdXcGeQZProDHAW5H/Gtuzi1\nUNStteZ5+j0n5BywLhmP6+qe/pISAhFqa3jeBGZ/vl6x14q9Njy/XHD5csHl6wXPv3/D16+/4uuX\nf8BeNgCMX//xL/j17/+KX//+VzycFlz3HTEEVEU6bVe/XDc8v0hWyXbdvdDUftnVsRXURhALaGp5\n+2Oef1qSWBVdCvhsWsVqe9lQ94qYEpazWLDbi2ze7XrV/NCCbXvG8/PvuFy+Yd8vEutnFuZqtoUl\n32DGRr9enwE8q+Gw43r9hlp35LxiXR+ncIDF0i0Pe9QLiFEswFolHFHKhv2y4/pyvXsj5JSwpIQU\ng7B9WWB4MuhH+RCilCTnmtuIxRvfYFjEARy7wpbRiU2Wx2kdFM0g6L1LmqAhCWp01b1qbmyFlUm2\nGgmGokhdeis7OeLiRPcdhFoaEgO8CMkxTuz+NckrRWFDMxTWJ4L2WUIggegsTcrh/WZs/o5ShwL3\n6n9KNrPCPNrDTd4TjgWGGAEcujVFQyDCmhLqmlFLRbomR1mA18jNj8aIH8ZBPEoZMSXNx13w8PkB\nj396wsOnB5weTwJxpihEMY2/W+gARcpht9K9GlxXaLF3RkwRp/MJL+dVqrGty/CA1A+LJEZOW4QL\n4eU/k8RFBZ1j1L0MaDhaVsz91R1trgYCMH5udeeFwxnQA3tdDSLNs1f0jzVsADXoEqRrXUwRaYlI\nGkeVlD8xIqLxIZYshlEgqd/g4TQJRdp5ipo5YcaEZfXoJhHlwK+Lav1opByRgpRGNZTLyFSlVrSn\nilofvfVvVrJm0jXPizLDl4yHZcWn0wm/PD7g03qS7BQwtlLwsu3qNSY8riv2KoLeUlvNeK69Y68F\nrbMY3knSmF/2Hddd9lHWc2nVNX2t8OMqb2+t+0za9bTuVrFrDYLeq8qbEVOPUUiNy3JCXhZNhUva\naVBDjop6prRgXc44nz755z2cP+P88Amn06OSGyWzIWo6YdTaH60294K9+I3e69wnRuTv+4w+AA6V\nE5EWrmvYnzeRT7rXWuvYSwF3xl4qrmkXHdE6vlyu+Prygqsq5N47yrXg22/fcPlm6Y07qla83fcN\nz9++4Ne//xX//v/69+De8esvjwgput5tRULIl+cLtperOg5QJ0rQzaL34yRPLSbXWv9jyt/a9UoM\nUZj9RYtb9NpAIWC/7Li8POPb19/x8vJFC1HIQ12vX7FvF1jKCzOr57TidHqEpZKktAzmpsUHW8W2\nveB6+Ybnly8o5aoM08VjOVbwJ6WsFqRwBQyKsmYyZsVJHPF+1lekUZbW3XG18Ey5V9YqYx6XIqQl\nIdfsGRJ2gAjqBSZGXkV4yzyObAJrgASWHFarlGiFRTzFa28KTwKm3SQundB7doJfQ3XI/j3Cv9eG\nrrA1RcnRFsWfkWfPXN9v8OKaE5IadSLALJ4vgtPi+nsVaKwqFB3IigRFAAk5WUMnuUbEQBICVa2t\nroZXr/59ZwbxzDkY0Pv7OB82X+r5TYV71vOKh8+POH96wOl8EkGXNI/bBd5QGHWvuKrFfn2+4vJ8\nxfYi0F4MEb2KQZgXKSJS9oLTw+ppRB3CbWlaItTqRRzg9Kll9lHeSY55CPluw0+u8ZqUaWW8BVIH\ntGG2GAJxanUco8fv7Vro7GGfZJkOWedLn9MhY43jpzaarLhSsjLRMbiHY5C8KIPoWUoHrOMdhD+K\najSFoGxugdItXe58OmH964IUg0O/OYoxHIlkHxBpSmxSozlj0XMjMKyFzBiB5H0pRM+eWZRJntS4\nNhljBknvHVutuOw7LvuOOhk3zGqMQwR8ihHnnO9+fudQQTgAnaFNoVjlqoRml3ySQlXqwVvp3Xnd\nexXnBST8BgDCnVkfcH745CjQ+fyEdX3Eup6xrmcsq2TQeM0LaNiZu6CGltVgxcH6nDYuYeL3ePw2\nyrVgv+xK7IxO+gu6Z8GMCwO/OsMezo2RDLgREvfzWSuuz1eUbRf0YznhfH5CCBfEGFHrjq+//4b/\n8H+f0WrFwy+PEnJorMz+Te5rM93bEaIamYuQg01PI0LIqh525D8G+1cXMJZyMzov1dKwby94ef6K\n5+ff8Pz8O67XZ+z7Fb037PuGfX8Rz1arNc2EqXV90HS9oeyNMDhXrpPiPgm1BrV4No2PGynLmKY7\n1vWssaSMEWS3VB9ZhH3b794IEpYYRDmLaQN8UH5OTLMmHMzg0wJANpBZZ3UfKYlvWaS9i5AcFbts\nvhVN6AOK6mw13+cXIIEFqYjVmaWlLoDRUe4++LfWJoxz1ivODWKma0i8PYoHk7MjAjbMSCrUlb8g\nhlNpDVutKJrSJALQavnr/KeExfgFIIQAEYYxonVNObSYGndURWFaP1r9pvitsuI9Q+oVREcN5lx9\nQCC1/bKLZ2ohnWB5/cbYkAN5fb7i8uWC59+f8fzlGZevUvyjqeEnIS6557JXPLxsOD2dhPUflcTl\nGSuDTGodxSTut+P6vOH6csV+EWSOG09cknC/8acCbY6be4VPe4sV1WFoAR9RviBC5DiuY8KxD4Ed\nY/BiR25AqjcPJ7emyXgefRPMyAgpjKqaIUxzr/1C9CzYmW3vMPzyInvYlL73FAG8wNV5WbAoojJ/\njqfZ0TH7p/eOrUu/jVIrrqXgWnZsRQhZtlc9o8iMIYO4Mdjtxq8JgbBmKSi1q+HLaiT0rumdkLLI\n+c6GZnPXVQBqOCqPIZ+QYhrwvsXoQ4Dl5Xumhu7LfQsih0JALcWRSkkNPMM4QstyxrKsTpRM2VCh\n5PPXAa+hPyOaLvt41FmwTBd5hvudnu1l08wajHK7iiqBhbTYqhDdLdXWuTEWEguj5wXCqDshacIn\nD3GI7loRQkKvFZdvV3z77RtqaYoIducKbS9Xr6kj9xaxp+KFskyuz8WHJJT2/XX/KeHP4tBWSazu\nwlLvtWHfNi/UUOuuXmtTJV4PcLQs9gkPD5/x+PgLTqcnLPmkD9m0AdAm+a3MaL0507/3ikAkvAAN\nDZjHO2cAjHRBAoU5zsnugY/KeD8fghqMtLY5Fc2GQdBJ0zIspzZ2xoJFLTvx1LwI0NR9d05D8uIh\nN2k8bgzYJmujKYa8x4SHwq8hIHBEjK95CveOVhpa1Dxx/azau9dStxi/xagXD5EMLw6QnNemc+dM\n/Vax1erKv6vA9OtbE/bpOuZFmyA2RTSnHA5uw4gz++udef7WDU8Dh7BKa71JDm0I5Ja+dfXy9WqL\nM4PLXnD5esHX377i6z9+HSVgr3JeiIKGesxLaOp9bFgfVi0lGtWz6L6H53rhUla7uJDYt+LkVjdO\nw/DMfzpma1cxfku9okijYJKHRkibZomhEsJQWJbtIDF7RcDmPhuKDEkfKi0ik5S9n6OiYRqzZjiR\nUu5lIABBq/yZcRrAwwibztg9Y4npsIc7M0LvUlRKPXKD2Pu0t8WQBaI20lmSKMZmhEvdq3utUgxm\n33EtRfoY2PnUa5qx1TS0NrcOl3uS9trezVQNhQbl65jBAAkDWCntny49D8dm5NmLwhVukSn9ofgJ\nwQ1AIwk64VPTUo2HI9X2yFHK1hbPpPFrK4P+LWfFDECEcDzfqjNarehTEzn5m/uVv8f8AdRNDHyb\n/2f0J0kAACAASURBVKrV9LryiQaZe6SRhzDq83uWgHFcUkA+L3h4/IRt+5OmJCZxWmPStM8icxrC\nkAfOsRvOjGfxVAJQHKkgIixNya9K1v/e+HFXPxVoUI/TiGwWYxAFH1SxiyCLKTnDmLkrVC+bZV3P\nkh/98Anr8oC8LggxoteKUgpKWVHrLtarwjatFnDviCEJZ2BSBqQx4Xnz5LwixWUqBWnV/2zi3hf3\ndchtUl5OfIJC7ar4mxkAmp7nC5ACWmpejcxT5cybMyatkoksVmR1oI39POdae2aDxoP1jh0NMYQi\naP1pX9M7n7/sRdPJNJVJc9Q9de6mVK6hK9XWRn9W1cPf9WUCbysFtYnXY7BUihHN6vq7AhreFGMU\nLDkWLhleknu3rDwDdZPeS/qaEQ4GgC6Ep32/+jw6uqAernmYZSsaq5bD/PzlGV//4Qu+/MNXXL5J\nr4taBOUKFIRQBV1nrRFe9oL1smM5SewUwFD+bXhXTQm4lp9crsXr/bMaLYGsEuadz0/TV/NkMJWG\n5pFtYb8/1OqflD/r+zuxNpthZ8ebxzKvTQgEpADu0tKblGxsYQ1HMSKNkthuVJOvW0d3RODwTHeM\nrEaMhfeS8ilGKEvT/AAnotbWHGq3vWrIXFXv0Ahcm3r+L/uGrUgs34wNgpyDqgz9mpKmjEkRLCvi\nY50Mmcd5k6yYUYRpcAfCKyX63aV3QzFp9tXqIVSRp3PRHc3DV0RsGAuWJTMMAYuhHzlQ2uPB/9ZC\na9FLaOsWk3PNQgUKWkzKrg1AiXkFpe4oynuyqoHv8XsMpSVg1NcguBduadiHqo7NMmEAjjyhhWp+\nGdchS+3+88MT9u2qZ5Kwrg/Ii2REmJ6Fhrt6YzWYAwJ34Tm17iHiWbc5v2pGH35g9P0Y9t/rQREZ\n7DDSCgKW5eQx0ZzF4wcGa9/g0hiTQv6POJ3kYfOS9YGTN0epNTts01pDO4lVk1LGXjaMik0MUfyz\nhZqRpxrrIWQMROB9xR6A4W12lriJKX/PmaZZ6Yzv3YPRdDX2+KQxohmxNLzqe95fC3e3LK2866Tw\nxkv+LR6x5bZa06EBdZtXfM9opaGlNiC8KsJtRj1MGdfeEZWVaml8Qbm3RZX+pqkvW60OddamHpNC\nlnWO7ZruZblmUtKVFQialf+cceBZBzfhCRPE71h9Xy8JTUl4Yd+3+S0HBKfXjrJVT+Mypvbz78/4\n9utXPH/9hu168SwVMc6iV74DW4qiVvE6b5I9sCRJB52gf+8Xv1cvtmV1v2d2vXgcEdHB4jvG9DaD\nMQECMeAkDkcEFCXRcIf1KHDSHUvcGFpwCsE8EkntG5krcMEVwtyiNhzq1tszmZdvxZQM/jfjj/sQ\nwvI392uAnJJ4kfqZlivee0cnkt9Bw0nq+TMEBUsHsh4d5EbtHVsteNl3XDYh6+278Ffm8uBGrq29\nY+3dOTYAJgRg9LQwZFO+Yzcm5gZb8U7ZNxNcs6K1y7Ji7qNixgT5e6XWiNT6N5ItqTHbQUEVdB17\n05R/slLtQWpjJOWBeKVMRYekMV/wENgMtYt82rUhkmQjWG+E96y7D5ZMpDlU25V/FZOU5x59SoLc\nHAkPhuy+POyozmoQGH45LTg/PKDsn1wvCcdhHamkWrinz2EFttDvcGhsxeX6GnK2QmdGOqbw3UyP\nn8b83QDwgikDPrXqe+L9JtS2qJcWHO4zAR5iRM4nrOtJFP+avYqWWSfWda/3pgUzClo7K5SYENMi\nSIAsx5hYZZvGmKYceVPIdqiixwTfO3iytk0BjnjyXO3uqJRmIh8gvZ6NlTyX/RXlwq/iulayc4QB\nBnw5oYQwotSt4hcIbvb2R7e/nz4zj/vxrlVtlA8Vxa8KiAiVRl5/DAo/6nzNyIl5Sa011C4vC5eA\nGS0EacZk0K7F9SyzwA4CGPYk5vUcJuX2Wd7p+Y9CSVqhTiF/gFHKBElOCE7dqsTbAjmKI53fXnD5\n9ozrVeqYS02KpnszHRAM6+Bn3rzU/s/ChofyPtj4IG00+diq94E3kROCGJ6JkyIo9639ITVP59a8\n/mGYjSZNBsV6jf3Z42BACnod5z+4sn4D3iVS5n8UL97uh8fny2yp8XGI+dMkd+bzcr8SMM6KKXWe\nFLwggfLZJuMAeLvhqF6/7XtHDltD6Q0v1w0v1w3bXrzPh7VGj9rBLaaIkhJKTqg5O4k2KRrgFTb7\nCEVanN9jvzEi88Q/uPP55zBqVCRVHDwtWuUnUmsfTN32LOsEGpNvrYM0dZNAw5nBUP5B8/qtd4iV\nbY55yEnZRxrCYnKUzKpfln3zTogG97MRYOl9a28GJfpALUYnvYaUpfJfnFArk8+YwMagRjIBHqaK\nWToxLqcFp9ODZvMQ8qIpwkv2Co4S/tvlTPPYw/NLf6hImvYJUXlhJcND/H43159W+KtleKCvrA0K\niA69RcS+YHoTDAEQCCoiLytSXmSTa1wELAvq8CFFhC5ejnEFpJ6/NalRTwLR0QXdGxrj0dhgspgn\nadqZWav3bwSDUdwzAQ9BoApIFFoXL9ZYsiZwLEWoMygYW9mEFk1wNPwZ0Mdmagq5W074MX7dbzaF\nVbSKsJQ+QQL0zhUifFexD43TzQzWovn1vTMaMQKNnuqNOwKCkq2OXAl/KTzaHDYfxokhCWZAmcFg\nwithHGQeaPvBw2NDZSaFz+KivSvua94MkbTynfuat1ZRyvBWu4Zl0pY89Yib1Koo1x3b9Yrr9eJd\n21rbwQwVsrIvhNi6I5YkMKMZXaWi5HLwNuz6dj8uUAMBpF6e1sgn62zW7y/04ryTPuBV2D7lIYQc\n3g/kRm1Ur2/2/MEsrbmn8ItcAxoeOJ5JMqVqMmFqSDWjBK+EoW+G2Ut6D9ojI8c4YHTju0AId5Z3\n7dwCfUU18huAMhu5qgRLrdj2gsvVqrPVUZudrYCLkdzktS8Z5bSgrot2zzT8RvbcbEx7IS6txWGh\nh1sE7GdjhFFGmfWchZQ2h2gM8YkhDadKoWppCUwg3Zsm67rKMb2xIbfC6JJpn++GYbDUNWnTbd9b\nLLzsm3ZC3NBacTkH2wrvdPzNkJWw0eSgMWu2gu6/Ls6nnz8jlAZCYiAlMVwF7rdS3U3TQqUwlVpF\nWt5b2nubvkAHChV4u2MYKpP8jBnXxvuCnBevOJrXkUb7PePnxzF/tWpmD3RmNepsIXRhRiKRC93O\nVoffFKi0ffU6yVoR0KqGuZCePJTBULZN93qhhjdhTFdZ/NiSQ7WMIcTe4/1FJXowILXpecBuzFIy\ntTTjAjSBbFXg0ARpHjYSRqEbs94PrUonS1XY/UJisRiuTQKj+0afD6yFWpinsIDnvY5GGD8bA+mR\nbA/WrANR4ho+UIHnKNQ0tf555qlMsLvf2SQ4XZj7z605zoBWzbsikm6PbIIfAxJ1TsZUKMXIfu8p\n7xvj4p8lSmAwsrk37SkhL2blxOzlAGOL0VSUxDqqpLU2hMi49rEc7Th72iirjUZVPscYJCPKBGSF\nzWkIzLorwzpYwaKfD49jejc9EW4MIDAJ2UobTvm+tjoCtiaOak0GWRvEqKGQxvk/biAc5oPmtabh\nefvnzrMynSm77nu8P8uJl307kJbKfIBQichhfmk2JfvgWoTbUvTcWmG0/aKhGTOmtZKfI6lTt8OY\nglTIe1ixacnnJSvznW9CkWX0iDd2dzbuDN7n+dr7/Qx6LF6yl+Z1MnK1a1hFyEyxS3lyM/wm6Fpl\nvMmjPhWVsnCfOErqrOmUz6l00nlvRynaAlm7yt7ygI7Nzn4+TA5Z6q7Uohgd8+Q+ZmdsEMmZu5Zc\nD76eooxXRyuCGnYpj/WxdTfvn5n19x29jswF0R0NnQhRm2FZq2Gv+vgg1R7zuiBm7QXxHZ33U+Wv\n3wCuoI+QoBMM3DsGeq8oZXeBR5rDbaUpl5NW9zNorFs/AHKz37xY6UdfUevmrXBtgXi6r64FDghD\n8I2yvypc3xn3zTGKN89HA1IUv+W/dm1CM4RMcIanCqh2zDH3sqMHzwqK1Q6yRkgEKpZG9HZ5Vpl3\n62JoSleac5Arfi2ROaVQ/mz0SYma8rQ57Dy34D0a2LYfDC5VB87RDuFCRCSDp0lKcvpBiKM7oGUS\npBiRohiPxrCePb7Zwzdlr7Pj9yTPct+6A9AS0sHXRzwfRZbIXVqZ11ZRmRG6Zp2kAWdLIZqExNnP\nxSBkDkFofA8ATg5KixGf6LABzWumEEYhm8PvRo9xZkYoDdoD9K4x4svdUwpt/3YiKauqc5xCmNZW\np4ZGQaZZgfeDQgZ6CLB2vaMNr6J4PPXH4H5Yby+Bbcb05IxY2MTgcTNK33Puk2VqEGkMvatc40Nr\n3BCCh7uIRBZspWkFxh1tn8qgT+28jaRZt4KijhUYr+K1aUk4PZwk/ezpJGVyg4VUp+qLjjQSKHQw\nD2M6qIFyb6qfDVkyk2kY8uzV+xT5asOxSCwoVOhHmcuKxpgssuqvlonS2noI09jassa/XelvO3aH\n+vdD6rMbwzCnSBC8e4cYyhqmPkl5YvPIjbNgtTVG98nu58MQ3pgT1ocV508PWM6LnKMiLYJJkY04\n9w6YqoWGELCfFoA0o6gxtu0FzbIXQgAgsiGvUt1xOS9aSnnxKqNWQKyUt8/+T5W/dWaKKY3qWQat\n9a4e/hBkwrrc0CYlE1MG0UjTYYWozGPiPmBAKQuqNaTZvHthYZaySVrb5CEYtE8UlGVvBsMx39M9\nqndAv0YeCxhkIo+fTXCoHwmS1ArCKAsKABWEoPH+VzAUTcLcvOAgaU6pS2va2MxAAvpE4Dta9KOR\nBVEw1aTKesDVQsi8Y8zksinMwNNzu3CdjL/Doxki0Y9xJ/OUZiTA+p5Hg/5okJWsxLIRqZgZVQWz\nVWv28IJ1IpsIATM6cO8gip7iBCIwD+/c45X6ApF71mLFazwwSVOpUJTIU4v+TZj2rSrq3mAFSojW\nQwtRDyXofB3Y7ROPZfa0DW6VHg3B0YD7Hh6uYKydsIUAACjTXmqzg1myEWhCcuZYrdyQV/1zNEjv\nNaSA1Gfeg/2JFtFSgrE9n322QeNMFu4Yf+cFX5oYL+9t52tzbGd4VvzzfVqsndULL9cd1xepImpc\nKTGcmoeGDPK3ol37tTi7PMSoVSLFgMxrHmXCt4LlcfeCUjOn4tXazf+0NXmX9y/h2jaHEwgjn1zP\nPbOS0qYOkoA4KsZhmHWD1yhpljk0voawI+cdZZfSwCFFUJwMAFYHqDV36pwUrg4epkJX+vATevye\np5c9vGiZ5nyS0tHcupfVrqgjhdMJ8OS1AdbzioenM85PZ6ScsF22w1wA0IqH4vVLVcjkxkY+ZTE8\nJ5JkrZuGfBqYk89xVofaXnnNSNZOWY2tt8ZPK/yZ1UdKpLEiPGBGbcVz+5sKLivtG2hig1rdbVX2\nFRhQXiBwVXJhrZKn2SX1zxicdt1SruiaFmVexMz2D0iAIg0Wl7LJZrA/x92bgCyWPogz3sxHGbeK\nPRz/xmAvFRzzJ5JDpaO2OREJjFyUp1BF+vp7YkBvwQ2AQ9gFQ+h7HHXyeM2zHIbQvaSvQapxAdYm\nsqMq5xQIMQxFfXjWSfCkKMWPkoVMNHfdlIZmr3ilQBO887PZ/CuUgNaDNxO08I7DiuZx3IQi7h2j\nHTVAlMC8+v53AqkTo5IXprGStWSkV83d7b0hbNGNM2V5YcZN3BgK5g0IiWeOBdpei2HqJU+TUcnD\nY5e2zJKb/659b+eEJ+5B7f7ZoWnNALH83Pgz49ayWNzTYwu7aCMXzfUn73w5hz1U0dAIX3jaIgih\nS61/3wYEISN6woR6FWyhte7I1XuGNbBKFNDwNmrYjciqWR2Xrxe8fHnB9eUqZDTNBTcvsZZ6yM6w\nDA22sIxmBQncHLE+nCR7o0ia2WkvDusmhXTfNLphlTGV8W/n5p61h6XGDWjeEE0zvphJOTSydrXu\nUsDHEBrrCTJ5xsIfEtSjtoruTllD79Lit9ZNlP+eHQUxpKe7LDdU92gEcB/e/9iToxLsu9ZeK3VG\nPc+n8//L3rsrSbZlW0JjvfbD3SMyz6m6dcEMM/oD+IUWWkDADA1DQgUJNBREBJAxAwEZMyQkUPiF\nFvozMLj3Vp08JzMi3PdjvRDmY60d+fJosW+sMq/Mk5nh7vu15pxjjjnGBOtp/1X1zrV7Zm0FDF23\naR5xephxepgxzMTgLyWrEZf4BghPwg1O+/30uYQ4iHR0SR36diNTJTkHrRAx9LNDaxmphsYP9ry7\nYP9esYgycgr4YvKQeb4y8avWyrP9RFAg0l6mDDbnRg5iwl/cCcqJkaCczPPUiYP+tt2wbwu2bUEb\nJaSwSxtwZ+jAXvayWbw+jrf0v5w1KAyhua7nLDa+ADQRUZcrdAQg4EDU43+g30suXK0GJnPQ6rTv\nD+RB0zbZ/oIe+qp8DtrDy0IhucH+9/b8c86w2SKlBlvmXA7M/P54NdHin5U/MGhVVLAWNQRQm5B+\nLuWMiGZeEZxTlUBnvq7X5XgtoyvfgyMFrVDE4o0tH6lejaFGknNkP1tsobl5Z1vg5yqdmLa0aehG\nWSqc37mKFd8Fd0AQ+pnq6cS2vidyQ5ONQRTGpIo8WOkK9N/BwRlJfQ3ehnng+L6ma1NxcG7OU1VZ\n9tXJ+WVdicL3sGhXKHmV5KalLZBigosWdudeaa0qz6vwPScyMCAycHV8+7UMQFsVAvcX0LPUoQD3\nLssJenAOyXuV7pY+uySZhQPavkYsLwuun694+fxCBmJs8EVjWxz8YlaJ1m1dsG8bT5C0QoOmokaM\n80TPsvS9jezDTUNEERYpSF4VHtou66SXf3rswtbXfT51qF9FLW2/SSkjxZ3Y9onIds45kqwuQa+9\ncEfinpD2HXFbySdgXxFT5OLHNWvffUAeB/jiAVGSpNtN97TGoclfocF0S7QE/V6ui/68TI5w+y1M\n9FwTX8U25JeFjKqzcADCHDBdZpw/nHF6PGGcB77Hi5pdWUtGTPJsOa36gxqDDdOgiIVMNIh2R9x2\nRdxq5sSKv7OTkXIrRlv4YdL7E4W/CvG1lgqUHrLKWRdd+D1udCHjhqzKfrTJDcPU5po1KeOeJpNI\nUoo8rrEqI3pdr8gpotSs70vZYWpMVmgMArqA+vWoUquK3xIADvCzc5QAcOA3ts3y0vdo79tn2a37\nzNB+wye1Sm+ENMlo6YFRIReuvDXgdtXw4bNrC/jqc90F/bdoXYsiYT/nrzDg4VEDvlW9WvqCvDFD\nNcupiggU9KxFTIl007l9Qv4AgTcyfVu8+i3TI9jyl+oV7vO1M9+TvuS83Lt60poxNHZWa4CtJDhl\n1FTHK0w3TAOGkQ1NnIxdViXeuOAwnkbkyEpwjkhdMtfsvcc4T5guE8Z5QGACkIhDoZLscn3NX5D7\nmwEFUQN7nezem/i2mXOWM9akn3v3ANALt3QiPMd7kDb/xGNtcW/9bevYwEkeh0JVjlR8gJBdGSZO\nrBvizLHPrc82Jz+lHhOHjrh675KqWfQlRHhKiXY8niq9++22Yn1ZyHhl2Vo/f2OzL/5OQvq7vbxg\nWZ5ZCr20JNA6+DDAwDIbvBVeVvgNhYIuITNOjXPoe7dxR9Ec6A2K7jv2NsImwjn0ooS+WmrPEZFx\nxx6JbR+56POebG3p+1WkGPU5SIn8Wpb1imV5pj52zjrrb61H8H27pBwIf7X7vwPH5xXyKvtRj9Dd\nu3LKMEH2EuHskD1xv8eLtX1hHQNjDcZJbJZPGE8TwhioPYaKcR61FRemoCqCvS31fJkxXSb4geyR\nrTVIe8S2zB1KVLWV1z/OxggPBtqq+oqc/2r9VN63FAZSrGUim5ErwH2whKQ3wKY3c85ZBXaE2NH3\nHlLysJZEewTej3FjIseq89CShTZbx9yUxroDB5qUYwt6/TicXLS3wX9yA/hakTpmr6tVe9C6+UjZ\n3QVlgsipT0ra/wLvOYaEOChF6Vf21RKxxWl+VbgV37uYPSGzvAr891f8sugGMyhdApJzYda+cC2g\ngUFejZTFyIcxKOhFeCyCE5Y0Q6s5IXJlJuxpCf6yuUvCJedVro0SfLpErM8SmhxyQyvuWQrDsqyv\nXNNSqyqQCdSvgX8aMEyBlCt9C/5+oARhfpgpMOeCagDvPYYpcAJAHANh/goJyGkmT8fihX3/KrGh\ndlL3wAsa8PZ2txo5Od8JrRCsQOe+ANXQJM+RGCryxtzakfHQnbTQxTGNzq9pEwVM5gobQ63e6abZ\nzIvovOVsFOlrbSERFzJKnBSltswSs28a8+TEpwLwDO0Xa7v5eh5VzWwWtjcjFyp8LGqgICh978Ju\nhCUVLqKsBlpCRwOGgSr+6XzC6ULV4+nhhPE8YhgHZZ6Tn0QfDBtC0Af+wFyZ4LxaXv/02J3vkuWk\ne3MIIwBxUOUxVt6zY9qY4C39aPp13zcWBOJJiEL27Ov6gm1b9GekvUPCPs2JU5/d2grR13wvPnq+\nH+h8VBj0lb97Y/C3TETtpwfkOZC9V+6vWisJnDlLCMEU4AfRurAIwWMIHtM44HSZsS4r1iuNeyqn\nzjtl64cxwMAg7pH2WYAJiFYnQYzh1pyT1iiOHBD5wZ88/D8O/vLQ1Nb3Z8xWHzzKiKnn30wh5GJY\nDe4AmtoejJI0YmRYf1+4jZC4D9Rgffk8uuBtjlP01unPjG5GEvyEECKkFfqhu+8DWGNRTbPFJKUs\n1vHnqqAnrwkTuNY2IlQ4SDrngAFa4VNrxMI6IkaWnJH21iqQUSCCfCKPh3VkM9MqMr7j6VyUY+Dv\nIbt2zn6+ciJzCZGQFVtJDTqvbiyZAHBAl5V2iREvAn/ovx1bwuZiYU0jEJZa4Yy0FKy2FUz3Wfpe\nfQLAf98jIjqp0BHW7lnijCUjXDYXGEPjRhqYO7h/mNqMbe9GJn33MAR6HyHnWdPc7RgtOFTqkm9w\nUinH4FC17aKIkfT/QBaeyrTveBBvWdZZ1F6b3DlYS2I0kvjV2qrs2lXYkqRKhfq1+QmP+xXTYFw1\nDstwPtFzwaQ28+o6go+zpIxsW2/TwcFyi0505XOUKpKlsu9cA5vJGJDEryT9KuRlLVLPswBtxn6Q\n7XSkIkgqYEDJfsvzguk84nS7IEUxkHEYxhHTecbpPGN+mDGepgNz2wmBi84IB/omXKYvw3sS37te\nrbjvc/UTXRUAHdeKgjwAbVnlHJHijtwJ66jOCqoWbv1qhd4OoKpIXAgjpumEabpgms8YxgFOP6fj\nS3R6J0Brlcj8P/WTCmxtSoTujZV/LVURJ0pcY0vqAitt1tqSSm7FGUfPOKGllBSmkOC9wzAOGL1H\nuVQs24zltDAPiPYUVeZjT4tt3XB7vuH6+YrrlyuW55WQAgMyz4Jve54m+bL31xYT+P6TFvRX1/pH\nJ4LIOfR76YP0/Wrpf7SKs43oOUea/8ZQ37jkzDeqh7PkaZ3SjnW94XZ7wrq+IHH/R6FW60FidRUp\nxa9Yi6rCdij1Osix9KN+VX/m7huh1tbbN1ytOgfL/X/XBX+R/uyrsaPsp1yEBiXtwcPYVSEktycY\nzjqpaor6sJBMb1MrBGe3AE9OdFDYcW782AK5d9EDYGkDj1L5U/UvyWXpjlVgUZXXRVehy3vWJuDT\nzymLGlr/99URXGmqCAWRoJC8Tw/9KdAnPU/+fesPap109yLIjgRmBMkCDLLNyleRTJwCuKeZ28Hr\nz9ZSgYzGcQls++udeoOHIcANThn9BuYAc6OKMFPV5EHOceW/61n0tbYT3j8Xb7kHrKUevuPv6IKD\n3cnSVvZZhfi7BCSnAiCj2KJBmMCwTuyEq18JYNJOEIdDI+8NNEdDOhg+R+0eKDmjpC5h4lHXmtmB\n9EBWfVvw1/vAOUQWmpI2kyT/OTX0Z5gHvYYi5U33QyAkoFTsW8RyXcnYaYt6HqwjmH9+mHE+E+dD\nSJ7NtZASTyJF835mGptd0UTX5HyNPSYA96wQRkoiO15XC/6MalQm6Clpj362FYTCM5JksH8G6R97\nP4Bc7cjd73R6IFvfeVJ0o+SMmqAIC+2HpGLJnwjRzwf4Hq8GRgiK3LYyb9jzS87I1rAj4U4qe3tz\nI9TnmJ9zSgSs7um1VuzL1hFlM8q5wJ+I9T8OxOL3Q2itKEP8FGoJ3fD8+zOePz3h5fMV221TMz1B\n4J032oq3jAKRBkLRlp+is5y0fGv9VN63sTxFxUk2ktbDNhyIpLInnX9S5zMw1BpIESVKf8sfUIGe\nha7uaTxRUMEjbplm1yWLI/2Arteh8FLb8Hp7x4OIyhuXMVCWv2O4WoJ+cKSnn62F5epX+4MsEGDA\nUCKAOvD7SUWQacrBx3QY6aLRRtGrFoUnAOAROf55gXYLj12WrwR9+uM1/en54co5w2RynOtJf728\nqFTpuVZYfoH/DCLcgf6Rp+slyn3Ngrd7T+nVcgJwXJV7sWjnmD8DrxAGPTd0QrXKvHeN86jfF2iw\nmkmAyEmLljkFdK/ypKhNF7yIux5vQNZY9p1v42o0+9uuI5HDkmbxfe+uyj3Psp2m0AaYk0HSZKud\nC0VD8DZ9e3rmWxsiegubLSo6FA2cdMpYqKi5ZdmIoedOWMihSFEhf8mEUG2H2dYi05HBltH07Ru5\n/0wp0vUDuFps1X9Wlcy3HDtdK6NoX48wuUqoX+JZ/FqYpDhXVenzwWMcB4xjICQBBjFnLNuGjQmB\nqutgaaxvPo04DyMCOwaKEVCvlLkl4hLkTNyJXtbYurYvUbLS2mbfq/5erxAm2nfjhswOrXFfEcPI\nSbC4W/J4X+3RSLKgpsAjTH4iDWogYmMgH5rTK/m9nHVEjZLCPnkjO/YUI02YcTJMZOlG+K3d3iej\n2RoX7lz7tiFwQWGdxbbs2JYd854QBq/7DLXoPEIlJFD+PEca+YRZsbwsNLt/mbA8nmjsj6ecrLcw\nhdqqaU9Yrxuun1/w5dMTvvztC7789gXL841isDU09ivoIE/vOG6PobKjoY7TdnsGCGH81voJ+cUo\nywAAIABJREFU7F900xPCn7WNCQqAsk9mKpdS1PZxHE8IfkBmYZNYK5BlXnHXTJA0zg1CGAi1sdyz\nzzRHWbNIh3o4fljI8Q+g2fZvb2gypiJVsFQmb2H9ag+bg6ZU+wRFF37QHJwt8ExgKtXwPHD7edmk\nIH1vZ8ly1zdXq75abq82zkGnuq/kMgC20ERTjztm2W0RDNaVhT9ZJWeUyMFfZltjbHOnOFbytTs+\noEMFumOS0aPKSIlupqioMGz7SskEtOffglimLEwDv6j5oXvvxsGgH5Ipibeu8TS2ZKFWHXXTwOAt\nbLAK/0tyXEohgZeY1QET4OfHW4aiB4SOtAZA+9VZ+sg8AqboCc8Ek+Sw9PgNYIGcQH8mx9lHfDkf\nb5h0kXtOxFccExtzKuSocYz+uuHKvdEfU188GOsP/16WEtU4CfJdu0FY0Y3NX9vcP5q2ujFGDZKE\nI5BSVgQt7veLHPVLNnrPQjIorQUYvEMFkVP9QLKrYSKIdwwBUwgqyyvBfJ9nbDGqmRXQxltlykX2\nDMeIQ+IKGtbAW2ofmsQW18pNMayFYQ6cGcFF733yx2FE5TZu5uKJKv9VEVm+SbQA0/vEeUhbIiXA\nmNQKNNO4DSGM6hkwDDPGcWZRGkZOGWFU34oUdeqg5KTXGdwWlPYWIQC9INSxT3/PIuMuqwVFGAK2\nZcO+7qrpL6ZR5PDnYV17HggtkFYBCTet1wXry4rTwww/MoHXGkK8GGFYXlY8fXrC829PePr0hJcv\nZAJWSiE1XEGMWSfEeSHgN7RLrN4PXAVrvov6/DD4W2/5rmFo1ZFamTUOqVI12hMrjLHwLmAcTxiG\nmYJaNhoIJdhL8kAs0v0A6R/1+iuMKTBkBUY3lwGQoO8hBBvd8Pk9slbLlnrmKb0p8ANkzuH6m8cY\nnjzhnrVAgAxbO2NRTPOmB3AIhuiQB9ksVfSCFaBkrMlA/LMDzfjXeghiFFBT9/vW7uAv26EyYvV4\nfwYc0wYYA89ujpIA5JiQBJVRlINUxfrvpseMYxqigiPew1UiUaZsYWXkj7+rbFilFGRjKOD0I5Y9\nwiIB0nRSr28HeA5rOk0KpZdSYR0RtpKhKsZ5ciRTwRV+CHNMNPd927GtW+sXiqRnbjC5QCLECzAK\nf/dumtrbqwCQGX2zmtjQdcX3d3ap/pggdNdS2N1QFcuERYGbCVBq96NC7Xz9dDStsCgPIziakEES\nGcPESj7GQ//aNIns7hm0VYRljihe/3sZD2yeFCyBe+dSQRzLiJ4kQoU4QMUQ2uLZA0A4P945DMFj\nFFVKvhcj847E2vq13GqpFXtOiIJudgVDqeQbIuiYcG2o98vJiZirCdwvSCWO3Kx7guAwnli7IKqJ\nWi4FMUaQBW0j1/Y8Iu88kfU4AZZlreN7hb4jWQQPasHuWQCOkri2/xNkTrovqVNp5avN721RTV+M\ntjZT4wtRm/ktq1YKpDLNEVcy2RomMq6DcE+MhXMV1VJqpShFZn4UCIUiH4J0SCB0AiKS4NNyXXH7\nciMy4E4TItaQRkd/rmHQtYJ6LlzPx6H7RBC36Tt8jx+elWEaEPcI1Ab79x7ZfYXZ4NGmqNT6UpIV\nFuRsUWuT/gWEBETVvPTF5T0luND7twdeKuN2xfT/IIRDAMiuafwLRHbv2lNixix9J6lEtbqsFdY0\nRy+BBCUoAV0lXJgpz7CnGntECvza9rDc1/UOLhEako30fPpjb8QOTZi65EIrXmOoD/bG6jelHc6x\ntWkSYRIe12K2s1T1pUK1+3Mph8/SvrygAnKfVOEIdJWUc9r/B6DnW65sqQW5fONYuuTMAFC889V3\neIvQyzDTg95PO/CbUTJgzaGylSpUiF3burUKwIqXORNCu8wkJ7YJdU3QpnQIFfnbs6gPjk56P1rG\nyIhYJwZ0J/RbctYRP+OkqmN0I5PpyQFcqgz9y8RNR2rsk0LAdmNprU9tXHt+VFtBWgCcHEA3NoNq\nK0wlDobpPh/2GPiV7PdG2F+/rW0CVgdEiT9ZRa34D4NzsDD6fKwxKrcl7gnbShr0sMeg3CcuPUlQ\nMmAhDRtuiTjv4SwHN/7V2aaOqW2fV8/APQjYNJ252l+RkldV0FIiSgkQslnfQhQeEhH4GheMbH5b\nJU7/zrMGjFcWPkkDt0Kmyf/2Y8r9ObKaSBLXqU1C6Unj47/nWfneomQ+qyBT2qMmr7qXSMFhKGmw\nlkaAU9fCSzHrlIjfvP57UbCMGyUYaSObb+KQjEoApHPMfX61UzbdGK60WHs7dCoeg/8+3+OHwX+c\nR6BWxJra6JMVhbL61Y0rF46ETMSPG/zlLYAB1rZADgiM1IgihlIbkAKaaZlgFsGJrEQ+SSCIIFbh\nQKNzlEnSCRAoXOG775AfvrXWKOiB4YetU/gzTb3PcDX7epaWDPVaUGzEKPJhV6JWpe/snIUPAX5I\nHZuUoP1ivtYv6M1iaj0KXehN0t34b4O/ViXkpI75GveEqO5c/XfhKh3Q/mI/BunoRGolUVHVIKjY\nitAhKJEZ42LSBH5wKl9XqcyadjtXAvKZ4ADbZcKkP/4G0tfUgn+KqSW8hSpgeci0tYBOUz4X3n/M\nIVGupSDHAmszjOGZ3WhhXaIgzyRBgRVp3MwotGq9jP4dgwdfgUPiZyz0mXX8quVtyBddMhpPlRZV\nNnzsjLK2KhBQEwxue5liqDN1fEfqd9oW2MUMRWaUbeflLsdaDYDcqh95yCsn/YbPMboZf6kee+31\ne1bmpBSgiRQJ/rKksjYAjyRCn/Eoe1UqNA7HFd+6bFivqyIQGvg7hra4JOqIpfhZMPFS2P9ChnbO\ntoBvutDX7cvyvXLOd/X9h2FWhn9KssfWw94j+5VW2RJoO04WtQL6ilPGgJtWjBi/AQUpccumtD1N\nAj+RugG82tP0/IEQ4lqNJmP6WW8k/LX3l2SegrOYMqlT66tCS8jwYaRk33pO4mWfNFQApBJbUpeL\ntqakSPDMK/CDp8JD/RuKajrQ6eu4Nq4pZ9buOJyzGELA8B2Bpx8H/9OoimJ6M4paFF997TPrRaOM\nzdrIN4Jl4YegxARVozIOpSTtF/WsfLkRRO+8J+z1WtLWFjjXWgq9ixN9ftU/k2O4d+0sQBO8g9NN\nuBFpwFVvlZPdbQQANMClnPUBOgqP1IZKGKtmHmEMesGrbnCpq/hLu/Fr1vfSG9FagsrRUBRZ9yYA\n63qFNTSxkfaRkAqe1Y6JbX1fbTKlVkJDatVrLAmA/CpQJipUh7+38I2sAJlyAjI4AejaJRbq5td/\nvqIEsguWqtKu+pCk+wMACW1UZCVhSoLTJby1EWsIeaE/ttbqPG7JHfLBla0GC74vbK/Yx58vAVAM\nfnwIaoMtPV66ntD2gV5jrgp6oR5Jst+yDhWbJh3d34OTDHke+r/kSuRb+YYRZI6/e0U9JFE4vo2e\nWyUyHQhN/Ln884Su9ZV/Z0l+5yqlcEXHGh/u6NWhlTXafacyv3vUSnG7bVhfFtyeFywvC9brSqNj\nSaYhsu6fqGCCpWdPB9oHhpHd2i4TzjjrOCFNEFGPX9CBXAuq7EOyV/Kem4rFcMexj9OEUmJTW2WC\n3etgR9dG7u0CawoJAFWn95vtUNxj9c7VPQoMKyY2bpYguu25cc5ApOLb2J7oLQBQWeF+dajcG4oe\nGaOWpL2U2qrznVA6w8qdmrAxn0e4P8p/SRKUWf+iVtUtqCxwRcieHJfX8yotQBm1TjHqc1BrQ0uk\n7eeY1CetXhUn6pD41+unlb8cdE/KaeplVHHKTVxUV18SAPGEHrT3IzOkdCMYlOL0hDcmJy3ngFKa\n13POCSU3fsBBdrK2IF8r8w889U1IPtXpCM69S0fQivR1upOLzrlOHwooFCgPYDIdA7V7aPq+r0B9\nUilavpFccs2auBaIq1wvb9knBLJRV55zNdbAlFad0Xd8Q/C3Dj6MGOOkPf+4EvFP+pj96pMeOSEa\nPCDBBLCVeqUeTuEwIfA1GB0w3EMutVVHkkW3gNtVg931IIGQ2vr23MO7d/ngIZ7d1bfPb173/BBz\nYiGB3VoLBArALjgdv6NAZQ5KmX28NEBX6dKDG4YAP7K+f2C3QPEMkHnkLpEUtEECYW+1+xZt/1pB\nHAMLbpXx6zV5ig9AnyupxvV6cOLVtaUELs4c+YvlKZECVDYOqwBbwbb7tsp7cSWlRkO2tdaEj5BT\n08kQyP8to36Rq+Rg3EHhk+5FKMOdDlMKj4I9Rqy3FbenG25PN1w/v+CFJX9vzy9Ybjfs64YUdxp/\nFhlyA77mNCUVhgHjNGGaZ8wPJ1w+XmCMwXSadG8R1T5JeER4iPLeHgOkc5W+Y+7yek2XCaWSQA8J\n8aygwoMveY84SdJeI193KmQcQleA9clz/WrPVkfLymPE3YSS+NdTYcUTNRz8W6H4qiBC24v5G+s9\ndM/yPjDC5rilwK08FqgqI6HTUpwVTa4NWTBPg2oBHJ7RLhEQV8BerEtgG/reRpX8BHEV0mHekkpG\nZxAabKwB5qbFI8+smKF9b8//afDfbhs2SzOeGpykkjACKxEUTxe5cAWeUKvXikMr9nKc+6zaqjEK\n08pNI+OD3g9a7ce4kQiM2xUROAoCtcyQ4BhimPrQhFfuXQJDC9nmdRVLx1u7CpR8lp1x2qO2ubHT\nS600BtWpoR16Wa8qLTKMKcjJUYasMHb+6iHid4C1DqQh3XrRckO1NsvP17bdIPLMcT+p/4LchFL9\nV0lFu+9v9HpTBfWa4KRVOtrEgCSPQGUhpZZECVogbTZroPrt6D8TAnF2CEs37y2SmvcsUl+kc1YK\nyTrX6jSrbyhUUejN2QbXalUK3vg4QIsGd4O9v/1yzqmim5CEKDBWVcWLMSn5reTWhpHKlO5V2RDe\nNupX0bwxGnLmvkoiaE849uv1vCRBBSsLBAlPhBjTJTHJz1kUV6gdwHtMcwZUeEArf7mJ5Lrrd+bz\nkFJC2iIZ63TywPeuLUYdtzPWwuJojlN5L+j3rMz3QdoTttuGl88vePrtCc+/P+P65RnXlxcsywu2\n7Yq4b0pia9fJ6fhbCBOm6cRqiAbDNGjLShBGUSE0gHIMSinUWuuCvyQD+52ch9PDiTUJVgzrxPP9\nm947B9jeFCr8KhEEbbJwLsL7oH4VIuQj8FSTHS/dPiaVce9YaiGtWhkdt54JhZIkVGkdtOKzRyLp\nfd6m7TKMg07WSOLaK66WUmjCgCF7cIGSB48wDir2Ffi7AmBlyqIouuwFkshKDKz8v5xbwhFXCvw+\nkDjeLWZtF9AB8v1o2wSAjMqK5sP31g+DP0FPXjP7Hrpsm7xAOjyGl1PLDDngOJeASi6A5ML0ym63\nkCBPn1FLsqGJBoJW/DFuOiKXtQo7IgC1St/D82jJoMIZ967CgT+VglCL+slr2wJopDfeLAlyoc/I\nfFG9c0glI6FtUHIT6IZtjcK8qkZXG0cgpePG2me7gFT8dCO1c9bB4GgtgXuWBP9tm7HvF8QYsW+R\n4cwde0xIOSNzn+k1xFY5YCe+qfvALYFa3d6kaukSmazXkoVVxJObN7Ykm4d8HohZm3LWzJokOFPn\nqX5/8PfBoRSLbI6Eo1IKJwalXUtL96CDw1dMdUlyGIGA4Z6uod639UKoY2IemCEvULtpVYCRZDuS\nSVDijUA3kO46aC9A4fSjIdSP1qG4FwRBExMHY49yuYJWaVutsliKyci50+LPBNPWUlEE5WB4srgM\nm5y2F4oEfykIWEyogp08Gcnro5xoRMiopFRrVPnfd+wABX99WxYa0qSfz4n+/Svwqe//8umHZfnW\namZYB/gQ2ImukXgNu8J5P2IYR4zThJEd/MgzYqBqVNokwHH/KY1Q2gtp6Ujsndf+9HBCKQVxvWDf\nVlJnlape7zFS1SvFoFSa2pLqXaywvR/gXYDjRECkjAHhbwmZr30vKlqsco0IBZkwDBNCIB0NoCIB\nIJkDCaSvPUwAq+ZTb2t1DVPzVBByNkD3FqE1hZJ78Dg0P3/WGYRxgHEW4zTi4TTjNI4YFDGqvF82\nFdge5ayoiLlgTwnbHrGum/JE7JNFyRnbbQcY8hdRLEjgD449RdxX4kb538bS13vuNbIi2UFvnDPf\nHlKWqjQlgeAqUAsLP7A3uFgwVkCqc1jHjHmn7wMOHqL/zLcHi05sLA6UVUFKNtrCY0jGQCH/EOjh\nCYPXEZl7Vq40c95gS04oZCPgkytBzKDyONVh9+Txtk52sxMHkcqph/0bglq6eVePZOIheemzW8Mj\nPqWYjlQp1ZYkBe0c/2wJ4Y+CPzmQ7Qv1Mbdlw7Jv2OKMPIrlJ29Gtareivif98Fdvj7FgiboQ0z+\nlrVLC8A7h4E1yr2wWzmZ6I8xl4rIs90iz9m/RCr53jVMA22ce5L6FwDgc0H2jqp/tO97kD0WmNh1\nveFqgMqqa1xBS/++d+PqN0NR6wK/ryR/ORUlA9H1l8Srff9ezlj4IXfu/9o6kMPu0ShNSNDeX58L\n52j2uXTfKWVkw2ptnRgQ0JKGUgqKc/CBNkGR6VVilXBjOIA7T1UQukRWWh9kJJQoUWVXPVVLvHNt\nKaGAoPLkaE4ftarYzxFta4mB9WTHOl9m0vD3DvPDrHoPtR6nRw5iLIbQDj8G9YcYpoDxNJHhy8OE\n8TzC+eYxIBW+cmZqUT6FJNc5F+xIGnR+tqbLRAJja8S2XRDjyt4sJMlLCKsQehOfe7Jgj3Gjvdda\nDt6jBnHvB2X3C1r8LfVRo3v2iHGcKfBPgYRqTNPCqFWEzYomAMo946JUr80bEK8wDqoj0HsJqCx7\nqa1dV8HaMUQEHMYd02WCNQbzEPBxnnEeSbSpVhrn3FPWfRGQvZvuqZgy1hixSMJQCuImxkg4mFSJ\ncRfxixxC8KrDI1wfQFCfb+97Pwz+wTmE0WNg7fI4RfYXD6CZzwajN3ZmhXNUfYdgNfMTuJkyqF1R\nAIIsSK6WskISBUo5srnP3vTCGV5aVnKEKl0WReNXMurAFf8wYprOmM9nTBcSkjjskHcsqab7Xp9C\nb917CcRtSoWx7YYmPgdVhMF7bOwCF/eAOFIf3a077CIVU0FSIaVjL+g1aiHQWKv061cPU0NEWoVy\nz4pxg4HB4keM4xnz+oBt3YjEdFuxLzv2S2KFvpYY6fXgAN5XINrbOhwDzzWTVZy2WRL/rE0JOwug\nDN7DM6myJ95VzqpjzjpnL0GAeArE1JVgec9ywcOhVXnatmLVO5fcwV2uMsydDXWzE0AaF50indx7\nKviTZNa9nyBp50irOUFNut596QK/9abN1huDGpPeP4pQvGHMEYD2MeX7HubvjYHhirzkgurK4boa\ny9wXbTdRlZJTZqGvdjpe07S++32MAWzV84XueOkCUCBNqY1OxTWqkt5bEL89ZySG5EfnMQ8DCQ+x\nzTT4fpZk1/A5ojtmRAge82XCL3//i+5dxhgtPISboAEARp9x6TfLaVHE1VuSC3Zep476Cj+XQs8Q\n50OFk+EtRZUZv2dN54nO457oeV8XbNuqRRZ9J953XGD31ghjIh8bfY/eVKyUolwvZ6ky9fZrxjwA\niBCQd4HbaK1NRhA3FA2SmCPFWe14Z3rfMGp17/KjZ8OpQskUX29B3yr32C2YvFvZZW8zZNV823Cb\nV3zxFNNiKRj74B8T9pwRE+2dsbAgVW7JfMmFZv9fVlyfrqTx/3TFtm7kCMr/jlqDzRjMONIFaMqY\n4KTv34LwJ32l8TSq8hiNm4wYhgHb5g7QJl281t+hLC6wI5SMaxkMdoIrHjE66hVx8JIg5VzGvvdu\nfzsKcwpKZovfLCYSRrNmK+pHzuvnjuMZ0+mEcR7pJH1H6vBbSxAIkaMV8g+M6PzbgwiQjLr1D5pB\ng7qLZdKUstOg/aR93bHeVqwvK9bbShX2jdyf9nVH3AUx6VTmbBNYAjqI6qD2V5TsVH9A/ni9KIsH\n1u1KLlzrFfvyiO22YruSM9XtYcdlikjjoH17yz36vtIwBhQsvvE5kvUaEDIh8Vm90ys52VVGYSTh\nkoon80aduOpPkcYoJQDsq5y/HSltdx070Prjzjlklxn2dnChINTAG2RkOLlqJWISBX4wJF9E9U5Q\nMiNjULaNz3LGDq6eKcBBg7YELyOz3YfeuFMHQakWhIYhwVmkYO9eiiRInxfaRxT4H9agpoySgcwi\nTbZYuH6jMYJidcxrf0x4jvP8BH0L8bH/LkB7HrVd4Gg8UBKdHBMlpeuOfdtZR4NQxrfk/JErJQOg\nePrhmb+HKvDVou0u1fcwFQiADR7+cqIxPHydtMuxtM58+3uONeqeaQ2PGguZWX62NjhfIX3T3jvm\njGXfUQFGzu4LgMM0oOSCaZ1wuszYbids2w0p7dpbB3iixYdurwGk3SriZM4NfD1dcwuEcGGkrUvI\ncZsO4p/1HoYh7MotnVort/Aictq5MJTWcUsC5H2JaMzTaXcuP3jq74NDZm3tJiHvVUY3iJPFiXam\n/WC7bXg2T9iXDU/TCyHnYsedMwsHtVakilAxMkZa/aIvQHoh23XDdl0PuiFenABnagtZ7/S5p5sA\nBxL1N4/1RydCpGiHeUQpFfu2Y7yOGE9MalhFrckjJXHCEvUugvNS2vnBtygloWTyMm/92L0jeoiH\n9I5tu2Jdrxzok2adbS7e8A1H5iskHBFUOWoYZszzA07nC6Z5pszImDfN+QM0q0/VaEaptLnJA+/Z\nk95JNcLytL0sq5ADK0NwMtpWcmMkx45JH1keUqqWuEe19RXOgzWtDylkKyFQNnGMooYmhQVRbP0a\nGfjeEj+BbVuwrles6w37umJbdqzXDct1wbKuuO0jTpEkTZ1kxOBNEVACUjGkVNb3HuXcOJB0b82s\n3S8wKlc3Ovsv92QXDCJX/FErfybKbBH7RkGAfOSpZ3fvsmzSYSr31JxDcQXFc3bCxyHOXj29QkbL\nhGjWV+8w0IAtMraojsZ9nAS8il6oSiC8ViHyxiZTIc4pDNn/HCWWnSfDnciHjg1J8LctURF5UU14\nJcFxFsU5JJMaWlIqjzb1ypPQ9+5taK1r6IVOEeiEkbTSemb08T4oudBM/bZr4ryvO2IkJvpbov8a\nIyUdAGJhmVZG7qQgsoXmywHx/TAw1RLRh4uR4BycoYAt/V6R6lWZbOYFvD4eWEIKvPMwsIfArpC+\nEJK5Paj7DD8vWrh4j3Bn0SNjxtNpxHaeqHC6nbDvZLMubHwD2nerH3Q/HgL1y61r+7Fws9r1a8mg\nHJTRtiUbp7H+v5DVckrIIMg/xYR937CzDoFwxioXl3JvKTLNRLh7lw9etSKQ6ctJIp4YTSy5wgXD\nqITTcd6cC7aFCtNt2Q8TOnSP5mbOFAmlVDvo1Lhr0KT9GCMy7zXU42cr8Zm4bO41Cs8tcSlav3ms\nP7wRvGcDg4IwBkzziOk0YZlGjPOI8TZjXW+cAJASlEHrhVP/f4dkdqVk5BR5dIt+v+8rSUkWCvDC\n6N/3Ffu2HMgKAv0D0D6IBkBj2RN7wjjOOM0XnE6POJ3PGOZB+6vNdvO+1cPmAjcJVO25F+2chcsW\n1dRDZg50bdEO3uorf/kHBtC+v2xytTKzm+EzqZChm7LTwNC+pxBgekGk3LUF7gsA5L9QYK3Dul6x\n3J5xu73gfH0khOK6YrmtWE4z1jFiCgHeOlhTDsQoacPYatpD0J0LkTKVKr/qsMYR1tcstqt+Mgf/\nPWXs7HZGsC8Jq2zL1j00b4O9m0xwRXEWuWPQmu7yJWsUhtN/X7i3y5B7H/gloIkxCDn98fy+7wI5\nCLp1kIpXPpHfykI1N4j4VnUDJFJgE2USr/nXAfh7q6Fj8rldle6bCU9OdOxiPkIaBl6TFPneFOCa\n9j70mNrf1woYhlchvBdJOpS8JO2r7lqWyo5mlPTtSzeRslMf+q3Xflk2CKponUXmFsCF71dnLZIx\n6Lp7tP9YKKmz1EqBHoRg7b1I1k5qblpFmu54GeL33B4MQ8EweBTv6P35OmcWrUpCeM4FxpG+h/gG\nJGsRGR3Idz73w0R6LGkfMZ0nTKcJ4zRjXUdy1OMgVm2g/dR4Ru2corMSeB0jk8147XXbUpIdRjFF\nKt41NJO4IlByX0pNg4C+T2FOE+1tfSvUOa/P1b1rGAdKmlKTq6bvWJlLQvwNmumn9zbC52CyqaC5\nySeNO/IsaatM97duj+vI6+IKSEqwSQsKoBK3ZGTHSNYWkEkBmqKhRHhbdtpnvkPy/vGoH2eMKWea\nO56aX/kwjxjnCeN2wrYtIO9nubj9AWVkE1GKkDU4KOXEfX/6VTT+xUM6pcjs0Z5k06YKJJhJ1mgZ\naqKK/4Lz+QNOp0dMp1ndogLPTN+7+iAFw1AcoIHNGTLkIAQgK4FNiDh9oJbMXq2AxTFt8PDJww0e\nfj8amtCZ/HG/vq/4m1AGT1OIpXFOIB/u+wMAZdVUTazLC27TE1kv3z5S4H9esL2sWC4bltOOOQ7a\njwyuI26iq1QBPTfozmMpBdVa/Tk9//0xmnY+hGAZS+bgn6iHxuxumUogyczEbY/7tM1l0Yx+RS1W\nJW6Ll4rAwHfs+WJyOy4WKalcHcEBpnaVuqOsXeyARbhH3f0cs39rl9CVfjQIek6bc5lk/K03GffE\nXIf94IV+zxKrUh1FzcJTIIXMHAjuJH4FuCLLgKBxMhaG1uow1iiRT64puV6WDvW2gCmwxUBVgTV5\noOCIWvsbQ2fcEyvp7bcN+7LRvdD5htyb9ALAuqwQO2JrDeI4oGa6nmMgLXprzOH+lLaVtAXkGqaS\nsccm/KNtqC1qEJDNQs5xGAPqWNt5Y/njahp3RlqRgiyVUmAk8UtQQrJ3jv/+vn2Pgh+QYsK40MTB\nMI0M41stKDwbi1nrAE+flXMjagr6UyrpOEj7p1X7x1YHBX+nqDHQeD3yDJA7LLXvmvBbE5crpSgZ\nXYK/D+FNaO84DwDD7jmTEBEqUCXBXIlEGiZKflxw8NlRv57buFq46P6HA1nW872fBw8XNzn4AAAg\nAElEQVQfxXyKUAAhxErCrjG0ax86JuL74HWsWC2xu2SlVhLn+17L58fa/t4TSW2Pyk6WOUaRmpym\nGft24qzPEKxfu5lLA7BsM/25BKWUVB+gjWoQetDcojzv+UYh76+zeMOsfoH7J8zTA+bTI07nC4Zp\n0o12mAaE6f7gL8vyTasbsnyyod5/cA6R5WlfG83oCwCsha0V3jU70JILfOJAwFmq9oB11e53XegU\niKgWbank7kXJVNZzR8JL9837yoYZI7BuC5aF5pSX2wvW50cszwuW64pl2XBbd5yGiEFmo7tjNkAn\n8GOODz0EwW0VVb8kgZIZf2Nw2PxiksBPM+/SJolr8+GWjLm+kfQlIj+1FPXpFig7gaBtebiToYfe\nVJBqGd8vIldLnBTX1PpYwc0Hry0ACf5yjVWZsBQ1PJHqWd+/Oxz6bgT3CUy4q2a49EXvq4BHljaW\ncUkAcL5q8E/8PKU9wdjSWhyxBekDrA/qzTtvdUNv6Bcfq+FfLU/OVNGJAAV7A1iGv/sRwJbsRCaj\nbtiXyEI6sbUN7xS5AUBSrnuTXN3CrpMiHy6nQyKunXsD2GroGZdEl4+vh7l1/5KHQ9BECYodW1tf\nplMYFBRMYGGWLxZBKtF9AKBtTmkN3LOIFE3jZOOyU3t3IAjfGAcSdUtIOXGglll+SUDzN2b2jfKx\njHHa53/dJtb7xrTz1NDMhMzXs5cdlldf1OjItSOr5LcUfNN5AozRMdqE1EnnFk3cpvMEN9DzUL/y\nJgAs2JDMFhRn4QKJflnmjAG0l+Qs5OQIvzeIX8nsPN0gybvt9gsZdc0p615UmZu3byQkZS2Zc31r\n/TT4+04ZzzqLMAWMDP9v00pKVNuZ2aBA5IOvRfTZM3j3or453zhizSjyve3iET+AzlGD+siytBED\nteJn4gm1HkYMw4xpPmGazhhPE8I0NH9t/t5vXRKcvhU6rKHsOnTBH12CQP+mZWMGkpVLz7urjLuR\nP+iG0QV+2TgYAq41NznTTLyAFHcep4wszckWmNVognXPkocuJSDGFfu+YNtuWJYrbs833J6vuD3d\ncHo84XaacB0HtS41pjNDoU49jGyCaEFdDkaOX86ztjX4XCXlXHDGzFWP9Pl31lAXrgQRvljghaui\n/MbqbxyDjhTBiIqdbaM2pqJWp0ZOLes/JqhCRtUxWZZtpbFTzxB6I+3phmYqVR3liAJ8dZ1AlbDq\nQezNiCSunADs+3d//ltrnsjOmJKqqKx2lxxssJpMq9NfKUx8JBVK66AbPGABnv6hwNZ1MEzX+2eE\nQO+9UgHbJQZSyXTiYpXPedpTG0O9bbTx8egZtRX3NwV/IdmmSPuWsRbbstH9VAvmcaR7lANxhTzT\nxF+xwgMyBlH5GnJDUBKk547vMRl7DMFrchg4QQws02qMQQaPFnPQyLHxSwBAxh2JlEbBP6WElO8R\n9wXCRLPqpVSMtxHDONC1VtEaClg2R+TkYIMDGK6XAyxVCrojUquwvrRzjIV1HqbahhJ1yIDs87XK\nKDDHDZVFrq3KRo8myHQFSyRP9x07AMwPJ8CAybyF+WlCmq3dvZHYxtkhM/TfJnCAWtq4oaA61lKC\nJvwWffG9X9GmeAT2z7FJVaOCn63G5q+VYH5CHIEI8ESCRdoTTZmN3z7+Hwb/eRBvaq/ZZZ4Gml3d\ndmIg7hEpnjnAVKAWxBThUFpPpoh8Y9Pkl7EMunAGYglsbcuy2qhTRbVOA6SMjVjr4F2AD6QANY4T\nxvFEr2nGMI7wwXGPJGA608zsW5bpgpkEtKarTkErOIfsPVeodMVlzE360wAUBkuloKSWvfe9HtTW\n+xep1+aSxe0PSAuEs+KckXJCTDu9ODvOSRIrrrYOD+nPj5uy2KwV1L4T+e92fcH16YTrlytOjyfM\nlwnjNGDwTglRAFAt9fphWHoYLQEApIpvSZFA2QUV1TZPbfDZq8wLSEycksB/0FOXfu8WdWMUfYm3\n9H7nYaDpBWMQnaVxHktiGyXSZIHCi9ahOg5YHV+Dnn5LEPaxI3YI6GQhatrIksCcPM//erNTZ00j\nFXDzPt9W0mHYFyE7RiW9VdyX/MxsAbq7fFCzpBl1j+wzIRfRI/mIkto8fnM/FLtbuoa9aJHgV7Lh\n960BEbuS4+vvF7kvpWqm0b6s8qfbbeWNmbRASJ/ihsj8lXtXjMIZ2bWqst5hWzbUUvHh1weMY+BK\n/Os9gjRLuPrsUC3xfEg8jkojZcyAFy4ImzcJKuQZuhe5cK3+S6HnOyaVj6216qQDMcdpBjzt492+\nFtM8IlqLkgqPdXvmoTjm7FB1bxJb/DoeP7RO96mEyvyO3MjPpvAzXVGrhYVju1q5ph0aoPsruJBs\nbc3So8rS5+4SAEkKSWuASHHjabz72k+XiRLfznhtX3YlzRpj2eEvoc5VJ3CMYUKiykgbWJfhsmt9\n/JSJ5+OdTvdAChqR8hWiMqOXcZeplUyJY+eU1ZMca7XIqWBbN6Q9EeKTqF3/vYL3h8H/YRxxm8jX\n3ELmsSvSZSLb0ttGEqMCJ3L7CtsNRBZj8pOcDiNB3kDYMv3FE/im9XDyIVFAbjcIgG6kj9j903TB\nPF8wjifM55Mq+llrEaYB02XG/HB/8O+Dvbj2UYUKoBpYDg4C/efiOaAVlNqp8fH3FbYv9afpopL+\nOPVkhQAk54rQFmaUFgfpk/bnSvkTHPT3nUYjSUtBIKtCD1yXOP1siXoiQA97jDu2jav/9QXL0wW3\n5xuWZzIuGbXyt+pv4NHJwco11k/o2ih8zCpcIqqB8mKEPdcmuSxzsntsD4s+NKLuFo8qkm9ZcyDo\nOziHzSdsMREcuEfEzQGdxK9WsF2AF9jecAuLhydgDBANSwanJgd8UM1EVfavQI7o7wthwSvUStVY\n3JKKMLXzQD1SkVC9Z40hwBqDoRS65x1BlZnJfbaTEBXFPxQhPYFm+cEy1x3hkSoW4ED+Mm3D730I\nnL43t09sE0dCRSMNM+IjwZokfSP2uGLbbli3aydQc9/Kkc7lel2pfcRV1+35pn3Y88czhkAsa8/n\nh5w/ZSKFEmCB68UgLA0Zey78bFZlqjfiMiUO/SRRm+ah54XIfsIDqZr4pT2RgdBtpckZR+5wke/d\ne9Y80H1vV6vXWfvIVMrwnmOYT+RRvei+WOTc+FmoorbKFS9X+0YLOXdIGpT4yF+V6j4hh6eDqBuU\n6Nf0ZQBSmC2VEvVhHDCfZ5wfz3df+/lCgVISs7gnVFTizvC0kCSawxTguao2MMTkj0lbMNIqF3Oe\n6Dt+j2+8KB333nYlU2/XtY2trtS+9TzOd0DLwO2DQojfyx/P2NeoxM1xHrHe1m8e64+D/zyT2lVt\nfatSKvY9YjpNOD2cGnHloNlMMDSdBN7Yi4ExFdY2hiu4im0Bv8Dawj3rqBdVbjqjmyzBRNLnD4EC\n/+n0iNN8wTw/YpxGeJ4plt7PdBoxDm/r+eumzqQUIaXlUlBZuRAQlT+eRc0FkcfaKmdE0qeLqc2j\nx0361JQAlNS83AW2IxnTAlc8V/sOgCjnZW6bRA7+K1LaOkvOXSteUf462mz+8MiZJCjXL2n1v60L\nltsN68uK5UpuZbfTSFmtayNR+hRbSzavaJthl/jq+ZNefpZf+ZVyUVtSFcgoBVsUueGNmf070kZV\nYNqk30+TEm9dUwjqK7DFiOtG6mVxDPDrjrg3+11tQym7maR4ZeQ9p6LVWUkUQHvb1mYK1FXZmZUl\nu/6ttRZuaLA7SZC2zUNERvZlx7ZurIRJzOh7iZ4ABX/PVZp3TT9ANi8ZX+pH8RS96mR023RHaap9\nMA0J0DYXV398/K/n/49a/80pMcWji17aqaKOcce+L1iWK5blWceN712UiBN3YuWRwRwz3JNFiVn7\nrKeHmWR7xwG2FORidVyzR5mEfGcM4K2DdwUpH02t9BrzfmON/aaHSENPZc8lhHW7bVheFly/XLG+\nLCiVOBrDGFByxXDnvjd6j5gSJZaasEtrMWnBAVSkxLr7tcCAIHxri871Z9+mlOg8HJn/Tpz6xNK5\nR5k4nqQYEVkULqWIkiM902jJkPgEpCTS8hMMF3znjyc8/unh7mt//nAmjQFDstTrbWUkgO6pWggR\nWV4WDPPAksOtiM2RUFhUQHwI5F7qtT0MI0OS4MuI8n4j9FIS+LRH5d0QwbI9c8Y0d8EcM17+eMHn\nT39gX1dM8wwfPE4fz981NPth8D+Po27EAsHHIWMbAuIcMD/MNNeYGrwqloxE3KuoDEcJga0xzhuB\nSi4iFMbuKlbN7nplMKNV/zCMmOcLHh4+4uHyCy6Xj7h8uGA6TxjnUdnUYQxwwb8h/29LCCQAdMQv\n1wrHM5SWxXeksvdWqjI5d1Uf9FwbxC+QvWSGOTVIq+cBtAfDMprSqND087FV+xL4Y0TvnyD9tJzj\nXcfcWyzL5i/vnfNOSEAnQLGdJgxDwJWnH6wBTqiAZ5tKa+D4RvXOEYerq5alr/x6HfTJpeovrIG9\n7lgl8K8RcUvak9vXqA+OwID3tjwACoCiyz2FwBKvwLZHhmQtNibYyT0sIz2+eoijl0q4ohLEWwpS\not6ulWDKQa50wf9w/0ngD0QWDCNNCUjVUErhMbeNXw0ylL53KYWttX++BucweCLbDo7NVGoldTJH\ngVRScgOj1XnNFZW/jytOL6wxYPKTgTG+ESEl4dE2V9vUen+EHhmBtKIOQk47QbOJOUVpZ32KFyzL\nM3JOcO5tI74U4FtQTVtENaBRr0zn+/FPj7h8vCBfCupE0LI1gbkKR+la+Z3cwxLMDyI9tarGRTVF\nraul1ZVzxhYT0k6tpxwT4rJr0L9+vuLljxes1wUACdZMpwnGWZzuRDz7tl3hMUoh8FHCIYhsgTE7\nBfk8oYKZ9tbBV4+SBy3qGpppdR/SaywBXxFCQDwajohmU3uV+KHnU4ufDd4NAAzCEHB6mPHw6yM+\n/OXj3Zf9l/MFix/gnEWOCS9fXnTKYd2u2OMG571OvPkQEIagJG1jwOOnha5hqXCFNEKSIhtNjbXw\naJ4K/jB/RUWqEiEP1jr4kT+Xp9dkX84x4/Z0w9Mfn/Hl8yfs24I9XjCeJjyuH1or7tX66Zz/wzRh\n2Xdctw3eOUzjgH2esG8k9TtfZiat8AZo5YbtVeZ6C8Z2Qwtkrb8yGVCY6s3XGQojibues6TZP00P\nuFx+weXyKy6Pv+Dy+ID5ctKJBNHRN8ZQn+aN8O+Bvc6bsHdE9EoGesFzqXAlw2W6qR33glBfSdyS\nTjAA6MWXdolks4eAb/vPtyBbX+jP55IRtSe/Mcy765hfTpHnlCnjTnG/67hDGHRaoMFw1P+XBzFu\nOzaGqabzhmEiZS4aL2FfbCH6VIPKpBZJBg2g6IAFmj5EyXDWoNZGtKwAcqWe2p5Y+YqVEOMSNehv\ny4YoD08vjMTCIfeuwTmSdeXAFxxVamukKtMtdB7FNEZGsnRGvlbEDdzrZC6HACFMWmv3lDmo/ElV\nAEB74c46uIFtfrnqL5wgJw38e6v6I22GYsva/NV/vrxzGKT6r5UQkFIRY8I2bAppS0STap7ukSpa\n2A3WkX9l6Abwsu3IRli55y//0kql3BIEeX+x65XWDo13Rm2dUVJ6w+1Go6nL8kIJp78f8RO4u1aQ\nzOr1hm1dUErC7eoQN5rTF9noy3ZGupyQ5hFpypw4NflauecLP6+9DXPv7NnLPPcwuJBcay7ER1ha\ni2t5XvDy+QXPvz+Ti+CXz9i3Dc55jCP1r6eH+W5XQyXdohlKNWRWJoiSFi/WOlb/O8EYsrVtSVo7\nvoqm7KfXXidWTCNxZprs6cfBZbyvjW12LZCcEOOKbVuQ0o55psJwvpzx8KdHfPjTB/zy6+Pd1/5x\nmnAeR4zeI20RT78/6wSE3EvURvZkuiQxxnvUadDjjSUpIlFrgS2uO9as50eS95wySizKLdBni2OB\ntDCmM5HYrbMsKZ6x3TY8/f6EL3/8hqcvf1OS6/nhAXGN3415P94Na4W3loh/ISDEHblWTGPALXj4\nQMYVAovLeIUw8vu+vYjOHObQOchLxdoSgaPpg5A7pG/kfEAYJszzAx4ffsXj45808J8ez1rlhyGQ\nNgGPeqQYAbxhEzDNxKf/vWxS3op6ExBdgUs8liMBj5OFUml06evVkZ3kYe9Gfo59MNPB5I0o13QR\n6OGIbHiUEhGfksD+5m09/3E8wZgVBrvCteLLsO8rCW1sVGFvN0oCxnmADwHGovUsO6Kkqc3Nz3IS\nR/QJ2tx7YmStgDEdUZLh/0PFf2tiLjreJkz/GJVwSkiRQwj3X3vHLZ55oJ6ezEsv+45l3bG+LBCh\nqZyYjFMdPLtryS2rDzKTF4yh0RvnvYq5UA+QpXO5TSXBTioFJYN5gg4LM+yVJCRz5BuN9gnhjVpB\n+5uSHzFTGrn69ywiEnPCbduwhKW17brWliza9JrzXY9lCMLnqpc/gLVsiMXniZCFAojkd6mAqaiF\nIfm9VUmSfInQz76vuC3PHPyfse/8Xd+A+YlwCkCft28rluWZEZSMbV24tdDaTpePO06PJ+znsWs5\n0n1caj2aTHXjp/I8A0c4V1shpiOHMuKw3ghtW15WXL9c8fTpCz7/7TO+/PEJ15cvyDlhGCZcLh8x\nnma9j+5ZiQWCeuKqJiq1aDCmfZrg+mGYuE1LAV7tpxmZQnd/CCHv9XrN+5JEIymqGbupMPoumaXe\nSYH0CqBinh8wnc54/OUjPvzdB3z88yP+7vH+4P8wz3DW4jQMiDHi86cnjKcJ1jlKptcbaq0Iw0A6\nN/OIYaAJuHEelbgIbK3/z9fPdEmzuLpK65BuATakc0TUFO8A5ywZPD1Q8Hfe8T1VsW8RL19e8OWP\n3/D581/x5ctvyJkmdLZ1QeSphW+tH+4GuZCoADFOKaiRsA1XdyGQlKOSF3B40kWxr5+31X4+V/mv\n9ftbT6nBwFL1NrenE06nD3h8/BWPH/6My+UjTucL5vOM8TSqCYcfaMyDslEOKPbOpwDQcR0R5rEd\nAZApHTCGDZCYmSsB3zuHmDNsKYdMvj8/HPOOAV5hUP47tHOqzHAmvPT9frKbTJpopbQjpuaBQApc\nXtGFn61xPMNah916VmkkZCKlxBvfSrDytmNnhvm2bPAjnesbn5PACoh9/9IUmoGFrdrjBJ/Pai2L\ngrRep5L8csa2R2w81rWvXPFx6yTFxKNtEU0vwnAPkqZC7l3GkFiR9P4l+K8p4bptuI1BJaNFOlce\nMj94fSZKJsKfZPBN1tfr+B9pPEjwbyQriQvy84bREun3igKYykNvSUl++75y1b/R81Sh42A/W85a\nDN5hDJ5HWWmb2FPGdd1wnQa44Dslz1a96n93FS1k7K8jeEo1XJnYZ2sFHGdMFTwpwq1AW/m4ST+d\nRIta31t733GnaRSu+tf1ipwinPfw/v5xL6e2qKw+mSI21rqQYBMjtVX2dSPuy8uCh18fcP5wxnSe\nGAUTJzrWpGeXQZkhl8TQcGKowR/H5B+gSZAcM/Zlw+15wfJMUP/zH0/4/Nvv+OPT3/D09Anr+gLA\n4Hx+xDie+HisJjM/W3tKqrZ54BtwUSb7NbUSiawnDnwh8HSVs+rhYGE1SQSAyq6rh3ulVk2EJPD3\nkH9fGMoeKEqwcr237YZhoO/w8OERH//yEb/+3Uf8+eMj/vzwhp4/V/2XacQWI377+JmqbS4g1+2K\nXBKC5+myeSL9GNYTmOxI184Z7OuOkkha3bhGAhUOVi2WOW6toKwAvDGozsFXGdd0GE9T+x6GzLtS\nTLg9X/Hl0yf88fs/4vPnv+J6/QxUYBxmUtPtjLRerzuCP5rFIf+5BCYXHIKjmWWgKlvdMssRhtiX\nMnMrN00ToGnZnfSRmnlE1WxJZvmHYWJi3wMul1/x4cOfKLudTgjDwLOxQcVIpN+vc6VveAjoOI0G\ncwn68t/gDJVaEG0SQMZ6PL8yV2mVEwYIiNEnNr3GucKcaC+0h0SU3uQBEREf6cVV9temV0JlXX/J\nOu8lPs3zha04F4bUImesWTUF9n1j/Xwx0IkY1p2OYXW4eYcg43/CmbBWpx8EUj4Qm/glff5UBOpP\n2GLE1jH6e1vUnDJtrntikt+rpHF427yvkFwDSzhbY5DHEY8p4Xo64fl0RRi8cllkLleRho7VS/8t\ns/5HdT+9T53j/nfrb2tQrdwSqmzny33CLMRRVgeT0T7RZCDCnyRBKjvz88XH7iwncByEtynhZZ7x\ndJ6wPA/Yhg1m4aDeER/l2tZcqZKP37jn+GeKt3CFbXyLgy2VXtbq7w2PipbMpihc7feVYk5JA/Sy\nPKsvSK0FDh5H/OHHS66LCG9RgpeR0oZ1fcHtllXrflsXrNcbbi+LIlHzw4zpRBu17DclZUpMeQQ1\nZ/GCb8ndYepDXpaqlpzJ3rXv7z9//oIvv/+Oz3/8DZ8//xUvL5+R0k4S58MEoDa76Dv3vch68L1Q\nkAT/lCLivmJjjX+5p6Qo827Q1pIrzfpXL3nfBtCgXw8IASUYUdtWpObX4H7hAkjgXxZK9HJOmOcH\nnM8f8OHXXyj4/+kD/u7hEb+c72f7D97jPE1wxmDZIz4+nDE/zBjnCd4P+tnOeQzjhGGaWPU2MB+n\nFcRiEtQnxvpc8z4ue1dOGTZl2GxRPe2CxhhYfp9xGjHM5JWQmTS8viz48vsf+PTpH/D77/+Ap6dP\n2DaS25fYLZyZb60fBn+xVW0EldKEbBi+9MED00Abdq4N1uzYyDJ+IT3/Np5hlRlKVZ+jz+iMfKxt\n3s7TdCazntMjHh5+xen0AcMwtQ2UhUc8Q6ptnEhcqJqK2j2rEe86EZ4ODdALKgxmcxzX0aTBksiN\nVjsdpMZfr/V6mSWvueAr7FTOXZKxl+7atHZK1EyZqn75/uZwI/5onU8fkPLOyl4W27ZANBsEwYlx\nw77SSAoFXgq+fgjElN4igt9UoKRUIvt53hRsbRwOZfnWyqIkTcBnjZFeMhqZkh63bBxCmKEpCRHM\nIcjc6bzv/cE/shGLfD/vHKZhwCUlfDid8PtlxvM8Ep9kYUMVntgwxmKYBhLwsVaJfaLqRyI/oav6\nG+lP7jdA5E052IvEp2h+d+YgaWtudnEjstu+b0g56j0AvsfuO/aElIlsK8kZAJxTwuM848vlhOWy\nYFvbPHK/kSshkBOAjKxtwX7VWuEqjQPKrzZ3TP/CL65+cyZ0RzlGPAOeEukbrOsV6/J8cKETx9C3\nPPfDNCDOkaDcacAwDOpKRyOvVz7HlGStC6ld7isF/8svF8yXmdtgDSFJbDgVxchFtf250HBW/21P\nhiVUIyOuO14+U7X/9McXfPnjN3z5QhX/8/MfWNcXLZQoAAUel7yf7xFT6kZku2eMp322fcG63rjy\ntwQxG4PgBzhLBV8pBSEMh8JLrneV+KHEvsbWp5HVyBMqq8L9bYqM/l4D/+0Jt+UZ+74hhIB5fsCH\nD3/CL3/5E379yy/40y8f8Ov5jIfpfmE3z2hfcA4P04SHywnnxzPm84RhmGGNxbpf8fLyGSGMGAYS\nQqJneoDzMycCNGmhKE8/alsFveOJjc7sR/4tgCYBH6SobVK+y/OCz5/+wKe//iM+ffr/8Mfnf8Lt\n9gygwvtBnRXD6L/rZ/PTyp/EVErTU+eqygcitVD104KiDx63Zw9jrgxhGc0SvR+wLC/s2HfTUbSU\ndtrQOTD2dr3ehxb4pwvG6YxpOuN0ekQIo2bMouQ0jEHJQjCAYRKWZtVvgP3FtQsVh6pf3kHnrI3h\nlm4zqqE+eevbHVZtr1bddf3T/t+hVbDg9y05dw8EMXAFBUipQaL0sxZitAEeLblnnS8fsO8LRNUL\nYM6E4eqfuRr7RgFHKrLMamMym7vFiIVH/4x8f2c1EaDeP30E6bSzEUrOWGLEsu9YYySWc5KqvrHn\nJPDn2CwxvXeoNainQQiBBUvuh/03/dzIrQtiwJ/HEY/zjI/nM54fTrg906jjVklwpsj8f6laDVhG\nIawl7kGD+6mHb7245ZlDIgQUnpumTTJLtb8ThKzuhTIatGxU8ceNID++PwzzBu4d89wS6SekEFAD\nbYgAcBoGPEwTPswzbg8nbNvetRyoBSM6FLKkWjPJICPxs9C4PLUSG7qWqiqHtli4YlE5Iaj8zGYe\nKe4rfmJHb1R9356wyvFzi8MYC+8Cwhtg/2EekLZEomAPJ0znE4aniYNdwratABZtrQipMqV2PtbL\nivE0Ev+IgyAxs3m0N0vfvyrPwRqr3g7SLlCRqpix3VY8/f6EPz79hi+f/4YvX/6G5+ffcb1+0amG\n0+lBW6MUjAT9vG/f23n0OO0t2ZQJDiKQ3vizMnOIYru3OOnLOVNR5jgJEb0G01oAtOW1vaJwDz+l\njUSaolj2tsBfckLcV674SW5834nMOo5nPDz8ig9//hUf/+4jfv31Eb9eLniYZ0zD/dceaPt2cA6n\necLlwxmnxwvm+QwfRtTbE9b1BU9PASFMCMMEPwya0LswI4wDwjgcCX1Z+CltSkjOhY0WJTTBN0kG\nBYGyLI0dlw23pys+//YHPv3TP+H33/+BUZ8/kFKk884o+TCywu2/dfBnIZWUSxeWqtokeucQWAZY\nRA2IiVjhblsnUGEQ/IjT6UFlYkk2dlOyWquG6ZMoeyHJXnHrk987FzRrFt3+YaYMrBT68zDQn4ux\ng4oRvWHletTrR23h2XDQb4TANrFSaptdV2gPUvkLiYdfXMEc/gKAML1F4ERG1WT8Eax2JbLJJSfU\nIqiJoBaMrLDYhvApfrbG8cSJW2vnADdFZDL7MJCLXoMzKwcphfP4HoqlYOA2SakV1dH5EQRF5/wr\nzdXvLOKzpYQ9J5bnrV32zCMyO1W+4lxIvfTM5COjY55hCHdvgACwMeKwpYQpZ5VwnkLAaRzxMM84\nMfv29jTAmoVg6S3SA84VzSDVMD8bPdxZSoXJBGsXutg01sNkORmvlWq/KGGMPImSojQAACAASURB\nVAz2dcfGLxE5ksDfMkzQDLZ1dzPe95Sw8fmXZFGQj/M44jKNOJ0mrOeZJwzEPjcB+Wg+RNeKjsVk\nAIanR7pZbbqcnBCYAlS2B8+FBcIsc3aEJAWFS/d1w3pdsKwE9dMkSgIhXlYroGG4v/oLQ9CxKqr+\nJwzDhBAI1q7qltlIaMZQJUuGLKS0Jz1aQXcAqeI7lzbeH/Aa7jdGRW1ypJbWy5crPv/+V/zxxz/h\n8+e/4vn5d+p3r1fEtHOiM/B3pSBALaWvHfW+txqhklC2JJwaIWOniH1bkUvk/YgmKbY1HDkKJRHP\nxgU4H+BUBbB9DxqBLhr4c96pqMi9PLDYUZOC6R5XbPuCbb8hpo2ulx+I/P34Jzz++hGXXx5wuZxx\nmSbMMqZ75xJSsezp4xBwfjjh8vGC8/kXjOMJznlsGyWbwzBimlhOfh7Z8yZgYNJ5g/kZvdkjnd9k\nDomsEXJo575pHaHVYi8etx23pxu+fHrC50+/4cuXv+L5+ROu188qZCVF9jDwPTuE76I+pt6LA7+v\n9/W+3tf7el/v69+JdT/77X29r/f1vt7X+3pf/06s9+D/vt7X+3pf7+t9/TNb78H/fb2v9/W+3tf7\n+me23oP/+3pf7+t9va/39c9svQf/9/W+3tf7el/v65/Zeg/+7+t9va/39b7e1z+z9cM5/3/xL/4j\nnhUnB71xPGGaLpjnC8/bj2yt6+ED2Qxaz1rdzqlGefPtbi5Ohgbkv+FeJ5K3RzvP3tmrf091QpMv\nbYzO6aq0pbEHDYL/5j/7T+86Of/lf/0/qNOWDw7DPJJu90wKSkasyAwL/hgR4TkKnTSJTJrrlRlv\nUfhSdTQ10KjN+IPtgNGLm1Qy+SCfeJ4DFo33fcP1+ozn509YlxckFeGgOVtrHf7Nv/m/f3rs/+1/\n/78AINfCMA4Y5wHjacLp8YTzhzMeP1xwmScM3qlvu9g/9+p8+97NC8dE8rRZxIBkfl3EPkQfACrs\nIeelNwSRe0h+L5agtVbENWK9ktNgXHfVKKi5IqeE//1/+x/vuvbjOKs/+DSd8fHjX/DnP/8H+Pt/\n7z/En//+38eHP3/A/DCrlPQwBtWZ8CHADeT0JRLTYhZDIj/2YHwk4lCsdaSKgl5MkURYiu+jmFIT\nP9oj1mXD7csVX357wpe/fcFv/+/f8Ld/+Cc8ff4d63olaWYAwzDhX//r//Onx/6v/tV/AbHk9jxD\nfTpfcP5AUqeqm9Bp0ZPwUrvfRWjLWsu65uJVQBfx4PjWvVCaRsLBH0Df/9V+orbAbMQTM5brgtvT\nDfuywxio9vr/+j/9d3dd+//5//i/MM4jrHdIe8J2W3F7Ilnd5WXBvu0oqag4T6+BL7/q3mXafdoP\nVYuiI+2RliV+m62x+EA48bq3TTeht4mWczxMAz78+QP+8ve/4tfHB8whIOWMp2XBp6dnXJ9v+K/+\nk//4p8f+L//lfw7vB3JMnS84nc8YTyOmy4SHX8i7AAaIa2QrbRL4yp1ZkejJG4ODSRVqEz2TfVWe\nHy+OlSzDPUwDwhTYnC2oOqbrVBBzzohrxPXzC37/xz/w1//n/+fszbIjSZItsaujmbkDiMyqx+ZS\neiXcBv+5CW6Q/+zD19WVGQG426ATP2RQNY/J8SwPTmQgAHc3NVUZrly58p/4X//jX/jrX/+Jr1//\nJz4+/mI9mR3/7//7/zz17P/P/+v/xu3vD3z7X1/x7e+/cF+/oZaCEGf2eRHeT/AxYJonnewnOjOR\nxcTkzDtWlR2HtNEeYYEyVvYThVIZWlVyt5HjECgRDDLWIM4Rl5cFy9sFl9cLXv/xij//tz/w3/78\ngss04cgZt23DmhL+j//+37+71yfGfBn90xhRIutTm/qGPo+M0d/6TuFODgpgWpfQHQcHnAdKVFhY\nmoLLU49gDEprMNWQup4GDF0cwxaSBW0Wp4NzOoG/u3Nr4JyFYxGh+UoPO4hS3EmyVBzV96+jk91O\n6yUfR37vbPT6hzCwAJozfb1aI1EYdPEgmYXQWkWcFkz7QipZrPk//vszVy2FDREp0YUpYrpMWF4W\nvLxe8LrMuMwTgggPsfPPpahKXW0N1dchsCG99lZlZvl52JHoG9XapU9VEazyfmiVlNDGZzoY4XGu\nQ05WRWca2tP65gBUKIac3wteX/+BP/78b/jzn/+BL//xBS9/vpC6pSPZ3q7v7fvQHlFXE11/DohH\nx2+tgcUwMloEo3iqoHMyKbIHv35wrGNwIA6YNPATqsogd0nmp549D3F5FJ0xD6pz3dnLsSIxL2MA\n0yoPxmmw4HkOLHAlrzOqW0ID3P45RCJYvznMODCNom5jDQ0HYqU8cbyy9gDp5uMTAk8yXwGgtTy2\nA9uNlBzTkVCTzLPnoJPDNg1UxOHL/8r6yf1DHL3R9eo/DJW+bcWgmYZmGlANYB/lYZsqiZZcYJ3F\ntEQaKXyB7g0JIJ6+f2M4qXM8qtqqhC0MONkQqe2qz6cHcRIIGf18JFuO8+wCK87wbP/kSwIcCRoM\nnwF5Nt6SLHx9XXDdD7y8v2D9dsf9Y0G4T3BOBG6et/nbx4r1Y8W23nGkjZ4zC2R5H2AMCabRuPIh\n6GvyXEh8ip4/+bWaa98H/D4k7tZ0KJmI+6h66eCvVAZelCAtjZsumWZdBJ5nQsEEJV4iS66v84Pr\nN85fMncyXI4z+jHapSeDH7wJb/PWIPO6daHkxxuA1geY0MAcoFawo6cxsGh909BLPRwyXhDDETP4\nIEgGAjc4ik9c6vinQIdqnjTj0aCF76OWpip1+hBlGejGNfPpetZjZtsDgdMTMCQ4SUkPGwFwRK0y\nqYCtDc431OIQQ0ScFkSe61xr0WE3wHNqVzVXuMDOnwdWTMuE5Trj5bLgMk2YfTg5eqtzBggBkH/T\nQ4sufXwKgvjsgNdOZDBbZVVJWRtWyqoWfPiA1igoAuiwWWdp4FQMJLXLh5GmaD1vBEopPAZ4xuXy\nhre3f+KPP/8Db//8Ay9/vtBoTUe6/XGiqJ/mSpAiF2X5glx1+WsdFGU52xscv2bKYAcpd2WgcyLE\ngPRzZNT7VjGgmRxWPhJptfP89WeNoEyONIYkoel90JGtWtmgDWeKj7vGY6xcKZ/rdA2BAHAOgPXv\nYlDRAwu5hSrnwAC28ihgPu99HkcfaCKZ9LOX5fkarTakI2O771g/VuzrTsObxlG8FYODQnf0/PzE\nr8t9y7OVWRtGkMvBIao9NUMABAqaZa1Pdob/f72teP/6gWkiWd85BBjQSOZnB5qJ01c1PgOdS+Gc\nG4I/zkbRkTqRK+5jrBuqsaDZRBQM8jhUwILOZBOlSxrOZIxBdrmjShIsOHNCUxRRiQaxTbi+XbH/\nY8P67Y7b+ztuHzNr3Iu0+XPX+rFhu6/Yd1KKlKmFIUwDesoTGP2Azjh+nkOmW1sFBkFVPS6c7Ooz\nPK3Z42AnCyP7zRjQTDlLksuF5KJLImn1zHMZjpRRYlGb8TPf90vnb42F4zngpA8vEo32/DUYt26o\nQZGuePlH26OC7jT721ijWX4z4lgbYCxI/PT7m9CFNqSvb2vjsaAUnVprUJ2FazRb+zO6/gBt+jDA\nUD56PUTdqdFD7pKtAtd3ozBeo6EbDfKPhBbFmI13TFYVw8ZoJ8chn3maFhq7y6OUaytAq2jtOSNQ\na4MDdI584ABoXiYsIWAKLOksBk0CODNCW5wFjFrutet6tyF7OUX+pbJEbs8qMKyF/jyGrJOzXsn+\nQ/SoJZymhn1OzLLBWo95vuB6/YLXtz/x+scfOrQlTJRV+Oh1nCeNM+3Zkjr+saT1g0EPTYJkQCP2\nyghPqRWAhUEFrFWSjmVkQJxDixXlUjWLSDuNPt7WDenY0Vr51P33TJ6/5BiL8Telnyf+Nw2GLQXw\nxvD5bx0G1ksDdyiqI59P0S/N+tv5Z8Y9YTk75ABJVnQMRGRfPHtZRwFFSRlpO7iMtCPtVD6RjNQa\nw6yp4bX5fxWxBJXOxP4YmHP2KwGdBixDMGBHFBT9vvXRDEGToZLX+r7i43LHFIOCHb9yAI8XSYG7\n4e90lvwwqO2EcLDjL6nwVMsexJFtqICzau71DsT5FTzsTSnlnCWJ5Xt6roY18sFjuky4fLni5c8X\nvP99Rfy6kLwx+65nr33dcey7Ov4YA6bpghhn8n2CKsnkRx4iZ10/C+OzG4N0vcXvkJJ68geyV6w1\nlBw7h2Z6ImSagaksvZ4p2xeZ85wyEktzB/M4K+R8/dr5O3eqFcuErD4ljtEAhUAk29dHpg+a/jhv\n3o5wNLRGN1v5JST6hqXoGhpI9EsNP/9/rRZotHGLMbDOwDrSRf7MZCu5nPc6MEgGdJwcsumQj2ZE\npUPW+uCZG2BghgcuRmxAUH5gmxVA+Inh1hoabzjrHFwEYp4xHRccx4ZcEnL+jOOjj0XrZzXyn+YJ\nSyRY8XEUL05rwhtbvq+Gq38GyhY6zNfkzzJM+hoRFHEW/MtiXGEZGZHMk0s11RNi0QdpVB4b+vwV\nAkH+1+sfuL5+wcsf5PjjHIlTYnlSX6Qyw1iL7s8HD5wXMebk9OljNw1uBDkxPBND9lXm35EBMTJZ\nU8oD1XuU2CgAKAXXP17w+r7i9u0D6+3G09Geg/3lLMtzRBuyu0rZjLFG11MyXsm6TRsd1zm4HWvg\ngtyMZ0Wdx8N+71nSUBYT5y8/63g1Nbh+vKfnLmstaqk49kRjeu87abLnwsEK721LTm20QzK3u7Fj\nlGRGbOSp9nsKhiQ5MR0NQl//MSOUn29AzxoBpEZ8l/v7HfM8wXsaRvV9wPnza1wn4jp5rWGf7W3f\nxwpb86yC05k1th/7B6SoVSlpAGgNBQbGCvJXUIs7DTZSRMAamGBOQafzDtMy4fJ2weX1imleOPMP\nCOH5gV4ybhkwrJFPE2WdD3qWfSQugnLcZBLrEMQ97refBt4S43acT/1fc41jS4NaOm9MEaLaAy/h\nVaWDpkYemc66wv8/uH7p/MnxW3X6Rjdwd6IKcTWCpZrtBxunZ63gtWYMj1FtbYCxVB+hiNny/zRo\n6YBOBBkaLQCI0QEy+vuSMyw6OUsGqzx7ucjGfQpw3vNDRg9mNGvtA1hq7jUrDAbPWNuztMGgjoel\n/cD7nwzLcPDGAMTInwaKTNAGnRDjrHPNn53oBzDUF4JOlQpTwDRHdv4Obqh51UqjnstIWBwztIdL\nA6Whtie/d67zt9PzMgan2i3xHx4DwiEI8hU+OJTs4crgNJ64rPU8TOqKy+UVlxeC+uPUJ6UpoU8C\n4AcHz58IAvdrkNb/6cEhGD2okkSf1swYtErlGxmzbUCTAn2t8L4S/2CKmJmfsbxcMM8L9n19OgAc\nSxEAD6nSwJZwTOsoCOn3IHsYFGiz03t8NvqMHrLxR0c9BgSnSwEBQQSGfeNdR4jYVhiYU332uYvm\n1wt6ko6s51qybePoPFdJTEYCHpcZJDARB21Oe0HScn7H0jhoAmAMbDPqCGA4CxycyoiE6v1X0Ge+\n77jfV8TogZlt75O337PPHvQTj0UQz+9//rtzKykqoCU5QSe++8yg511Ng2kypXQIAJs886ZBQNEy\nhtXXbo2GzU0XmsR4uV4wTQv2fXp6mJlcMgLaOa8DnQyXep33TEz0sEziVTRCPpf5/vk8ogBi1601\nAP9OKw3N8b2zra7GALkAsHqfNKCM9hcNmmp91PeReSIpTTKsrf107//S+fe6o3xgIjqczRI0UpG6\njLAbzz9ivnP6j+/FKwWK3isKZzqmSaQ8ZI6V4BI1UpYWrIKyfhgDK2MUuZ5ixEs+eckoWOcJyh2z\nfN3sFTyes5M21Ejw/TQARhzvaND0oAxr7M6f75EYpUhAad8dIGMNV0oY+g4RMUxIYaL1NERie+re\nuc4fJmLbTnPEFAOi9/BumFbVGnIlkkkqnemfa0XmiYNFR1kOdcrhcD8a+R4UjZkG7wvJgnTRzusn\n6I4LoICnOPjsUPLnAr/gaVznNF0wL1fMlwXTMumENoUguUbbeC8Y24NbiejJSVqa1teoXOE4M2TQ\n+jtDIfd2godlffBgdNENiqI1wTNHY0G8zPC3gJyP526ez3p/S3ZgpaBkZluLU0M9rbv4q0a4//ll\nmQCoPzigC98Fpmwd28MZGS/5HQnAx7XTDAySgD5/7mV8cj4yk9rKCZUCAGfJ4NK9fv8ZjTG6PuK4\n5Dk6bxWJPEH7bN9oDzGyJj9DRXJ95hqMDehYKxV5zzzm+cA6H+y08DTZtdYKy8RCKvcJ5D9yKvo9\nSnKoz6FBk62Rs9AaYFrVsoZkyoJ4WFkHzhrJRJyDp5oriisoxcGWCj+WeRo5bR895stM53VaEMKk\n3S7PXLKWNAUzIgTq+tD79VYDIh88Qginv+tYan22HfSUoFSRHxDypXV91wOoWhuKLVxiM0DKVOas\nRJS2ljcK3z/V/wuPuE7Yp8RJCf5rmX+/eBa5dTDWnQzSuInH1jwMG2Tc4L+KwI2e1O8fBn0Kc9r8\n4yULblFRC0O/P4xIn7+kVWP8zLVWZSV3kosQbx5eQI0CR72PBkjOAQxaM99lunRj4ICrQ3yt8iGC\nQwFgHIMjtcGYotmF9x7OR/oqCS3/wMj+5KJatteZ8zLS1j2UTijL7xB+qRW5UptfOjLSnnur3wAP\njs/jcW+MtTAxtgIh017rMOhpOU0v9QDglhg2tJ7mxD97OR/6aMyZM37H89at6S1YAwomTlk+y2P2\n/t26Db9HzgFwxvIaS4b3kCED3+2RKudh+La1Bi44xCViXmYEP2HD7en71xbc4Zmc2lNrQ7PtlOEM\nN/d9iiifu6FzeobvnX5uQJAe2wEVWRL4XwKvZtBKZfY9vb0PhAR8pssDQDekiUZWC8lvJC622jRL\nhzXEean99wFxpGwbjUHjfWPMuQ3wu3WqAFDROMgCny9rGo22HgjCUPtG/1sqfe60Jxz7gcBEvc8h\nH+T8HNe0nQS51sCcaDjmZOvHe++l3orC/0/dF93xK0o3wuSNgvZSCmy2KC7DesstoFXZ8TKmfSRf\nt9bgnONRzDPPtifS37OXBDdKcvd2QD0MdfRM7Oy13t9bM733PXg7BUXns6J2go+KtcRtQ6NowTJX\nAg2Aa6jVwrQiMfPpkva/kjKRfHca7+28Q/AO/icjjZ9g+0uNn9qapNZP33fnhy+H1bTOzhwiIHq9\nn73VeTMIdPKd9ZRNXx8NoJhSC6CgFG4rK2eD8ZlrrPELW3500KXITO7BYQmM/UAwMwaMYIyp6mjY\nac2k+4GX8xwl8vq0ypArzvdTW4WBhWmUdVGbHrWo5BzOXQhP3LvoNRDMzT3p+l7yGTvMXxpl/rkQ\nfCeIkHVWWdmtNlTTEZsfBYX6fSPZYesG1VZ+Tf6cxp1QJVlsDUaHwNR9wgk46zn7j4gC80mHhTxr\n/k/eXyHAoe9c75NXrrRKLGf+vgCS0t5nLBEtRdsBrcC2hrGronHNnxIKan8UjQXRNahMBBUCaIgR\n3j9f+6SszulZN1Jk1h+QBO68n3/1erIfvvv+cFF2ZGEly7VNz5OUGbSmz+hCo+I63bMGHhQIGn7u\nn/F9p+xaNCgGvsh5f3Z7rGerjgFOU2dvud3TBwfjba/Fi62T90fT1z8FYIp0DGtAH0hwFNRctQc/\nLhHTHOGHtsdnLoX8g4cLBG877wFDbaDGdESL4Hn0z8vEbTReB14QQQ4eH0S3y5S4NUe2xMo+H2y4\npcVRFKqWnpFrRu0MwuQxXSho9z5+ivBXckZrlHBZJ507hBpaY7QUKgx/ecYSxBIXiQvS4tkxHA0J\nYGu3IcoZKwPxT34OTc9NM1Vfh0qYEmEZYvkfCfGgpOvYE1xwgIlqqx+v38L+5y/bMwJ0qEY2rhgj\ngtms9rKSUx82iHxsPafm9H4iDsRrNVyNo2yjcBYESByce4OBZWNRSo8W3Sg48cTVHf8AL0r2wlBL\n47BWDkN9yFaGVxuiStms9oSQoHVn0j8D1LpohCtQWSPEwDaKfoWoNEbVznl4F9SQP+v8tTfd08En\nCOkcvMn9Sv15dA6EPlhUU08QoN7rL67TOg8BVQPonmGI/WooIGqVv0zjjMwoD0Le7xE6/+39W0fZ\nv4t64IUFLiiHrUbrsNL+44aWM7SGyq2IrVFNV+q94vw1s5e9r5+THnoFZ8Km74rK7PkqQVetrOfQ\noeB+H6bDk+455097Uci9PYA6/Tt62+apnslBwtDdq8+0ownyPXm9898B9BJBAyEMEvzUbgCrlCNq\nBXLnu+iz11r/57JeuVSEJdfhs5rhTPJpZRs2xjXWGQ2apfXTOkLQ5O/itPqa9zPS3+uh5NMeevyH\nn2mV+7z3hGM7kPaEnArq3L5D7H52yfMmro+Dj47aV5njcS55ti7O1ZruZUnaOjIwBPkSKJvvEzwJ\nBGwxqHbo+AH0fGlJjXvk9cHI84GB5W6fOBF65/3zzh/K22DUg0t8tRKq9Nh1MCI4kvAV/rxmXPPB\nHzS1a+cESpBR+R4FOlXXpWjZafw9Wj6p93dBtYKcC6w/B67j9Vvnr0tqBMboUe+jyhYtBmeL3PIk\nwd53kax8avRN0jdCb4tQpzKcX+uI5GD038cj0zeGK47qlAyHfPdAfnPRxxxr7gDGOuVAInv8mZ+t\npwZEQ2b4+PunEoUYfSOfR4yl+eG7GM6ENJCSrgzrUJ3HsyI/Yqgds/2tOjUMm7gp69xaC9eadgE4\n61BMG9OiM4TL73NCTIbIt5c45Pf45znTNc0JItp/3wDNWmhlmTMSOaS1PO8EfIiqdBYitXkKfNz3\nhHwi/v/h2alxZkjYVDbQ8uyt5brd8HxxDozkHhokoyXjJgS8wryY8f/HfSCaBy56uBgQwvTk3RsJ\nOwAMsCw7vY7kdYMtzsu4bvgfXvK7vxg9/9C11PMgjl8DRg54YQHXz6Ds5x5sD5nWYEM+A/pJaark\ngnIQyUzusWf3RvkdvfWs3ze1g3lu/+wqo8QQd1xCPa/Dd3H5Q6JsYKj1s4D5AWIbaQ9V52AyfW6p\n+x/bQcqT4bnMX5I8Cfrl2Yv+Rs6FVTqHzLyeA//x8w9/8FqRczYDiihZbK2AyQWZX8xaC1dcP2vD\nGaOArxCKCOF/NN0vlgnLgc/xs5exDs4YhBDguL3xvDamB37MMWvNYBT3oZ/F6eH1DhX5Rt/jALra\nae2iZKPaJSonWWPpiX2fqZT8ieM/oc6NguQfXb92/nqQRH705zCt1frIGNn2Vxod/+jwRlj0sSY+\nQuxjNvxD4/JwtSIMSCLuJJ8g0pnPXqMRH526RN+j8wfGnztffc16RPijtkPRRBiNQKvtl+pkp/on\nembSYW8H6zycD6i1oH5C8IIM/QMpqf8TOX35O7emiXKdPlv0YOUndwCBuCS6HY3Kj8hUUm/ua/AY\nSfPBkHXgAGY8nL+7YqSaYZxmhClyK99wD3L4i8WAb6Kx+EzjnlXpydU1ZCTAgBAcSpT760p/P5pA\n/YNDZ+IrlVia1voJ8h/OCjt+UWaME8mOhvic8xeRn9asOjT588QFeMhURbgIPzibp/M77CL5eWMY\nLgBIy6oO2U/7fg9IIDQ6ZNlzsgYwPVDFJ52/ZFLU4sdQsLdqe6y12utNwXE/1z0Z6hl/lwnvHSLy\nOTs4cV63ERmQBTh1xYzBdKH2ULFL+UjY1w37naS5jX322beT/YChUhNKQU5F+/mVBDmUVPXsFnI3\ntrL6oqUyjgSThp+7wQOHi5+15SDOutJb/XIm/6BRNgmbdZ8C9G400TzxrMz3vPP33qOhaXsjwCVl\nfu6tcWmltvPzAfDo0M/PTx7qOeFTFJmDtjFBOiF5Q9LVfSivk7ewxSL7RIEqZ/rSgfQT3/8k4Y+z\ndspwOgT2CPMaS+0Ojtsezi/xPex6CgR40eSW5Ht6oB8dPh9s2UiaCQ+GQjZOTgZmo+jbPRkB00sJ\n5D+6vIH49FBD1+gUPw5OxCmZMaORlO/kVQejxQHICP2LqFAtBVW18at+/9HpEWnHozD0/+xlgLPj\n10AMyqSWTZpr1bKA1QNotP1FPsu4sU0lq0fL1te1ZxWiItY/C/Qz9XWir/OBkqz/tA7hefhvnq8k\n7jEvTPbrancAKTpSTJ1hq4WrtpccNKNl58+tbyJ+Y1FRrYGBg95c4zp/qzDCmXrYQ9oixs+BRGYs\nmmTe4phE5ZD1xqfrhOkyY1qfY/uTrr/8jffnD4J3sJEeSVu/C8q1T3kIUsX7GXXi0sRlyfDyngYw\nID0/Kq3xJ7Ydbpaf+fWneviMpfCcjKRQrLHmVAqT9tcwByXFGdXiP5/pPqfEMBrQ24Y7cto7R4Rn\nZWxPoMgG9BBKzp6UNdOesH5sqK1iv+/U8nXbsc075muC/YTdGzNcRTgLuuNPmfhOkvhoIFxP0HW1\ngKkkCQ80lGxhLXeHNCPcNnotnpVgK9sV17U5SsrI3MaJAXVzjQnZDoDwFFRGm1Fo6z/FdRH/4MIg\nYV97r/wjNH/ai4+tyXIutAz5fXKrrc6lB1R99glJs1c977Z3gbC/dczrcp7Ksse2Ix+JW3KlLfDH\nu/+X1nCMpKmFrYDa6/CdIRBHrXAnG98xMxgj9MdaoS7YDwIEkcF8POe0AHRY9HVBWb8ERq00lFRg\nTIa1BrV+IvNFbycynJ20ik42ks8r2YZmn/0e9J6l7acaNEdQDcxg0OmHIdnQCH1D7queo0HVFBAE\nYjCG1nDU7S3X7Hu7ztOX6eUcuU9riLHsbG/HwZhxoUvYemtRnUPz5Ni1bPLgrPRZ1UYDiySoGVqr\niOMALSkpFN2+dwJSriCjRE4ZvqE929wCGuwzzxdM80xZwFCj1UxFogzHGUutp7KSsUTiE76RheUg\n11D9sBg1DFVQlAYii+FB0RA48QSkpVbfl3+u2q5tH2LAfJlxeb1g+0K69M9c1HnQNT0MmNfgOs+n\nNc0Jui2wfR/3oH2ASiH/3PpzR8+o9BwZA1IwaRp4yOsCA+SPfrbGcsTIEw4uUgAAIABJREFUUZAW\nvc9cBJsfyKyZDhCMHOeIaZ4QL1Elnadl0nZgcT5gpybJRw9guWvKe23plWclTHErbbqepaJ5/Rwb\newmwZX/kWrEfB263Fe/Th3Yq7PedSH/3Hcd2wMXP2L1ORlQ7pNk9wf4jjC2OCBgz1wpUwJiKZisa\nvAY1ANAYGRZ7TZ1KQLMVtZ0RAbEdpm+4U2Av7XXVVthkueNHODjuc87fkZiRc/wcpUOKEZCuw1/O\nTrpIVs7LYgT5cTyMiM7SCfavPJytFrRSkVJCKQk5H0jpYHXWBNEp8C4Qijt89Y6kCa01+Mljvi64\nvO245Aur9P4Y7f6lNcwlwcHTjVaRYCTDLjURyzW+WivMUFM1pWt/i+HQlflRS5v8nj5gcfwWo/zp\n+GuUMNgeXrBhNJ6dArg+XEmq8wDgPgH9jsEMlQuMBjkwZ61p+XmFaUpHSMQAtdbQDNXttD+Ws2OA\nMgTJckwz1Pohn6V2CU0SHaH620j8GddM6pG0+T2cI6nLZ4e76PpCMn0yQN6SopxI0KJSS5Jt5HQ8\nHxpbqkbLXBUng4UGmyuMKR3JKMTRyDkjKVkl65oJR6IJrGqMdpP0oKNfY7Aie6MO08Ceuabpgmm6\nYlpmMu5BplRaheqa4PYgB1Rqg3kQtqnO0loMkqTWGfjWSMHLWcBThN4aDcAZy2v0HIZzZAxptRsD\ny6iJHO7Ejkp0HjBxdpgyqdRtz2X+rTV28gJjD+U8ZY03cOQ9ZNcPmQ0oIFahEYvTfWn0gPP3v4M3\nLbiMdxaD+o71X422Ygo5s9WGVH9OevrRlZgxrYNrDNWA58uM5XXB5fWC+SoiSguJP4kGhqUAr9SG\no5DOumgF5ERiQXpv/JykpThEj+g8oqevyXtMIWDy/XvenR3zUQretxX/mj5QW8N227F+3HEYCj6O\n7cB2355GPLVUJ9wkRn6s7aRi+f9a6ne2fIzTWi20BzhYFC0Y+nvvABDehpxzsas+OARGSlxw2kuv\nf+cuhO5wHYwTDQVJEhzck0RXgDJ+mVmASpNAcy56n7U2lJIH6fSEwrMzuj2iThmSFw4c1InDPtsg\nCqgyck44jhX7Ll93HMeK49iR84HWKt8PdW+RANmFy5MLluMFJV9hnEFcJtqnb1dcXirmnyCev3T+\npSS01rhPkhy/cx4h0BhDK9ERR2YSFZEMJjsjhQU52xk2rmw23TgKHzLCA4K1aj4bBjIIgjwMA0Yk\n6n9IbjVDzRnlE6QvtJ7518GAi9SlwH9aC2LSR04Zmd60Z+nDWEZwZKlwOJrW10YugEio6iFDR00y\nZ8cy9lHu3xijw2U0Sxxkmj+T+feaOz1fHRQx/Ixh9MLzvwXnVO3vyBnOdAZ7YudPQ4O4Bs8ZkjJV\nj4ycEkrqJRUy5jiTb3hfdWKpwPtDJsUtOYcxNPXsF9yJx2ueXzDPg7iP1AAl6xjfz7kTHH16frU/\nHwlaZECK9VX3T7XUAikjfr0KdFh4RwGXdyKrzHAps/wTB0nHGAyZ3uY3XSbMrwuW+/bccx+QFCV/\nOaujm/Us8L443fvpGTWFamttMBXdZkjA0PAdHErr5CCFekG/aq3n0dCMfDU+Px5AcSSYouUqcUaf\nYPzpeOw9oZYK5y3iErG8zLh+ueDljxdcv7zg5e2C18sFr/OMZYqYQoDjoPjIGWtKWI8De0rYjgP3\nbcexHmfbN6JejYaJRe8xh4AlRlwiqWouMWIOQUdo11qRSsF6HEBr2JaE9+uC2yUSR8UfZIeOjP2+\n90mkv7msIY5QV/ckNENaJmUtleQ57Bn6E9C5Ha1qC3Zrpf9ck1biTow0hltd+fzGGBCmSOeOHX2I\nwq84656M/fRyLomDIfvpczoPrQEt93JKOg7OwCtyPganvLNsdtXsXFBJaz0PBJqp04Yz9FplRg4F\nCVT2qEhpx7bdcb9/w7p+YN/v2Pc7Ujo48Wb9Fi5jiHLrPL9Q8FF7ohSniOVlweV1wXyZftri/Evn\nn9KB4OXBchYZZGa5V5JXaYV7MQdZWznUIyzID0YuY6AkPFHyExlMdZSaZYkjYplMZ0+Zn0Dolttr\nNFsRZ8qL/BmJV90JgBoT4TM4RxrPtA5UAxbt71orTGZYboj8BUKEOA7HpBSeZ+081RWFFUtdC5Tx\nU3fDAxSnNfKu9GWMQc0FjmdfC3GO4FD3tOCFIhX8fqVWpJyx50zcAl5TWn6jWancn7EW4BYbmU+/\nu4RN74s2MwlUEJJREjn+fJzXyllyPt5zZP8wc8B7f2q98UOGKj3eVL993gFcr19wfX0hhbwlavYv\nWbkQvsJEvdACw3YJWCIKKTkqy/MBinNo1cO3Xp6RtTYcSEXnNNubQ8AcAjyXW2pt2DPpd+8JyIVb\nTAWIGIM2DohD9Ijzs2x/9GxMnivv65q6ot/I6WimodkKiEJl69mtljAM9+LDoNqife/OU1Bw7vjp\niYEEobRHhHjWZ54DnJmeSK8cSLMN+cwljj8fhJL5GIg3cZ0p039ZsFxnXHis9TxFzDEiMiJWONDI\ntWJn9Kk2KmlJG9ZI0u17KaBcZuSlUHDfWufXGINcsmbRuRQchcS0KMCgz+oCCdBYb4FE9udYD+xx\nf+refaBSxnSRWfUTBb2NS1OOR32fOrx6tq7xgOnrTtyEAmCHsdDykf1hFt85K35QVs0HDdqxvvT3\nGzrMJNEU3RWYnmh+qtQ5QPslZ+zHhmNfkTNB8sex0qh0HpFdS+5cKwCtMtrgyd5amwZyp4MLASGQ\nZo4BeP821BoUnZWvECblaNF+liTOMpIQ+N4aaitIacN291jfJ9y/3nB7JVTqZ1yn32b+jqVcO9RA\n9QUX/CkrLNJSlzNFKoOIAfkHcZ6iECiqSE4zi147Ivaq1Jl0fGEpSkaSQUOnTMxa1jYPsAwZGQDG\neGqxQheneW4fnOuQ2joVPMFRU2eECpO8lgrnKrLJSlY5Nmq5KanQeojz4Gl5YQ5oLQIwGtkKtGY0\nOy7Q6kdtqnglBBGpjRkDGgJRG1ogkpoEb7T+z0NgFHxRkJFywZpS3+SMBMn8+bEGP9alpUQgXQDG\nmA7z71m5HNKZkY6Mws7fcH3UcqAVpwAnU/RYeCfEPnhEBHaEZQ1D5R4bHMzdIB3Plzy+/PFPvPzx\nivk6I/Drk3qXsLb7qGMfPN8bo0zMfC5cxkh7QkbiFilplepcjcZBsPMOFgTjj9nfwpCyNZbPBBmc\no1B2ed933I4D274TXD2Ug6o4EUdn45lLDY2Vme6DpLfW2ytKMzAcgIoUrRU1NIACA/Q6J9WOqwaj\nrlQgNBgTYF3nKlCpD6pZITXXjimzBC/3MgvkqnyXsf+6V2aevjq6wLXW0Ad8eR7pXWvFtlMWv6eE\n4L2eBYD2wJYSbuuG9b5h2w7s9w3395VsATsYCdgFoVkvE6ZlQpwCYoyYWVbbOQe0ymeHa868dxoo\n0DhyUvTAWmk/S9jc9vQaTNOM5bpgeaWSxnShgLHmorpqIh8tSCUFbgdls5x9kgCaAUovGTcJhlMh\nATK+dyGthSlowCilS0GSj/sOY9fvgg3qnKDPIKWNnPOJB/KZq5aKxIS5UjJyOpDzgZyT/gn0uTeN\n+QQdcaP9631A8BPNV5lmTPOCeVmo+2aOGlDlnBFWD786RgQsgo9IywtIUEmcPsH9WnIZyOZkex0j\nYzTJ8/btjumvD/LVPxF4eorwR5DFgnm5YLkumJYIGFIV0jo3w90UFRFJoZSRKwB90FL/8D5wO4an\nHtWBqNM4AqOaS6IDWRJyTpBZ42NdUqKiECeUFOGngBqLOvDoLIxzMJ/I/LvAxBCkeHI4IfY+0CYt\nJ2MkzAhHrRS1SgBQS+YAQKLsGXOZaZ2j73XAYQhRSVmz/HyIbG4igkjOvBHENtoeaRujkfPI13ju\n3sV50HNIKeNuduz26N0OTVjUDO0z4crxNLGJs1WRGpbeXtm8aU8QeU4RJ0kbtasIz8L5rjEel4i4\nUMkpzlEDAMkelFHNSEEDGXLH7Gr3ZM0bAF6+vOHydsV0mdT5i2Ny3iJMkYMQDgSd03utlZQOD1bd\n2p3FbgywJ63B150DN86InSOoU+v6zJ8QbY1SK/ZCyMt2HLjz13ocWPcD+37gYJh6FGMR5AGti+D8\n7nLOI/hIWgdR+A59T2smRRgvBeocXIuegeq1D90iqI9saQ40vAW1FVKw5wLfMwcxNg2S4VIyUPIr\nB778HtZaEvSqFc4QRO6c5ZbM566xlc0YKHHMGJIQPtYDeU/4oMWARSdCKjEUQMqZnP6NyHfbbcP9\n2w3bbVe569aaTqRbXmbMMjUyDGgW25iciRhWpQzn+khp+Xk5Oz2gLsB9kN39zTUtPBjn7YLldUGc\nAtmwRN334rTkHJTsOBs3SIy45YPEhXLKMAdNFJU9SdkzrZskP7Sp0J21oEyDZkE+ukMXhNcPPIDA\nBExp6RVSsRmCwmeuKsx7yex5kBaV4oKS7HoJtZeq6X0kASFhrThNiDN128wX4g/FRYSHGqnx8d6I\nTC4+tqs6fko6AjH6hzJ7zplkfI/ubwEglwP7uuL+jWxknMJ/DfanzUzwwzxfMF8umK4zXPRnJTle\nBEoUub0u7wqP1FLQUCGSwFL/0KCAn7xpDZbnzZdSSKc4HUyu2E6BRVcbtD2QcAElJ5Q8IRwRmTeu\ndZbnrxs1Es9cY/ToeLOp3r+1VNsqhgd+dCUqFxx89l0VzlDknI4d+74qlyKECfm4orVGjh/QA0EG\nq7LjJ7hwX3esHyv2bcOx78hJ1oIhJSaUCDRYK0nJfn6W/aOBpb7hkf+gYhKj0Aegaz0vEy6XGXMM\nmHxAkNkAhlp8Ui7YI0WyGtQMdVZBWpS0GDziHDFfZoIjL+x8uc3FCeTPcLm3lCWnnKkMYD/X5jlf\nJ0wcZPjJawAAAyUfieMP3itM75kYmnLG5hIFXwAk9RKkTAiNJRUUT2s5EjYlSCu1Ys8UZG/HgY99\nx23fCeo9Eo6UkVKiWd7M5peSTGMEoubPlbukVjnNC+JEbHYpuYz7gw883Z1kYoOanWbfbSDqVZkM\nSEbeZgObHXzoAbQSwYTtP5T9VHxn6AYhgSeDkjNK6cZf9qOLHp/Z/qdygrQuG4OSMrb7Btx3ErxJ\nmRT0shAtRQee7rnwOUn7gX09sL6v+Pj7K+63G/Zt1e6LaZ5xfXvD9fUVl9cL8am4ZKGsd75vCbgt\n24npMmG+ztp1IBC5Bn/poeT4m2tiUuPysmC+zPDRg2aJ9Dr7WH40VursDmEi21dzRD4Sjj3hcBbY\nwM+GE7lMNWpjwVoUoQeI7NxKYplaJiwe66Gls7HkJ4jMfJlRckGcI72G7MlPZv6cnShnAZDOmonG\nA8eIeVkIARqG3ImtFH/ovIOfPA1Eu3Cr7WXCzKUUHz3QqBy5vq9YP1aE+QM+eOWFCG/JB5bYFmSR\nNReOcMCuK459JfJhzsi1YNvucB+EUBM/7b/o/IW1GKcJ0zxpdooysKoBZdbXEpQEUXLGkTZmQ1YN\nJoCmDlsWV6AcoLPjS0lIacOx37EfK3I+ONMl4yFtX86RgI1z1IpYKrHFc44wxmCaI8NRFtZ9Avav\nlWrMxnRY05KscM5Z65r80xSMBGbvMwHy2A9q/2EoLKUNx7Gi1opjX1FLhgsO17crtRMttJGtNUh8\ngFqjHttjPbB+3LGuN6S0EgtVyE7DWhpDjFWJ9aVbg57pZ0SOWCI5FyRjYLkXlwIzrtOLrCTXMa2z\nmJYJ+YWClnKZ0aYGayZFAioa7uywNZJlsp9kruJEhA/hGd736nhDH6/L7TSe4fIlBgTn0Zh4NZaH\nnr2EbCSOX7X9G1jPgstA1moHRPQegdvvpMQkZaaeLQPZZthMnR1Ss6ThSbS/hL1fGxE7C0O8GxPI\n7seB/ThwsKLX+CWdhNpJwUbskW/zy3sPE6Zppra2OWorm6AaTQhqhnXrhaXNDG05l2JIad4AT3jM\nzGFB7+k2jtrcaqRARYIAKWsJqVUgY+JQSJ85N/zVitwa7MGvVdlJDojFs5eSbA1UsKy1imNL1Gmj\nTj3pnh27WcZ+dwlU0n7g9n7Dt7/+ha/f/oX7/Rv2fYW1FpfLG/7443/H29s/sX68YFomtoWUwQu5\nVb6cc/CgREH74F2X301HJ4BlKS88SXicrpMGErL3pX1ZgtXMCckpK2e43wcPEwyX47wS4NJ2YNsq\nStk5q06orcI6R7wC3gPGGQ1y8kHjibePDfu6a6eEZ16DcEGkxCiSytZZ7Vj41QClH13yszSNlO7L\nuYAYJ0zzBZeXBfPrgsB19DLuy9JJf2EKmJZIwdTLrHwRIuHNCNGjAdjXAx/LhxJ/rbNIW+L7ZG0S\na5D3xIG8oBojIiWEx4rMaIW52Y6O/yQA+q3CH2XWBDv4GIiQBgM4MMu5KUmtZxsBOXvGoRu6PoAZ\nWkYEQo8aCQm7XQ54Yy4Bwf7U96g3Zyx8I7labf1pXTtAHkTOsc8+lxaoJ69SKmdyHIGXhmK6hrox\nRh+Q9V24o2YinaQ9dX18hoaIMUrIiNTLMqtXxTni+nrB5csV1hjs20GBxkFZv4+inCiKeLIW3ShT\ngEVZmWl9UwDSW/18y49Ah/nIJ9i4l3lYBW0/iFCXKozrpBsx4t5ZTD7AAARnW9fJgYOTUtUwKTfF\nDvUHdvTgA+1F0IPX1rv+p7eEMkgm3cCdGJ9gfE/XCXEO3YhxWUHqy7InhJk/DaQ8YSAL1yFwSYD4\nGGwgBkJiXCiwnnygVrFG/dvg9XIcdEbvcYkR3jms3uNmN6z8PNB6mUazZPLSkAl0/slebwn4fehl\nFSnPUETZyYSC+hmcCXrVyCAsVtlk4mvak9bShdRamcPC0odoLVDdOBdGNPJJWa4T+HpPudgYgJxD\nzZFKBnLmP5UACkG2k4obl79gOpwu0zWd95qRpaNgX3fs910dY06EYK7rN/z11/+Hf//7f+D94984\njg3Oeby8/Kn3cRwbYlyUqGos8V2mZcbluiAsQ7lpJr0Bml4IJRLmoRtC1u3ZS5CHwGRmyVAFffC5\nKGchbQeOjTP51igTnnkcuOdyEROwdw5OUtq1rS1nKvst1wW1NYSZgkxKLAbNjNa01Y6cWS8PtNaF\njkaEQ9QGlfj55CWBnCRM1jpCvpcrLm8XXN8uCHOA4VKccFMalylgSCXQRxoudHnjEsrbguvbBde3\nK67zjOg9Sq34iKs+s9oo6ckpU+l0ofJPSRn39xXbbcN22/jnixLJBdWtXFbJuftC0QP50fVL5+88\nwaqqb84Qp3UWLTZdZD0cvnAtpLMSKXhwaK0TzkRAZbkQm5rqql4z6mM9Tq1ZlbOGlCkAkJtNxhAp\nyVMdptaogQVpOnuCm1JRpv9nokAlZDHrHibDNSYwNZZVdE6JfxJ4SNQtGfKxJxzHjuPYtIRBxEWv\nJD1yAhEvf77gyz/e4KzFdhwIc1BSYJwCpnnCx98L7h8r1vWjQz4cpQIN3k8UcTfgUfLy2as1Ylfn\nIzORh9WvmESXGYkQLkNOGQZAmOkZEAok0CzFgU5EbxQtGrkdRcV9wBFwYEKdGCIV7YgeLnrtrRbY\nXYhyzho4Q/3W1Rqa0MUZ07PX5fVCEJ1kvzGoU9XaPwcbcwi4TtSOFZzXjD2Xgj1n3I+DerCt1QwZ\nkGEldD8xErPfADhyRi5FSxiCKEjLl7MW63Hg7/sd/3Yf+FqJpNQqcRxGpTExmMT4f07mVAaaaAsl\n/ymkUnH8j1etFfXovd9thK0lAEiSlQK1ADn17LEyLyUudE7TkbDdNuwsVCMO9cQFyokNddbP6Far\ntdUJk7aSPXvFKeIIBxPTpJwJGEeZbSsVLXoAkzLNqaVuo46SyhKwe8Kxb9j3FcexYr1/w319x7q9\n43b7iuPYuFzpcb+/IIQJpSRY6zWgAQiJWZYXvLz8geV14WxygbEWIXjU2vX3W6moSdC4Hjg9e/8C\n908yEZBLCd3RErReeJ8ZZ+EOh5I4oLMdkhcio7TC7uuOWgv2/YZ9X5XAdnl50bWe2OE1NC11CmnZ\nMjm45ApjMpeT6tnReQ/rGJksgzb+k1dJglJ3Urmc0Wmh512SiDf1c+Y5SfDRY7rO5Oi/vODyduGu\nCUJ0L9OkyGQuBYcngvryssA6i3mZtQw8XwiBSUfG/dsd9283fPz9gdtXBxgKGswxPlcu9bCWi3Me\n2xoQ3n987n/p/L2fEMJCvYqRbmC6zNzeZpS01WpDMgz1V2HkU42fJspJEECO/3r9gpcvr1heL10h\ni9vS7GG1tlfLzLCLR4wz1nViyFzY7UWhaWFhttYQPBlIytBFnvJzKl+6nEyukMyU+vPZaFuvhrxw\n+1E6aKLW7esdH39/4P3f73j/6ytu79+wbTfkvGvN39rexiMZbQgBr8sCby0m72Faf7jWWsR5wvWP\nK4l53Fbs9xXbbce+bdqL2rkY9tQqMvajPnMJXCldBqVQlHlsB/bbhvsHRaOEDDS46LEwxyJOgUh8\nR8a27/jgWri3CVvO2AfYVKA7Zy0ak/OWlxkvf77g+uWK6RKJNLPE3m41RT5IEUsI1GNtWfiGDVXK\nBaOo+2fafad54npiYMTFoPJAFZEmNsYgsvOfQsQSJ1wjlZqOnPGx7yi1qgaCKNIJE7vWBuOaftb7\nTu1YO8Oq1llE7zB5Yv1LIBC9R+I9KdoSaaN9d6yH7gFRPzOWEZIn791aT3A3Ezjx6Dia1Ht77Tdn\nmbzY0HJV5y9QtWSOY/Fd6vXFMPEtEPFNIHeB30c+iHBdBPEjvgs/XxD/RzQu9Lnz+Xr2Elb/sSdA\nWjvZ8YcpKKmCSl5F69Lbx4btvmH9WLHd7jiOTcsurTWUSmU6sqsTlyqpI4eIcNtgz3ZuL6M97P2E\neb4Sc5zJ15frF7y8vbHuwAVhigAa0pbo83DgVLm+/swl3KYwBZ0JYQAcvB8UbWPtA8pA6RxbHlcs\nLZxkK3aUUhGOwCjnjtvtG+73bzwue6FuEU/BaZyjlhNak3tZsW0r77UKoHcIxHmCdbYjSwe11slk\nQ+IqPW/zUiIida3SIcVqojAoqWDLJKFsrYFnp02JwkyJGqtqzi8zpgvJgo9Df1Ih/ZNSqUvktm5M\nBKeg0QUKukMMjAhGxJnO87REajN9veH2N32F4OHvAfvdn4PtWji4jNjWH+t7/Mb5e836IxMXLm8E\nCxlDbP/93vtHBVaPMSClhHkndruw/F2gXuPL61XbSEIMypgX5roPDm0KMAZM9Jox7Qum6YLjYCdX\nMjLXjqSrgEoMlZGCLgAyGpzPtv0o3M+wUnGFN15DYqOGG3TDSi1w/VixfazY1127IgiRmJUw48OE\nGCdqp+Qe1ZQy9pRgQkBW3QTohsArqGd7mbC8zkjbC44tYd92HCuxitNOgZBEwmRIHTOjn7MCeU/E\nXZBaOX8GubfbXx/4+Hqj91I+hcV+IwOYtgOXLanRTnvCOu9w3iKngo9vN9y/3XFshxKKfAwk8mGd\nsum1vs8G2XnXM2ImEXr9kwJIHXozlIIoS39e3tfPnktRFIzmVB5EmqwetMLs/pQzknMIOhCEFNju\nLPCyr8eZtWwtqYiljK0BhyFOy7YyksLscceohmTwYlhTyti2XYOwtB1akxbjNF0mDdafzf4UtXNW\nnaZkViYYEm6pPbMXB11yPnErqCRCZQMpEZTjLOXbCp1V2WOqoCkBBvDgyMnBdwJr6wRgY6kdeZ4Q\n5sia5/5TiBe/CaRdFW0QbmIOQy1NiVq3r7SPt/uqgclx9ACFSLgB02S0W4mCtqxt1DHO8K6T3qh1\n1sN7ut9SEnLasTYSmdm3FR8ff8P9+z8xzRe8ffkn/vjHf+DtH28I0ePY6IyutzuObSd+xbOdHkOJ\nxFqj5NngPbx1SHNWWWMJLrb7CsAw1D2pXkNOGWa3undapbLGtn1gXd+R84Rtu+HYNy1PZEYb93XD\n+u2Oj6/veP/2b6z3DxUMku6zWGdIICBIkV3JDh3rwShRRs7Pd/l0hn/tiRk6Qum8RYwBy9sFr/94\nxeufL7j+8UJqj/OEKQZGxw1KI5LuneF6czfYw4Z3DiZyLlhvK+7fbgPJj4KNHDIjp9QWKeeMpOv7\nWhdu5SWF0Yxas3IqWitIace+3X54r78h/BG5zXAriw+ela4WdQQC88yXmaImNvjiBHXYifRHR0/C\nKZeo0GKrDeXIKAlKLun1eSETUR1yzhdUJfSR8ELJCZm7AAQp8N4rpGZFHMY+T3oCoBleb5UDTDbI\nJiPtFua2EXw51KvFaFHZgqDraZ4ZeiK9hBoXwND/T9MFIUYYhtP3dcN93ZBKwbEnbCs59bwnFsBJ\nSDkzWcwhzDQsJC4RxzIh3CjzKInKJnZYYxnJ+cx17Ic+H5E2rrVi+1jx8fWGj3+/0yARNrzMcAOM\nwf19ZaOw47pedbb4vuyw3qHmgvv7ivv7HftKmQHdS4BvXo2FZn3OwbquSy8qg601lEaON9eCJRBL\nWp1xKcgMC1pD8sTPXiFSptJqow4ERqOURNQAgNb1yBm3fcccPC5xgncOqRR8bBve7ys+bnfcPlZs\n942EoGxnxUN4LplKXcd2YPt40OFvQB+oNczbKFT7S0cnngkc6bzF1BpBqHOffvbMpRKqqlZGma+x\nXV1N9MxrIU0L14jTIOJbAAbCJjngMhXNciQYIbnq1HVBROddWueC59Zi7iTZA0rOg/Pn3uxEryHc\npDhRychF6tAxzyd/QxmKarnpIKKfT4WGp+wJ9293fPz1gY+/3nH/uCMnQlxIS90jLJFJzIySZj4n\nEHlbIpLVkuHDRH3hIWKaLkoc1C4qRjohAYkxautqKdjud3zYbxRITJQ5b7cV+7oiHbvammeuHiR2\nKWlneahOBELzyIHQj2OjIEfKMtKa6QKLHXF5RqbyHceOlOhLUJGdy5bS+qjk1YPs37FS2WTb7xAC\nujEWOR9MdPbIOcNlp+fHOsuzGag3f9/Xp5/9yB2RZzWuzbQQ8vrxRociAAAgAElEQVT2H1/w9s83\nLC8z9+4HmGCRa0HaMpO9E9ZVENpd7RBMFzpLOwWR+7qTTWfVWB97p4BlHoTMa5D1lqSqFUEUyUcS\n9E/co1YrUvpx8PNL51+Z8Zj5AY6sYWsNGgv9CBlIWsIyC41Izykg5ChS8pq4lgHpRW1CVOkPv5Re\nXzQGRJiaqF9cIGhpH6klo9T+GWlTTFguvawgdUz/mal+paFZEjTREbOU7ijBh2Ro6WA6Fr+Qdj3q\nP+cBHSzPmjMbOjArNERFP3Iq2G47bh8rwkQw1nYnpiv1CffMvtXaW6GM6SjJFOgQWYIsRzlMUiB8\nTujmWA812jV4FEMbdbtRRL6vB7dwAkBvwxKUZQ+bio3I8y8pq/PfeAMLU9g6Cw93AmkKOzZqlxMO\nRaL+1j1hWyKmGBGDxzQFvEwzYuB2p1qwp6xKaUV66p+8Rq2FY0+dOGXo35K1cPHAfve4xYAQHJMN\nOaovBfuesO30zI6NhEMoamciIT+bWhvqUDLaRQaWe7pVEIghT6MtPwMLfiD+ACAFwcC96hy8P3t5\nlVEVsRWZkGaV+FiEz1EqQgm91WngCYguhihu5pRhvdX6MMBoQCnDpMrKSBDzDiDiWoQACbQ7Ttts\ntSHkSIQ07xAnCgBE/dFYA/t00YMuZeznAmMP5Z4YY3BsB0P7Gzs9Eh6jtuioZ17mIQB09jSD4+zM\nAEj5UH2SabpgWd6YLGrhAtkt5RKxs1expMadPoGQMXkfYuQndrYbjLEI6cmRvtLO21g1ciDkAmRu\ncspYP1a8//sdf//nV9y+3pAPQgql9U7khNN+IG0H1o8N63rDvm8aBBABcCPxHJ4UiEqS1trmFgPJ\nycekynfex77Wzp26ISTxyrwG6aBW8aefex24C9Z3RIr5YiQy1vUE1vcN68emQYIkvvq151NwLvoX\nQhTMiaYvHhsFj9IpIF+SgNVCJXbRizjWXYmWgpqLaqr3Qc8VQCTAH12/UfgrPGFow7EfSHvu4hSO\nWlGsNYB3WmcEoAFC5dnI43Q6Ecsxxiisoe1iR0IWoZfSe1r5RQk6kv5gnoJVG5NcODpu/OB8CJgW\n6oElyNgpC/Mz16m/eDB43VAxo5gdvQ6BkT7tiYmLhoxh4f74UkkVrRsILqNs9HBlLnPauF92PTib\n5hpR7kEHGeguo+qcAwJHg851KM904aLfXelgg+YdApMvqZODWqwEVh5hMVmXPrKUI9xEzGA0gstL\nrjh2gcC5BdQ7bY+jDJJr5EyeyoY6DoojHoFkCWlKpA62e+xzQvRULiq1IZUyOPx2co6/u6RFjFqO\nsmY2InAiNeCD659WWuE4aNUglvvAa2vUzsRBYZxlzrrtPeXcVSHXaYKjaDWMjHohpI4BthhCYxRq\n12EqT967OP6x1m+tUeY/+PnIvAn5KR3xauQM9iCiNcBsRrtktE3LdBnW1hrbF08IgpGBYE5iXHpf\nQ7oZ+mRbI8XR1sj5z12CXCa+NfuJZz9wFHIqMCbpiF8h92nPuXeI06TdMKPapGc1UGuNBjwEQ+9K\nygrs/C+XN1yvX7AsL0Q045q2tMtJAKBte6a3/blgeQ49zq14hboMPjPQi0S9qhKVN39ogNpA5Y73\nvz/w9V9f8fV/fsXXf33F+r4q0uknj/kyU2nYGs1Y13dqUT6OVYfhkINi4uZQrnIhYLkuuLxd8Lq+\noaFhXi8AhoE5PEaZAlI6TyEEbbWUzDqXz9X86fk7/VLnLyW+XLDdN9RScfv7A4BR1cqSeum3sp3U\ntlFOWB1PWaXnSftrX2noFvF8HHEt5oi0J7avnXR+bAchCeuq5QzpzBpHzIsMOYBz2Xu4fivvmxIJ\n0xCxjGo8crBgoPWGNmTpUjOzbMCb4X7c1hnnNYsEahfLEIOZjoMZnb1Pk+7D6GQwYwxMkHvrrE6t\nk/KGIEMwakh/wvnTuncCYu6wqjgsUdkSqNQH6lWVISjGGNQpkvHnaFGkkGvtLNVaKztJyq6lZ5ta\n6MqpnUUPeDlPCJMAwBhwrbN3JMgmeLbtR7KMkguR1iDCIhHTJVPNe0gmG5NaFPoMnPFxkFhSAVpC\ntlA1PzKIhpynNajWotRCCn2RB3noND3DiAt1D4iBMjBoVVooM6TnlebAdyf+2bqvIha1aXCqIitc\nc4MxMKUip67yJXCxqJy10gNSQnqoVre8XDDxa+RcYEC/u687c0oIRi+lwpaK6lon1DXpsZdn5TRA\nAMAOQTJgr/Knz14ytwNc0tKhIRxMSE87OIsRdUch+QHQ8+G4DbiWXr+XMcal9a6B1hpq6qqPtTSI\nVnzTz8EtgeidIvIXQRWlM0bPpCBfnyj5kOJg79eXejKx1anNVfQsSFOgT6XTfWZ6+UScNDmICxP/\nDOuTZDgXcLm84uX1D8zXi+pW2MATOX2fSClBgCBQcn+tNuxcMmpt2CcDf+KZS/Z6YhJjQ29LraVi\nva34+OsD68d2QsTELgr5TgLQdFDiQnwIGVIjZajeOpyPjHJkwBrEmQy7cKXCFLF9rHSeWpeBlgFe\ngrSKDkfJBYnLhJ91/BRg2K6bz3wmY8i5CpdAZ1tIFp/J/lSRnJYEUf2iJGZWFUcb28V923FsG7cW\neohqZaxR115bZplb0yftEhpoB0Znf+7sd39yp79x/plrJnfcbzfcvt2wvC6kyHQZhs/UMkyd4yV0\nneHcSu+NljnWov+v0CVrnlNPMC2ebHhR1BMCjkDs8u+Sncvry9CdLv8a1Bl9Bv4UeK2Uolm/6JL7\n6BBiJEPD8qddBKhpBkcZkwMMkRsrt/8lbsER1rRkPft91w2iugciA8rZRM2eDbAcas6dRHhFMgI/\nbDTeCDl99jD0LC5MZOidszooZ8xMyWh3/QNrHQccFa1ldnKG+BoiDOIMnA3qaFx1Wr4Io8yptMhx\nQCZBoYhLoVBkXYc2HMPZapwja4A/7wAFupYWVaA7IHV0st7N6P4T/kfJvcNEuC7UfjZhuk6Y54jg\nSIJYOjG0zi4Gwjk4yfqlJi4BQJXvgYIiQD+DqA/GZdIsjFppn3MAhFxxi1uRZ0zP0dhBrpuRpmYM\nTO3BUoOc94bWOmxZhmC1CSrBSF5rDbkkYOP14P3EHDsypI1FZh4CMdHXsMboiOyuNOh0rzx7Wc60\nC6v4tdbgVpH3bcxMJ3hbPqMmQWJ4gVPQpbM5tOPDssopkQLn5YqXP16JUD1FHvTlFFECwAPFWMCr\ndaU96boyR9JSzbiPNWB44jo2KnHkVHBsVDq0nkmvR8L9g+rXVTJ93tc2GbUxksg0EGpG7eBdKZGe\nG3dmWKfoGvE2yO6HKWB5XRQqd852wqyWkKUd2GvAKjySzOXCz7T5AUCrBYZFqiSjllkTObENM0bP\nqUyrbby/RZmwZulIGwZPtYZsDOyRTnvs2Hbs+53REIucZ0W+48QqqLVxIsjDhKo4ffaNxhL/Cjxc\nyRTUJhLVP977v6n506CAfbvhfnvH7dsLLi8LpilqdkErxr6OEQBhSUowUm2fbQ9IZCKCClBGbWVY\nmaImALVy21YnEI0MTGP5vZsgAtCygtQtRR5RjdknoF8iThTN+qWn1Fpy/BOLE4lTGssevQ3KKgwq\nutHWUUacbNLNLRlm2hM2Z2FZGhYM94hTDRNBxT55FbUQ4yiXOC4qQbDmNyt9+fD8cJtae6AV54gw\nU6tdXCLV7FcS9xFERGByGXxjuKUNggi4jvrI55UoXhwgABpqsrDIzjREv0MWI5mFZGgaRHLrkUDB\nxhjUUPQ9nr2MlQCsIvs8iFhRRC290yJ0JZ9PGLkAtOVMJI+lFcjzEBgBvEX/gBCboYSDfpZqJYY9\nfIfzNegdn721CDOVvKi0QC26Ljj9XL+7BB3r5YQeCMv3xvPwIzKh8w4Nlo2n4ezc6JpVI2UM6LpK\nW61Nkmn14L6Owc9pr0s5wPbzLpPPnPk0yReAltFI2IcgXMOwqjEGAYEV3GgAjxA3haTapYdZspmz\n0daAtGeEOWKp1JLcGim5zS8kAvPyxwvmV54kGcIpcDEGFNw00aCvSPtBiGwRCeehDMfPh57bc05w\nVFKUdjsRXEqs6aHQNt+ftQZFni23+cl50XknlsiPHOnxPvDauSEZrOw1eQ6S8PgYKNng4FpsuagK\nhil0pnzKWG+rBiOfCfo1sGVkwhjwvRD3B5UQXNAcNvqZ1gNU2Zq1tUGHQmZZVMiQHgmOhZC47ytK\nPgBj4f0d6ViQjgPTvCh/Qka403v0rF5OnrUO1PRGXL2U0k/r/cBvnD9QUVvBkXZs2w3b7Y71fcN0\nWQkCnsPJGTeOAqTOKC1JgPT0sjGpgypf7QcbClXwJSd7fCACb//A4PTv91+naBuD4fj1Hf/s6i1I\n3dDL90TRTQ4CIMiHRW3k3KurjHRQFFkykTvEmYlMZ3KCdoiWNpNvCmui8+cXJ9GqO0W3Uusxrg8I\nks8k6MQzl6yjNXS4puuMy+sFMMC2TIjvK9a4Yl9JDEVg9vFZqWOwRqNyjdqVGMaa8M4C8B0ujcNM\n8UitM60McqW87ihAMUWRAymnAIADIQuWpYT9Z5yAEYU3Kks44VUwp7HwCFUEgomN4VIPq9SdGe8E\nzwLQbDKzgyXH3qG50bGJ8aa/8LoykU6GWonSmQR8VIPmchcLQ4l0qGTev711N2SLDKmjKb7En5u+\nTl0Q/IytJ8KWc70nHADaJl0CHAybLuXrnEOB7FHeL/y8TAOMYdKt4VKa67M0dMTskCV3NPC/duAJ\nfXMatOdSYBniJl0S7ofXAJWInonrveKotHTC6yg2SlQjjXRAvSy4frliebvQJEl+D9KxF04JBTR1\nSBSEdKv7xEj5gYTWasn0WT/B9geIm7CvRGCDRRctWnclXeYjAwz1e+Y2RBYGcqzoJ4EDjRgnsl6I\nExoavI/ckdWJvqPzxfD0nKegVl4P6GUVzyiM8D5qIZKrsxYxzJ/aAw3dN32ncyFLzLa+KarJehWM\nYOeDNBuok+EA0JNfut8MoLGD3k7O33A7uPI6TA+cRTMCECSBba4GfFWR0EduyY+u3071IyNLxL/9\n2LDdV6zvEdZbxBT7xC85+LxZRQWt8BAPEa94HAQzPpfxEVVSVOkOd3D8FoNinem/rFFupQWh52ZR\nXEGJ7EDt8zDQ4zCLMcgpler2UmsEwHV+o0p01hk4EPQtRvLYD4CH15QimTsHSJy5Uv23S5+Khn4p\nVTelGAbjqH+9j8GU++7rBWNgWx+I8cx1CqwMZyfXqbNRoxy6vQ/5aTxCVgzfwMKldawouWeTspaC\n2HSHOQSTktkx34N+t2lmpXwTDjwFEh3VJX0QNcDnMwAx7jBg8g3Bl3lAKDQjk4DLAA69BivBD9jx\nVibwhImIgtnJ2WC2bqb2uSESGIby0BkIA5ol3SSqZ85Zlg8E+0snhHBKjif73TxrQyipsPXgtxWe\nd+EsPY4G4n4waiEEUx21LA6UWwOpY4KJstUqZ8YYA1+JLxN4vKuMB9bAnb+EYCV7c7waKMCy2TJi\n1mBs/VT2Z51RODttnuv+fd99976NZZVrlyAeSwDCEZKOCWst4Kl9ztrOkI/cBTWqhUq5wDrHQSbd\npZZMB1jZDa8v+hDVk87A086fky0h9o6OLe+JoHdu4xbyMoxRTQ4pMVklslKb4x7Z8YeIGCeIdC6d\nHqufW5IByep7t1AvnwkyJXvHMTLgguXSFJ+DSIFGfRL1AGjveN8DbSHOWmt7YsNIQGU0h9AS7orL\n+aTkKlNoaW25FMZaNNTySAJG+35DKZnXLTMKSB0NpXgukRi05nrSZD1a68gv7TnmAqAqcv8ztPu3\nff693kmtE/u2U2uDs8hz1nYax60Zxni01gkiAh/pKM/WqN4Ho8QGJfNwFCObvqGhGotWXbeHtaF5\nqhfBQx9OJyeNiALXxqxRst9ntP0fHSB/kz5frkiG52eHpn3OsN0IB55XIBskHVTTFGg6baJz3uBc\nJzhWx+IvRbogcieQNdLQDrO01HhYvi8JuEQGs6dtQDNVg4JnLmnr5O1OLNQYMMeIZYoInlqv9jUO\n7S3MdrUZELlPZ/WwFAlyBvQEAqYMG7Q1grgqk6TGy1pLraEDvCrwnuwdADDecLuQwxQDLqyQ9+zl\ngydmvuvDnIwxOPajt7XpITScCRsYR2WoCulCaUAhxEkCtHRkuJC0Dij7qbA2uBkMnsCcApVKq6qQ\n2yK3soZIIiwwrEbnHKbgEX2g3vSc8Wyro3SHyHntpN6KWg0cB5TC6wHo3mXipejOi3Jnaw3pgJKz\nbDU8d6ChRq97DbUB1tBAmYmCB8lwJJOWtR/bbkcJV+3MYei6hExB/Cc6/YRBLsGV8Grk9aWF6+Dn\nl3j4lsoWC/lSs3EzBFKNg1tW3vwRisnnQdo8wTaA7CuhhpJ5yxhn+jXJmiUYIPIYOY7n9n7l9lJK\nKnsbaU4ku77dNyXryoA1sT1xCjq2VgJgsoFgIqtX0SNru+QyQfdOg6BpIWSAuAwH/BaQdqr1j0Jd\nY0eJoGGVkbdxXT9T7hNkckz0DD9G60TyeoD5C89qSQTxk2aB6BkcyPkYsnhKSJprqCLAs9Ogt5QO\n1Jp1UqBzATHsyDGRMJzp5asGC1PotYpwnVg4qjARUNRcM+vg/Oj6tbY/EzIkCi2FtKq3G/cR5qKk\nOtI+BwuYMDO+FhX9kdG05Eh6HVP03x8h4gYW2TFdKlQj6eYgo8tk6l47GSiGbbQVjyJEjdqf3Qin\nckTfHMIopb93qMo6C2c61CukPdSGzK9D7ZN9XKUYdtLNlzG29JAFXusqeV0gJWbq2zUMd1K9zZ7q\nbepkB4b0s+egFGLtj10czjkskYZaeGauhylwH/vBvf/fIywnstqwlrKeUtOV8aAUyDU14JVhf8P3\n2+Vi+8wGxVWB3gLkqV1qChS0fMb5u8jM/ECKXcqj2MO5pjv6U96nOlxFdC7YeVjPGXDqMxOkFNWh\nRh6FO3Q4SHuXMdQ65aMMQIIGBHGi0cmOEThnjCofGkNDSGRa4G/vPTh2QEABZbEFBTZRim8Yfh6N\nrHCAhGAp7XYG1N1hbFFjLTVezW4roXXNcA3ZGM3qpKtoHOyj7bbaAtk7HSQJMAbI3sEnBx8+d+5J\nUKyLrdCj7Xom+cg4DLVmpa1PpmwQclgvl1Ag14m7lNE5tmvQREH1EaSUAwomJPCnVlEZDjXMw8jc\nT577TAeyCeRkiys82+JJZc+UyVaHcTy4tD1mlZEWuzV200jmPy2TBpBpp7HqPn50LpjpSLFchvfG\nfJ3x+nals+Y9c6Gqtj0LT0RaWsdOHrUvtTEBT4Tgnuc58acBP3RGtnqQovwRtjmVf67H1Z2QS7D+\nAZG6t9YiZw5mS0Y6NuS8g+Tw+UybBiPKtVUm2LZT+axyoiyoMQmAFZ1EON63kPZ/dP3W+YvKHwn+\nkGLSsR+9dUg2HDt10RtHzjp5SIZMCFNalrey0dcll4fpLEdJJOTRWgENa+rkouoqbOt8gPODY0gM\nZMNk8MRjFvm7a+QJfFfzr01HTEoG2EIbDKFXkZiK3jMsgzZ08AYPxKFDa7knmpj8iR1I0RZIiuIa\naOSk1ku9zMS2PdPmZ0NBwDCG9cklqJk24phdW2MQnIcbI+rhnJRM5LicshJapNyhWg+ARs56+NnR\njAQ32dw5Zfjk+qCZarTe9vjaYhC0BdM7hOAweY+JBwA9e/noVUa4DTVJHz2yPpPuiEad/ZKytkGJ\nM6Nn4hUdGZUTaRmkXHOGG+W51dIAQ+Na3Z5IJjn4AVEjxyPOXlnesm/R+Si/uxxPJux1zT4YpgHK\nM5Ayk8wQcMHpMKY4E9m01QbLjkvgfYXCbdbnKM+bHFfvnJHOA+cscuplAFRBRjrJstSiztOASkml\nBHwC9aV14m4LyURFmlazf0bxSs76eWR/iP36/zl70y25jSRL+PoKICIySUrq6pk53/u/15zp7lKJ\nFJlbRADw7fthi3tQCyML57BVTVHMzADgZnbtLtI4GGN5IJGpn+65RIsba9TwyTnJYuBhK1G2xsqB\nRmiicuEGQCY+UYPoOyB8Cw9X3mdvvF8pgpyIqVRwShlUUq3pz6Nfi/lf08KZ9cusZHBjOA/AOeUh\niJuhtZ7cA1t3VXTB4fF4wBwDlinSJ15p+Li+rQPbX1a/fzwPSiab9LTt6ih4970fFWTctMsx1f9p\nANMAS6uv1hycoFAusPlOVY8cMd+h94rQklrHAi3TegZgQdH03yGew8q2cfiQTPekRpPArKp/lxb/\nv2h+fgD7dyKB/GVCOqh8yGXPO5rY37DKu12Z+AWyBgj6Qx6n0XGfV7UQyEglX08aAZstmuuQmdol\nSkdoQQeDrBLQH6z3yj4g32HrO//vCVlCOHPe6r/TA5x/ilIK0pCwRVOM7Hj5YC0GYOc+v2cYa9Vh\nLOv3z6YVJTNERNN+mMIAf2MY73mqblX3pH+1//n+knhKNavJ36VntaYZ9tln3vdafUg7pF/0RVIy\npndA7rtK8M5YdpVgpjUA9RqopcIzVKgIEQQl6KEyzjptABynUMov/45D0HuvMcEGQAkOuQYOYCrI\npqtHWh78KraO1pBpiWWvBQuY3hhggMm1UeIpuObblYZ+fjCKKqTNwYeEnEiWlXNB9kVjhKkQAJmf\nw/KOxpeCh/j/GT/f1guWgQE84NiAx8oKZGDdW9/XNnLfsssQfo5L3fdd8tmVs2L7Ok9Y5A3slJcN\ninz2tRejViqKabBN9v3sUfBO0p8wyOMSabr3fW0pn0PJBZlh31rHBstqNK0+p8bBG4Nqefq3vXgZ\nA20IVPEkDeSVCv+V5XWtVh0qgAFRA5uhNTr/OonWaX7Ave/9tlJ0eExR4XsfHEpy2pBSY9a9TqZl\nwvKwYD7NmI+zTv5jMBi5rgbEeMBheYT3EcZYxGlhFDmr9XNwDh8OByzTJI8gqzk8RdrKWSfHHbp5\nWS2kt89bImO6dxZ/AFpYtWmq4OlapKeNYXzQeSMfPoDaaFLPJatHDoXONUXSIX8HX6LZF6lfaxHi\nNUCkSJY7s2KHkGF6OajnIP4Yhnj7HihVUf+d4i+71NbGh4dgG/pxB+Ke/A4zrhMfgtK9qmlP7kxJ\nuXli8iDQ8bauWNcL6gB7GOP0gREJF7GdB3MDIQQNpEIzdN3A/Ttv+bNGOj+RX7Xb/74xxFRF8y6H\nQanEVxByTu77M9E8C+dBPmv5HKzdYZzlg6XyTrPwny/IhbKf7ZVMQNQOMnpU72ANH4ilHyayhrn3\nEMg81eQhmCez4Y+VQ0t33mNR7YVKDl037Fxba1T4WfXjWFMrhiWeLVTrwIGQeyaSSpfp566h39vK\nTFdtTqQIeacBQPf+7ADgGDp3fOAJdCoKFlMsNaJtcESTeGPWoQNAc1zQfN+Nil0xAP2cROpZhsIv\nk55AqAY0AassdM/wa1KzE1iDXKqqGho9trqX78SVv7/8HFCvDbYIX4Om3QIqjM46FEuohjEGVom5\nfd8sHv9iRiSueeOzUninHueo6zpj0D0wakcdLPt3OG9Ri4XJY4M7KCNa601Z6yuV91w+eJVLShNq\nYCh9kN+rcfXTCa5Q5Cp5svEWS3Hrb58/maSLIIKpN8mOEZ31suL6elXv91YqGf8wqiBrAENViMnQ\nTVec8hyOevMfXZSgSGvJqVZuUD18LEyu83C56jsh0cnzQrv+GINKoPNO6gDjLAw7sk7Tglo/YCoH\nAEDwE5/r1Nju646UM5y1eAyBVqWt6QrTOtt/rlJumkv5fBurM2gHv2vi670Xkf56zWitwWSgmKqI\n1MgpqHpGGeRM6yCynS9aQ9GI3S+FWTgZ1jrImGgMnYUxUnLjshxxPD3gcDoxkuaIwGpYassTfx8z\nod/veP1V8/tDb3/6YEfTCKgMRzpX+cFb6yS/nRPGRmvUkXwhH4K8PBLXKdnX63phjWLjQ89rlxhC\nuHmYpXCOSgLRkgvZDDJ53gl9AtRFG2dh6HRhxucfLXIlQU5/HtXlMxOzVj0UBSIlGaBkLzeU0qcj\nKQha2JjMUYfiXWtFLtTd7mtC3BJSZFvUKrHIEr5Ck8R7Yo1lh5qSNGUbtnXHfsxq3lQ4PbFIo8He\n3Bi7T141GMa0R4ays/0gE9lOnKOyvEXGJbIrIruJeqJAkgqpwzXqCU4KFIJRnaVf1hglHN5zpVwQ\nfKX/pvUVlSIYGP3Wq663BO2i56dLBcMUMB9nHB4ozVLIQ0r2a0UnqciJlnUSLXfS9ENBuXLKMFdW\neCg/AKixYBveM2tp908oxn3P/jRT0Ssp98MLfF+ZbV8rxxoXgyJnQslwyen3dstaZ1taQHsQ6yy8\n8WoOJe55jYvAaBkbgkT1dre+NtwT+hpyf5sSiN9b+PX74iyBFBJcsqjOoMECzgLIqLWvRGqmHYTh\ntWdOCfWNza5YxjepxwXlVdxwWkpRxGC7HjEtNPFu1w3XV0oH3XcKcDG7QZyoKJoJ+hlLU0XWsQU1\nd1RJ4OF7LsnTIHSywke2+I4R04HWlQAhMM5ZzMeZgtqYfEwOoJxD7y22y4bIWRai/HBOeBRUTMWk\nBmhI64638xXn04bTPHMap1NCeWD3P2OAYqFD5fdX4zV1Stu7Uv3k+6qloFmvgyo1uVRHpCGXFWtr\nDcXRc1nSxKqGCcvygGk6QJj3QDf7kf9tjKCkPCj5gHk64Hj8gIePn/D40wccHg+UdNvI88WerSIc\nUpsJMZB6bf6kufjj9bfFP+cdwlDshh68S+UD23mvBxk9FLJz6JgMsTA7uacwjExwMnVn+75i31ak\nTCzJlFZQN0Q/hOGHmHgHWTsfedE11pDtKXXH5jmWdiDV3HvpTsk71GJY1vLnf7YbzGQkTx7goq9u\nDXDBYz4a2NBh8dYa1iuQ2YxBZD2kaavDmkUO4D5py/4MMKr53XnH6LxTb4DOfq7vOgil+JO5R9Jc\ngevDTsUXDblSDn1me89amzYCinBk9reXosFyNh88zEKfbybYZg8AACAASURBVJwCJnaiU9c0SyFO\nxnUfeJk0XfCwe6ZGxznY2iV1467ScdETwlt+T673lpC9R/aVYfPSm6hx0hxg51J6cyYuZdMy0R70\nMGE+LXj46YQPxyNZ+3IDkQqFEK37rmuW/bozgpZ0ahbTFS0aeWgOxeRoDp346IVgR1a696Yaxjki\npwS7uj8Qs0a2/S2BlqYrKfbblaHWYYV3A3XemOF0KJ2etRXreaPiz41TiBRP7Lwb5I/yTbXecJZh\nXzEiNu8I9FIWursdFkajKPlanQkxfBJ7BjhWO8xRSbitNkptPDNjnsm/JdN9DYEkpcePR/jgqQhL\nPkUl0xhk6Kqhlsa6egPT2o0Z1kie1s/ojittO1xwGp4kDYyQHUsqahpG/h8T5lN/d8mZsNsyy0dj\njWXJ7cwTcGXpMrPSU8Z+2fH29IYvy1c0A3z8cIIzFjujkDIcOS/+KACEI2W6BFcKY+IAoX+r+Lei\n6xSxFBeZnXAcPJMiAap7ga2pjQOmaVbEwRhaFzgf+HtvzJ3phErZs4UpYj4tFBF8WDAdyazLWIvC\nzrCo5La4rTs/fX3dq1wI0zkWxv75s/+D4p+IlOFGRzZDKVLL1E0WJjas0BeDp9yJWfZz1NxuQQLa\ndQcSTYsikUh5JZgmURiDhCtQfvPMKU7dJWpM/hPYUSYIQRq+3zm+p/gDzCsA0JqBaUZtTOXfoUEP\nQoLse4KeylBEAjUFTG3CNNMNnQ8zzs9nXN8uHNizksUpipJA6DO3/FmwX7+hm+p89w5vlQlCNkG0\n7roz1p2kwTvW3nQvS+vhHOcVl8uKsoh5SaMY5y0NUFyHX+k+VEUOSCtfyHzjMHFjNtNunmHWwHaW\n1hl1wsvMNBb5pxbYgeCHZrXxkabA8+QPALkUPUTu+rn5+5cXtVaBaLM2UyLP0xVO4eamNljTlLE+\nLfRCHx4WLMuMwzxhYQ0+GrDnjGtKWPdA+d+XFXnLyIKklV7c1VOh0t8vpC99BlOgbt8CPgZwx0Sf\nmb+f7R9ihI9JrbWB3jj9sfDzl8kFZjNcnPmzyfnmzxZ+PwUpUdeyKt7lFKCTE7s0+qDqgcN+0Lz4\n1qrqpVv7/nvqjbLYfb/L2pklmSohcyPSaeCN1+efhh1ZeXRUptU6NP9NOSDkc2D0vJQpjQptl34C\n0LWXZBO0WmnlV8uN14FxTB6lH7sXRozn3n33nlDT7tUhFrrjLtkHWitYbxE5R8EFBxdpEKylIacd\n19crzi9nXN+u5G+CUUEm0z7t+6/nC16/vZITZWtIKeN6XTEfZx0kfXQo2em9Fs8X1o5DjECEa7Cn\nlY1u3uHtMhTKkVjY/8AgS9TUPfrs854R5oDDw4K0f0BhBQZg9HOMSyQVxaBSEPSotUrIH7tzSjCV\n43VnAiGQlFgp5l2dNyMraQmGsoXJpX9x6P9t8U9ph/eSDdwzpaWQBTbxEP91edBpOrO6LxKGaimV\noKy3q+6rWmPDnBJ0TwLfYKuBdYFNIRbEOLM7UtBui8g83QhohBdpUhxIIeaPhL0fXXrTDW4egO8b\nCJla6N91p7nMkHZsEf7glUAT54jlNOPweMDb0xvevr3h/HzG5fWM9UL51uODJ85YFE/ambwjwQpg\n+0dpSFq7OUjoobh/7aHmGiDLVZlYrpdVO0061LKGXYxZBdZZWG46ciqEHJw3lJzhvKeHODikOSIv\nEyQcxFWWM1UubMYoapATQcLq8jeqAwAl14nBkmWyXgNNvqncP/kbA1JptKYcB7QxNU0S6CRhUBQc\nTOLkVUXYA1LKCOz5vW073hyR1yrf4z1nbClhZe/08+sF55cLBaKsG7YLcwkSE2dlmrKG0a/hOUyZ\nkBFPL/67gqz4CtGjzAFhDZ2QOD7zjHY0Q34G9Hkxoc1IcFXRdYWsD1ptXbq608Qrk2/meG5iLDOZ\nynmEMGmRuw3noqZGCYnfFc3+7vAa6M7GB4ByKAT6372H2fv06ZjI6HLnIEijKOZdzlKyKJn29MbB\nBU9FkguGhHWRJNmQhfZhUm95IjJyQwHORBHTGEiOiVcJpayjxIq582Dub37GJtMYMqUyg7IjTd2W\nXHg6kkvRWtNm/+3rK56/POPl6yuur1dq9ljiKe9sKRnregEa1Lista5mOH16IPc+7xDmyL9f+5ls\nDBFbAFiWgJD/wcZSOsonuPveu4BWy3DGd1WBFFnrWebMMnfriJMTJhpidABlYjc1pdTwiYJEaibQ\nw5REOtwJwsxfqPR3EBeGnkNZb/rgkXO4ef7HVEIZHP/s+tuTYd9XSPCE7psbx7nyxK/xrTcfoNWb\nJdnOstfbrps+KOI+119W8j2uTGiTzOYQZspw5oLn2AdAoGDI7k/IfbXxC2L14Rd+wr+zA7w5+Nrt\n5DmSF8Wwh9IKLRxrkw3YMawG0o9PAQaTpiMKQmCGwq2ohaF85hAjQoxKYrPhVtI1xpDiT5zcCALs\nVr8//JFFksnfR9oJ+t8um0rXyrCPlkhOgOVIoOI2EvaakA9rg70arJ4e0AaooZG86KocEG1zrcg7\nv0y83pFGQ5QYqjlW7XFv+KTY3ns535n+wqDX74ML/E1u9/CrpKyFCcLg5mK4XVaclwv5DnBhzqVg\nTxnbuuPyesF6XjUrfl/37pWRJRO8/3zkj9CniFoqfKmcaOeHA8H85e7v+yvOkRq7NdHh5rtRE4D+\nWdYKCyL1ScMt0KtC2kxmk4ZgXzfsa0JOO0mRMumQU+7ELGnyxQteOBZqBobegEtTUVl3Le+qTO8U\n7OO6Pvveez9khPjoYVerhQumEzGdp0kXBYrIgNd8cY4U0sNJn+B7JYe2IHbSwMkz7BgRGoOs0paw\nDUxx+ryrNhujo+DogyC20O8JNFMfAW7m3UDEFQlt5bNXvBukmNVCyXdvHPv78uUF5yee/tdVHeeo\nMMkad8dmDK6XC+bzjDBFxJnkooEjksMUEJ3TRijvGQXl5mxuDf1M2lbsaWPXvPc4e3pURkrGJkV0\n+hJsZj2HLrl+VsE7+Mgcq1wpWXADWuPizedk3jNxINjlUdd4jXNBTJeMapOU+5pPnjHLCgjKR+js\n/t6wdNfEP7t+MPnT3t37wMWfYFcY6jzE3Q/ou0BjSF/rmeQhTHTRDivTuUFJeCINCtPE+2/OebcW\n3kl2M0NFdrzZAxGpdZ1nJ2Z1iZ6VhuE9e+/KnswD5DWG7IAVFwY907k1Mhcq2cDYjJD7SyM3krTD\nRuG/yN0+HYyR4nwBVFOGm+h0EqG9mteDgT8EjKRL6YxvGgRmS99zWWG1C5eDSZz7SjpgWKMFLW27\nspVl19gM4KrrHuicFNbQ+RHreeWXdSdk4LRyGBE1OOqyxpOTrjLqd1Oe7dbGo+nIiApUJibee3k2\n93HOwVuL0qpOJQr9s9a/iB+DBKLsTOIUZzQu+pfXqLa3EsWq2mRufvZtJ/e4jYv+mtTNTaZLy5+p\nHB5u74lp9KH0MB36jHA34gMA02EimHFLiBcqXsl1J7nxGg+jESGjvSMjMYPjp+jCgdGS2sLYLktz\n/M6LFaxYxyq0jeFrtd543LCz0eFw+Tr3XiGS/bKslATWbnt/H7T4M6fCZoMEeg7E08BPHi46JbqR\nbTBPftH3vAgmhsIYTYMruXbCZG1Ic0K4BpRCZ7EQ5bTJxFD85fN4h7R3vJ8iJ80ps5eJUThePnc5\nY1Spw/cn7xnbdcX5+Q3npzdSKlxWMgLbV3K8GxrS1hxLvBtq5bRTVb1wOqBwD7xDmyvyHtRVtOWq\nj4Sk+e3bTvyxtL7b4Cf4gFLdTfHUs1Q5Y3/eRhMC0FVc36NfsvY0/H7KKkmI4jzH8mfTszGMI8dX\nSXKkRpb/t66EHQiYH9fbFmyG/6c/6w8jfSlicND41+4s5oaOuqLC8O5Fp7BBEtFJCej778N8UwBl\naqJ9atHYz+8dnGTSvoH2uUhLoe7EK6MwnujC771GiFUlPbyj1OLipJlpaFUOQ/5+DHf7RvzbC3d9\nAZYnZzHvEWhIpt1aexSvdqG2Twbuu0mmtdtmSJoefYgdQY4h3gcDW29ZU9o7T3UanNjh8Xuo2xiF\nB2sDjKW9oGMWsLCxpZCJ7bMUyPW86qQVbiKZeVLyVnhcJL0cCr80V90hj4me8izX+i7YP3iSB3pr\n4ayBt4QEiAnTjcuc+lS0GxgXEBvnHdsl3FiRyoOlO/3hwJbPRoo+oSoiebXcmBHrmchXhKg4X9Cq\nY95Fn35h2O7Z3PfwH04HGGuQtoSJiVxpS0g13bx/esgY+VrDmqEBiJwI50sn78VA6xwmfqrLZ+7N\nmbKVNb+AOTPhln0NdCmfSGK1xptBXeP+evr503svzZmVrAT6+kW8SizD6Iw4GGNQHKl3skmKTlr2\nIbH63tK5N59mQgTEi4Kbm9YatsumyXnKJM+FnPPWiFoJ/bKO4faBRU9uov3n+H7td8/VGMEcjchK\n6VbdIv+0zerPJLtpQWek4PUEwKJqpdF6VlwejWPoHH8k98qxjgZ4a9F4leKDJwk5P4u1EQmOZIob\ne+vvLKu7/zMIU4SvVK1USaNSvGGQKBWu0JpL32VGNgWpE5L0ft2Vy7JvQkLMur4RQre46RLK4BTx\n9TEo2m2N0cRabXJvEAoJ2HJad//qGfhh8afwAOko2T5QDjfT7SxNNbpT1n0FQN7HMskyM7zlSgdF\n9Ki1wjmLMguBKjNszsYywi4u7DnNL7u4Jo379z/7GWXa1TXDOy6RtQiEPZKvAHLOs9XeNhQNijwA\n0jHuSnJaLyuR0AzQKjggqPSUrMbICRhGZGtImQD6vZC/XyB9AyNKKmOUUCLwsEB3cbkv4MN7h2K6\nfe0oOSNo3nbi0fDBj4oKPcSF9BgcfPH0vCTorrwwWXK/7lT4Z/YtCKE3A1MgvTQH9VjbO3MrE558\nXemIh+ejvrP4R+FU8ATrbUPwnWuR0ScKKYDd06HbDrfWYF2CdduNKZUUvZK7BM6A0CSVZ4qKovRd\nqbMezoeOcggZkA+scQ1knBkOr/uv02Gm3e2yK/lovazq0ikToFyyB/V8vzRYZRJ71u6ECPRiLaz1\nUTM/TqvUCDPZDkYbjNYaWhlQIDb1IlOlYWq7QYHu/wzCFOAiEWqdr4o8domieDUAoTVFni3DwdIE\nCwFZ5GgGHrykomZmoZWon9hGuDQls9VSb0yzwkxSUUGeaBfPJNmZv19uBEf+FaGxRhMSf3RJLojs\n/fdtV7Ki3LvGfgyjk6FICpX/wlJB4udwoeNCDQhCRL+sdYTwhEnhcEETAq+drGXzKkc+CESYZH6A\nWMmz1HbfKFinlB6qc+8Vl8iDJUjqrYMnoVbEu2Ckjt+HUgysLb1p5+K/r3tv5vadk/4uWNcz9p1t\nfWvVAk3sfAfvJ159jWvvoGTJUqq6ZRqe/G/OPjPe//KXwUY/MPnhHQJHB1IXKnAQwTFiaWu9RavM\nvmZ4TnbgJmUmt/B+qnARY8JWs40QdJkinGg3E0oSSEikIXw41MZpYv2A/97MoMPARACTv//eK+9k\n1CERjqVy0yN7Rjar+f6vZDSSC3bViXn8l6On+6hxzrL/udHmtuGBSwMEBdXBCyNYvdb9ADfaLk+J\nc7zrZw9TgFHYr3990ihX2MbWtONPf9OISUAG78wZCpVVTzaZPtPUQ3rIypfCSGoqKLEglKBEmFYr\n4kxfUSCxm92YfPB8ydQF8GTwjuLvzG3JNIb18pHWEDZZVNkpc2DNWCBkxz1yUOQX2XEmNf2Qv18+\ntw5fD9p4PiRDiAiosDtxa0T/K++g9bSTlZTEsTm25r6H/3FeUGvDvmyYFnJ6895jN7s2OK00NFN5\n79nvr3A2+v3vGe2j2Zb+rGpElRWxqKyeEIWK8noEKRhgYVrnlEHjD3oPXH8vxHTo3us4TdgXWr+0\nRoRXP3m4jdwMxbTHsZeE3R2MJU5GCQ5y1uZMay5bCmoNfLYZ+K1L5+TZTyvB2MKroYLRlTTkuGmU\nNGytZR99ngzZCErOHXoOZXfdbWt/dBknJNnGEb7duMpaoz4MDQYOgPhYWOdgsqAi5jZjhBFROZ9s\no8mUXmriNMXpgMPDEccPRxweSeo2H2cspwWH44IlRgTnGPruAyCpjDhUTIrutunU/95rWiZCyvic\nF/SBmh96psXYTc245PthYrOkHtIadwzvIURiXc/s/EcE6LE4i7ItxhnBR/gQEeOOaToghhnON2Dv\nzbG8Z3r/jPgmDMX/LyTOP8aAGztmcXdSakFOZOBTSoWvTTszOKNTqHS9tVaYNk7O/PAUsF6//AH2\n1MCO0jvnkvo0JdaFls0h+tccJlD+ELQ46LR9PwS0b1Joq0Ja8n2aJsgDut489BWF/ExojffCRSFh\nOdxqY9e+JsWB9/ZC5tGvQd+/3wJNV24nx7jv4lO9mGh4kUh6NRQSzXec7iv+noOaWqUGCNzsiL7c\nhMHb3RoUlpmICYwULesI9o+gVUJyjiZhvjcCCcuL2ougPH/Qqc95chjT/G4uPPp1v7+3pq9DSm3Y\n0/37vymGm+JvDQWNeI4PVVSqVHjvUULp8LIxAMudUtq50PeCX3JGQ/dvEMnTyOKmz6CyIgA8FdDE\nbzLJXwmadPo5OL7nst5RXbo2YfcVgJ9OJ7TWcJ2vNJkG0W7fNncyyNGajw2KeE3gXG+MtPCM9Zm6\nfW3qihymme1ZWQYoiX0l9TCsTZrFPaPsZF71Pfok+3jhVrxH9fCPx0fkUrBdqQB7T01N3hIK8xa8\nd2hewoyyNmGtNOxcsNPOBEaWsuY9oiT63rfr9gffgMaMbikeEg5FqhLRnRvlUU2HiaRjou8v9HdY\nblwNumV2iPchfuO9Er8J2VmbSCodQauMNQhzgCR+IlB2xXrZEJcrkb3nQBNyozTS6j03KIA0JyFE\nTIcZh4cDlocDltNBZb9xjpinHspVarc/ls9GCIDULO1I+8ZOe/c3+3LFw9RrV+m+LrSClfAoydoo\nvHKoinBt102DzvaNzOpo0qcGQIq++Pqrio5fDvElyDlRA8DrdtnryypP+TdtzDlwaMbANqpXEiBE\nZnl/vP72jaBC4rnIcrhPTmSesCUlp1CGubuFm7ioy0sqD7PqoVmXLsUPFWpMoy996Xp31Vw32bNR\ngXfOwYH0jnTCQbX4YvChkyEfNPdeNZNkS8NktDGRD7zcTN+BFRCWo2gldcuwO1tOpGOmm0s3RX42\nKf5tIO/IXkwnO+s5IIMgyRgnxLiQkQwYhrN9CvWR/mkEOowUU3vP5ZxFDbQ/rii6a9VCbQ1M4z/n\nnLKtq5j9yJTdJOzIAs0DkeVHw9qns6TpVLBDo0L6fyJETsusqWECNTb+mnIowAC+eYy0nML66PKe\n4u8Ivq61W4sabgBCDCRfkx19qSiRGLxxjvoMp9S/v8ryIQo1mRTC09TMKvbLt2l3Es4BQP9b7wOT\nP5lQxpOX5CvcSmyNfhZj8f6763FZkHLGy7IgzudO2g3uBpofCXbye8YY1jOzFpnzEWQPTi6DRlEB\ngBUUuWAvElLS0+wyE6W2dUNrREJsVRjfSXMn5P7IWkidI5l4LOl891yfjkectw3nwxWJjZfEfTIl\njgi2hmyUg0et1GjV2nRPvm079m1DqYUtW8kXxTGfRc/K2pE/Qti4yS7l5vwzMOSyt8zwB4/lNGM+\nLlR8eY0k7HzLahN578IcMZ/mu352KSSCSiSOHs8sIR3PNYAQQjQykLLOYT7OOO5HZD3v2ab7bJA2\ncRusWkgtm7ipRTWoDqg65rqToyHoPb4yebBP/JI9knVVkXWYMBCTuHuv5bSwC2Nfbxtj0GxDq8Kd\nYeVJHuTlraHq97xiWy8M81+56F9wvb5h31dqSuSsYzmeDClaZ0tCKZ6Vdt0dUPxdqBm3w9Dk4BwV\n/FrlvJbgn3/D29+5yPrywPsVspRNaWcjjoI4yLs6O5Uh7JSxXzZc3q64vF6wXVbs+4acVtIss65f\nHnyC/MqNU5o8kPKDi/8xqQCCdqoC+3Yy0B/lEuOu/J6r5IKRKyISGyH5IPM9tAYATQG0g6PJp5ZG\nqVzntRMjGc43poLtg2BMA2BhjKwC+gSoBUCslgGAu+UyH4FmdLJpc+s7du8JJQhOd5FyiN9zEe+g\nsTyvr0yk4Ampk34msRCuyKWg7EW7/bEwgD+rEIIWbmEq9+VJU0Z4nCP5hh9nLMcZE4eGTJxgWGrV\nqUhRpoFvIKSwUknjn99R/EtrCKCmF1UmcIb+Jw+fQn8eCqXHTQfiZ0iqXVwilu2A1gqrY8gYJC5k\n9EREHqcwoza6g5Jg3zZs1xVpJUtYaWh9iN1VjQljcoA6aQR0z20UKr3nOk0T9pxxXGbMbEglJKua\nC6rp75ggLkJeleInPJuqRlh0i+sgiZP7X9ntbXSKJCllYfY4QeEScCN7aJmwRZWiBLTo6fOXzIs5\nYor3IV4AcJgmPC4LXo4HbOuOtGVtJDrSYbXBAsDa/77GyrnApAQwr2OrG9brFUTsYiiGHk/mCNWh\nARQmt7i0kelS5CK+PCw4PhwwH2c2ciJ0MVlK/MzJK/LhgsN8XHB4PNz1s8s6sVWR13JmxZYIPWj9\n92tt8FOgQhwjjKXPYT7OyJ9OhH7mqjLhWgubuNFU3kAW36UVwLQb8q4dEV3QqqXUqkjEzveFkMii\ng5nwZMArZec8vLv/3h8fj9jXHcbuHKE+NLhjfWm3XBPI+pb99gV1UK8KfvdCmOl7G3Jp+qDHzEZI\no0/w/zQdMM9HTPOCOE83KpAbbplxaE08HYzWGh3Evrv+tvh7H9lUJ+iEIjcwrezqVqp2LCJfaNXA\nsr49Z9qDXF7OOL++4nJ5wfX6in27IjMMKhC4Fgv+gWRnKTaFIUwIYcY0LZino/6Q3R9a0v2oq5JJ\nSPOxuRDde+VUwMmbfFD1Pb3hSbg2NruI9DV9DFhO5HVtDFl9TucV8XVCnC4IU8B2idQ85XSzB5W/\nU9nLhTpssT9OadOH6uaz4tpJhV9S1UbCjLsppvdcCkk2wNrByEUmbP0z0O9XWMLycqrDIO/prO/6\nZm89AJEG9vtEUhaaIsIcsBwXLA8LDqcFy2HGPEdE51FqJVMc7nxlIiWDDCIHOf7+S61kQ5zuhwEv\n24ZlirAwGixiAHhHcadlKoO2XQg7VjXyNT+QxwPvZo/HBQ+nA46HGcfDjHmZEdXoo0/6KRfsKWFb\nE9Z9w+Wy4vX1jNfnM9nCrjurUMhLXsiQNJl7Db+Rd0cmbGPMXxJ/vr+mEHCIEcdpos97jggTyRRL\nyjC1v0cy+eRERdpY0i2vomO2A1GVp8Y0TIVK9CtCDiv63EkzLzny8t8ILJ44BRNmkBX6Hissv2IM\nmN4B+wfncJwmPBwWXC4rwf8xIUxBD2iK7e5BTYJEGWcQl4jD40GNmYTLs11papXh5g8s7eCYOE2N\n4jRNmA4LIV58H7R5PEyIU2SuVYPdkzbnIp2eFrLKXh4OWB6Wu39+OeMEYdnXHft1w7xMA6eFGrbt\nsmGdN1hnKXOFIfCONBEKvG8bLpdXXK+v2NYLx8w2WOu1wB2uJ2zXE+/Kd45NpgbQB6/ohnAj6PPt\nvBlFJksB2dCTI6wP9w08AE3+skKWPb6etUYMqEaeGf0f6asJeaKVYZxmRUwbht27AezIxhfFS+3k\nXUHdvQvwIWKaZvI8iN1MqbASg0AEy9+NOCdCC/+/xfb3nog+zvmbaMicmVTBN6cJbGuYzewtMEUi\nDW07v4QRYQ/wycNuTslhrVUUjQseD2dDMAs65F1u4NOIiPmG1UtPpdwEq/a3Y5jCewh/BG0Ne/gm\nEyq/uA1AJehKUsjkIJgOEzzD5vNhwnJasD4esJ6v7IiX1bAG6DuwkQkta5O00dRwvVzUJMMYw53h\nxH4LsvvlX76n5Ikh07RMGhryo8sHj+ZpShFjHgB6gPdC2w2XSi684ujNgGZCMNe5eTH+oQfWur6z\nlgZOTDTiHGnKeTww6SdgiRHWWKTBrrfx5yXyMzFCEpe0Uhvvke9P9zqvG2DAaYB9Xe0srXdy9Dql\n+sLkVd+9v6U4HE8LPn16xC+fPuCXhxOO84LFe0RmkFvTo4FLKVhTIqvflHDZNryuK55e3/Dt6RWv\nz2+4vlxxPV8JCSjceAbPK56R4zIUFZ40pWn74b23FtF7HGLEPE3wU/fsSFsGSiFnPzMU/y3rQb2+\nXdHaYLO67tj3DWnbsO/kWihwOql7CsTspq+4WD0QCOUxYKfLQHGncuDKzyfKj27M05MufXhnnLNz\nmGPEaZrwPEcKFtoC2txJi2Jkpva/vO5YHmZ0sgkALiDbtuPydsX6tmoTA959G2uYQEjfo5eAHEa9\n5mWC5bWCrIWEyGxM9/UQpYnzhE4Zx9/TacG83Af7K4GtVtTGQT/rTnK1XJhXZJQTsJ5XJnhW5gmR\nXPHtG5n8PH95wuu3Z7y9POHl9Xe8vn7F5fLCLqZVi/Q0LViWB5xOH3E8fcDDx4/4cP6orHlZb7Tv\nuAijxLg1sEdMYQXBDLGHv/eKc7hZRSagc82A2zUmZG1Fa2vrrK4kxX5bZMqaRGv7c0t/HaNfjBDJ\nGkMN7PirEMokvhUWYmgnlxBr+9qmrwz/LcIfFf9pmPw53IGzitO6KxGvsvSGoBYDNweV5RhL3fDD\n5YT18hO5mF3fsO3k+JTTfuMlILtuYSrewGCDN/RY+HXq54u654H0xJ/ieyQ/1ElWnXhbF/DTC8jN\nQEbfPY1pgrIK8ZE8DQ6PB2bvJpU8UlcIQGRibGupRMhcVAO/vlHjkAtPftb2KWeOtP91QwEaNPNx\niYRI3En88RPJkkpwSq4RE6fCjF/VFjNHIdaIkjKTYUDGMOr3j/4w2949W4YZATOQWHDjpS4EnJQL\nrCGZ054Stn3UEXfGu+PiGvjvKLUisZzy3uu6UjDNFAP8IBu0Av0Hh5J91/u3impl5+wooOM448Pj\nEf/58SP+88MjPh2PmEKE9OgjepNrRcoZW85IuWur2tmN4wAAIABJREFUg7OY5wnHByJ8OSbRrX5F\nSWwYwoXDsPoA3FDI3ldgyntXXo6L/xIj5mnwW4gBLuyqqhknrpwzkMgVEIZ28+vbisvrG86vLzhf\nXrDyznPnjHXaayaFaeWSZj+EiTM9Jk5JO1FSWly646cUYTcU/2Hf72NAGM+AO3/+yXtMISCGgBA8\nEnMGRG1Aq7QuIxZUK84B8xzhQ8AyRcyBPDH2nEnbLXK+LM8sk+wGVYqxXa3gHRXbPRec11UJgfIO\n0oFf4QO7/eUAHxIReycwV4Y+q3uukb1uWpe0ierA84BhnEHdCIYHqCCHGFBywfnljOfPz3j67Qkv\nv3/Dy/M3vLx8xcvLFy7+z9h3uv9q5uYjluWEh4ef8PDwE9brP2ilw3Xl8HggFNM5aip58ASg6Ius\nzhoaT/2Od/73T/6GEeMQAyFWALAnVNP3+xL4JNN2LQYVgLGOpa7dl0WSDsNEu/qG1iOgW3+HRO0k\nEerKI2DUTDx1ZC1TqqzTRti/N/xUP7P++rPrb58I8tQngpG1jpKCGFKgMJ7bfav8so3grzAFHC3/\n88PxxuJ0u1I3KdyBwvJBymEmXSQxHxN/81J0LUlD4oIQZnoQ6XG9WRkI/G2dGdfJ7776Ad2bEgAw\nsGjiOligL8i2bqpXb6V2vT0/CM5ZCjqqHd7XfZUyngkmrAxVW2EsR69EF8MSKGPZg3wKiFOEC7ZD\n/syWDXPAvEyYl4jo7yz+jJi46pCthbV02Bhm19fCMLh3srqk369V0Q+FdwfdOxoxkhvAHWwDctGd\nmTEGnk2EciAoubWGfU9K5DKGPO3TnrCvSZnQjsleUwyYQkBwDqWKQiUhXe8v/vt117XOJG5/QqKy\nlE1efCW1gi2A0OpM/xxyyriuO35/fcWeEn5/eSN3RJClrxwuDU1NiFImRCOVQt+3SDz3MhDboFOi\nToG2o18Gw6Fgad//Hq6LsxbROUTvMAWCzIVgKGjW9+5x1CA3nUgKv9M5CUdoxbpd2OUt3ZBdwYoH\nymIwgPxvY5WkS+vBDI0tZYmZQO+02rJDM+C0ONvvBoMfXa01BOcwBY8peMQpKNGvNZLAwYyJgfSu\nzYcJx9MBp2ki3sA84zTPmEMnPno2jrLcSOpKplZsOWPLlPGw5axx2WvOeGW+wFn4IYnkjTQ8NJXz\nisRX0jE7Annfzy8eAq3R5CqERGk6GqI6jYoB1X7dUFKG9TQonJ8veP39BW/fXvD68oy3tydcLs83\nK9/C3hW1FoAJ2iKDi3EhpGiXzBB2fB2QLSIaC+LilWQsxnQxTvRMGQv7DsJfKYRGuOAQWtAzGqWj\nAc0Ifwnq0Ig6+M7w+kHWIs6veh4bRuCkAZBVRdoTqUmGYUlUNNY5IDYADqaxiRrXCiUkttYH2yZe\nBLQyFtvs76+//VTm+cimA4FMBrzvhhd8iI+hGsp8Flva4BA9wYaVIQ3xCw/TjrT04l/ZpUwsGreN\n9kzjnltkb845Yj0rcQy9m6pNDUdo6u962loq6jvgP7JMLErIEGRCDiuHCvIfh+7G1rdVQzb2SPsm\ngbn5vgwM36Ja5dGOszNqy40RjLVWC7okPQG84uBDKvC0E1kOJ+z4mZPk3J1yL+UPgF60EvuBb6wh\nbTWvelzoVsMyOdCz4pQAJv+Un5tuaA/QUJImd/ACvZVUkEAvhTr9sdRFPqPGRDjnHeI0TlwWue7Y\nmHR5Pa933/v1spKRFKCFyVmLwBr2wvK+nJ3yDGppXPQKrByM6463lzM+B6ckJqBzFAT1AZOHSu0T\nhhiAyB/RNdDO6AqjKQZQyaUcBB0tY1QM9+37+UvRoWodouMsCT+uFPhPtf4NSBPSbFO1TTxMtIX0\nAfNywmkjfXP3NxB1y+3kIpcCdszYJg30AT7QJDsWA7EJHrMyiAxMnI32Do8H+X4m5zHHiGmaCJlh\nrw7xsJcCqYY0U8QSAuYQMHl/U/DH3xO0zxijDWVhhGqjT0WbTEGIMsc+p3VHSkmnf64SNyiP9w5m\nhnJ/hDdxz0Vr1cEwywrRl95FAwM/eSyVjKA2s6nLp8mV7NlZbWC9V8K49xNl27eC4CM9A8M0Zg3B\n9PNywjQdEEJk2apTzlIIdM9bayiMYMqZuK87eSHUxsOqZXWUx3u8/RWuR19zFyYxN1+RkyAMVSd8\napIqrx26r4v49VtDw7Cs38QjRrxTSBm0Ie0bRFLfw9zI7KzVqqtc4tV0KaL19JlolsdG9SrnpBLD\nP7v+tvgv85FlbJ4lRvSLCm+koitFlZmOALPUHSMAhrXvrsEWo2Qs6y1scXCFzTvM7eFExC8L3zwa\ne4MbkJzPq9f/cJDKRNmg8J8+uHyYkj3n/ROA9RbIuFlHyP+mry27pIaSjKIajh9QIkplSuNTgkgP\nehkLvBiYQJooaVgGrTMaFG60gbPquTPs+Qi872S2c5wjIkOfdnipf/izO3pQdbLhblLvNeuxQwv8\n8xEaEDgApPgC5yx2RQoqbDGQ2EmgH/ik0qTC6D3J1/zkGco1/OJ0vbimNg7fq/UO02EiYt2yYA4B\nuRRctx1vb1e8fXvD67fXu+/9+fmMMHcLXe8ICg7OkaN0ayiBzWYCT7m529ba2o+2kgo29OcUfHh0\ntcN3BE65mkzvPNU3sElWRwCMNSS7VEMrmf5vPzcPaLzxj65131FaJdShNXXWhOxWB/8NYwALC+PE\ntpjvNU9ncQpYHhbVZqdtRyk9KvnmZzWyP6X8dHnfFG1j1E+Nq6yD2GGTPLRbzcoqsNaKbU/vmvzl\nstYiWIsYPHKJSmITa27h+Ixrx1wrci3IlRAcYwxyrdhygjNWi39mBYmERrXWsJeCbd9p6m/UXKVS\ncNk3XPYNOxtsYZBaGgOd0HsT5lR+bIxRf4R7LiGLkVWz2LcTqiNxxJRl7xSR3C4bMe5bgylkqBQm\n4ho0Ia85cqiLcUHJO4ra/QqB0iOEGfN8wOHwAYfDI6Z5UatlYcaDhw9BoeSc39cdrVVY5zDFhetE\ngPexexfccUkOSWsNeS/6DO1tQ2sezhcNMSum0LNoCAVWlQ5/3iUVJWLbaomOxtwMNf4a7qU0egBu\nzgKqXw0wFZkZhoK2i8OpETQyS5ARredz/jcn/8Pxgx7QwUfa/7MEgbTKlotV1cMAoGhPgbKbEhv6\nnrNygaOwgm6WIlO/Qkzlu2nFiAKAiwA6qUasQvUQFMYzjO5Waq0w5f7dX4gBxRbUTfYvUvwzRuag\nQDRpS9jkQbV9AmtT6w8wfxAKD4umWTINBsOjcSXQuPB74+GN16Is3t5C+CO70J6BHmPoyXit3hTf\nv7tGuaQkdglERS9a49z2BmMB73oCm3UW2WX6mpVkSEJQks4X6Lv/DuU5TYocEw99cKpdl5fHgDtq\nAxhD/gWH44KPxyM+Hg5w1uLlesV63fH69IaX31/x+vUdxf+Jir/wJ4L3aIyc2MBGTtyg5exRsqep\nvDXY1pQfoBsnfuFLKeoJf6M+EeSkftcEGyKW3jQ73kGGGdktOiZ6ynMlkcZyeV2P/fh6uV5RasXb\numLdieglpF5ZhUieAAA4/nnbMH0S8sfkS37mZV+uBx5/LpIZIrwhQcRKKmqGIkQ3QKRxgnb04B5x\n8lRnw0a79Vzzu2SeW85IJeO679iZhd1UOkYQrB12rd1NkyfF2pBLxW7oa5ZasSb6d44hf3oXuTBw\n45AKrT0F7i+1Ys8Z15Sws1ugkNsMP1syXNUmhcCqnNcYo06T9xZ/YdVLUqTBqNagZ1MjdsVt0Fjs\n207ntTE6HAgZ3DC5l0jKESUnRg7Fx4QbrTCRkms+YZoWhBhhPZ0Z4vBqwSjcEhXtGVMRvY+YZkJt\n6QwJ7+J5SaQyGpD8ruu7vGVFKgUNuHmhaFbqBVzWBUDnCwkp3cl777SwW2c5QI+aIUG4JaNE1Rfi\nCNjYXZGblRADN0HEeRJpvmQq/Om9/rsP4nB41APEOeqivKOwgThNPZby5r8aD/bviHitS+VkupXV\ngUyUcqgI81f+PN3cP+/exy6pO9xZnfwBPmTa+4r/xKYeJfebTlLHBAwObc7JKsBgv/L+1Q8FLXid\nzgE28ijcUfPPbUtFQd+XagPwnX5dGgd5+YLEXoaeoOfF/Wum8BBjgFwkG+H+CUgOFgkeAdAP0dY5\nCwY0sauDnHyrpXbmu+f0rtxQx29hgIwlJERY2k4KP8N/hKgAzbRBwUG7/nmZ8PF0xE+nEx6XhabW\n8xnn8wUvv7/g+fMTXr483/2zvz29YVom1lgHTFNEmgvDaw4Aab6rQPW16hqk+aYohqx85FkeCRJ/\nmPSBm9XPzWfJz4KgaePfY4zpsCJLX0V5UGtFs5QJcO8E9Pn1FbU1vK0rztuGnVGN0aNfYWcArYqT\nYF87iAZqfH/5mwU4bXOc/kVXTioRnqByUiIwUCFe7mQN25/P0QN/jO+VRlXY4fdeT+cz1pTwul7x\nfLngsm7Yt0RwPLjY8zPbTYwMYiCSoERBVy7oYhJVhd0ucLERVUDrCAvf1gZqLkvtHhPBS7w1B24Z\now+INNJCdgT481zpM5Wckh9dEuIkZDNdrzGpFq0heI95mVBj9+swb4YIegBKKPoulxgQ5e8oJ0JC\nctJ1qrzDANUYIXjKuhToyK1MusJpEmOl9cIx4c4hzhNJw4Mj62OuUfdecQ7MKyKEUdUc49/BjSwJ\nkwHdvWHkonjISyqkb/HjvxlaBZXO4YY7d6MGABgdLhhlxdYRyiv+JzIkei7+kO/tL64fFn+R+N24\nCzl3E34hH461ornvSVZW0AE9+aA/1PcQh3RMekoaw0mBQm6yWtRvtPvDfyJFd/yZhbCHalDbO8Jd\nDhPs7viw25GSdKCJuleWHoYw2osyBOsJhvHBI7ZIE4JOBwTfajyoNQzdGH2JZSVgGq9ATJcWCXlk\nWiIiS0t6BrklpvlhxsQvZ6lVXa/ynXKvWhucJeKkjx6RO21phDIfEGDo0luLZow2GK02FCe5AsQf\nqIUibE0rUOGE3mpza/LhxJudTYq+d4aTCdcaxBjwcFjw0+mEj4cDDtOEy7YhpYzXlzOefnvCt399\nxbcvX+++99e3K2qp+rPPhxn7ISPXimhITUBoCjsASvPpLE07fEhp5gI6JCshJfJ7VCubHrjSVFVu\nWGVlpRIgvm5CcIZ3ghjolldMTZGpe739f31+QmsE/7++XbBdtxuP+TbAm4BB9WKDahQh+j7xcNxv\nElGtZ5MToiZaZ1mHZZYAF1BUnWFo2HLza294CF3i1xPP9jWhNTaEuXPyBYB/PT/jsu84X1dcLivW\nlfbaxhjEiZA1H5yqAYTA5x0lQYqjoXzeklbY0OB4SPBWHA/pOXCtIQ9oUZEVEf9esBbwnomyWd8X\n/SdM93dg3kxOmcpPub/4z0eOc1535hTQ9+2DY28S4mtM3sOx7HYk8+b8/dcRfgPXD0dGQcUYjK6m\nRuBxhtEBKNJICqLwBym1dVYJv/KuCsHRB4/5NBM68Q7YX9fJhtdttpd4qVEyrEoBh94Ddiz0HmGC\nrsGcs7qKFbdcRb3G5koI3wOPbrSDF5TEGAPDfDexeZ4PszYHntFeax2ZiP3F9YPi/6Afnk5nskuH\n6bCsMm271lj/POvAHYDqi76sylCWX64XdXJ4A2ohKBEM8Rnb0/nGzuhm6uebpzeM2ZnNyM717ucA\ny2nmbHag5IR933iaKtjTCpN31VG22i16YYRs4ztDfy7w1cMINCkkJSPFkeM9xa+a1x9iI2v5AYoz\nE/gOE+bjTMWf95zi7BbniGWe4B3ZsaZKbHnyvb6T+FN61+uDI6tdhgStMVjdbZqhpNUF7yAafzFf\nUk2yLTC2EmOVDwUhqVFx6hHQOtUMzQTFFo6NEv3MyzThcTng0/GAh2WGsw7ndcV13fD67Q1Pv33D\n189f8Pz8+e57n9YdBsB2CdjOE9bTim2fNRkwOgfHMG/lSbfxgVEyMY77TpLZ3byHdc4iOq8QsCAI\nbZgAayW3xCxSqz0z0az0gmoq0OsuVDomE1OpaE7UKfdf//ryDTAGiXXcl5eLOuvllPX+po0c5Vx2\n+g6Kh77yWYTEO9iwCiu7sqJBpb2lMAO8hyABvXDI++JDt+0Vtr9nhnuco9oQr+eVsgDWTeN47/r5\nvz7hum7Y1p0an5UafWst6nHGIThYS4X/yL7zlonF1lol8Ukhr6BzyoCVIpaklHL/BS0xTDDNN59D\nNwMy6Kip/L74DdDnZNCJZwVIYOfF+2H/06cHWO/wmgsRrvcNdicd/3xasF6Ie2AAHCRsR5DbXLBe\nN9QynGGbsNiz3lexiZd7P95jijMvii71SOtOsBQPh9aAVjcUjgj30WNqE2qhf394OGA+THDhHYS/\n1hNjAXqflTcmGTOCBluL6p0S+aSGachSYQifh0FBLaR+kuV76WdcY0SndrRDGuGcKaHQGEJIYIKq\nOubjzMWfmu0Qo8rzrRME4o/X37P9jwumw0T7BMcuWgxjllx5ygycKuVv0s0MT2uWoSoxhhAoSTXd\nkBpCHIDGk0qtdHDRpCOEJjMQP/qLYCD7vy610j8jf3sDy6XuPwROH09IWyZ4ieUYaV+xGqNFXyRL\nFMLSDzLyrO4yPHIAk8nMKTdhjGZ0weuOLu8enqUfjff9IXrdSc2nBdORnL/IeKNLm+YYMbHL1MYv\n0X6leElNF/zBpagMAOMsYvSYJ4KWwJ1xTmWAl3j/aSn0pCgSwbC9rwz9c8dwk7t+y/QW8iZlZdO9\nExmgsQYO1NFabjaWGPGwLHiYFywhMsEq4/x6wcvvz/j25Xd8+/orXl7vn/yleO3sL75dNvrnKaG1\nGd5ZuGZRPe14a60obcgp4M8OfEAL2dIOv9SF0ACVESEnhV8Oe9C7IM1iq5Uh5W4SI59ZLQ2OHd90\nVTUiBXd2vr/99xc4T3rq/brjKvbc5y6/SjvFvdZaVb88Nv6K/hRCm/JOOvecScIrEiTV+TeJNpVn\naUD6xNLYRYQ4Ic5kVhXnoKSzyO9FmGjqqbni+nrF2/Mb1vN68zn86Pry2zckbnS2dVdDJecsNQGc\nnIipwTt6/rx1SEWkwEanegAwfE8b3zCBfgUlk8IuyZN7yST3rAW5VCYRkg+EWCAD6LwfVtsI4azw\n2VoKGfRI0Mw916f//ATnHbbzhsvrK9b1AgODkhPiHPHw6QHXX64oHx/JCGoi07A9ZWyXlVDBwRiI\n3D43lu6RdJt8HlaVcTfe+YsZj7Vk3e52jxLL4CJr1DzLWIsq0cGFhspgOd2w0fri9OmEw+NB5bX3\nXPuaboizYiAlSErakyJh1tNQJE6ntdB5bgV1U0IfqdEysqIXQOfAiJ+L2gNnIRBnDebJic5t5z28\n7/bny2nB8fGI6TBpEyY12ft4cz5/f/092/80Yz4tWI7L0LEAMEBJdEOiFDYnGt0BjpIDT0l6nQTX\nyXvfdSWmS2l6R8SHAfMIhHxSi0yE8nV7QIJyD3gf0xrZId77EgDA488fkPakrO9SKlJKWLcLjHlF\nSleWIo6xt5kJSlCzDiv72EaMeCLo9RdWOQ1gw4/QIWIhjUgwj/i5z8cFcYlqfKH8AmcViqR9dMHO\nD+x6Wd9V/IWZbmDgncfERiHStu3b3tcwphPMgvcooaJOf5SCAg0mUfOosC8ajCVY2Ox062qusE6S\n8kZEiF5u64h4N4VAVqzThOM0YQoBaV1xXlc8P70S5P/5M75++xWXy8vd91786gtnmu9XmiCve8KW\nsh740TkkR8oLz/eBnrUCbA0lZGR+PwTZsM5gtwXeyeSnPFD6bxkul7AiiXYlAlxWuPDPinlrQNrk\n+bPEJSkVxTfcewT+9n9/Y9KY2LsmnaILM5nTljg2NaNWcl/Lgf3kmXPTXINrdL9kWi1FGr3xjGCL\nXljAdHOn7m0fEEPENB+xHA8a9xqi19Cq+UBoWIi0481rxuWN+B7n5zPqO5r+l68v7MKZNDyoVuJ6\niEW0MQZTCHhcFgTnFQlKpTuvGcMwMIDmOjqSSlEEyUAaOFIF7CmRIVCp2NnzgRAgOnvyzkggFyVR\n+ABEOM4VinrmPeP6esX17Xr3uffL//cLjDF4+/aG+rngenlFKQnbfoExFoeHIz78xwekf2R45/Aw\nzzAGOK8r3mZaDVaeaCXWduVwG4q0pWS7XvypWRKZmnMOKUUEH1EjNRYyUIaJzj/P/CE1O+JiDwCS\n9rqcDvjwyyOOn05/rDF/c61vV1rP8npOY+QrnQXbZcPl9Yx93+A9SfACNwBWzy2rdQeATvCjh8JI\ndJXGRhQVmf0xSiZDt8oOuMZY+O8m/sOHAx5/fsR0mBQpmQ8z5nlhr4O//ln/fvI/LTh+OOL4eMB0\nIPITwAzuTK5v1hmdMBQasQWu9Ydd97Q8CY9duO4CS0WrAQTrDjnoAnGNH2attAVs9EFXgWWYza/d\ntJgwGKNGFdtlu/tB+PDLB+SU+WeX7i9hvV5wPj+htYp9v6p/Mv0sARLdKusAaWIIgiOPcDfI//pD\nACVFAhi4FLaHlbBF73JalG0r8iPviJXueZUixWNfSee+nq9kz3rPxf2TmMM4ZzEzmUkuawyaIaMf\n2XVWa2FLQYuhcz10p91Z3TADe7xWCknSHVhnLo/Wv84Tf4QMbgKidzhMEQ/zrIXfGjpEn65XfP39\nGb9//oyvX3/Fy8vv2LbL3feeJumih9h22QjyvO5YFzqgw+Qp5a8W+GT5c6d7Sva1hSZ8PkxkNyvM\nYGVtG6EMNC2URQmwXUYooSVi7PFnPAk5YGA7lyQXj1wK7J0ub1/+6zN87CzpUggByRtB9fuekNNG\neeQsfXXOqwmR5fhfMboqpcDvHmklEmfMM5ZSda8vZj+9KeCp31gYDrbxnva5h8cDoV5sYmOthZ8C\nlhOF3BhejezXDefnM54+f8PLtyek/X6Ph9evr7g8X6jZyd0b3XmL7bpr1G5rjZrPeWYZqIdBwQ4h\nLzZYhxsCoEz3ud42b5WbPmH7Z24QVAGQe9NBMD99JvIMEqkvkQlXA9resJ5XvD2fcX273q12+OX/\n/IxWK54+P5FRV95wPj/Dnl9Qa8H8Pwt++t+fcL5ckUqBsxanacaHwxFPhzdGIgnlrMwB2Pcrx9pu\n2gQI4kMwtuf9tIc1Xtd+lpHlMJFD6cQhU4JKFV5BCd9DFCl+Cnj46YRP//kJp9OC63tsvV8vurIW\n4p+8y4kNjdb1jHW9wFqHWgqWeiIOAqsumhgqMRVoVG3pyq50kyapj7JyyJkyIcgIaXj+nIP3E+JE\n5//p4wmPPz/i4ecHTMvEhks75tOM+UAmeCKx/LPr7+19OcZyeTjg+IGgBectSqq0P94SJIErcWxv\nrRUmGWSXVX+rsC5P78IFkH2V9x4tVCZPgKUwbIEpIxGgHRgAPTQFGxXXOCe8A2+VsAF0n/F7J18A\nOHygJKzD44EIb3ywXM8XvL5SUAY9GAmNIf/gI0FWbmdSIO22yLe5IJ1mxIkMLKg5kWIqk/bwDQwT\ntTyMot0XKZy1DCE7h+AsvKUJFCC2cNoztvOKy4scAvdPQG0o3hYGwTlE71WGJIzu6Ej/7p1FaY3I\nTM7BDb4CynClGwzr7OBm1YmfgvgUfgEV8nYUCWwtPSPei/86uagtMcJxw/N6veLp92d8+Z/f8fuv\n/8LT02+4Xl/wV9GWf3bVUrHz7s8Hj30h6P96WXE5LXgsNPF45zHVgt177CXrZ0+JjknXFR0hgR4A\nuoqTFZYUfz5AbhqAwUlTGc/sGd78yBrOigRla2GShfMZ6R0Wt19+/QxrHJussKNirajM+C6seMm8\ni6SpOCo6EWdGpKygfSLjLbeEpoHdrCFQta/wtBFgxGc6TFiOC6bj1KFcazgil///2rBvCZfXK618\nPn/Gt2//wrbe3/hd3644P59xfj6TJTSjeI6TGS+vF6yXFXlPsCBTHW8tlhgROURm52IOdLKlNT2r\nhI6tJq+QFn9pEJgHq5fyrkyX9YXgmW9AJkGCUG4Mt19fL3h7esPl5YLyByLen1//6+NH1FTw7T+f\n8Nv/O8LA4np9Q9pXpLxhno/4+q+f8e33Zzz/8gkflgWHGPHT6YinxxPOD294O864LBN8uPIz1/S8\n7uucHmntfVCm/zQd6Nc8I86TFn8fPexAmO5DCevya0NpAExDiB6H0wEfHx9wnCe0dr773m+XFRLZ\nHiZ69tfLxvc7I6WEbbvien3T90JIiJE/f5nexO2ya/nReVLGAEYQAEn1KxyYlIjgh+H5sQ4hEN/r\n8HjAw08P+PiPj/j0j4/46dMjgvfYUsL24Yr5MCPEyNLJGTmf/vRn/UGkL3VU0zLh9HjEw+MRc6Tg\ng3Xbcd123oNTx7EagpdqrSh7RrJsQMAkrTYW8aaf0aDXtbCmojoDWwHjHcxAhkAR5qghi8Vh4r8J\nObCWyQ6iA5VoynQ34Q0ADstMrG4AhwMR62ppuLxe8Pz0O56efkOtBbnsGk4U48JmSJ7NLEZUgMlP\nh8wGPfamKOjnAVCB5Gm3eZHQGSXQdZKcTPw0fRtyzEEqBduecL2sOL9ecHm54Pq23p1pL6zUfr+6\n65ggDAAdWoFZzt45faBiawyFdmlaJ4E67H5nq2JBfcRTHPq1AIaLUdH4XhtLTekyRZzYPvUQo64k\n1n3H59dX/Pbr7/j8P7/i98//g5eXL1jXC/yd1sZylVyxrTtc8JjWCft1w/XtgstpwX48AiDW9uQ9\nUgjENfBZw09apQaApramcKzwXhpaR7e08N/6O3Ttuzj+ASF6TAeytrZS9FkemrcMP3mdip2zZK7j\n7mc8f/3yTxjj2FOf3fRgv2vSJH6YLHf33cP70K1Yjew/nU41f7aquHFzVLtS+bfDc8NE1okLghy8\nAFhuTM9oTgWXV5Z3/v4V3779C1+//optvb8A5C1jXzdcz2ds69qNhnhKjdOM69sHFCZhxkjOff94\nfMRhmrQByIVMcYS1Tz+RYaKohaGtGoSZJKjGiP0qAAAgAElEQVRaa005A4HDnyRVsjlqcC2TBsPg\n3+CdRzb0Tm2XDefnC96eX3E5v9397P+fT59QasHvv3zA8fERPgTs+4q3t6/Y9hXLcsJP//Mf+PK/\nf8HPv3zEx9MRM6/e/vH4iLefr7i8kZvmel6xXiakfbk5+yWH3hoDx0Y85N44Y4oLQpwwzTPHowcy\nLgtdugo9C0ge3FpF3ouei2EKmKaAOXi2qb7f3ne/0pQvMj/SzidcX68ULb2tur4Q4qp1DmGaKUWR\nm1JV7jDyC5nwRRo/7OLFp4DqWNbnxUikOIQsHBGniMMDQf2f/vERv/z8EZ8ejnDWUSjYw4q4RF6V\neMR4UA7I99fffirWsxwlOMxzxMfTAY/LAmsstpxx2Ta8XVdc1g1X6fIM6z0bFVyROrnW3b+UsVqH\nqV6+KBc1IYTZBtQqk1EvkAAXhlEbKTJDJ7GmDKUI4Sj1wJR7rmWKzOYNeDwesMQJpgGX1zOevnzD\nt2//xPOzZcvNXckZMS6odUK2ibv/vtMHaC8kMjCZ0nQq4+6YvAIcAA/nx8/q9vAUyN+ZLpGptWJL\nCeczEZ7evr3h/HLBdl7vJjwKqUaMaXIu2FJSQxJnDIz3qPK/ddo3yvzPzGimm9W0+OsUY60yZ4v4\nGSjHQ3bCGBoh0tzHOeIwEdR/nCbMkUI89pzx+/kN//zyFf/6r8/48us/8fT0Gy6XF7ov4b5EQ4A+\n/wKy9dzXHetlRXyL6uF+fjhhP2ayv3UOcwyEtOSM6zxhmzc4ziUgmDgrzKcSnkqyufH3pRii9alA\nmy+wwc9gICUFs7PpC0IOPeFvkN/+7QJwuF5fvwKg1MjgI6wVxzgPZ52qNNAqwZy5wZoVKUTs24Qp\nzYi8mvDsu38jSzP9V0Pj8JZudT1yffQZ92Ld63iv3JQV3bkjBdt1x+vvr3j+/A1PX7/g6ek3PD//\nhv0dsL/8fTlnPuhXbNsVOe9UXPzESEIlAu4yEemZ2ftLZAUA77/Hz12USM5Ziu+lLwgLwNYKKyl1\nfE5pQ8DNYWFiqSgHRE4on6W4+RFJ84z1ckEpCdN0X6Tv//rwAalk/PrzBzz+9AHz4QBjgG27YttW\nfP36T/z2z1/w4f9+xKf/+ICfPn3A47LgOE346XTC208rtsuK9bJy7O7Gjo9OJ/2UdrRKv0fOsbTj\nD5H0/SFQEBqFEk26SpC9OCD8IeiAB3Qlmax/FKF8x7WvqUtDTUcoL68XXM9nrNdXpH1j0uqGUoj3\nFENEjFEliPwgDYZmAzG6NRjDRPXhmavFMgcG6J427N/gg6ayHh65+P/0iJ8fH/DxcIC1Bpc94eV4\nYZJ+RIxkmvS9cZhcf1/8rdViHLzHcZopmcyTdeqaEp5nMsJ45iIPgNmQ5AKXd9aaenoJxA1Q9nzC\niLzFRMBTLggGNLcdQpdd1JupUaR+jnflrUFZ+jmlP6gMfnRFZpKf5hnBOjzOMyyAt5czvv76Db9/\n/gnfvv1LIypLSch5Z/1/ZfZqw7Z1jau1HmiNswdYHil2pAp1GlhHBC3hK+jkJ8VAlvKGpgnZKbbW\nsOeMy+WKt6dXvHwhZ7vz0xlp2xUu+9ElYRkS13pdN7xGjyzkxgZlrIukyfJz4riwV55aSA5XFbWR\ne2gMkJ2FzQVWffpZyzrssp2jvW5c6OU6LDOOMxV+mfpzKfh2PuO/v33DP//7N/z2X7/h65fPeHt7\nQkobTaLh/mhPHz279xGJZrtsuMarSsqeP5zx8XhQy9/oSXpUSsF2IHgwbZl/JZRcdQ1QW3dAu8m0\nr8PUz9OCvhaWCkYYzEakqRVCYGXyUy1VjaUMGHEztwfQ313X6xm1Fnh/1X2htQ7TtCBymJb3UTkq\ntRakvGHbLgjXiOk6KRHYR4nX5jWFF9nn4NUxKIA6/6d/Bjdol6UzhFQxhGIZY1BMQakNl+cznr88\n49vvX/D09JmT5L7hr5LN/urq6oqGfV9xPj9j2y4gQplDzjuc95gPC5lBMcnRGYNPp5PyT8T7ovI0\np7Jk9J/fMOxXrEU2Br41ZJ7624gYNTYNqt2wS/7uUmov+i8XnJ/P2C6kcvA+IM73Nb4P84yfjif8\n/PNHfPyPj3j4+IhpIpRr2y54fv6C3/71/3B8eMDjzx/w0y+f8OnxhDkELCHg54cTLj9vWFlhUFJn\n85NffaDi3xpPptP/z96b7FiaZWtC327+7pxjZu4ekU2lhBDPAhJM6gEYMYAaMUJiQI2YFYVUEySE\nYAhSSUh3AhOeAl6BqryXm5kR6e7WnOZvdsdgNXsfiwiPY3dY13bKIzLczc3Ov/+9V/Otb32Lnb5n\nUSxCm8f9iOkwEbdp7BVBovPyCkGSZMFCW8Fjypg3Ijmu4fZSb9gC5uMFl9OFuSx0Z9ZlxeV8xOVy\nxLrNrPtCHStVdrrDPh0wjAOL/EB9ngS75AwNkHlQkTE6wVQDBZPUYYs0cj8Qs3+6m7B/2OHuYY+H\n+wM+7Hd4mHaMFC0YtRV80imY1wWkur7p/KVeUwoJuQzeY9/TxCqpUY19j16gPX4ThmEZGeQSuE+T\n2PhFMxSRf7yqiaCSH/TdChEuVwW8sAXOpgg+Ndxi5rgrgSCX2MjnJoXdb10Cu+37AYdhwP00AaXg\n+d874+ufv+LLD79T579tC02rioEOO3c3OOdYYzkghAXbxpAls/6dT8iZB1jYauDA/aHZ5ZohMhGs\n3TPDwUIGACYKneeFJW1f8PLlhTP/s2pN37J87xn+KtjWDZfzDBhgHTbNRqTmaKyrbUtAbWdzDiOg\nRkpK/gJvG2tgecKVauMzVNpyHUTNcNwN2B0m3E8T7sYRh2HA2JEO93FZ8PePj/jbP/+IH/7uR3z+\n8w94efqCZbkg54K+J+3wW1c/9ledCpG5E5JZvDyc8HzYY+p72HGANQT/p2FAyNSuJdMqRRAnrJuS\n9trJXm0AUEcEV6NvLDHhjUyws9QyKax7+bcQAXPOOpWRPn8m2Hy4LfjZthkxBli7qA1wroOosnUd\ntRBp1pITAk81c65Df+GWOxmty1lbFePpFFJVIlzDb2j3AA1kDsi0xIS4smSv4Xp5okDo5csRz18e\n8fL8FafTIy6XI7ZthmgG3LJKKRzgdIABQlixLCcsy1m7eWLcYJ3DMFDWL90o1lnkAjzsJvQsyiOG\nnIA7hqubjN3wO0apgbHo/befKeaqNCqEQNGDWLYNl5cZ56czXr6+4Px0Rtwil22oG+LWNXUdPtwf\n8N3vPuLTb3+Dh/vv8OXL32Ndzzifn/Hly99jHPc4PDzg4/cf8fHTPXZ9j/vdDlPf4+P9ActvWRaa\nu7G8d1jnHtsycM86JUD90GT2Dbl5PIzUp38YidNhK2IoMLqiukL8c025bQuY1w3FAOsbpJ1TiFjO\nM04vz4gx6FjgnCLW9YJ1uWBdLwiRkj0AWJYzkxY9MfPjHcbdRPoCNbWvqDYAWOoCgePzJu87AEgG\nxtCkUFJt7cmZH4h7t7vb4cAo/N1Is0ycocFQu67Hbjdid7fH7nDHKr0/3+fz7cyfh7LIDGpBAEbu\nIc+lqFQlgzFVkYyZv+LohfgkLyysUWu+dKFqW5hsVmHmM2UG9CuFhI25BjnS9CjK9rnNiIk/YQ0K\nuwjsSj2wtx8EcWYdC8lYQxPCnn53wef/4Pf4+qcvePpKsPK6zjiHBSmsMNYRXOo8ch4ACJOTRiyK\naiJBeQ5CbILj0aao8721DszBjzLBY0bpoLCfZAPLFnB+ueDlyxEvX444PZHjX84zM6pve3YvdUsW\nHRLFu4WJXNYaDCwm1Av8z3wAERtx3IEw9X0tZ5jKeHcyDCMmpOjhggSJpjoL7uvthw7TbsTDfocP\nOzr4U0/Kiad1xV+envC3P/wVf/7jj/j895/x9PUzzudnytCsRc+z4W9d/djXGjdH5NsaUF7OcN5i\n/7DD04cD9tNI35+JV733mLoOd8OIdJcVBZJfwvSth4z+pVA4nQb6vUYlkxAHgv1c55jRvmE+L1jn\ntersW6cMaLlvMoPjVsIflbCuh+HIWG9BegTClS6OlBPWlWr9wzChG2imvZO5DCy8U4qr5R/OeFwB\nzdBwZCOyTUjWVARAEJHCpNklaGIBUBkNKJjPC16+POP4/Ijz+QnLckaM25scPyDOWjhFtX1XSgDL\ncsSynIBS0PkBvhuqvr8XBbeC/TBc1fFF5le13o20QYPt6fXnMPV4MIRd9J6nwkOEUsYWInF7ns90\n35/OWOcVMBb9MOok0JvePQffh2HAp0/3+O53v8V33/8BX77+CfNMff8vz5/RdSOm3QH3Hz/g4ft7\nTNOgd383DPj04V7r864jsuZ8nGlaJssA+446OIhIXs+FY1ExaWuWxMhxqbRkVmvlTqZ1XrUsmKUf\nn7UoPHdC3LpSTNjChnW9YFtngDX2S86kTxBru2LJCcaS/Z7nE7wf0La492VgJKKq3MqEP3q/NdjL\nTlr+ABMMcraMKHj4waMbO9V3GfcTpmlQztN+GFBKUe7F4TBh97CjAGC6r7Lrr9a3M3/um89c28t8\niTomwYnmtCiSiXxs4LYIMXYJiYVHIlIgRrI4lcQZuYgcaLTfOv6cVTVKxyWmDYCBNdRGRWpqtLEi\nmkAPUaVyyVi84SDkgsj91gbA2HV4mCZ8/3CP7/7wHX777/8Wn3/4A16enzTDWHlmeddR3UXnj7fM\nd85UrLXIhqK8xAGtcwAE4pTDodyGUh0SytX3y2DIed2wnAn6uxwvmE8z1nnBti1vMoLOWzqAbIAF\nsq4Ki0DYjwRFDh16qVFyZE7jSKn9rXMONAGxljWMswg+6LtJIdHlZeffZv2+9xjHHvfThO8Od/h0\nOOAwktNdQ8Dj+Yy/PD7hxx+/4PGvjzg9njBfzohhI40CJhONw+7m5++nnt+b0aA0x4z1ssF1xAY/\nnS543k9w1mLX9+g7T73/vsPYJxwyt7PFWtdXLYw1IBqWac0ULNlCrG3r2UlYo3MNROOh6+lzBR5T\nfHkh+V0iKFHXTOZMX4IGhZrtjZEfaJJleWU0Cb3aVDWs6wYyaqgTH1dD5z9uE8K2wa88Ytc6DXBs\nU+aSINhYA1NMrZPjWrRGynthDSRRzV5RbEwM1NN+OZ0wz0fM86k580aTgluXyCLLnaOrVhgFuGBZ\nLnDOY5wOGHc7Csx2Q+3G4YBYCHmtX2cMjETTXqGcSpAFmpkARc9g5ExfhH9iTFjW2sq7XBbqUDA0\nmMx3ns7Ojc4/ZlIXdM5h3I24+3TAw/ef8PDDb3E8PmJdZ2xhwcvLZ/z1h7/Dn//4Hb77/fckqMMc\nHKBg7Dzu7vY0kCgXJY5fjheWHTbcwUEwtRDlZC6KdIH53qMf+iuSczY00jYEQuPCFrXsIsmE847a\n5crbSL6iHZNTpPMDKk9QW/fCd4Dq/QDgYOCczHxZEMLA9p+4MuhKw0uoJTDt9y/SwWZVBK0UwLIY\nUPv+RuFATD1GHlvec2t3LoUSj77HNI3YHSbsDntM00HP2+tlymv67ft6X+/rfb2v9/W+/p1et/f/\nvK/39b7e1/t6X+/r34n17vzf1/t6X+/rfb2vf2Tr3fm/r/f1vt7X+3pf/8jWu/N/X+/rfb2v9/W+\n/pGtd+f/vt7X+3pf7+t9/SNb787/fb2v9/W+3tf7+ke2vtnn/2//+lcVHlliwF9fjvj7z1/x5a+P\nOD2ddDxuq9dNQ9uo317mMItUpvRvyoQ60R1vVd9U3Kc0et86D5mEGxKPWY08SXBdaNTqelmxnEnS\ncrqb8PH3H/HdH77Dw/cPGKYeMvjjv/hP/qObNuef/8v/hSf5ZRZUmWmu+YXGelpvSWt+6NDvBpaj\nHNFPAw01EVUqy33+lqZywdLzqoJcO8CFJU5VAIn1Cej/07NHUThk5UTpBxbRDBr1eIfDhwN61sbu\nxx7G0szv//w//g9/9dn/6T/9L9GPI8ZpZFEZmp9Nylx1Zrt13LcOkBa26MjLYmlZK0omYOVI/m/R\nMpBlbRXBAFC/r2gagCa/6bRBL+OOq/53KVmldrd5w3KaceG55v/7v/6XN737f/W//g1iSFjnlRQS\nn8/Ylk17c62xOrr2avw0iu6NYUle13t0fYeu8yx447VvWXUtIAOpZIhTI/xjofoAuq18blKsZ0NG\njuZE5//+u3scPuxhnUNYA5bzgn/+z/7TX332/+n//L9QuI868ve8vFxweqJJd8t5Zr12UjscdgNN\nEmNJX7nj0q/eDnZqhYaudCpYJ4Dkgku986yMuC0bjRUOkaYLpqzCYNbSMJd+GDDsepVWJd2QzDog\nHv/Dv/ivbnr3/81/9z+TjvpugAHJlR8fT3j5/ILT0wmBzwGpFFZVznov6nPxg14JV7VKnq3tbAcn\nQXvA6SyI4BWdN5quKDYzbgHzacHzl0ccj191dHXfj5imO4zjHl3X4W/+5l/96rP/Z//sv0U/9tg/\n7HH/3T0+/OYBd5/u4DqvA4NWGW19WVjKPegAq6rW2sypAKqYlU4qJQVQ14m8OWlZuK6eH8u+oRXD\nIWVM0nwhgR8S+Uksc11yqSqSrIjpOof/8b//r2969//H//N/424kFdFcCj4/PeNv/78f8Of/9894\n/MujiiidTy8q624Mz8HoBvTdCM+qiqRMyraJP0/Xeb4nHU+8lXvCInYsb51CUvsftqCKlqIESqPa\nV1zOJ8zzC06nJ6zrDIDlnPsJ03SHu/uPePj4Ef/6f/sXP3nWbzr/LUYVndh4sMsWAuJGjldncCce\nPti8YJriRwfAZhE2AIq1gG3kfBshEXH68qJF2fP1MBCgajlLsCHBhfUWG4+0XM4L1vOKcBfQDR3P\nhb9d6MSympnKFqtAjUFnOxau6NGPA8Y9GcB+V1+0szxnoBE0qRPOSB2K9sjAmKxGUhwI+NnpQFvE\nYGFdgutIxCU6SxKyIqMJVFEkFScx6jOss3D9bWInXSdjh00zbIYnPfIFvXovts6W/4lcmX6G9t01\ng3va4KFxGqXdc2MAw4NuUrkKJFQpztMwJBmC5DJp22d2kir8dMOSz5BCrOOrEykktvMj6Gsdii1V\nclnOqDw7vVTIpC/EpIp7IvlaAJhckE3WYKDuC1Q6VgNmeXZrUKxB5jvgvEeOZIzXy8riIPL3bjv7\nKZCBTSxPHLaIbQ3Y5hXrZUEKGTCNdAgHsikl2FTvqIoK6bkWbXP+LWOv5U/RfHlzpiDBIuR8lxoY\n54KsQ4v4bvJsCHWiJr9N1tvXM64T1tRh8cCtguZ9VGlfUkU1/MjVyVtbAyB5Rtk72QMN/IDqQC3f\nAblfzpIYks4+4H02hqc+kirpui1sB2j6Yko3ihzJHefBRAVgcTQ6A+tlwXohh7stm/oCSVb0cxcZ\nRvZK1pb0bMjsmwyk+nMj2wI5T9bYq8BXAl69HjLEzVrEQkqPpKh5rfv/lrXvB50UuoSAlLIGNyIp\nH2PkSY+kyOqch3eelFttlbHuWKBIRhNTsEyiPY4HXonzJ6GrTH4zFyQWPMspk9R6HxC3dOX8fVen\nd4q9TynCWkqCUgo0hOgXxth/0/nHlDSrXMKGZduqtG5KV9KzqkfOL9p5kiek4RZAzgY5GxhRcMts\nPOoAJHX8elDEMVieMFg8ZBa6aJZ7n5F9olnbniIrOYjbQtn6dJlIKtJ1VxfslkVG7VpdT9SnOh42\nM+5lAlWvuuWEenB2aKs6lXxPUywBAAUoVpwbj6vMBcXVvRClNf4tkmvVTCrAGJY4NZIZyxhYFl1m\nlUZb7M3P75vsVHT2TWd4spqHcVWBUA67qLYVdWL4ScYnDr/NmF4HEOb1eZCgAgU2F2RbatBgxEHQ\n57bWAPyMjkdippgQh4gUbh/sI9ln2Mj5yRQ+nZbn66AR3WNGAGp2Y1lv3LJULKM8aD4re0Oy4zwX\nI1/r2dMzcmDhPcATwfgPq3NiRC1yxr6cF0ZEnH6fW5ZkV6KbHlYy9stlRdgiBfRNICGS3jKh0FiW\n6eWPVwP6659jLU/ilK/RLzA1AJDgv/l5EkTJ3TRZHEvdP1FRMwbsbG5Xt+z6imCILDm9A6sZOAw0\ne5Vz73hIl7wXAwBNptvugQbD8lj8nHVT+bcZLbxy/jBXTtZYg96S00wy6MxYHZ72Fh03cVCuZ8VU\nGLV/NEeCkFaa2EfOX5AnmQUCPssSnLxGfkzhwI/3IYslMQbGJk0YiyuwrPp4dXjUL7BSpBMJZVHo\ni7qvtD83Pz6GrsPgaUQzQqDJjiuNH9eZEznpsB7nOpK+Zsl2CgasqvL1E/kFcv5OExTbnCWa61CQ\njVX7bZt5LuRvHGKfrtA+x6PDUWgscEoB63rhe84zIOKGdfn5iZbfdP6pFB3msYaIdaPoh7L+GoUJ\nVC0DCgTyzCkjO85UChSuFedUYOCMUzsnF6JmqxbFSHTbjL0tzdQz1rp3KcOnjDyQ5KVkLttC5YCw\nD/Dd7XOd6cdU+FGCG2st3GDRDR26ocfAk+YGhvrbaEycQPv9WkcHAKYAkECAv8Y62xhDGhRibSHU\nxIOkXLNDkVG/AJJJKh8rA5DqpefnMOlmB3DlfKyB56jWd77J1qBa3DKYg7LYn0K7rQGQ76kyvrYa\nVzWARWD0mgWiAHAGznJ23QYVgBoeveyGMuGOo+10o8QpUCHGxEND5Ge0Y6PluTQoNIYitCZgsiw5\nTV/HgZ1Y9kKZjRhJzaXF9zcGzMpdeP2amv2xhc6H945g8oWkngX6vHUp5ChZ/xKwzRtN6hSE6Wqz\nwKWWoncGAGwxV+foJ1lYIaMvErc/66Pkvrz+BQkqBE2BOohSaCqlOGbbTMa7ZfUjGWvfOSQDDfiM\nq7LTbYYnKJj+mXnl5NvMt0GG6iMaRbh0WcCUNvj5qU0RG5Hlz3ZATntOFgqWZa5fd+OS0o2ie65m\n4jpBUsberpvKswuy1iZJ7ZkW5y6BsLU054G/jJ+ZpNjhikT/gHMQWXINEBr7KfsnqK+JLDvP9g78\nZ7cuQXsBIGYqfWnwn64RVWc9YGjcrnMdo6LkF/qpVynefuzR9TToqpaBDU31E5vHZxkAoT0JJP0u\nyYQ1jGpSwJtCVLup01fTxiiPzK+hAFmkiF+vX838vSNIIaaELdCIUq3Ds+67QHAwABxQ66AZOVtY\nvQn1e5eSgWzpQY0FBXctJGZ0brm+ZImQckUArKcJZnUkakEfM4KhwSSJeQHbusEP/nbnp5+zedl8\nySmy63iWN/3qen81t7wabv5HY8zFMVxnIzWT1tvQZj7FwjpyrKWwc+FsTy6RDO6RAEt0ogW6k6z0\nLcuw8fNcjwOgZQZjjdaxrLPQR7XM+5Bv8JO95OfVP3tVNoAYrFyPTIsKvUISZMskI7SydwB/RkJp\nbh1nDEBnSUjmR7MOrgfS6B5Znsve8FcAGozVBq2vz14phWbSowZxvB3VGXBJQIxoOx9Av9bUYAQA\nXOdheJDVfJoBY0g/3d1mBOMWdWSu8GrCsimsCzQDmuhD0XMUXAVKuVy/q3YZQzNBbC4oP/Ox1GmY\nei6cs0iyn1eBNQ8XSkApgUspgOsKPBxQ3JvufS+ZGt8VQfEcIznZM9/BX/M36K5ez/DQZzCCgNez\ncGXbrpAe2kFxErKfipJZ2Xn6pwV0JkZNxqLCwEDBrQGAOH7febjeK8qhvKOQmnp0vnKIGtw2P+uK\nx2CMooElm+tk3hjK9vk8aInM5IqCmGpbpaRAKBkhlannqaBNiUfLDzeuxIOTYs4IMWILVGtvR02T\nb3LwnoIyGVXsfcdDlMg3+KHTvRT7KfZN7Trbs7ZcSHtGhsQwwlR0oqGFdZmHQ1nAGEUktnDgwVNn\nDgLq+Ouffdff2oiYEpylumTMGVtT65fMUqDBIpCbNSjF0thZHWlcnZi+lFwAU6GrNjKQcwI1APT7\nMvvYeYcUktbSBD7JXYZLNAhGDh2NAOYAYN7eBvsb6PcvpcAbr9kukcz4JasBgP5MmczXHn4CM4zC\n0uotBcLLGdBaXoW2Zdyjya+cnhhGV2ExYwQGahCLXDTDfMuAMwOKhP3g0Y/kPFJIiIUiS8n4ZSiH\nfmYAP3F0AKMS5SoIbH9ae7mRgXKVNf7UUMrXa+ZcJAByQhGi4MVbdGOnJaxbVov2SOBnS72wwne5\niuSby1tQUYL6Z8JiaB+reW/yTFojNlAi7Ovd0qDn+s8EOXLOYgsRy3mlzCBmdMNtQ07iRrVOJdxt\nATGkOpyoCTwEgpWyxuvsvt2X159TzkMtD1z/nRYtqqWlpM7vNapWSmIKkdHsPLs6WvvWpVmvIQdl\nHN1lywhKQdGhSdY1Af+r51XCGr972T/5vLoPPwkKdfNePV/l8NR3YGiIli9qg6hcRbZ6W9efR1R+\nYWn5qPdEUOWsWZ1/SkiZnKxMGBRHLGUsSeL0czOCd0VyhSQoGQCXaHD97iWRkWdtkyVJbgAen+6I\niCyoEwVP9ir4vmUlHpqEGLHGiMCjt4uUU0D32vkONIPNwfLwLe94NLtzzfltgpyru2yY/9DY/194\nT1TegNpOU6w6/lIy+qFDCgnDQKTDbVs020/M+fi59auwPzl+IfvFK4dSyWWZ64D26vJfHVLgyoCX\nAhg5GObnL4Bk93KBWiNYs6/qAKXGKsGBEL1iqOSl/IbMt+s7Okxcd0uWai5WavpKfKvOvA06yFk3\nh7cUmGK4Nlkvr/6LD8uVd+D6rjxru+SAWWMAZ2uWZVAjVS0fmCtjctMylNlLtwBATlENxGvW+qu/\nS3tSahZuICPL62dXB9m8Y8mcGxTgl+zX6yxDJg9SPUU+Fxnut5Z95ENba1D8zzuPIu+Uv1aNmK1Z\nvxgxAYAowMvK9ZA90PIH76k8v5B8XpeRaI/YuCRQlB8iBzkGJQMpBs0MhhtnugcO5gnOjZVp3Bhf\ngzqSlj5/Q37Tc/wqoCs/fV/0gNAR1lcOnXBjDkIZJvWWu08I/bO5aJYr9qUIB6FBHK27/ex7HR17\nXbcmiJ+mXWq3C0Pyr4Oc1lkz/t0kOg8taGMAACAASURBVNfP3yIY+oEh2f63kxVjKnNezks/ZgzT\ngHUeOOu7PfOX0bgUaDnKTxhmzinphNGSq10ixIGD21cBXClk32UvKs8FWs7QwEmRDf2Sq/3R7ydd\nUs3PRzHKxdKgwtTnuXXlUrSEvcWI2Ey3NNZQF5tzQOnYDjbdbK6iw3LmdYqfoIIGGv4T8mcAcx0Y\nys+yxlakrRjlChljCGFPwnughLcbegzDiGU5I4QVJScknsT5c+ub1rCAxtqGmLBxvT/FhMJjD3VM\nbvPC25em7ShCaMoFxWaCT629dkYtMpDrISpgdjftFkpqDFDjz9poUZwJ/ZW2TdC/6SD0Y0cEJzTP\nYQj29x2NEfbS2mOMGjB9fiELSWsfp93OOarpNQ5SCTxNrb4lN2rUrUxx1KzCGhhwbVM2pdS2kcKb\npUz8G5e0zHR9r+Qny4QW13AbfnYJbFdAAZCtzqBcfVkhPw3zM8bw+uvApYbyM1/z2vAi132XLPNW\n2BsAETe3yJfaXX2+X8rEpTxgXT3br51Ca+jI2XNbrOW6sTNXf1dQH4XTfyGAK2ha5Lgtk859gs1F\njdAtK8eEUsBtRhFRCL7XD6zP4NghC0nOOX/VWXOFiBSj7+N1hi/7IshVmz3J93HOIbuM3IkDkPsR\nlEuRUoSNQtZrAutb188Fs/zzrXfwjEzqmRanBWiQk0tBicS+bz+/7IMifMaoc0QW+4VqT2RfWmQ0\nA8ahIkuWSG8OQCkeechcc+4Zng/4CSTzC0va4wQ5AZ8DKv8wox8gtM8YGE5yhGyoDl4cYIHWqRXK\nb8oVbSeG8w6WbW3lFVFnFNp7ZK6TKtlPAMTL8BY5yvuyb6r5x5yxpQTL45KF6NueH0p4/FVwK2N7\ndd/Mq/ctCCKXUSCICb/PUgp1IzV3QbsloI8tW8L/fW1biHPQEwHRWOSS+X78AzJ/gDLYLSUi0Ukf\nek4NAtAYJYPq5I1Rp0ufGCjFKZcDHkBEPfgALKy2LplcyXEwBFtKbUmiegn25fGNRa2HWQMkaH96\nWCOcD2+q/zjvG8iGP6eD1vqctMIVACVzy169ZopO5OaA8stycGq4NMsKsq+JDkSuf95mxJpFqjNl\niwM0ZDRuGUmUsRGxshrkX1tyicWZSQuT7Sw8qEfVe6+/L33a8tkEDQKgiJAY4defoOD6PUqWIM9O\nWgYUZNrCEBh/nbbK8NdSxpYBa5kfUWor5BvgT+01Fmeun+v68lmBg6Xu615BjUbeE2qt3jJc6IzC\nxq/1EaS1UrKXtq7cHjIh3VKrKAWMMcSGmEv3s9Yrf30ldlopkNFXyJ+NkZEgojF+joMk5/1PAyB+\nx6bUz3vtW4n3o3tlm19q8WoHhrWGa54UNBVP3CKxRRTwR6TokFOn7+rW1b47gIN2PqDWWhRboWV6\nB+WKoyCBfObfp+C3ZnHtuzAW2gGiZ6DZ1/b7oaCSPk0liwliJPst2a7vO/RDRMDr/f7l5TuvHSry\ns3POVAvPmSDwfJ3wWWuBvtre2p3Bfz9mGM6mr2B95k9JKdd1dI9ktTbPtKCy2v62bMxXS74nANFW\necu7DzFiY/u5hVA7GACyP5YSv1KEqG749xj6H0jTQ1AIss/UEef4DLVIqZTPJWhXlDhl8gPybA3h\nUxB3SRRlL60lLoJzHZzvYZj490uoz7cz/0LEhy1GhEgEosp6lLo/vxyN8rjNi1fcIkoGH5yExO0O\nuRFzkKzfGMOwsLl6uSR+UMsLteWiVKcsn6E1OLKpoX72tyznLWCqYIhN9UW07Ue1rpUlqNPft9aw\n0ZdLYa8MvSkGGbVdSR2edESUXC+BHJz2ZwoE9pMXLIEDGDo1b7oItcZaYSwiOXlkW1vexBG18B/5\nNxYzUud3neVdQVxGarpSy6r7lyMf8GQq6a0xtsI1EReTIE6hOh0AV2S12xYHLmLc2OC0wYvjlj8V\n9eFnawWN+An1XwXE3Si2ANwHLiRCLXNkboZu9qd9L/IZjDHImQi5lHlWwyEsbAkc4xoQbyx7SCAn\nLYOV/5A1Y38dwbU7K/ewZiYGRqDnIl0P0pPOe1Ykk4aWAcWxSPApTi2z6JQkA7Y4Yo/njALJMKUl\nijUe3tDqJ4a0SAYqPqixdZnh7ysyGl7dT/llACNBWBMk0zZZSnZs4V5eCwvDLD5ot5MYlQTuqEoG\n2RA0nHOBAT9/5vdkjOoVoP71X11tR4EkLILuinYCCZIROtSWt6y11da1QZvNsPnVeW6CWvklgnCa\nMYMTYQ6qDWowoslOYxPlrKE9o+WnJeVvrXnb9OsX7mYgB1/LcRKEUzePq63dHesjsPGR8kHLTyj8\n9+Suys+SpEVaWNXHSZeZo+4uWyzz2AjVU/soewAKiL33SIa7tMzPI37frvmnhAhgC4HIfmJQUuOQ\neBFxh5wYqW5Z2Ez1sZQyXHT1JXsHFxK1y/UsjMCHWeTvckqqeJRCDThqNiiCEtBLarjmb5qsQwIA\nQS7ecA64rY2eV+p78rPs6+ZRAyATW1QMrs229oUzLOR0DzyMpWzGJNNkLVXEpK1j1sMuhrWwMwEd\ncDYYGpBkKpfInmmP8I3P7ztHbN/O6Wc3pcB56r3VtjxBbaT9SVp5mkCMDn5RpyWBXXuBhGAkgkgA\nO7EtwhiDZJOiBxVpoSxPj6EhGFIjddPUFEvRPb5ltcxeoNYX1akJ0ZRVLFuI+6pdUZCw17CDGLVM\nv1T0oUEK6FtUgSXoJ8FPyxiFAwsx2m1mlAoCItz282Ifr5ewuinrj81ZhLYtvsZvxOxa27a81WAz\nC/plmpCMP7N+72x+AplWI8MOg+9O6Yue/ZIzkjXI0lJMB4SNaGKl0NsDf2lbzTHXfcyFYFTN6ttz\n9jrAbb6ZvH/DCF9I+n0oIM+aAQMehe+0LTWYbJ0/MkVKyVT0q7DuRS5V4MsYQijT0P0qb+DqPYoN\nMfThBZJW596gT20BT8pTQD13+qYNrp16m9Hr/3i7mkRCgqlKmgWETCioX3EOLst9zTW4Yqg+GfOm\ne78sGzxzHdZlU/SMj1TVljAGzrPuSU8+xznHqp+ofDhz3SYuOg3UNspCbhzUWGMQY6S21VwAa5EB\nLnVXpEBa66+T8MrBsM7D+x7OFVj7y50u32b7M5tzjZGkPkNU+F1rVqiXo83As8lc96OMJrmk/+2Y\noFAvjUU2FsZSACH1DyLrsQFq4JdCRTWFouTnSwYqsJUIw4gBFsjw1uW4hRD1p3I0rL9Vo1+QKIWJ\nBhlJM2BaksUInFnhOrpgtVbWGj26ZFbtpbGcCTOMpOx/iaSbNrRSmPTXqP+JGtYti/pVuytSH6xh\ncmq5upBtNgzOwqsUqSA2Nbip/fgGznviTvTcDuNFQAiIMSE6B+vjlQPUqLjU9k4xUPIZauxh9LPj\ndhvwCnki1MowqUeV3V4bNFSnZSDGmNGZBnKXLPL1vaF2HtrP3Ih8VKi8XP0seS8U3MaaUUr2BVMd\nFsP4tywRbhHHn5k8JWdXPH1FcuqdEti6Zl81QIS++xax4n1gXogpDWJQCrPJ89X+GlfLK0S4Iglf\nmzNKMQ27uX7/HG9/+ULakhZmSj4oy6qs9wa6b+9fS/57Fb8U+Z8EZ6DATIBSYyl5Kka/siIPberO\nwbgcaM38ckX6YCrJVVC/W1brbOWHV1i5Jm+CANSSRNH3ecVPaWI90gyombDhz0p7YtVOo+NW4RY9\nKJkzZmjSIN6rgBOoQGJnQk61bDhTvJ3oKxLTMSUW+ApNayORX1NKNZFNCSmSXTRg8rU11SZZagNW\ndNNVlEDJgaD77ryFCRY2JpTkKZgR9JODmdYO6uI9NUz89L4DMIHuIP5hrX6pMHkoVohBjHl7+OWQ\nXsMypOoHJGVCSiCQPYuASHZg5UIbYjVmgZZqu5Fk/5LdSBQmjiCzkRJiiWZgpcmGBbq9cTnvENcq\noiAiFgJbF5Ra89XWoFLVD/nFVGWwigBo1sYQp6jG2Zj4YHvoxeOLWEpBcgkmGlbFpD/Ljg6sME9b\noycGTEoQ3Y3Qrx88+sb5W4HjHb1v0hhw6gzbjgcYaGsVOed0RV6kQIBhSREU4bZB+nn0vmJMiB0H\nfo0DidJ+U3J9N8yTaGF0Y41KAVNHxO1kTzE8ifkuKSRCPcTAc2YiGaE6bWTYQuUr6oMWHfbK38jM\nTZAz4bmrpFWlE7IdEWwJ3VBxrFfOv0UH2iy9oJaGUF7rSvzyiqKdr9LR9U7p/tgmqAKjVYk6a0oB\nbL4WMJF6cdXtl2ClOi/3Sg1TPn+bZUvwfZVsWCYCJpKzNYrKVefxluxPWntzzCxd20jYBtF/aH5+\n41wN6N1TksP3lo2BMNCFLZ/57MgnvYoX9Lz/vDqhdYXuopDCxNZJYIYWoTI3Z/+y/1bON2pHh9S2\npcUPEHS2Bqrtu6roVQ3akAVFERud4KLT7ytJg349yPGLbxE0z1niTWWfYTZD814QdC6KOP9SCkIX\nf/5hf+HdW2sB9id1rkzUttecM2ymNrsUIjtcxzM8yIaBEz1JdLupQzeSIqzn1kCZA1HvdkKXek1q\nJLBKMevshBi5zbo4TcBT5I1m5991PZwjnYqcC2v+/3T9Sp+/wChJnb4Y2hToQ7QEp5wzMVwly+MX\nnw05LOqPtCglXxFVtE/fZoC1jfXUyAYw/E/wSJVLbV9O5ohcej2lDtKut9R9jSUhEmFNJ2ZBW2fY\n8VsVkFGRH8LfAAOF79tD1ZKZSi6wiRx5AeAS1dKztzBJ4E/wgacI0RcasJG8u3J8mR2q1OUEVlcR\npiCa17dlACJ0QiQcgacAY5wGPZ5JjyQz+VrghtvUcqbuEM0M6qAPxxfGe19rZhxsAECXMtKQrpjm\nORXEGDV7oOeLjBARykHwm2MosrYmvkXmUzNPySQiO3jB/2C07kzfn19Wkr/b8FykFJSLCuiI43ed\nRxcSfPDw3mnAGGV4VQhEwBMYMWdlNksAASOGn15SSglXgXHOLARzW+Yv2b5mvol/pkD+7FwAzrSQ\nmNdDX98xguN4cEmBoFBZIfhWMU1RoK6SUjXYlH1snqeFPNsAgMpyUtq6RiXfsqw1CFuVN95Y1pbO\nWFDHf0XKhIVJLFVvHJw3ugdy53JOMM6q80+pzpvQ8tErwqgE7a1AlfxsI11TMCSZy6iLZfuj5xg1\nAfr1Z6/IoXJJTGXN+94hZ8r8k6HkLnMgQ4FRQIy1NExJUkWJahBW0QTfeVVVpPNSFGmqpEarXVU6\nx8XSHdyWDSXT8DVBlVJIyIbQN/cG5++8Y/Jqld4tJVffkiM9X5BuFL7HzsF1HYaJ5rx0QwfnPMv8\n0u+Tzn/PSQ7bKEZ26RixzWfZFrUZMeq8GpkxENeADfx83iGGqHbOdx3AZUFKXv4BrX5bpBqhQm9y\nAWM9iK/rOAIHtUujPFMhTGOjXnTnHZJnKMXW7EcGdqiTZ75B1owDGp1XqckmSubgQQKInDMcbs/+\nIg9ykJ+dosCfHc0R6DuVcLxylCB7LJwFRSKaXwLJp1fwYCl0AaWuJBFwaxAEEpbMPoSKjCgbNFWC\nUVVpC1fZ27dWP/Tohv6KwFIKteUJOc93HVzvqvNmAmebJbbdC7lcZ3AyJMj3VSzIKwnUaMYjdWv5\n/7kxLDnR84mcreoQSNnH1yzxLU5A0GrlnGRSdaMzEBlVaJ3/tfLfTwJTDgAkg1Rdis4hjhFd6BTC\nl7ZOyTSrbnqTgXN21k5Bk7PXEvVaZbLob3P+gnBdaXqUcnVzUkooW2GxLeiz+M4jjz26ISMXCuzo\nezYiMeE6ABAH7jar99oae4VsvK4na/DXOH/jLKzmDYQpi/N5y7rqmFirQqhk/LoEztZ3LgEynYeu\n9zS9je9PThm+j7qn4lCAlmgHfb/6vNkA8br0AUF1TEU5DUP9BkBODqLyBkCTtV9brplUaUt1vMYY\nlEHYw5Rhxi1qtp1yxrbSMLWwbtRtkWkAjrTqWVtnTND9dOwgO5SyJ95D7zWx6Ie+QQWd7mUlTNMZ\nXS8rSi485W+FsZu+RzTJ4K3P752DS6kmT0yui1vAui5Y1xkpbQCEXe9grUfX9UhxxyUgh25gBdSh\nI0VYLm127fwX7aa6Jg23JcEUE7ZxQ+AAVCZ0GlszemX/y7NyUGCjQfA/L2v+bdif4RnKXKCqX5pB\nvHIkRcLgZonRKzybUmRgxVgJOUciPcW+BC4XjgCLNyQwbBZLdWwLaxCkxEQJhsNl8+QAvNEK0GYn\nhBC0ZihRquo2s3bzMPQYOo/Oe3hL+tApF6Sc6q40znuNEeu6oSCS0/eA50tRxo6QAO7fFUfUHgza\ncIKEJCJUAxsTIiIHPIU6HRiyujHxJ/XCoVMoul00ypjHbzLJpY3eK+EN0LHMuUpBtwNwVEqUs0Q/\ndM343lY86RoKlWEIKSaEZVMDGEPUlkpjqYxEsDNd4LetooFUikm3LqWkvBMUuuiuc1R64f0S5y2k\nOXk3op7XZmSeZT8Lew+B2inTLRr0inFX9rtrJGYtBcQllTrdrIHuKci+HfpoFSIhgX3KSABiyFyH\nlRIOPUfHPJFu6NGNDWfEW+LoxJaJz85fSiNFJkLQGVcmeIOWWc6apfZeUS/ukW6CAaBmTm+B/OXZ\nBe4PG92tK84RI5GiO2FMrYX7zpHYikz5nHp0HTmtXDJ/Hy6nSlknF73bUqrUz6L6HlnvUltnv7Jp\nbD/cIK2pQI4UcF5N2fy1589cVnMFtrN8tvkPmacT1oDNbmpXcxQl1Q3Lcsa6zghhQdhWpBQZAue2\nSxAK7H2HrpswTjv4rqOsuAmGuoFGlA+7kYanTQO6zsNzgrGFiHXZKBDhOS7bLOOFs2qi4A1ob+88\nhq6j7raYsF4WXI4XnJ6OeHl+xPH4BZfLkXQlmGxsLUHtfT9it3vAtj6oJDAMWFmzlmY0aJQgp+/g\nG5EmAVDFd62W7HbXeeLuxIRxT2drPl6wnNefaHhkLnc6b9CPPy/u9U3nL46/Nd5FHiLXul+NxCky\nbNW0rHVc7yU4p0I4VR2rLXYJBNLCx85ZJG9pmE0pFJQIAWMLmqHrxecRw+JojKmiIW9R+qIMKiBt\nNavuBsr2xbj1Y49pHDANPYauU8dfALickRmGlJpVAXcEoCBmx/UsqBCF1nO5M8AwtJXECIMiWpl7\nkGPWACmsAQVFDbZkGMESdGm9u7nu6/s64EM+l7D2K5lSarA1gBOHVFvkcEXMlLoVGTwextEEFykl\nFCOSmJWs2dax6ZxwgMB1VN85GDtUiU/eczVOQoa69d1HqfVzmyifn5I3wBiN3H3vtPQhrT4wgN3I\n4ck0MEUn1o301gsQIw3igA7soSEhMpJTnApBeZQFiXiLbybPWQ50YyB40DgDBNobUvgKsNYihhtF\nfrj+2LaREvQOIAIxBqQUkVJoRpsaOEefp+sG+K5H1/eUvfX+iuUMMGkzZkWDJAtu+QwS/MqZol55\ncqAhEPkqNy2c7Weldjerd+cN9l+Dk8BZv/AoxN5l7v4RrlI9C17VMIcdOf9hN1wRZ+VzKsITKvrR\nOnVBXmScrDEBoRQYW8j2sgMVLQqp1ZdC3B/fd3omBEm89d2rAeZgRETJLPMHgqX7G0NUtELuZc4R\nYVuxbTPW9YJ1XZDSpohmPSsd+n7ENBZ0Xcd7XkmrACiY8U5hctdZDL1H58gmeUa6who0IRt2g44b\nLqUgF7rLty7vLDyXMFNMWOeNnP/LM15ePuPl5QvmyxExbiqdawzg/Yhx3OFyOWGej1jWj9iWj0gx\n6SwY33VIju6Li2SLJQAwxrCmU9WwKAAPJ2K/yEPDSimEurLInAT/YvdLBk/eJTvY9T/v5r/t/LOQ\n/aTWlTSazikDCUiQXkaBJCt8JbKf3tRisLFEcOoGnrbG2aO5CgBMhbP4vwkMKOrwpAtAIqz2wNCH\nJ7RBRiAqE/oNbS8FVVgEQDOjucluOo+h7zB0HTpH2V/MVRRDngcgJ5hyRkgJUYgpUaJHFnzRC1wz\n/iK1zpD0HUCJVO3e12BMjAt43+QzdOk2fXeBqSSizCkjoRK0YuQBGnypPZceUqA5095XxTdrDZUG\nrGViWEJqMpGUMwVxISEv1ZBfkTqzDv7UzBdo+A28h6bzKF7EeCoZpgBvgv1zTAhLIEOyBr5UjFZ4\nx8aGRnb2I50Fz/wFY4BtCVjOs2bqK1YdFLQuM2LYMC8nbNus7HTnOozjHuO4wzDsMUwdxv2IcTfw\naNAB092E6TDVLgxrkCOPrz4tKKUgbIHQE9k/nsx5K/JRYeeKVIlYVM6JnX/g3xO+SU1pRGjEe1KH\n7PoB3veUHY091faFwFXjnoo2ZFHmq739UcpBqLA88SFqWe06CKAAICWr5+fW1RK8kiJ+pPIoNkrG\n7ErXh4zz7TjYkfqu/BLxl67zcDz0KueMEMkWEMGTEIbMpTwaplTH01prYbp6B3RsuHR9NBEO1efp\nZ26ev88bl5YdLavmwcAwUpFCJO2IV6UQsv8sMlYKBQNhRYyB2esZ1jr0/QhjDMZhVwMX5rVIm2lY\nA0vpUiLWzxvCNKDvPUQ0SWyctt52PIqYa/XI5k2w/8gJXM4FIUQuJdCwnHk+EaqxXZBi0P2mcltG\nSgEhLJhnIkhb00jOO4dSgGE3KNIppcS+65Ccg0HlNQjnSI6cs67q5xSDwrLrxJeRUk1RO9XqSdhf\nIDp/m/AXE7Z1w3pZsF42Ir2sgSQ/GcoEqgKWwJLCZlRyRjMKU1ndTArquJfcKRveMTuWOAIQG8Sl\ngKzBiPQfg9mftbWolIK08fjawpONItee3pAC1Freq155KUfYSmqTACRlEkVKKSNRobshKmW66Imy\nnMRdAcL49gPD/rkgZXIUYYtY5xVh2ZR0CEZgSNwD2jooCAdFvBQUVLGgxqnesHrOLoVsZApp2Bfp\nwhA1wixzrblfn4kszjtFRoaxxzgN8NOAofMoXadOO6aEZaVnW04zwho1G2wRpQKoaInKjzb1YGEo\nt1oGhestlDk75Hg732NbA9YLzS2Pa6Bsv4BlnQmO3B0mjIcR42HCyBme9RQAhi1im3cYDxfMxxnn\n5zNggHVZkS8Rl/kFx+NXzDNlEcY4DMOEUjK6jqZEHj7scffpHocPe0yHHcbDiN39DsM0aFBL2SFl\nOdsa6v40MydSSjAm3+wAyDBV7QKCH3myWY5syFO989aqYY8xaDAjGV7XDeg6yox26Q7jbiQ0wJI6\nZZv1psjfX8qE3M5HA1Scompyn5QEiXoOAOhneN1mecsKS6giSaUQAtd3OthH9vi10BUA5TAJoiVJ\njbWEXuynESMnCsYYmpYaIy7rhtP5gvW8YmXEiWBsqvVSqdUor8h6W5X4JJ9pnJwxlFHKiN5b3722\nMRYGZNnuir7/xpnw5fmMy3GmcelbQNqYhe48+n5kvkFAjBtry9O5SBwsUgmgAMbCsghN4iBW4G5C\nslayjZ1HN3j0EyMpcsb579HeNyI8fG5L8+e3rN53gKHy4XpZsJxnbMtGrbSZJiRa62A89dMT3D9h\nGKZXQe8AYyzZtfOC88sFRYKYke53YXuy9Ru1ljoH51gts2Eny0TJXAoSdzhZSzX9fqRkrpTCBGGW\n9W66vH6J5/Vtwt9GxIL5OHNtYWFIhaCouFX5QMNQqB+8Mk+v1Ptcw4wfPMEWEqkJa9xVwlguIIdj\npCOgnZbURHoNE11r6ikhaNZYNOvZloB+uv0gKNyZshoRgqHpvymToqwwpkQiDZlGQeYknASZfU3w\ncWhg5JIJQu/HHuNh1KgQ8nnnFctlxXJesC1bdeLMxXiNSPjeowCqi6DBAvCmPmcAGKZBa3106Knb\nAAzDS2aUU9KsmjLw2s3gO49xP2B3v8fdxwNxN0aHvtUA4OdYzgtevh5xeblcR/PWKqRqjYHpjTpz\nGCLdJX4/MYPGRws1QIy+EULa7f2+83HGfJqxLQSfybjmbugx3U3Y3ZEzFmh32I3k/B0Fb93QU9Z+\nGDEfZvRjD2OA9bLgdLQIYcXl8oLT6RHbtsA5j5wfME13cLbDdLfDw/cf8OF3H3D38YDpsKPR0XzZ\nl/OqJMe4BayzqJGhCaSFI5IVcbllvR5LK5l0zgT15xwV8TG2TnRMmRwUZXpc0sCCbVvQ9xuAgq4j\n1rMxBo7hSB0Uw2cipRoAGCaKCTmMCMGCikmAmPhnU7Qnf2YMj5l+I9lnvazY1o14BIzCOea0jPux\nzmfvGY0o1yRLgM5eCAFmqa1+Il07dB0Ow4DOU3vbvBFB7bIsxF6fV1xe5PwRibUO7zE1ANEWW2nt\nxVVABgdqLxv7mx1gChExOIiwjaKfW8RyWXB6OuPlyzOOX0+4HM/YllVLkhKMC6QvyFApgPcdQeUs\nOUvtaKRDb6wlxz8TauWcg+fEoR86HS3cDR3dNQ4AhGQtZ1VFdFpydIpIb3j/MSfkUDCf6R2s55X3\n36LrRkxTRtcRatH3I8bxgN3uDtNuz90BnBjyiF/feXb0Bdu6AYbOiowYJ2SFNA4EySOFPlJTHbxH\n33XoLHUCBQ4WVwRGfB1clxWllWBZRhx/a33TGq6XFctpweU4Yz7O7PwpGl0vK1KUmo/VbEzqq9qS\nVHBNiOlpPKyoaKmso8LxtaarAys6r5tqmOHccX0IgMLBAiNGZraKYyFuQMR6WW8eawpAmZXSQpY4\nIo8M0cEYhRQjcwIAYqZL5LqcZoqQ5xXbTJnkcll1DLLvPPYPe9x/d4+SCvqpZ9h4o30/UXTd6rQr\n8U8ISC0rnrPOdVk5g4kAZwG53M587UZquxH2fPv9BZYVTQHKQCtMqiiJtVjnAZEJZ/3QYeg7zXxi\nzpg3gtZPj0d8/dMXPH9+xrZucN4TzH2YsLufGIngUtHQwfedQsWW2/xioClchIzQEsU5qdPfus5P\nZ8zHWeuaMt1wkux7N+ilDktQKNR5B3CZQ3p+OzbUOWcs5xWn5xfSu8gJ63rBspw1Q845wvsO02GH\nw8cD7j7eYf+wg+8JLbm8zFgvdjanWgAAIABJREFUC86ceakCmdw5QPUerlnjlIndumqZSfQUxPlz\n54MRxUpPTrYUGM6MADqjZOwTlwo2CggSnQUZiW2MueqmqIOMIlKkoVrUTkUBACmXeZp4Bm4vzE2w\nAFFOq0JTWmK4cV1OF621U63coOs77O4m7O73mA4T8y68wq3bumE9r5hPMwIjWTJGnHhCPYCCaTfC\nGoOp77EbBoWOny/zVflmPpO9lRYuYxzPmZDODpkWV/vEJchoGfoYiLwridqvrRAiXHAN0ZNs6LZu\nOL9ccPz6gqcfn3H8+oL5fMa2Ldx7XvQsGJC4lHMeQ7/jwDZz4CiZv8c4HjAMEzrfEVoWItJMgU63\ndczrSuhCV2vaRpBSVfjRBLP1JRSIXCNRt6zzSp0D59OZfFwiAam+n7DfP2Ac98iZgoG+32HaHbDf\n32F3t0M/DbXzpilTaV2fn2GdV02ehUCfQiLel4FqA4zjgG7n0DuPfd+rj1xCwLOdEVMiFULJ+ENk\nhCIr8mOM+UWy5zed/3Imx0NEpQZSEJ18yfY7bmVoIrFaw6+oQDd2mPYjht2oWSVB6gI3JdhIDyh9\n4957zvqs1jNFbCHMQTNzYc2K46G6d2HGbkFeA72E4fbsLzD0G9YNBgbRWX5pPCGQN1clSW2NuPMW\nkBe5zDOjJzOOj0ccH18wny8opWB3t8en339HL/swYjCDdj1ITUvgdSHNCVogfaja6gMSlokbwXPL\naab+dGeROmmzuhH65e8tREnQq9SAgxQAqaSyKdGQA4GUSKIS0GCj6+nd7+/26JzDru8Rc8bpMmO5\nLHj68Ql/+eNf8PnPP2LbFgzDhPuPH/Dxtx85IOJg0Eo7WJ0MR+puBQjQcyC4ZXGWS0Ov5HB/ZV1O\nF6yXGTkleEcwYy9kT2bvChojEXw3UK3X9RK4jDRcZRoUHo5bxHy84OXlEd3jDxysbogxYF13zBCm\nbgol93UeKSXMz3R+zs9nzQqFiKrz1zsP1/EkQnZ8NaO+zfmXIu1N5ICF3CdGXnrLpa6LUh1wy18g\nxyv8AIJ/Synoeo+R0RJjjJYtpJ3KBYcYhRzH5FXls1TFwqJ11k2RBmMcOt+xI2LUwF0z6H/13T9f\nlGyXYlJSXT/1mO5GKsPsRg1UYpI20AScyG5sLA1bQDK742ECjMX+wwHOGBzGEXfTRGWvjfrU13nD\n5XTBclno70rNV4Il5hPUIThOVSYLCnxi28bEMRjAREMTKvuf7/V+veIWEb3jdr2Bq6QsNLNsWC4r\n1nkjvkWKCGHDthGHhbhLhNBIIOB8B9/1HCDyu8wZxlqM4x77uztM+4n2cRMdl9o6K8NyvPfaWdVP\nvSIAFEQzcuSMol1CICRC6htG+uaCZd2wzCtKyeiHjj5fJrhfuCTWUnljGKkLYdhTUjDuRxZHo+Fn\n4NKDTEWdjxe6v2dCdMW/jvOKrusI1XbUPbVMPZG4cwb2BbtxxMits5dtQ44ZC5cU13nF+fmM5TTT\nJE9jtORTfiHh+zbsP286xleVm5hpTpvfY9iPV0xW0FlRVSnpCe96qpNOd7RBrnMoiZSL1mWln7Uy\nxOosjZHtnNZ5+rGH9w4xJqzziuVIjq1sNRttHZscHMOHI650md6S+S/nhYzsvKnx3gaPfovoI2UZ\n0ucuWWhJ1Oa1XhbMR+JKCEdhuSx4/vyIH3/4Ozw/f0YpBZ8+/R7d6PHwmwcYGPS7XuUzl9OiIhbb\nvLFATNS6Vz/2cH2FvYQopO1+G6EUmvGaqrz3a8t3Xss5kjURnA+YUqHHwoX1FBKioT0uKWsmbrh+\nZp3FuBvx8N09Bt/hw36HmDOeTids84avP3zFn/74R/zlL/8GMW7Y7e6wrn+AcQS1W2exLZtm/gKz\nOxaRUYi/IWgaU1CM0bbAW9scAWA9L1jXFSgG3nTalSK19XXh97oSAqUcBzZQcYuqgjhOA/q+A6xB\nWAPOzyc8fv6I3e4eXT/yGaV6eimZyyUT9vd77O73GPcjlVdixnJZ0I+9BoFho8DLe5rnbb1VYiIg\npS8a63or6Y3Kbi0DvXI7rPVaBqDMqpYDZHa8YZWSFInsFcJKZ896pBzhOofpsMN0NwEG2C6UGYYt\nIqwdzMKOXz4Pvzxr2anAKNOaAuLEAUoEaIadQr9COn5L5n9+PqtNKTnDs5mkJKa2uErNXmrV7S8y\n6kGRjm0JsM7i4ft7AEQsuxsGrJHY+CHR9yAUiWyg7z1soudotTRcJ6O2qVx6hQLKXeWzGpgH0ve3\n2b31ssKA2tOStEdbsadU+tjf77gXn6B7ax1Wc9HgTuB+5Xv0A4Z+0iAUhVCCYT9i/7BXDsu2bPAz\nlXWkY2LYDRgPI/qGcyEkQO3AkDHRDUKZc0IuSev0t67OOZxjRObS3XS/Y/4U/TndUSa2GwOZHglA\nyemOAxXq8nAK/a/MZ1jOK+JGCNF6XrCtG/bLDtPdTt+laEU8d894vJuw/7DH4X6Pw0QB87xuWNYV\n67Lh+HjC6fGI0/MZ63mBcZZKjmPPXT4/n/B9m/AXmlYUPly2cxi5dWfaE3Gn1R9nDgf9fW03MOhG\nMuDd0GHcDXDWYd02ROYUvHw54vR0wjavMNZid7fDdBgx3e+wv6faW2Gjlxl2F6a/tOZsHDzQCYAa\nSAOjsH1Yb4c+55eZg5JNyWY50LCZbV2RviYcH4+1+4EhqQJoi1eOSaWHwxZwPp3w+PgDvnz+E8Op\nCfu7e3z3u98gp6Q1bjpYRII7fnkhiHcNyCWjHwfENWL7SJCiXPQUE5bjjIVJOKR8VRmpdDBvy36t\nt9pbDlSVNQBK4BSBjdPTCcdHqtcvp5na2XLAtpEh2R3ukFPG4eGAFBJ677EfRsSU0FlqUTw+PeHL\nlz/hxx//iG1bsN8/KDxYYsHLlxclWbWZ9bDjwLDvtKZKnBCrIhrSgfKWzJ8g18CwtEPY6MznnOFO\nM0R213lHJYCxw7gbMe4H9LsBu8MO492EaTdiPw7wzmHwHtu8Yf9wwOH+DrvdPXa7e6zrBTEGjOMe\nw7DD4eGAj7/7iE+/+4jf/uYjPh0O9Hf/ScJpnnG8zDivK+bzgsvLBfNpRkoJzjnEEHB+vlDttKMa\ncS4JMUXYG3sdi9RwIVWD69KcTitTgyt1ec+Epx4xrgRph4WMcErYmAuAUuA7h3E3EJnPOW1TdL7W\n8wEmfCIDCA2RMyvMT84mUIABynhTDMieCWW4neQq6/R04rMn4is1YBbybojSQketqyIGRj8PGuwv\n5wUpUgdM2ALGw4in33zCGgkVUNLrtmG7rFhOdH9LKdQxw3ZTSIQA8XdCoUzeWOa/WGm5vuY0F1Qi\n9i3r8nwmqH3oqTRZCryj+7Z/2MN1Dvef7rFcFpxfyNmE7TdsfymJU2GvQkzzYSRujDHUgoYCdGOP\n3f0Ou/sd7Y2UWOaNgi5QkimjcWFr2+6aKAB3x0qsjCFg5hL1Nm/aNvrWd++syBg75XhIMmUsFAnS\n0cfe0d582BMJ+I7LdZ/u8HB/wP00Yeg65FLwp69fEUPC+fGEbVlx/PoCGIPdslHCzITpFJKS6kWj\no+POjWEa4GUgnrNqf18+H3F8PGJdZ3Q9i7MdDLrR/2K589uZP6taCVNehit4zqhb+c/MbNAYWLSG\nCXcU5VPUNx1GQg2Y7Z22iOW84OnHZ/z17/6Kxx+/YpkvsNbh7uEeH377ER+STNRz2gIhetJJHGsr\nKcy1VwAEe3EEZVx1kLeu+Uy1etIQYEGZEHA5EVS9zqRotc2b1oYJAiJOwzANGBiep1aPjG1bcLkc\ncTo9EqzUj3h+/IKXx2ecXy44XBb0qVfYMEYiKs6nC5blgpwT/NypyhO1INX2p21ZCYIrBaXQSEch\nXlJgcaO2f8eje1OGWY3W/nJiwHUhHsL5+YyXzy94/vKE4/MT5ssJ2zpTt0ImyLzkgrsPD5SlCpTJ\nxE0A3NWwYZ6POJ+fsW0LUAp2uzucTo/wvkfYAnxH3yulQHW4sccwkQDI7mGH/f0e091E0Kir2tkS\nCLzF+Wv3RkrI88xiQgEy1KfwHu0f9pXctxvQjT3Gifu7+w699+hYxtNag37qcf/pDh9+9xHfPf0e\n6zrDWocQFhwOH/Hhw2+xf9hjd7fDp4c7/OHTJ/zm7g5j1yHljMu24byuOM4zjvOM59MZR2Ykl1yw\nMmS8nBYm0Dp2zLc/OykSMtHNeXjf1To8E7a8p8lhfT+wLgENwvI9BaPrZUF3HCAtSylF9P2Iruup\nZDQKakiw/LZsWM4dk+lGuGVBipTFU8li00yfMsia+Us2BqDqhtB/aQfIm0o+xwtKzvT+poG/E9sd\nFpMpOWtpMkeeGirKhSEibBGX4xnHx2es2wznPGIIOHw44Md/8og/P9wTsTdnfHk54unrC54/v+D4\neMK2bHRWuE1QRY6sjL3m1uEYsc00uKYV45K9EDXFGMLNQ53WeYVhlE3smqC8fvDYxz1KKpjPM+4u\nd5TFs0jVfJrx/Pm58pTYDkvCZ1mLJKdM3+uezrl1lgIuRnVTrDMtJAE1zminUx0Fb5TsXUrBeqFO\nhOU0q+6LoAC3LseEuW7oMR0mANDunrBS4DLuCcHuOTCz1mLYDxjGAfu7HT7eH/D9wz2+v7/D/W4H\nZwxOy4JcMs6PJ4DFyJb5gsS9/ru7HQo/m+iphDXw8ywACstFO9ZesKrbQgnACZfzGTGuGKc73H24\nRzfSM/xSqfebniAsXLuS7J8Nf+R6qspU5kzKTvOMbV0AANY4WEd1KusEemAtZ2uxRarbX14ueP78\njK9/+Su+/PgD5uUM5zzWZaZMup341l3Lx1KvrNTFWSzHWRhDaoD097OyIN9U+AOVPRIPKkEBM3EZ\n0ptXzOcL5ssF67IgbHRhx90Bh7t77O53KB+ozNB1tX5OtdMV6zYj54jz5QXn8zMuxyMuL2csp0UJ\nSn7wnE2OWC6LsqjXRUQ2tkYbgLocqPbZaC70DdmJ64e3LOUygGvATGBsCYMbkz+1NJSytvNENtzG\nD/B9p6In4zhg6DoYACERUZKOk5CUcGXYCb0x6Poe426gSz6DuyE2yORHMhYV6XEjq0eq7kQzM+Km\n5+d6btxQQkEMAV3YtKbZDVSWioECWKpZRgy7qD9HFcYAdN4hZZI0nu52+PCbDzg9/gbbusI5h2U5\no+tGbhmy7NQoig4pki5EzljChsu2Yd42LCEglsruzjTZiJ+16iO0UPAtK3EwTVmlV7Z8CPTvvvPw\nPQVewzhQptFIl6aUcHk6XznpGDeM4x7T7oBhP2rNlowd1NGFNWAYeqxdz73UGaYUAKL85vSXnHVB\nHuS9SbIgfCHvvWbNt6ywBuqt9l4RxNT04uMEbDKUpdDXz8cLLscZl+czzs9nnJ/OOD0/4+X5C9b1\nohyEpx8/4PNfvuLv7va4bJQl/+WvX/H44xPVgo+UWAC0J6ISWHKBdw5GZG6VGJl1BoS1rupySL88\nkw7Xeb3p2beV1OK2lQWpmOk+TAPGaSCBtZzQTz05ZSb+Zi6p6hhodjiCjO3udlRK4ITReacBcwFg\nj9xbv61Y5hnLcsEyk0OdzzsM46Doo7bzmVrNE7RlmzciO4eN6/0ZwO3vHqAAYJh6lEwdNvHDgRKX\nnOF7T223uwHe13b2oScy834acTeN+LQ/4LvDAVPfI6SI00pTcreF3se2rtjCipwTwjYicfJoPQnQ\n+5goMOb3TMltlccO64oQST9h21YWVlphjYH3A4wzGHfEQ/gHOf92nK62uxUgpyq+ItOI1mXFuiyI\ncWPSm4W1UAOiRI2xR+cdNh7GMp9mnJ9OOD4/43j8ioUvClAwDBPGPYmaTPsJcYgkNpHqARACiYw8\nlEy4Yl9GSwRvEXsAoNGjkBdTSsgLEeqWy8JZdkRhdIKyIZGrbcYKc62LREmk7FB06EIItHeEIqzK\nb9jf77hbgtnmjz0uxyOJuFjJ6pp+98ysYCtjTasynjEiJHTbRWiHqYiw09XiqNo6YvFPhx2MAbqh\nxzoviJGMzTjt8fDdR9x/d4+HT3f4cNhj3/daL42ZyyXec2Y4IoSVWz+pZWi3P+D+0x2m+x0RxLZA\nugeM4nhubWkDm8JM5ZIzTdjLpItw66qZVOF2pQyAVOO8p+y1pEJCLCFhPs3wvccwUbS9fNhjutux\nGpyB2U1wzmIYehw+HpSxL33Np+MTZyrU6XF+OuHr12eMQ09ZA0D15ZwwbwHzQvW+jQmwiTsehElc\nR3Bzry+oK+eWpa1iHkDhAVKuCkn1fY9uZHnrode2K0FcwhYQp4DuQv39fU890MO4w7gnTQQhTgoR\nathRZwdxXAJCZG2FbVEioTEWQz/Cuo64Ed6z4BDVdpV4yVKr/UhITKtUecuKIZAkrxWxGMrCxIHa\ns0wrZCE0djoLl2EuLxecX444n15wOj9h5YTGOo/nzy94+vEJP37YYwmE7j1+fsbp8aQtvfP5ghQj\n3NljuQwYdxOmdQJKwZBHjEzmMtYATGoumTJw4XtQaVRUJSmJu2Uty5nIeKxxsc2rzqawQhyNYhNY\ntyRSSXN+ueByvHDWn1QRdTpMOHw4YNgNOuBJSsC+94hbrATjkqlktGUiwoaInKgUJboVGtBKUpYb\nOevIgnRs/xzPtr91XbYVKSeC83cjhl3lTxgDJdUWgGd7ZEa2ElISxVbDehQFW4w4ryseT2c8vZxw\nfD7h8nLBtq56pq11ylHRRJVtQQGVksMWrngcKUWkNWOZL5jnIzaWUR76kQmJFMDsH/b/sJq/LDFK\nHGMpcUoirzY767qeYRm6cNMdvfjDwx67w4SBa9QSBS3nBZfTGZfLERdWUJL69DgesH/ZY/lwwLZu\n6ENP5Btmb7vOwUUHFxIKw0DKgBe2K5cnMh/QW7OfX9oHgGrePbOwcx5BkIxDP1QdalGAo35OnkGw\nbdwm4rhfuX7fnKqqlUSY0tImGte7+x0uL3fY5k2DHTlkcgHjSnKuUnOrrS8slnJjBiQT6UrOnAXR\n5xX9gsy1r36kiyW1r2nZY7tsXNsFht2Ih+8/4P77e9x/usPDbofec4uclJKcRTcMGIY9xnGPGDZt\n6fK+Qz/Ssx8+HujiFVYY27hFjOFQIUFWAiqPY+a+9LcEf9pWZT1ERIYMSlKiTwyWM+TIKYjBcvYI\na2Q0g0iOORGDeew6dN5j7DsOGBZqx2QBl7CtzH4POD4e8fmHr4AF9vd7JYEWQJXHhCQrg3/CErjO\nvLIefRW2MnwGbn72xDoKAv97T/3DLOClGTWXQQwbg9qdQgEHzeXo4azD0FOLnMyAkPfkmcOR7ncA\nDE1NY1LYtvQ8t4MMWD+MmhzkTM6EJsmtVKqw/z9nb9rlyJFki11fYwGQWcXuNyPp//8xvSPpvWaT\nrEwkgFh80wdbPJLdJJEdc+qwp1isxBLhZnbtLhYxDhimiZPUesbAs1fOG3F3ZLLek6JH+0rKn4aD\nk1olq+J93bE+SKb3eNzxeNywrjcsyx3OWoQ44H69kU/8r1dVTn38+GCJIGeUpIxtW9Baxbb2FWDJ\nBSPneHTURGKUG4IgaM7qWjRxRsGzKp9luQMwiFdG604jrT54bahEYuYU0a/Ea5sFjytB7sZZcj+d\n+h5ciKslsxSNuWPbY+tZImzdW0tGOaDLraGbKjHKqT4upatTKktLqSY5eBcQhn/vbf/vrvfrXRVD\nYtjk2T5X0MO0J1I9HFYbPnpMpwkFDVOIJMNLGRsy3h4P/Px+xRsrddb7qrVIXDA9y4YbE+pJ2gsm\nylpeh4hU22hjUGtRB0XAw7HSxXmrvIo/WnX/6RNxdE2jA0EOOyk6jpnO5F1c+RVJUlEYo2rYz9/P\nmE4jrLFYth2P+0Id8vsdj/uNrRPJF1kgvGE44fTxgovImnKFCQbWG6B5DfJoaLA7rwN4ByM6Z2E7\nyr75K9Cv9Q4mdXIThU18DrsRDafE+npOcJLdDACst1XDQYRFTVGOVhsBMJdBjHn0gOQkK4LJBqy3\nlQ1dcnfxYydAKQh2oX08GriJEG9s97TRTc2U1S0yI1nzqLmPqZ0BDKMIjzQbtVCTN8wjNX/fzpjP\nk+r7a2vYWJPqPJGCpumCabogpU279aINQncMNLwe6EZJRr93shUOynoXlys5MJ694hCR96hOduWw\nhjCGJE7GGCB4Xb0o2aWvnFWTHZzDEEidkGNALsTc13xwY7HcH3ovyPRPzxXZgupOF+KGR+5+acvK\nOF+Yj7KzGoGCpbos8pnrKO0EnwESPiTmSrLvFnRJ/ekrTaLyuZOV6wDAYBhnnfYsO1IeeTmkYoB6\nYMgk5EQ21xpbR/tPJFd6huj9OUfW27RGIHLUVyd/WuE5uI05JkLeZYKnbA81pZKbsrxl9vG4Y3l8\nYF1v2LYFOW9ollCKnKlgLtcH7fxTwfIhO/Kkxl/kk1BQKylOTKPnL230jMcxKqeFEE/6fjQjpDZt\nLkR588y1bQvxN95Jwh25cSICpNP1lsgZJUlPVguZTXpIqkrNw/wy4/TthHEe1JWShsWgBOfltmCc\nR4TIskC9F+mXqGmORlrCrXCJfS1Wg7wDLVeYauG9JTnePD393X/89kHfs6OQMXmGYSxxEh4blo9F\nuQU5ZThPQ+5RIVUbTf2pFLw/HvjtgwjtK/tAoEFXU86TUV3mpt57p6vmnuHgiFvXJD/HIsYInF9g\njcUwzCglw/uIcZ5pJTcNmE8T/zf/ev3pE+E8WaI6LaiG/J2tBUzXYwrELki7C466PpZynF8JAvUx\nIJWCZdnw8esV11/fcX+7Ybk/sO8r9m3Bstzka8c0feDxcSMo6b4hv2YmWfAuBI2thYFsmaQkTlgH\n/ataYlqDrwD/gSEpCfIIMXRrUv5MwhDI11281o1R5IFYwNKRilEKdabOB0SAoG6ejACJLq7s0hT4\noK9odeTD1yNOu5oPCTlGMwv4OxCbYJWfSezwk1I/cUakxDoAxuj6R75sgdYB6GRE3gwOJnq46DFf\nZjKqeZkRQ0AuhckvDevO0H0DQggYxxmn+QUp7UqSpIx4dhKsTZsM6y08H7y6K3NC8KMTo1ICyr/I\noJ65psvEnyWtUrprnUjckhLqjGNmshMJVvi09nGHf3prWec94NvfXnpiXGsI7xGtVGL0Rkp23Ldd\nd8ACvInqgAotQf1ycGzLxnbQhACVIihQVRvVv7pkotUAJmvh/dFEpTdb6vrYKHOjpq7EkUIWwkBN\nwDDAefZlByMx4jzIPCKBg4d5+NRQiKJGHCSVwMgNhHNk/euCJ1c7zt6QhlSUIM9c5FlACXWUI+K0\ngSTttnxQ9LOl+aHpd8W6Pkj7ri6HgGE1hGU3N0rjpP3vcluQ1p0QImcRQiT42HR+Uylktd7YqCvv\nSQlgUhBLKTCJPfJzDwUqv/Pf/9P3XhK2jWSZj9uI8X2ihhucTsd8EskZIS5Y1aFQUBaSdU84fzvh\n9I2MkUL0yKkwg99hHCjNzjuH5WXBdJ4wTiN8iEi8Dye3RzGuodWl7NqP/ioig9xXB7ux+ZQ1GBiN\nffa6v92p+AfiKpB0mRp6IdKKiiNt5O/vg1GkduSEvtoa1pSw7Ds+HgtuPOiuj1VRLTpf6XV2EzlO\nuqyEkAiJXp4lay0NIjzIhBjh3DeM+xliEHb+fsH5pzPOLzNe5olWq//m+st2WJimzllUMfcwpPUG\n+EAVshY/lJ41/TPboIoXQANZBi/3hbWJNyx3MogohdzZ9n1Vjei20UO0Psjpbr2vdDByyIZC2YE6\nb6t2iTStgP6U7CYg7N9nL8lKN4Yeeheoow1jUGIV/R41AM5ZncLFG7yWcrDbzSg5wRjLfucDhmGG\nY4tLQQqMMbCBpqxWG6r38IGaApcLzJ7pfbe+kgmDMOj5+/IWtTT2Sog6cT1L+BNYStYo9DH2A6RW\n9vtmUx+Z1HT/GxziFHF6PeP17684v5C5z5YzCt+M923DynJQ+kxGjNMZp0xT9TDMyjRPOyUTCpu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lAB/bdyHVYWz156czNTnGEl8T0wjjrQYaLkqfk84Xw54XKecRlHGFAD5TztsGspSPuKfV+w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\nAOvXWo1xJNJfUVZ4ODzCDBoCQJ1lUEmCfdPhXHVLoAj4gGcVQxCbx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\n", 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" ] }, "metadata": {}, @@ -936,10 +11664,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The results are very interesting, and give us insight into how the images vary: for example, the first few eigenfaces (from the top left) seem to be associated with the angle of lighting on the face, and later principal vectors seem to be picking out certain features, such as eyes, noses, and lips.\n", "Let's take a look at the cumulative variance of these components to see how much of the data information the projection is preserving:" @@ -947,18 +11672,14 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 32, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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G0Wj0ZEnkJqcQyD9ajm92FWNfYQUAwKAPwC1XxyJpeC/ERBp8XCEREUnxaMjn5uYiKSkJ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\nMsWQJyIikimGPBERkUwx5ImIiGTq/wMjrvJM/BfyVgAAAABJRU5ErkJggg==\n", 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\n", 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" ] }, "metadata": {}, @@ -973,10 +11694,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We see that these 150 components account for just over 90% of the variance.\n", "That would lead us to believe that using these 150 components, we would recover most of the essential characteristics of the data.\n", @@ -985,13 +11703,18 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:58: DeprecationWarning: Class RandomizedPCA is deprecated; RandomizedPCA was deprecated in 0.18 and will be removed in 0.20. Use PCA(svd_solver='randomized') instead. The new implementation DOES NOT store whiten ``components_``. Apply transform to get them.\n", + " warnings.warn(msg, category=DeprecationWarning)\n" + ] + } + ], "source": [ "# Compute the components and projected faces\n", "pca = RandomizedPCA(150).fit(faces.data)\n", @@ -1001,18 +11724,14 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 34, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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pLP4/AaxAgjrBavD03zTWJAIxrR1CEWgq/HgtTR1+LrcsHKX4mGdkLZDYtA8xQtSFwvjI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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -1034,10 +11753,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The top row here shows the input images, while the bottom row shows the reconstruction of the images from just 150 of the ~3,000 initial features.\n", "This visualization makes clear why the PCA feature selection used in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) was so successful: although it reduces the dimensionality of the data by nearly a factor of 20, the projected images contain enough information that we might, by eye, recognize the individuals in the image.\n", @@ -1046,10 +11762,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Principal Component Analysis Summary\n", "\n", @@ -1068,10 +11781,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "\n", "< [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) | [Contents](Index.ipynb) | [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) >" @@ -1095,9 +11805,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.5" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/notebooks/05.11-K-Means.ipynb b/notebooks/05.11-K-Means.ipynb index 981f9b042..25ecee5c9 100644 --- a/notebooks/05.11-K-Means.ipynb +++ b/notebooks/05.11-K-Means.ipynb @@ -42,9 +42,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -80,9 +78,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -135,9 +131,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -215,9 +209,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -287,9 +279,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -327,9 +317,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -383,9 +371,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -418,9 +404,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -486,9 +470,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -517,9 +499,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -550,9 +530,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -586,9 +564,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from scipy.stats import mode\n", @@ -609,9 +585,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -640,9 +614,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -681,9 +653,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -741,9 +711,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -774,9 +742,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -804,9 +770,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -835,9 +799,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def plot_pixels(data, title, colors=None, N=10000):\n", @@ -863,9 +825,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -893,9 +853,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -931,9 +889,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -994,9 +950,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.5" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/notebooks/05.11-\354\274\200\354\235\264\353\257\274\354\246\210.ipynb" "b/notebooks/05.11-\354\274\200\354\235\264\353\257\274\354\246\210.ipynb" new file mode 100644 index 000000000..a0d6490ad --- /dev/null +++ "b/notebooks/05.11-\354\274\200\354\235\264\353\257\274\354\246\210.ipynb" @@ -0,0 +1,1309 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introducing k-Means" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:55:02.199117Z", + "start_time": "2018-06-13T01:55:02.191144Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns; sns.set()\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:55:02.299132Z", + "start_time": "2018-06-13T01:55:02.199117Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets.samples_generator import make_blobs\n", + "\n", + "X, y_true = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)\n", + "\n", + "plt.scatter(X[:, 0], X[:, 1], s=50)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:55:02.323106Z", + "start_time": "2018-06-13T01:55:02.299132Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1, 3, 1, 2, 2, 0, 3, 1, 1, 0, 1, 3, 1, 2, 3, 3, 2, 0, 0, 2, 2,\n", + " 3, 0, 0, 3, 2, 3, 0, 3, 1, 1, 3, 1, 1, 1, 1, 1, 0, 2, 3, 0, 3, 3,\n", + " 0, 0, 1, 0, 1, 2, 0, 2, 1, 2, 2, 0, 1, 0, 1, 2, 1, 3, 1, 0, 0, 0,\n", + " 1, 2, 1, 0, 3, 0, 1, 0, 0, 1, 0, 3, 2, 1, 2, 3, 2, 2, 1, 3, 2, 3,\n", + " 1, 1, 3, 2, 1, 0, 0, 3, 2, 2, 3, 0, 1, 2, 1, 2, 3, 2, 2, 3, 1, 3,\n", + " 0, 0, 2, 1, 2, 3, 1, 2, 2, 3, 0, 2, 0, 2, 2, 2, 2, 0, 2, 0, 1, 0,\n", + " 0, 2, 1, 0, 0, 1, 3, 1, 1, 0, 3, 0, 3, 0, 1, 3, 1, 1, 1, 3, 1, 3,\n", + " 2, 0, 1, 0, 2, 3, 1, 3, 3, 2, 3, 0, 0, 3, 2, 3, 3, 1, 2, 3, 0, 1,\n", + " 2, 2, 3, 0, 2, 3, 0, 0, 3, 3, 3, 3, 2, 1, 3, 0, 3, 3, 0, 0, 0, 3,\n", + " 0, 1, 3, 0, 2, 0, 3, 1, 0, 1, 3, 1, 3, 0, 3, 3, 1, 0, 0, 2, 2, 3,\n", + " 1, 2, 2, 0, 2, 0, 3, 1, 1, 3, 3, 1, 3, 2, 0, 3, 2, 0, 1, 0, 2, 3,\n", + " 2, 1, 1, 1, 1, 0, 0, 1, 3, 0, 2, 3, 0, 0, 0, 2, 2, 1, 3, 3, 0, 2,\n", + " 1, 0, 3, 1, 3, 2, 2, 0, 0, 3, 2, 2, 2, 3, 1, 1, 2, 2, 3, 2, 2, 2,\n", + " 1, 0, 1, 3, 2, 2, 1, 1, 1, 2, 2, 3, 1, 0])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.cluster import KMeans\n", + "kmeans = KMeans(n_clusters=4)\n", + "kmeans.fit(X)\n", + "\n", + "y_kmeans = kmeans.predict(X)\n", + "y_kmeans" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:55:02.459121Z", + "start_time": "2018-06-13T01:55:02.323106Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis')" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:55:02.579084Z", + "start_time": "2018-06-13T01:55:02.459121Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis')\n", + "\n", + "centers = kmeans.cluster_centers_\n", + "plt.scatter(centers[:, 0], centers[:, 1], c='red', s=200, alpha=0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# k-Means Algorithm: Expectation-Maximization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Expectation-Maximization" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:55:03.216286Z", + "start_time": "2018-06-13T01:55:02.579084Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.98,0.98,'Final Clustering')" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets.samples_generator import make_blobs\n", + "from sklearn.metrics import pairwise_distances_argmin\n", + "\n", + "X, y_true = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)\n", + "\n", + "rng = np.random.RandomState(42)\n", + "centers = [0, 4] + rng.rand(4, 2)\n", + "\n", + "def draw_points(ax, c, factor = 1):\n", + " ax.scatter(X[:, 0], X[:, 1], c=c, cmap='viridis', s=50 * factor, alpha=0.3)\n", + " \n", + "def draw_centers(ax, centers, factor=1, alpha=1.0):\n", + " ax.scatter(centers[:, 0], centers[:, 1], c=np.arange(4), cmap='viridis', \n", + " s=200*factor, alpha=alpha)\n", + " ax.scatter(centers[:, 0], centers[:, 1], c='red', s=50 * factor, alpha=alpha)\n", + " \n", + "def make_ax(fig, gs):\n", + " ax = fig.add_subplot(gs)\n", + " ax.xaxis.set_major_formatter(plt.NullFormatter())\n", + " ax.yaxis.set_major_formatter(plt.NullFormatter())\n", + " return ax\n", + "\n", + "fig = plt.figure(figsize=(15, 4))\n", + "gs = plt.GridSpec(4, 15, left=0.02, right=0.98, bottom=0.05, top=0.95, wspace=0.2, hspace=0.2)\n", + "\n", + "ax0 = make_ax(fig, gs[:4, :4])\n", + "ax0.text(0.98, 0.98, 'Random Initialization', transform=ax0.transAxes, ha='right', va='top', size=16)\n", + "\n", + "draw_points(ax0, 'gray', factor=2)\n", + "draw_centers(ax0, centers, factor=2)\n", + "\n", + "\n", + "for i in range(3):\n", + " ax1 = make_ax(fig, gs[:2, 4 + 2 * i:6 + 2 + i])\n", + " ax2 = make_ax(fig, gs[2:, 5 + 2 * i:7 + 2 * i])\n", + " \n", + " # E-step\n", + " y_pred = pairwise_distances_argmin(X, centers)\n", + " draw_points(ax1, y_pred)\n", + " draw_centers(ax1, centers)\n", + " \n", + " # M-step\n", + " new_centers = np.array([X[y_pred == i].mean(0) for i in range(4)])\n", + " draw_points(ax2, y_pred)\n", + " draw_centers(ax2, centers, alpha=0.3)\n", + " draw_centers(ax2, new_centers)\n", + " \n", + " for i in range(4):\n", + " ax2.annotate('', new_centers[i], centers[i], arrowprops=dict(arrowstyle='->', linewidth=1))\n", + " \n", + " # Finish iteration\n", + " centers = new_centers\n", + " ax1.text(0.95, 0.95, 'E-Step', transform=ax1.transAxes, ha='right', va='top', size=14)\n", + " ax2.text(0.95, 0.95, 'M-Step', transform=ax2.transAxes, ha='right', va='top', size=14)\n", + " \n", + "# Final E-step\n", + "y_pred = pairwise_distances_argmin(X, centers)\n", + "axf = make_ax(fig, gs[:4, -4:])\n", + "draw_points(axf, y_pred, factor=2)\n", + "draw_centers(axf, centers, factor=2)\n", + "axf.text(0.98, 0.98, 'Final Clustering', transform=axf.transAxes, ha='right', va='top', size=16)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic Implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:55:03.336277Z", + "start_time": "2018-06-13T01:55:03.216286Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import pairwise_distances_argmin\n", + "\n", + "def find_clusters(X, n_clusters, rseed=2):\n", + " # 1. Randomly choose clusters\n", + " rng = np.random.RandomState(rseed)\n", + " i = rng.permutation(X.shape[0])[:n_clusters]\n", + " centers = X[i]\n", + " \n", + " while True:\n", + " # 2a. Assign labels based on closest center\n", + " labels = pairwise_distances_argmin(X, centers, axis=1)\n", + " \n", + " # 2b. Find new clusters from means of points\n", + " new_centers = np.array([X[labels == i].mean(0)\n", + " for i in range(n_clusters)])\n", + " \n", + " # 2c. Check for convergence\n", + " if np.all(centers == new_centers):\n", + " break\n", + " \n", + " centers = new_centers\n", + " \n", + " return centers, labels\n", + "\n", + "centers, labels = find_clusters(X, 4)\n", + "plt.scatter(X[:, 0], X[:, 1], c=labels, s=50, cmap='viridis')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- https://blog.csdn.net/tong_xin2010/article/details/79846169" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Caveats of expectation-maximization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The globally optimal result may not be achieved" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:55:03.452354Z", + "start_time": "2018-06-13T01:55:03.336277Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "centers, labels = find_clusters(X, 4, rseed=0)\n", + "plt.scatter(X[:, 0], X[:, 1], c=labels, s=50, cmap='viridis')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:55:03.588333Z", + "start_time": "2018-06-13T01:55:03.452354Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "labels = KMeans(6, random_state=0).fit_predict(X)\n", + "plt.scatter(X[:, 0], X[:, 1], c=labels, s=50, cmap='viridis')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### k-means is limited to linear cluster boundaries" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:55:03.720269Z", + "start_time": "2018-06-13T01:55:03.588333Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets import make_moons\n", + "X, y = make_moons(200, noise=.05, random_state=0)\n", + "\n", + "labels = KMeans(2, random_state=0).fit_predict(X)\n", + "plt.scatter(X[:, 0], X[:, 1], c=labels, s=50, cmap='viridis')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:55:03.860234Z", + "start_time": "2018-06-13T01:55:03.720269Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JT\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\manifold\\spectral_embedding_.py:234: UserWarning: Graph is not fully connected, spectral embedding may not work as expected.\n", + " warnings.warn(\"Graph is not fully connected, spectral embedding\"\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.cluster import SpectralClustering\n", + "model = SpectralClustering(n_clusters=2, affinity='nearest_neighbors', assign_labels='kmeans')\n", + "labels = model.fit_predict(X)\n", + "\n", + "plt.scatter(X[:, 0], X[:, 1], c=labels, s=50, cmap='viridis')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### k-means can be slow for large numbers of sampels" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Examples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example 1: k-means on digits" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:55:03.988473Z", + "start_time": "2018-06-13T01:55:03.860234Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1797, 64)" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.datasets import load_digits\n", + "digits = load_digits()\n", + "digits.data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:55:04.164437Z", + "start_time": "2018-06-13T01:55:03.988473Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(10, 64)" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kmeans = KMeans(n_clusters=10, random_state=0)\n", + "clusters = kmeans.fit_predict(digits.data)\n", + "kmeans.cluster_centers_.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:55:04.368421Z", + "start_time": "2018-06-13T01:55:04.164437Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2, 5, figsize=(8, 3))\n", + "centers = kmeans.cluster_centers_.reshape(10, 8, 8)\n", + "\n", + "for axi, center in zip(ax.flat, centers):\n", + " axi.set(xticks=[], yticks=[])\n", + " axi.imshow(center, interpolation='nearest', cmap=plt.cm.binary)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:55:04.376420Z", + "start_time": "2018-06-13T01:55:04.368421Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 8, 8, ..., 8, 9, 9])" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy.stats import mode\n", + "\n", + "labels = np.zeros_like(clusters)\n", + "for i in range(10):\n", + " mask = (clusters == i)\n", + " labels[mask] = mode(digits.target[mask])[0]\n", + " \n", + "labels" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:55:04.384419Z", + "start_time": "2018-06-13T01:55:04.376420Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7935447968836951" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "accuracy_score(digits.target, labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:55:04.664421Z", + "start_time": "2018-06-13T01:55:04.384419Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(92.68,0.5,'predicted label')" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "\n", + "mat = confusion_matrix(digits.target, labels)\n", + "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n", + " xticklabels = digits.target_names,\n", + " yticklabels = digits.target_names)\n", + "\n", + "plt.xlabel('true label')\n", + "plt.ylabel('predicted label')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:55:37.789688Z", + "start_time": "2018-06-13T01:55:04.664421Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9326655537006121" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.manifold import TSNE\n", + "\n", + "# Project the data: this step will take several seconds\n", + "tsne = TSNE(n_components=2, init='random', random_state=0)\n", + "digits_proj = tsne.fit_transform(digits.data)\n", + "\n", + "# Compute the clusters\n", + "kmeans = KMeans(n_clusters=10, random_state=0)\n", + "clusters = kmeans.fit_predict(digits_proj)\n", + "\n", + "# Permute the labels\n", + "labels = np.zeros_like(clusters)\n", + "\n", + "for i in range(10):\n", + " mask = (clusters == i)\n", + " labels[mask] = mode(digits.target[mask])[0]\n", + " \n", + "# Compute the accuracy\n", + "accuracy_score(digits.target, labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 2: k-means for color compression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:56:45.237604Z", + "start_time": "2018-06-13T01:56:44.542385Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets import load_sample_image\n", + "china = load_sample_image('china.jpg')\n", + "ax = plt.axes(xticks=[], yticks=[])\n", + "ax.imshow(china)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:56:51.965182Z", + "start_time": "2018-06-13T01:56:51.957180Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(427, 640, 3)" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "china.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T01:57:44.873256Z", + "start_time": "2018-06-13T01:57:44.861206Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(273280, 3)" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = china / 255.0 # use 0...1 scale\n", + "data = data.reshape(427 * 640, 3)\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T02:03:18.198688Z", + "start_time": "2018-06-13T02:03:17.296405Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_pixels(data, title, colors=None, N=10000):\n", + " if colors is None:\n", + " colors = data\n", + " \n", + " rng = np.random.RandomState(0)\n", + " i = rng.permutation(data.shape[0])[:N]\n", + " colors = colors[i]\n", + " R, G, B = data[i].T\n", + " \n", + " fix, ax = plt.subplots(1, 2, figsize=(16, 6))\n", + " ax[0].scatter(R, G, color=colors, marker='.')\n", + " ax[0].set(xlabel='Red', ylabel='Green', xlim=(0, 1), ylim=(0, 1))\n", + " \n", + " ax[1].scatter(R, B, color=colors, marker='.')\n", + " ax[1].set(xlabel='Red', ylabel='Blue', xlim=(0, 1), ylim=(0, 1))\n", + " \n", + " fig.suptitle(title, size=20)\n", + " \n", + "plot_pixels(data, title='Input color space: 16 million possible colors')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T02:05:18.504185Z", + "start_time": "2018-06-13T02:05:17.088055Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# import warnings; warnings.simplefilter('ignore') # Fix Numpy issues\n", + "\n", + "from sklearn.cluster import MiniBatchKMeans\n", + "\n", + "kmeans = MiniBatchKMeans(16)\n", + "kmeans.fit(data)\n", + "new_colors = kmeans.cluster_centers_[kmeans.predict(data)]\n", + "\n", + "plot_pixels(data, colors=new_colors, title='Reduced color space: 16 colors')" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T02:07:46.105316Z", + "start_time": "2018-06-13T02:07:46.097306Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(273280, 273280)" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(data), len(new_colors)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T02:10:47.160556Z", + "start_time": "2018-06-13T02:10:46.905524Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5,1,'16-color Image')" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "china_recolored = new_colors.reshape(china.shape)\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(16, 6), \n", + " subplot_kw = dict(xticks=[], yticks=[]))\n", + "\n", + "fig.subplots_adjust(wspace=0.05)\n", + "ax[0].imshow(china)\n", + "ax[0].set_title('Original Image', size=16)\n", + "ax[1].imshow(china_recolored)\n", + "ax[1].set_title('16-color Image', size=16)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T02:39:41.217973Z", + "start_time": "2018-06-13T02:39:41.002509Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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YlSJtTyCsIzqZU2FkFjuDsv3WkrpE4piOst1BI6l7vC/nGLu0H37sivp3KQXfTDw4fxnI2g1mmoNAKVV12u/AWdYhOFuuiShDXYhnirwmGqQJkU7JBD0ZsixD3fvLCKXTM/6zL1e+XwdBvaTt9lFRdIBWDs0sWOqVyjDkSNaFbreBnC7ViyiDkXFEZygjmIYq+Kan7fH9KS5OTZ6Li+SM/unpfBTmRqnoKkjnMQ7vk4CtYt98Oxz7PNGcKydJWcwSny2+y4rwnQK7lapjGL23c1mlpujASHWsO216AWH7TQ8oZ9+xZXWQRItrLfXEifF3Vr1R3U8Gr2+W2vmtmxejFDoXUJA7XXDKPXZiikL/lHtH6HtpnRsV6vJj4xGgksRN1pcJXh12Dzi5G8lsPzJPXTAuKCYasr1e2TY5NRqWJIzgBJCqcqzaTlQkaeKCp0RMYQ90d1hZfxnI2q1ur1vWRjAdDDGQTJixi8dHe3kdnDEGMeKcSXTBSBjeV/vXLtQyQah99yT9mUqZOgSUuvKVuWfLWRQDczoZBztJGiISkHaA3NDSOQ2lyHLnN6o1Im3y1jiZ6kEzQos2mgY2pESR2MsK/ajjyNLAIRUiusoGx4k37kTjWvjNOHnZ1+cCkST0yyTGUvf8BC6+wSaSEOeG8sihzshbh6yLjF+lr0owef34pZDnxhGDtExiaAtSji6sek/Y7e8GpdKtnxp461Vv4a8T78vh2IBzRSu1w0mfnuB22otliIgvImlUjEnwz6rCmMbYCuXeHcfDvV/ZiELtJAcNFEPai/vPvSnMaTR8VgmUNcjHCFUyK0kp48a/tMej254K75iIud7hyDoYr/z3pLHBZao0n4GyusGQ8Lx72DmhK0BV9MLFhesXfh2XWoeYp4KEJ4MyJ51OfPn51NOkbAB6LAQh0nNPxbPIMWSKLtPNeCtnPHO6/F5B501625CPj2Ck2yI07SwMRqyBKFExdSJk9b7HpjDm6diUkYXfkRMTSnGbNbK1vmydgTO+d/L5SlUNsSuddYv1rUtKJ/oAHew1ngOr5zY7E7KJRWelcP7w6fqyCFsSHXJZ5PdRdYXnHNFLe5U2K+tA8cTV6b2jKvNb6qfWKrjFmbzoo6z80HnEl0cC5FV2PsBJZ5H5ChicHIUiU54Dl1CvdR2M6Qo88kyXXKrSsO0Nv7iIZGKwSx6ymDhCBaJ0ucLk3RPP5g5H1jqPgRBKKXLEhvUC4tI6Onti3GR+8yQ5MKwFuBRU4R923I+49JHOl88VsX4+RuziLFDjKSDexwN1SLnMXaa/p77V5SRN5UCSeN9zA2ViE531/W8zWjDcgL5derj48gt4wV5v4NunvYqd585jryWL2G/v3eh78vPoZzHbmIG4cRJlPW4MCXdlUq5VCu/EBdFYFyiHplx7rV0rQcpEvOCjSBXKRrmWxskjoJRzK45DacNLjtbFpR85U0r3iz6+xtMCo1wKUSn4Ck8IJorEKrQR28a8hqgUvhQJuuc2w/jp2Ea7HwzaMUKKOB8Facy4mAbxqgHLGFkVT8O5OKrg1ujRiedIUyO/iJAXOFRPeKDhXeqU5TJJVAdVKSHWV16/yiFjnbggZv6dkQlGJfqgsvtoIyDiaCwXiZGn4MO+y9KImyfHIReJjMvHkpvSeolzZvBqG2ohrr/OIt4OR9Zxsenwt6yrThFUwffXDajSRfEoPmgLKV3ULVmIYjkQ1SnJu9N2+bb+T6ETd5iOQ8fyhsImVUoFy3TavnJdqeTgjUkRmRkGZkKjV/Pyg/fngGXz4HVvYGDNFoYe3kTf6AiLmuMMbHqUVnNndn/q33Jf9240yRA17jZ7Z68Vr/aw90M3UBNwyWVkLKbax7Qf6XPp8+G7loCQROznypy65SHiOK7S2HsR2+8loyATXP6Nsmhf2y284c7rhCWEl3uvgDh+WsX2Fgm3Dx5J+xj9m31/vbieYoeoo7btKDMNInYHeIeQqrTkJVEQZY2v6diXRgFI9PqiohnBh8jngnRQ/Bfa5QYpN0KmIdfFMfFRpX7niFhZIkuIqCcKPnlSylBYDruKgL3Ek7oomtigQnvz3Pph2+6J46B9ezziLrkTJv6mKbfeCXY4svaQqiBQLtY+WHhimXL2tLoNX9joIoGzCpMhlisvC8KdxM9OyLqsPpkKTNbeuvKd2lXdbPW6tCqHkLZF0c7bzJ/Ry/J5ffRssodBqHFB9zbYsOFR7m8Ms+vCXhbMXMcjN46in/UetAyR5VifsJr3T6SymArRm0j9VB7z8nxXoKRXLmflsw8nxYXCxqkSeoeQJaLpYvs6I+w6mGj5pOPppaXyb5U2i3LeIALikkeFeASHlMOjRUQf7oolqJZoGGt49oxNMMKFtztiNrVOexwf4x8m77//LET1RbqfLaJUNueGUmRKaKCcwTmuxTyoLyK+qeei7e/lXIgT9Sn3xmOr7yhIa3XrIa5VqazROviLQNZ+8xgTxWrlFpbxFF5RUIVMhqiK+l3PtfgJcqKkLZy0I+pxy5t+Kkh2otSp5Wf8c54AdUI0FWmCdAxqkE6Htvr2lZGcUjBnXLEQzcD2rfTMsmqVrl6Fbgpatcl6unhkm4HWCFnfOLStKiMvBSnVqXHK9+o44roxmihAw/clLT+V/k/19/p3VH+3ahAT1AyPFToSl0nbFbn9znVHNUueg85S7s3psKkSJsAFoTlVlM8qJ8X2llUwSqmCvclDZybBSikTif2Ajd0mri/EcuSZtgZqo4yNZ5C455XvWyInizM25BX3Pk94VPK5CFHRKJigy0khxnLYUp0RdBnC2BQGr/7BvwhknS5YrXVBDLMOT26BpOOpKFqW3corc7tRJ2USBGAznNmQGRXrJi5cqRF1fH2+zVXRpSg2emSY9rGK0NM6isSnHqpid/pMVXQt/l53b0xnDGwXxsZysmYbgM1bB1i8dCe6uzO6R9v09vXS6prBLsv2pZ82beliPDN0l+qsI3bpeDxeKWSiPk2EuO29x8bpxnrqxrLKZYeX+DY/BvxblIQ6S1Dxu5kQwekkX5MYixCzEOhi3Dh4xOfbTLJO476zOlbvQplm+KOiHhSRYFgrdoCS1FJyNezAoIT+lI5ty0LKUfd7iOX2/bf7yRhHkoJOO5abSOpL21lok1gcYR2BvS0gNj8vuWBmaa6J2vpjW2KfC1nRK7DDkXUV+VVVApmVZNwD9r86Km5KSfdTbjzdFIEjSBcFity08CeYRFFLheivokjmP6UEoxUXvQvTCG6FOgpTKeLWQDsE+6SeH8WJFuU2qcTfRdoFXX1xAUYPgIo3htPhdkvOWEPoajfZOCrM3Hkp83qHAdg8ArJ+gJk79TCweBbZEMxZsQC19GnMyMcZy5p2QWpBtXHRbUIjiwbM2twlxgRyU94wZV23b38ci4gsjMmTvNyx/x7pxc2WkZX2TNk9EmxaTCCchyfk5GREu4eiFU4USZgLFO3E3dNlfSUr5QeJiZy9zjgLxCv68laGK4xLUlFHAqL85k84V8/5GsAY7caiDWTBE6vQI/fBQIhADdrIlGjkxqVNJfAPnXC1dyKUJJm/r88Tik4INM0fYqdWQdgGxjFsMdGWteHYNovRFZ+DMIZJnfX8Q1QP+fUqTuLICyemQJxX12ZX1hOvXBHOWoxllI3odDmLYuRoZ3h88tv/Akyko/QzagfMTWp61TwP1CL0Tu8r6MzRFA0Ouvay6pSM6O2gS38nf3eVE7T1xve4+qToX91JX19Xf7msPXbIMKYUWW4Dh7qGBnnFS1/CwllzAOjRTZq5oj2as3lDP+32IL3d+9A7ezGD3RndeoSGsRvC1z8VNZBv30SczVTVFJO9Y7LF36lNU+G8qu9ynzutuTB/dl79+6aisplKufCaGpWQ5TTFbRexebONsoa+9HIQZlFJ8Hipa5ddplNvv1V7RK54IlWBJ2SpNBakXuOusE9SqW3iFLXV96Rt9VKDrS/3KSJ8O5Wp5CkPz4oN+PHeJkpZDK2U0MAaRhu4zwoaWkKSMJ1NTfL7i0DWhQktLcw6cToFEQl+1hUkZaSA4NLAjEo9NdyDUvXiSKpqqEO2jx/qAiyqnHOq6rEnTsTLcu6Jlq1OzHShtqIzQGOa0KCL15z4Hm67dyMAPWaQJsP0zu6iO8+ZMa+PJYe+jC1txexWF2PZuDW+iC60ayowmTpkMmL0P4Wpqoj+Z+8ofHtMz3Yynsa6J6+vjOjAieYU11Fhzxkf0Wese6NIxyhNW4H1tEr/ppfSRVyeImrXykpfy202yl45lnC0HeExRqw62x3JnjJWxjg1iKnWJ1K8jErfS1ChKKXIMpf2VAnGJR3zLFTmPvscNA1t3VkD0TO5zYuiJGQx9MSggf2bZTrq+5VCKT0hw/O41SBHH300s2bNAmDXXXfl2GOP5aMf/ShZlnHEEUfwtre9bcp1ddoodUigjrsMn40UkbZy6gStAuLuiAhUZ5VMXVtTw2An6KS/rC7Q6mbstEkLkrVS6CTJefnZjqoIrSx3pQxCi7YodHcPA7nhvtUPAvDqQ57KL39xDa2B7ex7+N5sas9ia6Zpjyl6VQ55H6IFK2d21pPXjclj5VzrxqKuTFWNEudzKl5EjxXq2uVpa3qCy0Svemwcs0o+V/dHWaT3elDPLXpXCMHpekvqMeUqUS6xkeQSuEy8fSfxa1Y+cMW7VpZOwfGuteV+Bg5Z/PO2wnSpdppzpYgn0jiBIOwf8WVcUIxK97KlIWVXQYfrEzc/r092J+WU9qoSokqzICW4FKpak4ugsyz85jVTOqQaAH/0kld/xSo7S4SPC1mPjY0BcOGFF4Z7r3jFKzjnnHNYtmwZb37zm7nzzjvZb7/9HnPdBaoPiW4pjcarxYGVZ3CIMBg5pLxopuZq9r8FIt7iXl6Qnb0ElFIFb4rHcxYhQEZGbnJE2zrGWuOM9Q/xvL95FgCvecPLeM2bj2Pd6tU8et8NzJy3O+3RIRq9fbTbQtN009ajlXGb7L2+zFRBHLszGZJ/PESg0/ueuHWQjguFRVtGrPF+dS3U11uOwq2WSiUsHTaB/Yaa3CNHuUanaUm1ilHAqb9xWnul/eLbZ0tFBJf2pp5p8zEXgegZf9/VFpBskWB6xJoSsjq9ut+D9VBMNqaEEIhXXidpUFfM+CjxOcC7Xour2yNmy3UL0cukWduax4Ws7777bkZGRnjjG99Iu93mxBNPZHx8nOXLlwNwxBFH8Lvf/W5KyLrA6ZS5wwSRelAqcb4PSetB8AdQltQFKjFSlBBJRdeolTWA1XD05Wcm088qfOhz7Kd/vrCwnP+qJFxzmZrrJBZYSn8zZQpaunjYSObG0W/YKAVoIxgXBpajmWEajDS28Z3T3syLDl8MgFkCotssWrIHPU/q5qA9n8emgW08snmczXOXMrcF0jZIsxmQabntZZgKh1wuC4ScSuXnyhsmU/HwX/se/9ch8lKGwTqEOWHbVESQdaoc7TZlTIbl1XRipbuCt09sg+VMi5na6vrnngycYso1pnXGvqQn8ChnBxM0LskTbdKQ8rC2lMG7Olgu09kkknd5JCwJAus0doLVN3iEG+ogwfdiOW0/JuW58SoKNw3BPa8O0/pw9DR3TYpPfJkUUUsYQEcUkr6mYIyhoaNUEcc6/o2EyY2Jt1P7LIfKYgc7fznBTuXY/k7OPkoeh7Lunnvu4dZbb+XVr341q1at4oQTTmD27NlcdtllAFx66aWsWbOGd7zjHY+16mmYhmmYhmmogcfFWe++++7stttuKKXYfffdmTVrFv39/eH37du3M3v27CnV9d83bQifLSec+oK6+4nVFyx1sx5iJbKqskpuitxboKUoHmmti8cKidhz66QsUndITlPSBz9/5Wx+cddgqUz1XMGJz1zUpXen7/FSRMzqJSJkYlxUV+L7iUZpx1F73bTYEFxvJEnTNDYWzeTkf34Je7GF9eNdXPlfv+aE170EnRn6Fs1ilz7N/Lmz+ae/+xu65u3MQ9v25qH2PAyKLpWDargkNjUSwSTjVriXeCSkGRRTDtmXr9M7Z2XOScH+SzS3ryvpHXUxR0aFQ07cLMM4Z3Zt+TS+Zf14ji3jDyrIM0Xm8mB4MTc1FttX+Pfn6EytIBfZAAAgAElEQVSRHryiwnt9Gdh9nmJVf1EyLOtFQQrjYEy7cCKMvWk5/cwlY1ZKkedxrRbH1broTSTVRBtRie/znHL5tsCCWbBle1VyEFMIip0URKj10BAFOhfE+V2LH5uS1tBKDFLYq96l1kc+x7QNto6ok8/D72VJy9sL/Of6fOL1aszKfDl4XMj60ksv5d577+W0005jw4YNjIyMMGPGDB566CGWLVvGb3/728dkYEwXvdeTiVEuFzVkqhjSHNNclo1pRferzBkgtIkx+5lTo+iyyImbYKoLtg45+O+d8lMAdhWZ+GydPldltoy9n2OjEm2LLPESOw4+K5eMlwYvQyufVMipfFTbiY3ufEMXsdnwfc1dboqswehom1Pf/zYGHlnNLdtG2NKwY33fnQ8zNnc2PDxAVzujL7sX82gL0xRWHvx8Gvu+klx10UTTSuZgMhXHVH9Pj+xK0wXUIek68AEM9nMaKBwjPwt2kAnqtbm9nabRqdXKhKPpo7US+T09EBaToXSeqFCqhz4UdKDKJxuqaoPLyMkjas/klOusmxeLQCOSyTKvlAjKCZcKNwMxFURUqwbsgJDK7y+oUmofKHajpMGo1GO9N6KKSgsY1zxN26phxM95etKNK6MV2qkN/VmVmY+uNHb/Ffatn/+kUblx5X1/UVYtq9xJOUnUY1h7RqF0VqtuqYPHhayPOeYYTjnlFI4//niUUpxxxhlorTn55JPJ85wjjjiCAw88cEp1+cUUkzOJi1TsYADxB+jWIL7yosiVO9DAT4rKSL3kp4I4JlpYkXCEHUm6yqL+OJYXkWBRt64+ERmV++EJjiRBAOl4eSOPSDQCASgyxCH+OrA6YEV3I2P2LjNY/4ebWJ710p/NZOdsOwCHHXIQazZvIR9pIM2Mp+81l6tuXcXKJ+/Og1f+hNcf9I8MjozRSl5RQQgJonis2rZYl9SM89QIg3aZHVQYtxRlJ/MBhQAp1wA78C5ToMf8KaKulC9BagAUTDgjVKnIzYc+SSQelvu3WlkJ76vX97v89WhljXFp+lBb3qdQqBshG2jl22BCbJ4fP1+mSJgerxE2EkYgMEVF3bRK+1MOLCqV95+9SScQesd0GZWDKBoujUSmNFqZGkIjpMKUwmDE2RhUbGcMaLOHOhifrdAFtMR++M9WD2314SnDGfFCXR87weNC1l1dXZx11lmV+xdffPHjqa4GYpYsgFQ+VMFdrsShlpCdLSvhOCDPuaIMamLv0VCHXZRV17hqudowqYo6w4tGSlyWL0CpNp6V8AYhPwZAkjO6tLlDx2OIamEzlYIVwkKGINKPjI2xdvOjrFi0MwsRNg2tY/GcuQAcuv+TOGBgJx5eN0Dv7C6W9bRYceRRtLsVmzdvZuPWfrLuXuvGZEylXZ2McAXxWaK1fyKYKmIwfjzEirHxMHuf6U6Fkj6fR0dCknpSCMFo6Jtb5RZL3gEmd4yFQ6ITtLsiRosUXP/StV5YUwrvAQb+cw2k0knpzbXvSMGu0+L7pwJpOR0QXnUUTKL6quO+q+XDJ1t3KbNkrMPYABXPGyllDYNKhfjFVDWKS5GM22+emIf22CcsURDlnqvvRxXKezF+9pJTYVzLIbcOdni4ufcDtUEdTozxekE/AEHsEoonKpfVD3aDiUrEZR09RqIOOiLxMnguLm6IvDDUPllLWYRO/xb7lkYe+nkQBGMd/gMx8RumEXxb7f3U/Sp5l+9U2Eh5It4libFKXESmFK3WOA3dIKPJjK4+uhYuZfU9t7BgToP1jlW+4aGtrBvZzIyGpj04wvVrNnL84oXsvGhXZs8/kBaaZlcTabfCWBj8IaOdkVj47BLZ+9bZQ3mLXGfot9aFZ2v13k4aswUNLRVVEKpdPBnGbsUSgc2ryKyQR9xV7Tdx3fwnnbRHStkvNmI0cbXzz9jvVmQujFFylJSvow6ZKgnLO1kPk0CCVPJknD1DUE65qxz7WqdGqSDumvNIPVcfzuVMVBxedZmCr7FQtTNnWI8t79PtmA+nLstwEZIizu7lglaCwO6omokn6ARlj/ZRw0VpuaxnDogVP2eFEXH95jFBOsclzUoF/gKQdRHKusmiuJiaxuLnuGkcx+z1fAmrYfVDGpQN4pjymJpqyYrqZgLorC+0umZr8UhyNIQTOIs+nv695WO9ovSR1FEaF9sP9wyCoguUoqVyZiyex/qtG+lvjaOHDNJry43tupD1t25k09AAze0Zyxct5v6eOdz94MPMb25n7wNy0EJbN+gRQ7uETDySKwejFOwSUtzUncYPmFQNkhmFuMgG70IXum5ZKmcHya2OsvS6MuJti7GnidTYMDwU5qb2t6qevaz7FfFysF2zWiAXj0DSpE01jIWigHg6rcaAMKXI4WeltZXKHgVk69vtDJNKqeTE++Q9pXfaiiToNpT/7PtaODAhqjNSyIwnkK5/4utyz/pgErEINNOCOGbLPutzyLjytMPYASga0cgnGqXB5MSDl12f7XpOxyWdyzgOE3PYfmzTz9VnGx046x0ebp4u5E4cahwgxzmLkCJqjxC0OP2isle6AXWG47zcO5IYfn9pioaTTro5ZWIou5aEg3P3lbH3fa4AH5YauE6tXPvyhHNLEI1RoXzmNoZ27TGmeBxWaL/PNeA4DyUEXR0mHjqQZRntrA1dXXT3ZbzxTW9g4aKdaTRnMGfRQsYHhgB48KGH+XP/RgYHtjNj/nxufnAdX7j0pyw56Gk8+OgmFu00k6GxYboza6jMDDQzyPO27Y84btnF/tpoZBfOnHD85Tmo4269lFC3ZjzkyXowqog4MrFzo8P41nCpqqi2aSgNyckfkxETSoyFzRmjCsQqtt0FQRTqcX91ugY995aoFFLuNSFKZeJTBKe7d8hGiw4HC6dt1pVnIr+jlEJlOqwto6x06C+I2WwsR6vItF3/GUKGjyBO9yUupF3QifTif1MqIlXtK3ZI3f+uNTS0oplBU7kIQ2PVUIJVRZk8Mg9CAyMZSAN/oLCLPbRGabEHAxsjtI2Qi80R4kPXjTHkeV5YKynUrefq+o7ctEX6U1Mv7XBkHQZxkk0xEaSGBYgLrOwWE0Qhx4eUjTxef+WXoA9DbaDIhHBppci0tqIX6dCDJiNTjRDEkfYvfVdBv+nrRNHQzUQgKIZQ6xKB6czZJ4Yzx523TRuV2bwJDbrYxlbO+urnkO4Beltttm7cyvCmrXSP2OjUZ+21kn2as3nl3s9g2/qHyIfHaJiFvOPUc1n+jIO5/PLvsmrVrQyP9yOmbU+CJqORqZCLvByOX+YwvbtYOv++XJ0OfCIoqF4mKddpo/l2lb+Xy/t8EuV3d2pjWbUWUZr97In+RG17LPvCsSz4XDH1DIfT56JCOg+Mc2vFE1ubuKiU8sM+X0pGpbQLpEk3BKAcc6G1pqE0TW0vsLlKsoZKLmhof6CtRd42W4SgiJdlhKR0wICK5ztqBSoDlaEy+92IJjeKXKBthFYO423FeNt+buWGVm4Yb+e0cuPyXitE6cKF1ojKXPpVHVRJU5sfi3tsEqp0bcVDkCda5zscWQOVDQ2ETvjPqSGqKkpWOWCPpKp1++9xEdsjvQicR8r0GLF6vBTNo2IUpf8doI1gyDEuA3cuZYpa7GuW/BbqU61aN5/JkFUKSpTlFAInY+hpNMlF2DY+TruvzXe+fwHSHGGnuTO5/a4/MnN8kC333c1hB+0PwJga5KmLFnDe985lw/pVzMphy32PMra1n3O++A1uuvs2zv7wR9jQv57hsUFo2jy/ZlwSf/b6XCx+zrq6ugquj3XqnTKRi89TuqKUU8IXRNWZST4Xoa6ddWWgysWmCD22074r9sUk93KMacd7StF25436p1MTeJHIdWpb/ByYUIuBw5rOlEryP6dpYiUpW5TY/OcKJHYhe+nA7RbapYvqTJ+EyX42pasoMVp/62Tfe65ax3f78c1FLCJGAuJt5Yax3DBuYNwIbVG0cuMQt+Wi47w5u5GOWTT92opjbNx6jeuzLmYgRcJVG5vtV5ZZNZD9axM6aV3vM+5hh+us00HP89SBXxIR2LszOeOAVs4/uXRcV2mLZi7/cx1yF9qFfMiZQzCZN1y4qvKSfkk76g6CTs5ZA6sG8IYr5TwSNJlF5h1EJi8igvUayDAYr74IRsdYPgWpMSBa8MZYQWPDWcdHR6DZoHdeH9/55EfYto9mp7GcfMEC9uztZcnfHcro6o0sOnxvAA6eKay660peNtvwzEP24QcPbGHzqGKnpmJGeyaPPtpPe2Y3p777ZD5x1megqZjRmEtXs8FYBllbLNFwQ1Seg3SjpYiyrKMOm1dHVCZQ8XaxvXbrw6sgXRFbfxqgUEQK6V/PBZYZAKv7zJxKRwUPj1RNUeiTW5s6iPMS5tRLRSKCzpQV7X1MsoQX2s0ZnIMc9xi1Lsl4pv1UwW9fqYZdBziPltBXqwaUvE04bcgZOoHgfBURUvFdaRv8Z5NHz540FargD4t17ngqnqujyJz+2SFGFwYf6nV6njBHUMjmam97nZebV4ULoiuHi9td28i0PTlH+TzfNYFbigLDJlIc7ypTUSRsQTXv1VgVNx1HxC1LScH4AJ3OHvjL4Kx9p7OsUbgX/zo/4mSmJI+IejIRpMrdpnppwHri2s2uVeAGIBphouCqKldcvBo70ln4XhbvLRXViUpDkyO03Q4xybNltUCdGqTM1YkIubTJGoouNKbRhckztuuc7t7ZfP3Tb2fGXktYOtCiOT7MPy9u0Bhaxz3XXM9Nd9zG4qU2N8iSxjB/vu8h3vraY3nFIXPYctcd7DFziDndcxlvbWfwoXW88KmHMrevm5Pf/Q5uuuV6tqthdHcGLYMoRaORBRG1PBflua/jvP3n6jFK9eBVVzYgJlEz6eK6moraovCXYiSqofOaK6usQp2G4C7odZ/2i8dUYiMrRKKnhVJh4XnTYEN5vbsPgHLIJPF6UMrrl91YOoISOF+HPLLkoAh7hq8dvMAkuX7mRjC5vdotYz8bU0CGSoPOVDg+zNbrkHhu+9+2WpngflfmqMWo8N2nQPXjnOc5eVsKiQK9RJUbO3QtgbbjmI1YRqssFec+sFcUeY710Czn9S6kViaMW0V6L8yxhDHGcf/WLmXH1e9NO27Fta4zLLfdsFcn2OGctYfyQFQ3kt+8wbGvYHn1SN1TdFIfoRqYaqJ8cPulkL8xvrOwiU0UOS0SL0be2TaXE0UVVSV1Om3/vU6fWodvunWGahuGtdDIM4bZjpoxl09/8T0sXb4nmx+6jVau2Xfzvdw1uJjFK5cwp9HFnvkCWLEMgF/94Bq27LI3Bxy+nDNOPZ99d13EAfvtyrlX3ML8p+1L/+qtbG3ewfOOehE/v+4avvu1L7Nx0yO88fg3MTo+guguUJpM+SAn3y8rTlrO2LtuJXMXBjjta9Fjpg6iGskHrpTXxtSgTq1WpxJIvVNiPFf04vD98e93M12oy7j7vvG+/3EcOrUx/i0gzGSNiMSTkLRbk4GpcO5nJrFQKuWTYPkIrOirGBC+ckjdcY3tsrtjMubBj700bQVvd0UgJOFxRWFNi3Ez73ygxd0rnprlDklwXhupNCwlz4pGKQjFj4nGRA8Q/GoTdFYSKwJ+KUJWqNTLM14ixErh4Z1e6kkhEr9OwWw7nLPupCdM9ZLeK0JjLb/abYQUgaZUrhNiK0Odrru2jUIULZONVa4jcP81ffFtTLnjYl8jku48HpMjHbuxFMOmTU93F/3jAzw6tIFf3HgRG7cOkg320zWac3jfAO845Z3stHkL0t3Hmt8+wLIXPp0Zzhvktltv4P0nvJTb//gLHtk4wJnvfy3P3HsfvvYfr+GIXRcxe34vq9c/xKqbbufI5z6ffZ68B1/62Ec548OnsGjJbEZb21AaGo1G0DFOBFFCquGiZXKewtde7xUxNc48bUvxRlxnZcRdR2Dr/pbnT8REZCrRS0UZx2F3gFSmK2vXfO4XZaQqedeAyWOi/jy3XGucB8vSWynFGd1LhCHL4lrWWjmxXqNKaMWT3soK0NrmzlYajfurtUW6bh2TSiO+z17aSF6ToWgINI112dO59QppuKsp0BR/EEC8tI7nOhY8sBSBkUjLd1zGzvoqJb1/nYE3agRyvK0gmZWO87XDkXUKEyGjiqFJGXIzhtI5OrNJ9OsQQp3Vvvx7WZVSQJwp1zMJwpwM+Zef7YSUfdm6swLr2lt+1hir986lzR9uvY7NIw/zp3vu5inPXMkjD9zPp4/+O/7+6Qfzi/O/wz3r13D7r29g1733oLmkl6f32GO9BhotnrTIcMklN/PCow6lV23hvG98ngN372PTVb9k5kxha3fOtgce5t5rbqB78TyWLd2Z635xFZ/81MfJZYyBoa2ByE1IaEqpbaMxro1IjnfDLMxR6YLI2NUiKo/ZPDf7OCGujVT1VORo6xiFzv139VEmBJO3ZULkUeqlbUP8a3LItbGqArApektrz/eN0DaNcfp6gzv/UHnxfhJVJOBPCgiqDRPVtU5DQpVeOyeDBFmK2MRZIoLJVDG6VJmgUtDOcKpUXCV+vDrtRS+Zl1Wn5THvhLy9+siIJPUW3XSl9Ft8r6LT6VTwF6EGKeZ+sFTWiXG6Xi1gkaYC1bTU3i2oTPsMbV68NvjcG0BBLeHfnRqTYvIgSK249hlTEFcJbUy5rqJuy2+OWIcUDt81Jnfva9BoNDAmLyDoqJO2wqNGgsxtowU1SsZBmoDGmBGM6qF/uJ+unWfysysvZVYf3H/DPSzv7WNJazsnHbE/v7zyIgYYZ05XgzV3P8SSPfdgt2c8hRl6Z2bOsG1/2ZEv4fd3rKKtRznmVS/g/DM/x6JlT+J7Z32O7qVzOeUZT+Fdv7mVhzY/zNDwBnYbG2Gf/fdmYM0Qt17/e/58x108/6iX8Xd/+3Law4LOFV2Nhl202noFhEx9Ko5tyj/Y8HuLuOO42vXSMMVFLQrayikkCxuoEf2utZV8GqTcqSewbTcn7vGS6sJb3WIb7PrJtLbqBHs3Ca3WwXMozGfuPGSMQTUyJwWoGpazyDEn1Vji5PSoRC2GdbnzBEOrKuetldcnYHL7YAPHQScIzK678FbH/Xli6iNwFWIstx2RDEheI72ioipEK7Ik8KXh1DjK1ZOl/fZ7TVtfaO10IEqRHADg30Dog1I6uMb5pRTVVRoluTXqJYg21BP2vpPQTElNo4quuqGtyVIM9rcER4RnK7YX6zg+FcLsS+9QKHPMaYNMzeSnz3k/USu6FI1tHuziy9A6o5MuKNSXUNMo3unK5d9R9lio466yLKsYm4pt04GTSd8XdafatVswLthHSU4mAu02jdZsuqUBTRgabtG9E9xy301c9uvvcPtNvya7f4jtg+t4ZqZ5yub7eWTDney6YBF7zm6wYr8Xs+vTD2Cv3fbigBe/gNUPr+ORrVsAeO2rjuBbF9/K3x57FNf/7A9c8+f1vOftx3LtnWN89qxT2H7/9Sxccz/Ld9uFed1zaT3wAA/f8Qh3bVzLcw98KkubTS4865N84bzP0Devm/7RTehuRSu3EZReDJzK2oDoaVD4XcULiuqEcrCSFo3OfeRidKNTyrfDH4Dsr1TnnIf5qnDN7q9WKiaDqjFYgfMwqmHHvKGtuG47j0Wnzd2JE/R/vZ+0dxmre97+rfqBp9y2L5uu0+IxZsn+8e8X56PkWehS+zzHn/Y3lQbiWAl5Lk5tI0jbMm7eyNg2uTWIiiR+48oxNgDFeAWti3sOYuCR0l5VogLjmOfG1Q/BEYWUIEDqCmnrprSelFM/meRvvDrBDkfWqT66TI1Sy3IKIlJ4Tok4B35dRYzKGntSgbmTTjHdjLV1OSjoxxN9bOrhAd5/2kZSKS0hT29apzEOATvxzpeP5WK7hcSIqQSTCcPdLdaOb2VwdDMPjm/gqqt+xOo/34i5634WLV+B5A9w0lP3ZPfmRlY2MpbMXUDPThtZsngP+sfXs6BrBgv/9nAGzDB77bQTb/t/JwBw02U/pzmzwbP2PYxvXvwDTv33t/Lo5kf41zP+lQeu+wnn/eQWfvTFj3DI7AabHvwTA2Y741mDOTS57PIf8o9vfj19S+dz5RU/4ptf+yJr195HS0Yha2FMTpZlbsOZWiLn+6uURbANUWgjaOMyEdYgM4swhUaWWdcsrzt0wru/wtIozb8ETOLblPjUTqAEDiK+Xx9CASEoZTn6gj49j5szRXzh57yKwG0/cd4THZtTA14fHZ8t44SU0bG/V5FGed+k9+vUdCISjIG+7ZWWiUVeyqgC4qqqC+oZqly7+AYFop3HVEPZSzmijQmXzQ/u0yl7XOOJQtImktiHxDc8HqLrNflxr5ajUm1dKlzePgAaG+HqWXJdc68KOxxZp1xO1O14xbsPHKhCHRcc0JrjXgKKVsZeuk0IRXcI1H/vdEn58pn7/LPFVuGFpIB4J9hVFaKikhzODmlL2h4IebhFC0Pt7fR2DzM2Y5Qrbr6aVTddw9V3Xst+8+YxJP0cKi3esmwhs/sfYNHYMGp2N0O06e1ewZpNf+JXP72CWctWMq9vIYM33sa81Q9y/D88D4Dzr/wN73rtS/jiZ77B3gccRN/cJu9//zfZf1GTs8/4AR8/7V+4+srLufmaq7j206fytMXzMP2bUYMjjJqcU8/6BE89/DAWLdyZW/5wLT/83rc59/NnM3v+LAa3bbJ97nAyuh276lyTXlDZ0CK5i6ZsV35TKg+It25TFY1CVbDqmrijC4QldcXy3HxuAnHxV6E9ab/Sd1BE9E8EeMTkWuY+F9dup/c9njaU9b0pq5RKQiKEQBD/Go0zOoqGGgSeHkYhYlVaTWWvbgUNY2iKIRNDl85sZGSWOWmiGLVctzct5x7HxqqcXH+0gMrDd62pkVC8ylURpZKIsHFZqSQ3TspwOWuMCm6OnWCHI+tyKGuRwy1ZlZUKxpC8ZqBVckWdY3JJ9SSW2jYlv2lLfmMddc+lZ0GKsWHqKnUhM+GvSbK96YIeNOmnOH2ue39DMkxD29SmCkaGtjN/zhxuvvtObrjpN1z3259y6x+uZUZPxtx2i/b9q3hls4c3PWMRza5BxtotZu00h3GznbFZXYz2w9z5KxjL5rP0KfuwaHiEA572JD738dN5+98fB8ChR7+c7sY6Vm8d4p3/+e+cdfa5vPcNx/CBE8/g2PefSmPWHL5x0c/56rfP4o8//RYbbn+Q4w9aRntxH11tw5b1g/z+iqtZv2ELz3zBc+jftIY/XvNbPn3Wx7lv1V00Z3UzOj5iOUxyxNjTStJ80XkeZeYyt1XkgnN8zmrt1E7+8uPrc3V4hG1/8pxzMd1BxUc6TYxlRblQr80n4ebUSIgQlNKVK9yBAhLVIRQ50XT+y3/L3KV/SYHed1jSxa1SJUpltWHxd1NruJ1o/xTar4T0XzHHC5Hj913yyFxjddyZJV5ZZq8wp9qGqvugmUCInaudpqo6q2ecvNdZHJesdECB8u3Bc8qSfE7HUSGShTJFCSNByj7CmKIapawKKkN22mmnndb55/99eHDddkhCt1QSnhqS4LgELuIwulLiTgr2iM8a4TJU0FV2ujxRyJTu+Luvxx/RFHIo4MrVvGfZTr08vHnE5c4VMhe6J0qB0hgjNFUGqoWItjpUlaPI8FlGtInBP40sI2+1kEbTpjpVGUNDQ7QXz+S2dbdy7S1XsnrLfay7+1rmL1mE7s8Y2TrGm2f38JRdRnjqrgvZvmU9aGHm3EUw+iijvXP56s1bWNq3gHvWrmXn5x1D1gX7zc756Ld+wFf+9VX86ue/Zq8jX8MBw+v5yAc/xete/xquvOxHMPNJvOBVBzB7pydxyOJxTn3fFzj19Hdx7+9v4/Kf3MRnz3kna264hs1rVrHn3nswMNiGhjA8MMTgo+Ms3HcJ3ePC+Kr7+cUvfsa9Dz/AYS98LuOjY8zVDcRkqN5uBrdtpae7ibSFhtZIu00jE5fDQtDKIs9mE7IsXjpMLoU1sWhWk43b2slmSLmmzp4qQeTWVXWJ/WxFYv8+7YKYQjBaFgOsxLmiKXdqkKaaja8MVRWNMKdXsW0sRkX6dtj24kT11EBYD6HuxKGu7Poa96Cygp3El3VE1InkozK7l72XSVnVOKOpGB6PUZM2jEkgU3afO4Oo9h9d1Vo5blzZcRBHsELq4ZQTFmyaZbHvEWNIZTalbJQtToWWKRXykQSDpXgmzI6Ynz9EuUAiq67NTZq9pMImFj5KsJSmRVS4l3WYvx3PWSeKeD/AlTI+w51RaGP1WzpPyF3iEzrVy/pyTqFcCaZSzqoqEnlGhKbSmHaOanWRSQOVZeSq4bjrNkbZ5Oe5gNZNWiNterp6ydrjbNr4EDNmN9mw4UGu/tY55Lffzt6rt3PsznsxTxbRuvZOPvLiXXn7Tg+wdPcWc5u9jHZtpzlDY8YMbNsIqpdd5u/J2jXj3DKrh4UHPIvZC3Zmrmzn7o1Njl+5NzO3buHSy38LwCc/cSYHv/TvWTxHuOHOBzjp5BdzwWkXcvg+yzjjixfwt//vGMT0cP6Fl/KFy07n61/5Frc8OMgPz/soM++5l5kDaxmWBrP6ZvPwmrVsuGUVtz60mucc80oWLJrLQ7/6JZ/64Cl891eXoXaeyVBrEEYHefDOGxjauhHVHmZGJnQboSfrpqvRoJllNLOM7qyByYW8bQqXyRVidBCdy9xqqhsuX7Vrs7Q7KqqaGm44lHW0wxu56upI7xUJQfU9Kbea6lD9a5WKz6U5Nure5T5N2CZbd/2pSp3qLksE4b4BXBRjWlfM8yE0NOjMeqg0VfxrOebiOKQEyoOTUZ0XqOVZI9J3QT3aG+s9hswKe1fA+m875Cxi63WBpYjYLHxtca6GSmFcY9pIuLxboUjiYphEZkLZqCiFeewEU0LWt956K6997WsBWL16Nccffzyvec1r+OAHP9n/HdkAACAASURBVBjEmnPPPZdjjjmG4447jttuu20q1doB6jDBlYYKFHTbie+tlE3MdNZJld/7xELiM6md9wY23abfsDrromXatPNRm4YTnG47t+qTzLoJZt0NRtrDDI0MMXP5fH5/63WsGV7N1i3buOPaG1i6zx789KbrePrCcd70jBXo9X9i2c59zJBu2hm0WyM0hobp3z7IA2sHeXjTNkYNfP/iS8i6ZpHPmMH1G1fRNbuXn9/4O565fCd+fNOD7Lv3cgBW9Y/z/ANXcuPv72H+ot1Zd8ftPLBhM7f/8TdsbM/i5S95MV/+5lk85bnPZt2f13Hz7+/hjW86lt/87LdsGVjDR056PUva43TrUYayfgY2b2XxvAV88ydXctgRR/LkZx7Bb37+K+77w8185atfYPHuC5kxt4f3/sf7mTO/j/Ub1kJmaKucsdzQynPaxjDebjPWajnxuaQLdBDVHlVjTScuOtXFCYlVrMOzdQiuagysEgtKiLeufNmwlkIdgUk3utaxTL1qpfhM/K26f2w9hLEo11feY8EQW4hkdBy8l4YVycEMqaqwOuRpGyyh9f0qlHJlFbnjdnOJyLasjqiCHTRRnZm38CZlTdS5Q9r+CgPqrgJhqSWcZc477XNNEx1M6mf91a9+lR/96Ef09vYC8LGPfYyTTjqJww47jFNPPZWrr76aJUuWcP3113PJJZewbt06TjzxRL7//e9PVrVtdspNGC/fpZnYrH+iVoq0H2Wf6fQEcFsfWP/dBl6Dlb7Tn6LSSX9dvh/aKe5sRwMtl6LRQ46QSQsj3pMEWnmLcQWNtlXrbNk+yLzZLebNnMPmdWP09Wq2aUOX6baqk8YYm4a3M2fuDMZ6hVuvv5Mtf3qUtY+sYfD2+1n2jJX0bljItffcxjNG1/PihXMYHLiLmTMWMaoa5NkWZs+FrhyGdJvu3gYjupfrNgxw08UXsWLhbJa2hcZYixmjhnvW5zx74SYevKmLa/77Wj73rXMBeOnLX859N1/NJdfewle//TVOfNNbOfkzp/Duk07lOxd9g2+dfjo7z96NE/75lbz1De/hA595GwNrt3Hu5y7kgsu/wKfe/VGet+sKmnNXcMG999C/ZYgFY30MPryKqweH2TD4KG942/tYd+fv+OGXv84tt93Mv73lHfQsnM9Pf/pT9l+5ku2j2xgjo0kL1VBIO0erDFGKLq1CxJ1Fgn7Ti2XRkkNKvT6z7rT58vzWITgv8SldtHdIbqyOVFQQrz33Xs6/rZWKJx+FBeo+lu7Vrbs6EENAgLa9frMXbSFpm6NI78F7S+kkFNw4lYNHjO4h522RYrAUCWb+3EjAHxQNoFVuE1Xh1SmWsDaUN/Z6A3KVgOUGgk0oeFglY6BsdcF10LlfGrBHq/nDEpQnBrHt/gDtXMTmBhIJfVPa4RghBN2IuBQBShfGGee7HvoLhTZWpbd4Qs5k9rMUJuWsly9fzjnnnBO+33nnnRx66KEAPOc5z+G6667jxhtv5IgjjkApxZIlS8jznC1btkypAV6FYZ3zTSE9qAc/QQqc/olCQEN5cxSU+pNw0BNt3lokrkCLJtfQJQrJ4ykplqPuwhhoS5tGyzBLNa0Y1JWxZWw7PYuaXHjBefzoW99GyyjDWUZXDuPb+9mohpC+HoaHH+HqG67mx9/9Jl/70QWsvv8OZptRdlm5C7uvH+AtL17BSctHOGLfhWwdatC3825Ib87I2CZ6x8cYHxa2DWf0Sg/Zgh4u+81t/GlwJ4ZHZnDUscdBW9jaHmF5Pop55G72bO7El75+HmdfdCafOPNjABz6jJV87We38MXPncEnTvoAz37xUaz+45/4zkXf4Guf+TQPasNb3ncSp/zb+/ngWaey7oY/cNGFF/D1H1/IRZ/6CrOW7srRRx3CLy79Ph894mD+7oCV7DS4kS16K+RjHDw2yjUXnM2qRx9knyevYN+Zu3DWpz5Hoyvj+5d+i+Ne/Qp+9N2L6ck12ghsz8nyjAaKHq0LyZmUV2b6tZK3QVqRgzNZYZ3VGYA6rYmA8FTpO1jx2hd04rZ36KqoNiQ+H3XGTrRWxeyOE0FhSSrrT2yMFLBDHWetnPBhxBneNMHrQpOhfS4PV7DC9SsCqxnGzHHLPoFTOpYpcVAur6RHdj66DyW0VU5b5bTE0BJDG1W4lALEhrD7/RzVCla6znPv/uhJjU1VrDL3TG4Q55dtkn8ihrYxNvmTEdoG2iL2chx6G03bKNrOOKjF2rqy0lx26Yym0jRVNS4jSgd+/iLyr7s6waSc9ZFHHsnatWvjpCWUYObMmWzbto2hoSHmzp0byvj78+fPn6x6UO6YHUjEVk/tY8d8mdAXFd2fcH/rxVRLvYuU0NdWTK4SuXNv/SeU8f02CpTJQSlMq41qZJGiCrR1TrsJM1ua4a4MRkaZ2d3NtvkZ53z8dA7qm03fUxeht43QM2smG7cOM2f5PB7Y9hBjv7qDH44Ocf19N/CKkTksffq+HLP309i4/kF652gOXP8njn7xgYyOrmY0U/T09DCrZ5ytj6xmwbxZLJ43h43DA/Rlc6BlMDMVZmQ3fvXQ73jTfjPY7SVvRbauo7Wtn/7BTey2sYdlaitnf+n7nPaF93HBpz7PzPm7AHDG+z/Dez90Nl/64hfZ89kvYuX+Gf9x8je4+5F7GNqm+fc3vpoPvv5ETjzl33nol1fy09vWcs65n+SkN57As5/3Ao59xYt4/9s+wKWXf5GvnfExlqpdOPyw3bjnp3exIeunlc2jO2sx/KeNrNxnT2665TpuXP8QS3sXslob3vGeU1hy0L7cuPF+FnbNZI+lKyDL6O7qZuumAZoN0I1m2BB5npObPHgU2fRlzpNEt5LTy+vXSPleZz1uqj9wbnFCcqSj5ZryJHrOehQpl7jIIRRxiA57sIWWxCOihtuKXHLdPbe+Fa7PNcmoXGSmEovYNZZABD23ryepNzcuL0bNOJT1+aGf1Otd/d7xbQOrt9VSUlWVnrUMcyLNlE8YIe59Pwa2rLJMVKaBLPp6F/zhVcELxXLlsT9K6RDm7jBUyEsE+IS8tk/JczXxW27eoioqGnB1jWdbBwZSpqC8Xbt2Le985zu5+OKLec5znsM111wDwFVXXcV1113HihUrGBsb44QTbEDF0Ucfzde//vWpIetpmIZpmIZpmBQec26QlStX8oc//IHDDjuMa665hsMPP5zly5fzyU9+kn/5l39h/fr1GGOmjKh/dcu6kEawDFafZbldJWC88cRzT0biUT4lEHGhpoleWkQwyqlbvPGCmNO3osNWBntOm3fjEpswt6GhrenvzulrCWZgG39z+F78dtUW1GiLvNlk3rjm4Xwr24b7ecqcPVmlRlna7Gfs19/m4VVrac6Yz3euu4HnLz2IWxbMZMGee3P/Q6vo6V/L83efz7MWLWP7w7exy5Nnotf1k8/blfHGGGpwKzO7uhByhrcNk/XuRKO3i3xgC33zZtBqj9Cftxjq72E9Czn2W1dw4IIFfPR97+RD536Jf3jh8/n1nTdwz29+xYlvfD2Hr1zG2a97Dy9732tZuGqI86+4nLNuepQrjj6UO+co3njws/jYRefzkdM/ycdP+w/e+s538PFTPsJHzv84X3n3Gex94KEc/NKn854TPsRnf3I+3zzxw+yy73JefPQreOMbTuQTH/0IP/nZ1cxbsojnHrwXH/7op/j4Jz7CRV/+Hj1778nq+/u5fPQhmoM7MTy2jpEZi5jfyOhtaFbsvJiB8TH65i1g/sLFbNq6lUfXrWfjI4/yuXM+w5LdnoTp6qWnJUjeZiiDhXP72LR+IzP7+pDxMfZbNpc7NwyC8X7WUY2RqguUFDk2v76UUgUVSKrOMMbqrHVOgSMrp9/trJNUFc7VpoERd7izKRjdls3LWLM16t2jd0TxrMfQp6R/Wcl6p4K+uOaZpI/xgajPBQoujR4m0qmKWOOnT+w/pwe2jdSXi+qCqiHUGuCj8VBncS58mwu2hvLYm6rU4u1XSll1iA0t92OQ6MOVAkySDtXe7+7SjI7FWAuvg5/Ia2YiPXV3V/1vj9l1773vfS/nnHMOxx57LK1WiyOPPJL999+fQw45hGOPPZYTTzyRU089deoVSo4/Bw6KC0BERbFHC9qAyiXkW0hFj4CccecvOD1m2bKrxaAN8Z3Ssm1QgsKEv0jLhhNJK4Ypm5xMnMg4QzNvtM03Lz6fGXOatol9iubodvoWNrnmV1dw239fyUFLlvNf//VdehoD3Lrxfk7+/C+4ab+Dua5vBns+85msevIsBvMHeeHYo+z25yv4wL7dPG/BCNK4nj13a7B9/ThdfQtp5MP0jEGfnkmX9NLNHJqzFtPTGCUb3Uqzt4dtY708vG0OW/P9ef0lt/OOq//Atj+uYc3D/Zz4oTP59eVXgGkwYBqc/8NrOO+mtbzizG+z3+ln8u2bNnPWvQ9x5BmfBODu3Z/G01/+Gv79Kxfyhg+czVmn/wdPf937+OBZF3HGRRdxxsnncuBLXsbs+ZoPnvJJLrjoy3zs+H/jmf/yKnrmzuUbn/0Sl15zOd/5zHksf9IKMpPxuS9/g/O+eiGnvO1DvPTtx9H80z3s0gXH7bIvM1bdwev3PYR56+6nS8GoavCL2+7kgUc2cOsNf+RPt/yRNffdz+CWrcyd3cfZZ32YXRbNZfu2fsbGhml2aZr5KE89YG/6ZnczONyPdNt5aaqesKaCO12SjlQZv/tNwdVOKesbnRlrUA6itIoBF5MZLMGlCCj/DvZ95eAe7QKopGWZC4p69fL7Go1GIUeNR1BZ5pGxz4FS46poFBhV0F1jrC4aTdBL+6AgpQk66iJStFdZ55oi3ccLqa7XI1FJ58erJFzgjCdcnVJF1EHMz+OJlr0fVCukKidvkHSe4U4fFN8ZD3gu20W8rr2Qm4Tq1XEspqIG+d+EX928NmRfs1nWIknTyoZqKmVDvO1tFbgikVK6y8ewMrRuVI0oHSly0ZvEYANcNqph7rn9WmZv7OdNb/lXzrvk2+RDI/TnuT2xeXAzja5hVvbOoTFvDtv7h9gwtplrH3yQF8x5EmYRjN57L8cctg9dI+P061G6zWbmz12KGjNsawq9vX1s7B+gR8ZpzpmH9MxhYGCEAd1goNHDlnY34+PjrMlbzNlzJbc+8AhXXfID9lq0G3d94VL2/YencM1Pfs/z3v6vPLO7jyv+68dsHxtnl91XMNqC/Y/6G37/29+wYpc9OPCp+3HuOZ9l9e9u401nv487r7iWz537ed70+n/iHe9+L/991WUc9Zp/4uz//BBvP/md3PXHO7j1upt4/gmv5of/+Une9PXP8OMzP8u+e+/OTvuu4DdfvpC3fOlMLjn7C8zPGix6ykquvez7nPTBD/CVkz/IS055Jz/48Y+Z2ehCL9uDO27+A095xnP45a9v5TkrV7Lr8l35xnnnc8TRR/P7315LT08PT9ptOVf/98/4yrln8+vf38JonvGPJ7yJFYvmMW56OPYf/p65C+Zz2ofez67zduKAXeZz2yMDdOkuO5OOIzOqJHWVmEittUXkCYuntD3VJ2QnMoJuZKh26vvrIiBL663A0Zfu2zbUGTmTyEk0y+c3WNvfLqzLtFy1zsiBZ0oq20Ml+uJYNq8EAqVQxxWGE9dLvgEqua0eJ2ddB2Hbqk7H2k0AJc66jghO5CGWSk6+SG8XgbP2MFG7wjslCizeCAnQaNY/t8OR9a9vWROSIWnR1YEUHYyQUBzU8oKqMzCm99IBNMZglI1WBBCVWeuxCwTwE9MIhMSXE3LdoLulyGZ38bLnHsLzj3kh3zzz87z+tHexMJuJygzj4+OsfvhRhhHWNfpZ0T/Ema99HXf87hJe9LQDINuGGtzOrHYfG2Z1szBbwP/H3nuGSXKVd9+/U6lzmBx3Nmlz0CpngoQACTDCSEKAhREYP+YhGLAJBkywANtgY2yLYGPAIJBBJIkgwCBAQlrFXWlXG7RxNkzO07krnedDVXVX9/SsxIf3kt/r8tHVmtmeqlOnTp36n/v+3yln25QTbZxeLJDLdKAqMWZFCbOiU2lvw6maLE7P0tHZxVTFxLZdBgZ6KBaLRKNxhOPyzx/4OIWxBSK5YTZs2sYvf/ck8a4EiWyKiYlJzhnaRKkwy+Y1q7j5zW/kk//1Ffp61tPd3819D+3i7e98D+/4g5fzorfewo3X38BffOxD/O0H/4Yvfv6zvOqFN/KdO77GbV/4HB/99N+xY/sO4gmdn//ql3zk7z/JO9/6Nt56wy24mSh3fP7f+OvPfIL3/Nlb+eznvsCPvvcDqgps3rKOn939Sz76kffz1g9+hA98+L387Ht3E4lE6Fg9xNHHnqJ/w2pGh48wfOw4V77sZey6fzeV4gKdqST2YpGe9h7aN65g49k7+NF/3kVHWz9dK9pZTKQ4euAxzurv5dSJEXLzC5x65AEOj5cbaC7XT1EafOe6XvRr+MWViidVN6+zZhBEVTzj3RKwbpHTJlw30pVN1EVwLRWP9lsKGANtOiMLdss13dq4Xv+9nli39TF14UfWacbQNWoG9pDLa+DaF1xaUVtDSc07xgcmBfGswFrKxinzp416Th4vkKxuYHQInBRqkr/bSAct6ZAmGkgufc7hFowrTNPEI1A1n1nwq2ts9U2r1Tn6MjTIcw7WD+w51fBvb/E1+z+HOOYWO31jTunWXFHzcQ0vpi9peRtG43mu0hj6buGCJbEsh/b2NB/68DtZiBR58D++x+fu/Ao/+d2vOTk5ytuvejnRVIxST5p22+DUwiRD8QiFVIKYbWPNL9Iei6Jn2xmplFEjcaLpKKOj4zx/9Vr2Lcxjo1KYnqO/s4fZiTmet+0c1M4k9931cyKDPQhXkh4Y4O/f+X5eduN13PHRvyNpOfT0dXM0P8f8Qp6IHiWeTVPEpK+7j9zMHEktQjYV46ldu1m5ciXvfNc7+MQ//AM3vPmP2XXgKX7znZ+y7uILaevsRAhBtrMLSiWUrixbNm7iv/7pC7zkja/BLFU5PXyCF137Uv79m1/nQ3/ydr581/e58OxzGFi/itu/9O984KMf5WNv+0ve8t538dTORzm9MMNLrn4hP/z6HfzFB97Drbd+lFv+5C08cf999KxZyaEDRzEcjaISgXiC8T376IlCRzzCgaf2cvnLruHnv9tJND3ExsEkx2ZPkxncjDp3hEqkG7fkUNVjJNwSj9z1Y5SIyvFclbmZRVShoOs6EdXAVQS6tDFR0aWJK6II4ecneRacc1A0Nyi6XAcM/7umAr0CFcHSTQHAK8arNMC7wlIJLwDrZxpbsL41xcs15/W3VPrGrSclCjh6Qqk8wx4LtfdhmfgE77j67w0bmqYgHbem4msIkjFYrLDkffOuG1xL+r7btat4fatKKPvk8oKakC3G0gKsa2MONh0JUvHqOIrg2QoQ0uOrA8+ZoMUjSg2s69damvcnsIu0emR1fFu+DuP/KLCu7WqiLmE0T3Z9IQE05RBpemDhf9cknkY7iZenIeyY39Rc4S98P1+2rfhcZ1Tn2MQJ7rjz6/zs37+ELNq84bWvZvCqi5muFrh4zRaeLE2yNtXH6NgJXnH2uSyMjdF57sU8cGAvbQXo6uvg6MkjlHu62Dy0nr2H9yBm5rl0xzkcHJ8gG42x81f3YrSneesb/oQrtu1g49AQf/MPn+aVV11D29a1pBYlpUKOTCTK8eGjbDp7MyfGxinbknQ8QSGXp2IWufbV1xGLRvn1Pb9kfrFIW7ab2YlTnHfOBlypMnbyNGcNreLpAweZqFQ4d/s2drzwMnTVYP+Te3n5Ddfxna99gzXrN7Dr1BGu3nERP33od1zzvCt54uFHGdixiWwFFioVFoRNYXqWdevWMTU1hW1ayKiOOV+ga+0QI08dQxgKIhEnLaKMlhZIGwZTc7OoVZNkOk67ruLmcqxcs5I1/av4j9u+wKp1a2lfsZLTs3Oo05NkB1K8/5Y/wj7wEJ95Kk8snqZiW3T19XHBOZdz5Wuu48KODH93239yzcuvpq+nn0cff4wN69ajqhpFUyKsKraiEVG9RFm1gghNrXlthNVXmiTwcAsDmxIK9lqu/1rgjlsHzOBvfW0ao/PVhrV9Jl42HMGpNPuT+YUYwsDsBf4s3SRaUQSt6ZFloCR0LUV6NFAqLlpK1vVr+j8Jc9SyBriNWLCMJNzCmHhmuPMAVpWAUHGQPnB75wRbVoA9QYsYAtNqRWM1jiW4o7AWtpRSopZKubk992D9xMmGyW458WLpAhG+LtGgwoQEolYAfqYm3WUWfiBZB7Fdqpd/QNMVrnnFi+nv6WbAULnnnnu45PLz2LdzN9/8r2/xbz/8Fi9/7aspJ3XGnj7JwKXnUZifo7Awwxa1i8fKk2T1KDde/SI+eMu7mSyXefeHP8Cdn/oX7n7kfj7+0U/yvTu/w8LxE2gJg7nTk6RUjUgygp5JUVkoUq1UWLAKDPUPMDU2ytr1m9h1YD+xVJa40LArZRzLBE3Hcky6ulKo6CzMFzE0nVylQsWq+O65kt6eLpKpFIcPH+XFL7qYmalpTk/NsHHVWvJWhdGpCVasWMHsxAxWsczgWWvIj02xuJhj8wU7OPrQbgbP38HJx5/ilTffxIMPPUBWGCT7Ohk7dpLnveKlfO2zt/Gm976D3971YzZech75sTnmrTIbB9bw21/+gudfdik/v/tnbL7wXH770EO85LJLOHD0BB1b1jFfLHLyiUOs7xmiQIE7v3Qbt//0Z/yfazaROvQIi9NFptuG+MwPdjIcybBx60X86POf5I/f8UHieoRrrr2CK15wJZ/75y/x9j//Mw4+tZ/de/fxyle8nGKuWovSra2J5bhLAUIqSOGi1qqoNL10NEnWwuOWlZpPrke5hSkHzx7j1qSJZpAc6IgyPm8uqz2Gz1kCUk39CiGWSNE175emjSCgP5qpkTCtspzm6p0fUEU1yx3phLIErAP6wwl51wTJmsJSe1069pL3L8sPN/PTApRWBU1UAUgvoaIQCEc2FNoNG2+bdC4AdANsa6m2vlwTojFkPkypgPyfS4M8+GRdsq4BbEO4+TLcUStHltDO3lhtpd5Xg7RNaOG6y0xyEw1SW7gStJTGV77xFe749D8ytzjFxqGVDKwaZOfTe+lLdlAWGjf+0etYOHqUH/7oB1xw3bXc8MJr+fp/fpWj4yfpNFLkRmcwdUklCm0VlUQihVkuoaUMcsUcpcUSPek2Il0dJPQIJadCbiHPZD6HUTAxNS/RfrItjrRscvMlIpEImVSMimORTqc5MTzCQN8g8YSGK3WmZ2ZYLC3Q29lFfqYIhkImk6FUKlGxKhQXc2xbM0i+WCBfNslXbJKOQiHiZb7LCI05WSVjxPxpVxDtcWKmBEuidWeJoTJXLhBHkOrppFwuI6pVbNVhXV8f49UyzvAsqbVDiFyZ0cIiq1b2M3FshJFSAUVCxDFJZOIo0SybN2zkgssu43vf+jYHDj3N2q4OztnQy87fPMJbPvt5qm1Vzmk3WDFxmNzEGOmeQXblIvzfd/0d3Vs3ccUVz6NaNTBiKq5pkkykiEUjTExO8ZlPfxrFCtZCnfOsLaszUA2KQo1fbs1x+q5zqgpu6/zmDbRCyMup2e20vz3C+LxZBw5laaRhuM/mAgLhYh5CeJF9rYSj5vegDoYu4JXNUlTBmcA6TCcpfo1NJfR+NkvWUnrZE6X00qYKgWfLkmIZsA5S5/4eYC29hFHNLbjXYCNVXHywbvQOEY23XDvPiAgfrGt/WaqJifB5rYcbtP+xBsYH99Qla0VRGjisZmJeCpYAZ3ih2XJ5NTPcp6IoXp6Jpods1VIyBlwiNRAH79q2cIm6kkLBZj4r6e/K8pKB1cyW50kkEsSTMdq72rEtGOzp4PiJYxQW8uBCNpXGMVR0XfeyyOk6cSPijR2YmV2kXDGxTBOzalOtVj2pRKgIFRTh4toOkXjM50FdUKOeYOBWUFVB1XaoWpJ0PEZHOoFEIV8qohoRCotz6IYn0XT2DGKbC+gYmK6FWzHJpBJMzM2QXyyQbUuS1OMUKlVUI0Y8GsOIaCwuzlPMl0gkYiAs4vE40tWYLS6QjSfpassyl8tTKJXpzqSYn59DUeKYskosGSEeMUiqGoWqwJRQLBbJtHVStIp0JJOMjIywcsUqZuYmyaSiXHDOdh7eewzFdZgYGUNRFNo7O1BVlVx+jjff8If86gff58XX/SEnIxEyK4cY2LYGp+pQPDnFJ//07bznK7dx9MQwbXqMR3/zEEosTpceoXtVL48/+ChP/PdvGJnPk0ymvdofjoWq6g1rJnj+rUC82SDnAUqTgUl6kq2QnsFNVeu6bsADNy5U2QDGQgh6Mwbji2bDYcFG0NI9cBkArhkIQ9RIQPU5LAXpsFQdHlOQezwcednq+hrCCzH3jbEgSbegQUStT3CbcoyHtW4p6xta7Ro+iIZHULt/4YbAshlE6zTWckbaVvcUbroBZjWsjTSOfbk+Aizzftb7XY6z/h9QMLc+eJV6QpwGiXgZ/A2rJ636XO5vrRY2+O+XlA3bYLMBQ0oJDiy2aXz+ts/yyTf9Gab/YrqagmmavlugxYM7H6F/oBcXQcSIkCuVqeY9f1rTNBG4pFIpVFWhUCohtCgRLUapXCWdTmJEdRQhcRyHXC5HX18P5aqJY3vfpZJxSlWXimlhqAJNVRCWg6YpxBJxItEoiqJgCUnJtNCjCSyrTCyVxrQqaDhoqrcBrVjRx8L8fI3uSWfa6GnrYM9T+8lGY+QLi2TUFIP9Axw/eYR4OsnYyGna0z1Mz02jqRGqZZt5WSTV1kaxYGFZCtFElrZ0G+NTk2giimnCvHTJ5UrEkzE6OrqYnpvmrPXrmB4dZ8e28zg9OooiohSLFkePnGLrhk0cevoAsUiEbDpDOpPBdCWZtjS3f+e7vPjyyyjMh16VzwAAIABJREFUzHHy8Z28/MbXMDs3zXB+gVXnnwtAhx6n2taFkk5zxZtuIj5TYlGW0A145+XvYr6UI57OYtpe0VxNM5asraUvXj3tkXdMI6gQnOtztPgSmhQQ5HBelncOBavUXvrwGm08GBzfjHgm41kLGlG6jUYw18OZGse8XF9Bc/x0pLV+ZVgCD43XF7JUSUM+6Vb2KMeXqP2B0Uy11wG4eRpEQ74gL/CtWXhrQWlJWXtWze/6kmueoS2niS2HTa3aM0rczziK/49bOMm/RLZcWEFr3mmX9NUknShNxsTauS0MD+AbM5r+XY9o877TpIIZVenPJtjz81/xxgN76OrtAiAVi6PqOlPT8+QXF0imM0RjCWzLRVE0pmfmSKfT6LpOuVSiWi0jXZVoIo5puahalGrVJJVKUS6XsFyHSCRCxayS6WhnZnYRW9pEdC8nRtW00dBJphIs5OawHYeIYWCaVWYWFqmWSyTiUVQjgqq6qDq4rk2mrZ3JsTEGOhIkUlkmpsYoFXNYpTJxw5MoDcMj4lYP9EI0yomRUVZm+qmUcmTiSXRdpaOrnXRaw3JiWCgsLubJxJJUKzmMiEKhWqQ7lUbqFqoO5WKFeNQgmoyhqjrRqMHs/Aw7tm7h2PBxzr/4Io4cOUapvMiqoRUgHfr7+zg1cgykRSSi0tvXyaGnjyAUg3gmQcfgSh49NowhBBvPvoDZ2QWO791PySzT1uflOdl538/YdPklrBnop3xymgecSfqMFCfuf4xNV3dy01/8GZGyxje/eQfVctWrOi8FqqIgZS38BQ+YfK1ONmeBDDwr6qq669s6PECgwfYiwoutmaMWEGwGtc3CP7S5+klAdwTG81B3S6TR2liWwR7Fk0NqhQZavYvhd1DzvbZc4cVIiKYCyCJ0T6pSp0I8MFT94LP6sYqsU5I14UhpvHZAxYQDl2rSNgHQ1zWIcFsONxrG3PT3ZzrHG9PS+TlTq0vS4eRXwRpY/rznHqwRDQ+s8QVoxf20BvFma3TNbadpZSpC+KXs6w+1tpDxotWC74UER1UbetClQtV1qRw5wYvf/xaypTKff58XsRlNJpibzeG6krWr1lKtFJiemyVmxFB1DdXQEZpKrlhARWDZLqomMB2XZDrD1OQcXZ3t5HNFIkYMaVYxhIEe1ykVCyRiSUrlHJoAx7awpEZKQGE+T6YtQ75QAFfSk8lQQaMwP4WoVrGki4NkoKeX6cUFctOzRIVKWzTD6GQO6QoMTeOcyy5i15NPATA5NsrAuvXoyRhThTzr166kMx6n7NgkdYPjExNs2bYJN7/AhsFejoyMs3LlAJglulMZTrvzEI+guxIqObKJCBYabdksmqhwYnYKSNPb3cPw8DBbN2/iySceI7cwTzaZRpEVioUCx06U2L7tHCamJhkeHmZyZoHu/hVe1RzbZeT0BEJTQaicmtyP6pQZWNXOWWuHkKdnAbhApMlO5Dg++hAdXd3Ip08SHViBPZfnF3fezejp47ztbX/O7PQMyVgCVdVqgkOwRqUrPfAOJE5JrapRsC4bJWUvCKUG3rIR9MLUgisdz1Ap6317/6u71gUSqdqUelW4LoqUCP97B29j8PyZ/dJRMljX1CX+0Lsk/MtJ6foA2/B2hRI0BX7WvpYQgKL0xh6UVqvfpwxtdqCIRo49KCzsJUeTtayaDfeHl4EqmEdPWwkNUAZ3IBF+2lxvvnz7gPTD9puk5jPZIVr9vtyxzWAN9ZD1M7UgU2B9XPWwdlVrfe5zDtZQN0YIIXxfRNFgLAya6/8p4MqaeaEGCQAfqFv83Q3NhadxKUghUXCRIhR9pvjqVGgMtpBUqjb53gjKHUdQXnQ2Gd/CXClVvcoQjs2Rw4dJJqO0d2RBqFSrFrquIg2J4UCpbJPMJKmWbIrFPC4GkUSKiclp+jo6mM+XyaY08qU5Eokk3Z0ppqeLxCJxkvEoQkgWixVSMZXOji7yZhknppM04qi2QIvGMVJt5Bdn0aIR3KrKxeesp1LZyoOP7WfNupW0JQ2K5Tx6tEwqXuHY08MUS4sAXLh1O/sOHkCUTRJdUSo5jdHxOQr5KaSMMrh2FWbFZHFkkfiATlu8k4VcnvM3rWOgPUW+dIipSoUNWzbTFY/w3/f9jmIsweTBw1z3gsso5l3mFiskEi5mZYZDhw8yMnqarWs3k0514sgCvb2drDxrBY898gRzizmSySTVikOhUCAR06nm8gz0DVIqlJidnKRvoJe5fBE3b3Hf/U/xc2s3H/jcbewz83QfPEFxcZ6xyFES2RT3/fDH/N0/f56ztm+nQ2tjprDgcb9IgvJgdQrCj/5bEgXYeJwQXjSfK6VHTfiRjAJRYwhqObeRXgY+V/o+1V5Kzxr9oPjAGFw/RJ20UtkDY6IivIAeicQRIS+m0HsTRAoHAFh/P1SQToNd6NlIitBIpzSe0xwcFLxNKlKRSOy6+2xIE66fLxruz/ubG6Jcal/6DGawAak1bR03qAgTAKtEipA2cgZt3e/a61uCkJ6niGXZKIpWK5grhA/AEhStHv4vBbiOhaYaCFUgbc96Gsyv49RzvTzTND/nYO1KMIwItm2BsAnlJPTBuy79BlmJg9238XHiuVOF/ubtzKKmSnmpEnypOpTMqZYH11H8dJZ+h1Iu4QgdBJVqmZ0/vovf3vdL7Ht/QXtvt3d43MAuFolFdXRVYts2+XyeTLoNIbzIuYhQcQyXSlmlLR1nxpxEJDIoizlSkQqxVIrp4jTdqS6iIkIpUiaq2CB1ElGNgm0j84uotkom0UVhbhQ3YyHUBAPZJG3ZTsq2ID87C3YXG846C1uXlGdneP72fo6erNJ9zflMTI6wsj+D0CtsGlrB6NQJRqc7WLFhIwCZtdu5dtNFxMvjnBqdYt3mc7Bsm660JKLBT371FBtXdRJds4XR0WNsW98L0QSLc8PY2TTXXnEhx08cpCOjErNctmwYQjcSmKu7mDw5wlm9nZQHElQqBaz5FST1KJtftJ7xk5OcOrWfhJFl0spzYmSUmYU8Z63u49TJEdo7O7CrZXK5Mv29PYyNjZHJtNGzejVVa5a2WIyZ+RIrhvqYyxUBmDwywq+P7CeVSnH29u3kD4zxs1/dx0LZQbo6+UrRK2rga3nSrbuMCZoEudDG38Bp+0DTnFCoRo+gImXdfOdxxo0Rc4oja1G10nb92rw1Kw7AsiAapjyEK1Gkl0unQVetLWZfEqUeMeyN2/E0T+GNW1EUXBm27yg1OsM7L5BYA+BsxUeHPER8KbhGYcgwg10H2YaufGqlNgafdiEEzOEeahsGeLnxRRNWSDU40vO8Cc1dcwuMj4GtwVsHAsu1iRiKnydbgC5wpGfEVFUvXa+mqeCALUFVdG/cIe0maGFDczBvy7XnHKwFEtOsomlavfBALWetCKg+T7JQ6su2JR3SFNXkuq6XvSysXfrbpHeIi5dG3MVLSK80JIfyNovGa6iGQn9bJzdc/zquf9n1aMLk1a98JQBZ28BSdCqFIrqA9mySkmkxOztLpi1LJBJhbrJMT3+SSsRgbHIOVRroIoFbLVGtKphRl87UELnqKFo0QV9fhj49Q7prALPoYBga1dI8BbuAZURJukkWypMQW41WPI6jlKhEV/Damy7g4Ud/x+BAN33d7UyOHIWszlC8guHG+a1dwtQWGRxUGFqXJN6dpSuXpa/Ty6Fx88v6uP/xeS7d1o5MXMCJE2PkiLFudRd9XZ2UYhm04iHmilO8cPtm5qZO4kgbK3UFV2+L88SR0wxueTEydxIl7fDCC17Lnd/6Pte8/MXsPzxPR2yBTMREi3Sy07U4e0MXi8U1bDh/G21d08zO5BkdKTM6tYC1mKMzHqeQbGfVwCZ2P/kYGzeuYnh4mL6+PhYXF1EcB4U4kfYMWqyKdEDzk0xPz01y0bYdDB8/TVRJ8M2ff4eZuQpGNIPAwXJtROBR4QOei6i902fS4AjWp+IZ7Go8aS0kOpA2nYZzW1F0rqCWuElXwtmSl1fPwx4eXq6Q0Hj8+68dXRNb67VMwpJ6AMD1McuGnwEt02wYDCXzrs9JaL5qhSHqg/b/HQQgLUclsYy4qTTYt8J91+6HcHHcOi3jNbdBezhjU+o2B9WXrB0pQXVRNIFlO+joqMLb2Bw/jYVlexufo4CK4m1MQS3ZsEeNImoJpBruu0V7zsHaEBaOpuN48UKe36pD4JoB+MI2DRvSEhtJaxcZCYpsOjZcBDRIj+pnwFLrUlF9f26cQMOR5NwyURSIxKmgct/DuwH40n9+gxddeQXptiy6dChUyqiagQLMzi9Srti0iyir2rsoRjXWbrmIH9x5F1ddvp3YuTGwihw6dIh4exelQjdZ1UFEFVa2xTlSXGSgfQ2qKshkMqxPRVmYX6QzOQORTsZLHfQpOUwlzaPTEqWUw7ZtXNmNIzqp6lW+9t1jrFrh4LoFNm+4kKceP8ZFF2xnoGuAs7ft4MEHh+nuSQJwdGyc/uxKpian0NpG6UpnEKZFIbdAMZZFGhEu2rCeiZlZxqdGOXvTRmJtnfz4SZPyiRF6+nuYWjBRXZ0N52cZP5pHxGe5aKvK6IQg3ttD1pims7uDn+4b5vTYES6+vMTqnpW0dbdz8MgKVq/pp1jM0xuPsfvAI8iqxa6dv2D7eWczPTaOLqPMz+RZsWoFo6On6ers4uTpE3R2ZbFLJldefBEAU+NT/Olb/i/vfudfcGJkjMW8BCOCK20U20FF4AR1F33FTojGVdYsySq+xhbQJJ4EHT62cV22MnaFf69reYpPYfgmOLe1O2rNaNiQcc+tGdqavSiAUCpiX7tcUrdS0AycDaDbdB/N3jKt7gdYYgwMJkcqIhC3l/RXB+0ld770voQIbWiBAKY01HoM7rtuCG6+zvIgGYzLxkUEaWuFRrVa4eSxw2zath1HujV7gFf6zMFybLRIBAE4Fe/ZaJrWVNWqcZPyfrbeRJ5zsPYSKtkITUG1HKSiNoxVCFGTUjyDhJ+7VlF8FaXOF4JnJJEy8AetJ+Px/iZ9tzqnzgn6FnfhuKi+scMzttSlgvBireIQkXjqoQoGKtOLCwz0pdAybfzswYf5wF++m/ETx6gWLKSI4NplhKoS113a01EuOaebk+NlpsZ284KLz6IzoVLRLHr7u8mkqxw+PcnFl/UTV3Pk7A6SqsQsa2zscEhF2nji8HGy2XXMOtDZ04XW6VI4neTYsROUzHm6+wf45akJbD3Cj+79CUahiKI6mFqGyZMWb3rNq/nJt+5FcRR+ePQ0OSpEbYuSZVOyNV735o+xafBC/vFjn6ag9ZGQ0xQqAi2dxSnNMT2Ro3PdWu4plLBkEi2hEXf3E0+0IdM6Jwvz5GKTrDF0ZHuax35yhI0bd5BZsZ45EaesHaU33sdQ5ypkDC698HL2/O4u7FKSYinG6P48bclBkm0KK1/QjlV10ZLdrFnVyYuvfAmVssvU6jGe2neYxUKZybFpkiQ5cPBJ2rrbOffsbex9bBdTMzMATM7kODk6zvD8PHYsBgqewVBIUAS2dBCuUquTqPjaWN2gBUHmnbrRK0ixGyhr9ShFD+wbwa4OukF6TF+QkIrHrgpfMwxTBSIEJv76cxwHVVVDwOdVVRKKgif5ht+u5ujK4KdbG3PwXgTGw3pCqcZ1H6ZZauf52C58DTjwNmkGvtoeIT3qpZZNMBTrIKU3zy71+/Ou1yi1S/95NG8O3s8gkZMfvNNSgAtA2tOm4QxBNXhkkeqH4EuhUnUdqvN52no6iMeifOur3+IT/7QdV9hI18awBK6mMDk5zZ4Te3Gl5JKLn09SjaH5/QT1ZL114S2i/19I1tJ2wTBQHBUwPXc76qqkgDqN4U++ircjB8mXan017ZJBMc76bqogMfFeBtXL36t4koireI74S5Jbus0qnoJQ/aOCRDj+hpCNJ+ns7OTjf/NphGvxob96H1aliFBd5sbHsaSkYpVIdLaTVUwmFqY5PVfl/C297B+dx+zKcnJkDxUnDuk007kO7FSB3IzJwcPjHOiTjE6eQhZLuI/sxTZL/FyFtkgKjSpGppOEiGPm9lNRFNqNDEORCkRUbBGlMDNOJNHDF794Oy/YdA6F3AJHZ8awhY5ruQz29zLjO/d//m+/QH86zujkU1QKJhE9jmVPEBWSdfEokeoMrlOgahSJaQoJTSWarKIWTKJpgVowidkFcjJKmiT7fvFbTh+cYPDGGxm59/s4XeMcj0jMaoX5+Qojw5N8ryDYsCPG6aePcvaWHl5w5RZOTx2kI9GOEY8wMChYs2YTczOznJpU6OqLgmOQjPfwwMOPc3r2FC8970UkYzE+cevfs/GcHQCcml2EeBLpSjQJ2NLLWx5wnzS64XnP1PE2ezzpx7NnBeAhluSzFoFhMZCkhRIiSut0mrcWlZAqrFCjHTyftVq/9art9fF5YBSc2whGUrbmXn8fl7WlvslBU3CpCzfg59DwxXgpJVJtDXqNPL7dAEw1jcLXapqDKmqG04Arl2GTUuBKGcyZiuvUWR5FXW4u6vfrLuNPXmuOg6sKUBSP5nItTHeR0VKF7nQPf/rGNwFw4vEnmU9rjI6PccUFl2MuLHJo9x6qhmD7ueeRTBq4UkdXBM6SXDJnfjZBe87BWjEiKNLGVlwvDMnxE/0HRr/wQhNgu3XuLwysjUDt1igTLwJQ849xQPo7r/CkEBEOM/efcqMRqXnEbmjT8MZXe5EkVBYWGeofIJVKcdNNf8TU7Chzx07z/Pd8kPe8512MT80xnY+Qq1TQ1BTtKcG/3fEd2rs6+O2v7+baV76Miaf38dXPP8zKvg1IpUhXxCAd0YiOnKSvLMlXKpx2LRxXoT2aYmp0gmxPlmoBOvp0qjOn0Nw21LYEMS1OSaicc+ElLMxMcezgIbpTaRxRxTVLbN92NhsufQG6I7nrW18jEu8BoDNhoJmCNYPrGbjw+VQrDsmOJN0d3eRmZrj9m1/j5lffyEKxTO+qsxha2c/UvgNEMkmSg/0UC2X27d2Prrg8/J076RvsIhVz+Oo/fpG2pMStzDBlRRAlm1h5llgiSXspQv+CRf9Za7DnnuCfP/ldCuR4762fYue+SdauGaIj0kF0VQ/G/AKDbYLuXoPZ6hyv/+PrEGmTwRVFPvTpzzM+aZM3i/Xn6LioioqL423QUjb4LAvqieZdV/oalSehIah5bEDA0DVTCI2J6qXdIuuan8VI4Hr8JaDg1OsyCoEWBkph19IHh7+TAZ8uGymJ4GcrFb9ZSvYkvPo5YeoiLDl7XHyQHweoud+52E3vpqjx9CFDv294C7wulJrbrDcP3oUCO1QwNic09mB8oU0k0LQBKW1q6g1BSL3/rFoU366pA9SBW4bokoAKktIF4SIVB6FHcS3PRVKi8pN7fsGBqd3olsaV5/0hQ2dv4Jc//SkjeoGRiZN09SeQT09z3qWXYaMiTTBLRfRMFrdiE5GKp9ERaGrPrj3nYO1Kv2CuraCormejlqEdT0rfL7quTrn1+W5odbeo8AIN51cQBEENSiCZ+FJPoJaEm5Reqfq6iug/ZN8g4BLQNHU1LBaJUC6XqRTgmhe/lHRXhp/c8V2IRsm0Z+no6yRXlBx44hAJU8FRBVs3rsSsVFB70uy+75e0Z7pY0dFNqTRCe+960j1Z1IpDYkUHF5x7CY//7gEu2LyJtV2DfPWrX+RNN92AaUfJFRcpLebYMzaHrlSIWTmwHCKJBNWSjRpN49oOuq1iVV1s2yZXKTFnlVhcyFMWJeKy4M2PazFTNOmMRpHJOH1r29CMLMMnTpIwVKquYNYW9G/bAY7L/UeOcf7AKqIGHD91klzO4tzzL2DvkQMUsTGrZWxFQUgL1bFpG+piw5bnEYkZPHLnlzFLDq5lY1ZzFDFICYljK9x4wUt48ovf5twNG8idHufHe/6LA8d2Y/T3cuM1V5PpznJ092GQVdyFKqW2MqUylKWDoXmh/Jg2mq4R1PcIeMLgGTdLkp5Hhb/2RGB0rFNxvlrVJI2GcrGHVF2oU3Jg+5K1hhA1v4faetVqtAY18FlizGwA7sbAmaWaZSNnXDvNO6sBmBspDi8YSNTODYogQPB+qYEkHJbua0OvuT3USeeaNFubwFrz6Mn6XDbfZ5iCEUIQuFXI2rjDfQUG0uD4ZmlLC1GnjcJe86bmJdpSMARMTI9y/8O/YuW5W5l4Yh/nv3A78Z4OktILIrvwygtZU55j50N5vvhP/8Qrnv8HHPjtbt74+lsoTU/wldu/zk1/+aes6dpExXRQcb2cOjz79tyDtetgSw0dHSkdVL+0kBry4KkZKPxzFF/blDQuzuWbp0oGZY4CD8zaDuv/00U21G0MWjOIK4EVSkqErAcNKHglGqPRKEJCxIgxP7fIdTdez+jCFOeds4XczBj33vsrkkaU7lQHbqnCyOhxjEw76UQaIglspwK2iuKq3PS6V2IInUf3HsS1bPY+to91G7YwXC7xyM6HiaXSlMtV+voGKbpl7GKMYtWkXXeQVYElVaanx9h+yWUcPnYYV1O9CuxWCR2LVf19aI6nlidUFWlbAFTLFeK2jhYxUG0Xt1JB172figJSlmnrzFAu5uhtyzAg2nj80Z0IQxBfsZoNm87i4MFhtFSamDBQHIlwFAQqrl0gmorgSsmimadYqeJaLiIZQdUSbDvvIiaefhJwmS9O0r16gKdPHkZGbNb1bubCiy/h6ILJlz71Obaev44dF76AquLwwiuv4IldP6JiO0jsGu4ZRoSqZYEiPG7XdbxYiRrehF3IvOblnQ5ee0/qDa/B5tYAlKK+PupSKyhCrUnKDUmWpOIJDf76a5D4A5BqogJr1wq1Zk653v+Zxxz8LvzKTEELRxfXrgEQBKo0XScQhMLoKUX9ffIqxizPzzZvnFLKZVPW1o8JXz8MwP4zOAMcNm8I4bus0TOuwvzMDDMTYzz51KPce/hhrnvexUzMzHBk+EnKm717vusndyMNKBRzVBYr/PrRnXRkE+QK0zz8i/uolHJ8/Y6v8+e3/DWpWAQEqLZESOFHgD4zbD8L3xXYs2cPN998MwD79+/niiuu4Oabb+bmm2/mnnvuAeC2227j+uuv56abbmLv3r3PplsACvmTzC+eQNUrGFIisBGaW7NcCyG8Ba5IhCo9o4ziBbAEHyGdhuTqSvjGXBsFGyGt2vHhiKagCSFQJZ4k7UvUaouH6RkaAqm7rqbVjgupcNK1UQyDXM6kraubw6cOsmbjehRXEpNxLMvGqSww2N3LxZdcwIrVa0HqKDKKrjnEU2n27N7P8dFZHKEgHZuorjE+PUfKiNHZ00u5WkLD5fj4KbLZNqSzCE4ZpI4aVVFcCyUWwSwUiBoaijTRXDAcgemqzE0sEJEaaTWJkHE0y5vHCArTxQJ96S46Mx1oJEhEEghXxaloaKaKbiqoVZu0HkeUHNraMqQyWTK6gVQMstksCS2CrkepSgchJJYrsaRKNtNFxbJQbAXhKChmFdVxsB2TXbsfx1Ah4oC7CMaW7SS2beMPX/8mVl58HqdHJpgbPsGrrngxs3umOfrr3RiTo3zr324nGR1g5wMP4woXx/FoC0u6XqkqnJoNQjS95OHfhagnJ6r547t1n/tmqayZg4VGntZ1XbwYAk9SC1cvURD1up+19StqJZ+CmpB1WqHxI6WkVpPUD8oIzgnXIw2Pub6WHYR0UP2AMPAkZlV43wXvnaJKrzak4oKw/Gx4IKQXxKb4/9XHJUIfkI4DrosqvajKQDJ2XceXhEPvuv9+Bp9wLcPg/QvfTyP/HD6+sQZibW5DUaeNx/vHSYmLi4qNrSiorsCaK7Lrdw9QXpxgZHgPn/jGPzKcG2fi9DjZbBaAeDpBcW6KWCzG6dOned7553HV817IPV/9D/aM7efcbVu47MILGDt1kIiuEnX8eVPquOI0ZUlsbs8I1l/+8pf58Ic/TLVaBeDAgQPccsst3H777dx+++1ce+217N+/n0cffZTvfve7fPazn+XjH//4M3Vba1//6mc4tucREopTz/jlSlzbAel/3OAFCB6Y94CXPAgCHks2UBg+ydEgCSxnKV6iDrmy4eO9DK7/kSjB36CWctIbq1ujSAxgIV/ggsuvZO/+o2weWEUcB9u1MRQd1wE9laUsVVzbvyfh8aWL+SrJtgyVapVVgytIZ6JoES/yK5VNYuZzlOZnwbTQhUpM02jPZlBUgWPbVG0LDZVUto3etm5s08FVVBxVxRISmY7Q0d/BwEAbblRQ9HNIChyycY3c7CTTsyewnEWGxw+zcfsaiqJAOQYTxRm6z+rjsX1PIbqS9KxexcqBIbra0hw7fICVgz3MTU+CWQUUpGWhSAXDNrEXqmQTKZKJDAulAtGYiq5I3GIFXdVwLBMDhXRngq2rtlBZcDi4/wTTtqAUy3DRH9/I/QcfI1+xSaVS/Oz736Uvk2Hz6u2s37ClMVmX7aAhUV2l9lFcL7GQhr9JIxo+Hr3s+tK3V0gZWfczbnS1qreaxCwaQadhs5eyDsZ40qvibwa1Qr7QIOWGgbYOvI05MoTwx+0KVFSvz9BHhK6hSFB9zTAATSGb1rq/zqXjhta+d44XRt74afVfjQ8WLvj8cgP37H8kDkKRnk0hHPXYNI9n+ng2gdB3LZrrBraFOiXa8BxFQBL5EZbSRVcFjz/wEH0DAySrDucN9PH46EH+z0c/ytb0EAA333QLo4dGGehfwXvf/3bWrurjxNEJ8gWT8ZlJHn54J+sGN3DepvOg5CVxU1RvLTmOg1CUenbCZdoz0iBDQ0P867/+K+973/sA2LdvH8PDw9x7772sXLmSD37wg+zatYvLL78cIQT9/f04jsPc3Bzt7e3P1D0d7b2otopuazjC8aRVVG/Oai+HC46vZslQDoEmzWGJ9ue4nn0ozOmJMD/VKBk3c3+tW6MaGjYmh/k0BY9b14WNrigoruB6dZofAAAgAElEQVRXv3qYfumyUCqjumBZVWzbIRHVEBKiusRwq2i6QsW0QHHRYlGKiyWGBoeouC6anmDNWUPsPXqEqcVZtEQaV49iZNNM5eZZtWETjz2wk4SiYpaK4DgUF8oc2PckdqGMouoIx8YyKwjpEpnP8/g3v0EsHWdbZy9HT3r5NFypYdsFOns62b/zMTLxKAWrymQyhSYstrdnMKYneewHdyEqKka+SMV2mKsW6d+wBbNU5ac//SnZni6ggmKkkUq5JmkuLixg2ItEO/qJqBqmLTFxUaMazz97O1Ojw5h2xVsHMZtIVMG0JIvzc+QmZ4i6cXrVNuaVOXrSHTxVdognoDyfQ1MlLgpa4JqFZwiuJ+XyOU1aq96t1PSae5u0oVWtUJokbln38qBJSFhi8PNOblyDeDx4QE/Ur+fWHSaaPBmE8PIxe2DvLknRJ4KxBhtOkJQq8K32D1cENV/tGmEYpoGCewhuL3T9JfNBXXKWimjQIJqbd69q3XAqW/PbDXPVBMqtbBGN7/PSTTa88UockEpNMlfiGk/ueoTVOzYhIxKlHGHNmn6uvfKl7Hp8H3/w4msA6Ooe4vwtl3Lvr+6nEinwqpe8knN3vAR9ZpGzN1/Owokp4ql2ivkcblwQVeLYro2UJkIzQEps6aItlx8Vnl0+65GREd7znvdw55138v3vf58NGzawdetWvvjFL5LL5UilUmSzWV73utcB8PrXv55PfepTrFy58pm6/t/2v+1/2/+2/23Pov3eBsarr76adDpd+/3WW2/lqquuolgs1o4pFoukUqln1d/73/UGotl+XnP1H1NIxNBtB1t1UaUX8qso9Zowwb4SrgJTD6GtSwo0Hd+KvK9THuCpRI39B61uCApLPo39CiHYunmIfQdPNBiOVE2r7da6jDCByVuefynxRISYqmM4LiW3TDySZGjjBlwBhx7dhWEYVBbm2LJlM2NTUwihoigKjuJSqVSoVqu40ivaa0iJKz2/30gkAq7EERq64tUTtKomwlCoqgorhlZz6OgxLBUuS/UwnJ8g2z9IbH4BYZmoiSgWCj/Yv4837DiXqZlJIvEYqhLFtlx03bN8x+NJisUiigKaoWLhEtMT6JqGrjlYlo2qazVJLWY7iHgbTzzxBGtX9TFdzXFW/2ovlwsOjlCwY3F27byPv3nDmzhSmkcvwz2/vpdXXPp8iimdzr4OdBEltX4Nu358D5fc8Eq+/S+3kSsuct7Wc3j88d1IQ7Jt3Xbe8A+fBcVAUWDlYBcnR2Ybnns48VBNVlEaJTLPQ2FptF4raU2IxrQEy0X5LVl/wud4myudN7vr+d93dWaYnllcyj27voTpZ5jTvJRkDfcRvq/6mJZ6RQTXalWFpvm6AdXXXAyk1b02t0QyQiFfaQrPrweqtJrLVq2VVhPw0M15N850fm2siq+9CweraqPkTT71qffz8MG9dK3IcPzgMS6+9GwG1m7j2iv/kMxgOysyQ3z/q9/i3HO3c/fd32W6Mk/M1UhvXcPI8CgfePd7ccbzdK0b5C9ffwsb//w1XJhPMKFbPL7zYW54/WvRFJ1oNkU3cdRkrOVYn5WBMdze/OY31wyIDz30EFu2bOHcc8/lgQcewHVdxsbGcF33WVEgAE8/9Rv6Ow0qdh7hOh6XAwjhVbJQqE9+GKTdmvuO67tkAcJp+ARGEQUHVXi/h431iuIbLwV1I6bigavE89NUVLyPIvzjXYTmfd/AkQUTqigoqlp313IFrm2BcOjq66dUzKO4EtO0EEJFlizMQo6n9+ziwK7dCDQs00XJdvDkyBjzZYvZislkucJCvkq1Cq5rYFsqmohjaTHQY8SMDDgGaCkc10UTLoYKQlWoOi6KGmWmM8WGq68ifdmF7C7lKaweQlu/DtONkbATUFWRflCMW3awixbCUrGqJRS7Ak6FSnkeu7SAsIpoThlZyBGrVpGlKcz8JNViGU2qCFPglhwUU1DWDUoqOIaKKyCuRikrMKtI5lyLsnBZrOa58W/fC4tFtl52HuqmVbzq3W+hvT3KfHuSPQf3szh7muHH7mftUC9HHn+M5116CalkhmQiQ0TRPQ8cRUE40nf/9NxCAy8gz0+60WBVcwtbwssuNeaF7RaB8a7ZiAc+4PrGOwXXM9b5vzcYxvG+a94AgvUdrPtgnOGf4eNr4O5HM7oybLgLjG0uruvgAXTwDjTy6GEgX86lMfwzeB+bx9rcXzPHH74n71zpbTgtWnPf4bE0Ux3BsUKIMwJ185ganp1/vmlb6KpKNJ3myMFjDGY7GJ+aJ5btZsNFV7F99TYOP/g4//bFLwHwzZ/ejrupl3f+9d9w3dU38ZarXktXexc33nQDxcUq446FLTS2rd2KcmKWk+UZxk8Ms2HDBlRHMjk5SVpPYJ2BJvq9JeuPfexj3Hrrrei6TmdnJ7feeivJZJLzzz+f17zmNbiuy0c+8pFn3d+F553LkadPcsXFKYpKFYHqW5aVWiKaoIUT7TQHIXiOfCGDDGHnvMB7Q8ENqkcHz14COAhX1K314Ll5QUhickPHKzUjEUpjcEWQCF5RVXLlMqlEFMNWcSwPOCpSYCoCPZZkdjaHhu4F1lRthKbjai4xBOVyjqSQSFcBVcGyXa/klFD8cFywhQ3Y2ICDiRaJ4KpV0OPMujadbZ3ENYNKoYAWiXDJli2YWozpg/tpW3sWk2qJEwvzVDf24sZ0KFpYVa9sVP7Cs7GKq8kZBlVL0tfZzdjYBCAoqCqZVApTSiqVCtGIV2YsFouxUCoR0XxJ03WwqlXiqosrdC5/zU0c37+HtqEhpkbGScQiVE3QjRhXXv0CfnvkAHp7gsWDBxjccQ77ju7jBSsH+dwnPsMDX/531oyNcnzsCDPSQikU0auC84ZWEHdNbviDa/iXr/8HEUXzc0KEjb127blIKVrWJ6ytmyYAa/V9c2ughgNtre6QXdPgGsGv7l0SXKteg7Q1KId/r/+9DoLe/TWyvK0AdzkwbsXRtxpHM1C2Gudy41/u+LCWXNtAmzaH5fppNY7lNJozteB427a9d8zPK/v6N76J6amTmHt2UrCKbNmwkfyeE+wrjXPT8zzO+oUvu5K3vuedvPpFr+ZP/+B6PvSRj/LmG2+kmkrSk2kj1d2Bi8tLb3gVTyyc4Mdf+Hfe+Ffv5vTTJzj01H7Ung5iusFPf/gDXnHT9S3H96zAenBwkDvvvBOALVu28O1vf3vJMe94xzt4xzve8XtNDoCmpHjDzX+OIxM4ogT4LkVCIoLIKT/ZeaPaFX4gfqRZqDU8Vj8iDVobIBoequr4xpfAOKU1HePlTxB+5q+axIX3kKO6genYlCsVztq8ga5ohCOHDhFJphkvlyCTZONll7Dz8cfBskgrAsURqIqG6vhbTCSBnc5QlgJd11GTCRxVxS6bRJMpcoU8RjRK2bIxHBU1YlCQJlXXRnEFqXgEQ9E4npujvTONnc4wl5/my//yr7z7C7fROzfPZGwet1xgqHMFMSVGqqsTO18hokcBaO9fj1IqYLouCU3FUgRda9MIIYhHolQqFVRDp7ywgBPXEKjkqjbJjjT5fJH2ZBJpmaQNjbGpGRKpDvbmcmS2ncOh2TyJ1WvZsHUDm9at4en9B/jnn99Db7qb3w3GmHnqNMd+/QDtMZ1XfvhjiM42rvzgO3n3jS/kJV0d9JRirOyJk3VilCIRRudmya5Zwde+8hV+dMfdpFIJcmWrtiQURaFSqRCPxwl7I7QEnBAGhoGx+ffm8xrqdnpH1o5flh6QQdh6E0g+Q+L64LjwumwAN5dwpuEzjqMZMFv1Fz43fHwzDdKKNglvDsHxjXP3zOP6fUH392nNz1IoCpoQ2HYFx5X81y/vQs9XKM7lKBRy/NHLn8/Dv/wxuckc39u1k7UrBtjKFawcWsP/MTpwhMm/3vYPfOqTH6dSKqMZEYozs0xPjrJvdpy1mX7Ku4/wj9/+Bn/2ilfz6ne9jdU9K4h0t3H68BEG1ixv53vOg2I277iWqq0jIjl0N+oDoLeAlVp6QhcloC0A6TpIRYT4LH+BWfVE3orPPSE8n9lwvbnwTt3woITqa471IISSAZ1VhVwEVEdiCB3huOQNiJcdLE1i+jkILNvFUmx6t6zhBz/+CRfeeA2rs22UkyqV2RlWrN6AiCW5+7/vI5Nuo3ftesr5IjJiUMZLPm9oOrOmg6ELdDSUTAbHtohEdSrRIjnbRqTjFG0LN6J6ic6Fi5krUSpViCaSnB6doOKY6IpKuWDxtx95N3sP7yL62pcy/PRejoyMEJUQkWUK5QKPTh+mZ7yPQkyhePI0X+YvuHvnI6gdXRQm59CSBkKNQsIkaSWYyi+guGCnFHrTGWZOFnHtKhFbJyrnsI0MtlNFoBF3iiy6JooYw7JMutviTJeLdEiFnQf2kbGrFE2NNsOke8sORkoFDk3NkJ+ZY0TVeNtf/S0JLcFMscjr3vxR7j/2GNs7u5GOjVKtopcsTt0/QWF+gY58DlW3edUtr+XNN13P+VuvggGIRQze99d/xV+970N0JtPk8ovE43G/ooenymu+m5YqVGzbrkmpXpolgVOTzB0Q3lYditOrJY+rAVfgxuk5nngeF1LWNnYhBC5B3osAZO2ahBms9WCdBi0skkgpCUpgNQgenv9q7b0IAybN51MHwzNtTkuANsjbg8SVdo0jrp8rqf/fmwfFD+GvK7VOiO9eOq7lALuV9hMe2+8D7hKQjg2KikRBCBe7WuU39/yME8PHGFo1xA/3PYiUEm3B5fH9x1nT1Ymt21zzgss4vP9BePWbmDo6TnsizSMPPExMGkyNjJDu7sARsPMn9zCflSyOjLGQOsTh0wdp3/1rqrpk3Vkb+e/bvoIxaLN336Nkd1zEuRde0HKszzlYr17TiyN1LFwUUUW6KkHqUggmvnG3l+CHiDcutqUVokVNUlnuYYYfeNgQEzTddKkqgpirYgqXqmuDaxMtShakg6JqqH6V5qwWxRlo57obric/v8Cq7gFcc4L//MpX+cG3v4elGmSzKUpmmUQqjqkoqIkkhUoRVwHHsihaBWwksUScSqWAM59DUSTpTJL8glelPGZEkLaNrqloqQyqUNAsl/ZEkmjUIJGKo5suiupgWRY7f/Mz4luzaPv/H3vvGWZHce17/zrsnPfsyaMJShOUs5BEkMg5gwk2HBOMDAYMxxgbzAWbY5LBBkwwYLLBJopoBEgI5Zyl0YwmaHIOO81OHe6HPVuaGe0R+Lzvvb4fTj1PPzO7u7q6unrVv1b9q9ZatcyZNpuNB1oYr0us2l7L3CUu9jT3UTTeyrp9Lcwem9w3ekKRjT0tHUzOtuJXIliddmyRMBGDn3wvOEQPCT1KONLH7JwserQg2aqNLgSsugtVjRGSYxTq+dQpAYwmlWA0iiWuc/y8JYRbWskpyUcMBQj2t+H2FBHoasI5ECTPaCCSUUCv5qc/0IHgtnLmrFN5ac0q8nNzeey1ZbT29OLMcLL4pAXkn3kyXfEEEbuRacfNoOaTb+gKtBO0JW0DzGYj27Zs4y/vvMntN92EXbKhJVSMBkNyPz86giwN8ro6smwYpEkGnXzpyjDQFUhx1kOSeIS7hSMxB0fK11CfN0MX8VJyl5o1pvIIKYI5jbym04KHnh/uijP9Ynu6mcJIiigdLTHMaG3wGBr1ZOii49A6jATdoZRFOqrpmFz9iPzDo8mkB+y01E+qDXQdLRpHiicoLRvPpyuW0RDrwhqJ4Bc1zrzqahZUTOL9N1/hpdWfM7ekjEZfctG9PR5loq8YPS5wzfX/wT+XvYpl6kS66ns4fcpkXlv2F8ZPnk5l5UFmn3wKzpCBm+6+jbKiIqoLi3n2nadwlfi4es7JaesN/40Fxv+/0yOP34DZEMIimVB1YdA6UEXX1UGuURmdsxpiuaWrR1s6pVLq/EhrpaFCMlIYU4dNl4hoCoqiIMgSPeEAdmTiOU4yzXZMSETEJB99oK+W7pqDPPHre1kydRoOUUbPzeGjb9eg+bxEnEY8vgISioQuWmjtDxAUDagGM2aDGbfFjc+dhc+XQ6bHS0l+PgXZHnLddoxKArfLSE62FUHvRxYCZPsMDFTtwRnsxtzVQqxmL95gF6aOWuJt+7FHezF0d4LbwaWzTqPs/NPojHVz4rgcGjev5OoKNzWrvuY0lwMqN3Gyw4WhbhcAnvY6zA27WZShY6zaRFasnsSuVcykntjmGiY46kjs3MVc3U/v2lXMoBepeiPW9mYyuw/QtG0Vx8e72bf9S+Z312Jc8w3T+/q4ZVw+dS8/yuNnFFD35CP8ICtMcWsn4U9f5rY5RTR++AWTDDJ9jfUUxUWMVg9iwsC6NWv56x+e4um77mFqSRE+kxl/aw9vvfoBHa0xnvnTP3j/6yp+99E3HJStDBh8XPaT2wC45/GnKJ4yjcqDdZxz+tnEBqLE4zF0NbkQpyTiKEoi6fpASSDL0hEOGHUYKBwGhhGmIKkFvRS2phb1Ur+Ti2nq4UMQjrbgSy0CjrZYl0rfBUTpAPBo+mE4WI4cNIb2iZELfOmUnVR7jcb5D+2DKVAdauWZ7p5096Xj74fWazTQT1ffdDigRGOsX78em2QkM8vHQCJEfpaPQEML2eOKmVBcis3l5MILL8CiQlSLArBgagWm8V5+cOYpPPbMo1S1NODqCqMR452v3iOiRHC5splSPpOJxWN47+VnqWrcxvLln7Nt41bKZp7IkkUX89k/V6b9tvD/AFjnFjjYvPFLBDWOICajlAikAnceWyhFQRh2jNQodF0/vIUplb6PYA19TggFdB1TSQ57duyk5lA9jhwvTz7+BJF8F3tXr2ddQyUA9/72frxxgUQ0xufffE0soqDFFMK9IeI9YXyqRMOBPRS67RgC3ch9XUjBDiZkWLBFekl01JKhhXFGekjU7sbaXoPX347QWosn2ElZPEhOWxNjA/2UJxJktLVQKMdxBDsoNmtMcltwhXspNQrMyvFgigRR1QTL3nmHWF0fpvpupo/P4tDbb3PV4iWcPKmE5oDK00uPY29LDzedNZXGg1UA1OzZygWzZ9G5bzPl7gIuMil0hzUqt9UwpySEu7obU+QQur8TVW7H293HzuYqFrsVWvfu5aL541i3eh03LilnTKbCgiljuf14H8+/9SZrnryJO69/nKce+jEb3/yIydkhLj5xNvf/5n7+/vB1NO7fzqUzMwk1dTHbZ6V57w4yPWbcikpQVVj2yj/Yt2EtzQcq6e3u4qHHH6e5q4mTSrKZne/hlOlTmJzrYYw1qfWs2fQNnQE/zQfrqNqxBUemi+UrviaciCGaDJisFkRRxG634/F46OvrQ1dVEon4IPDqQwBYP7IrRNWP7CAZEYdwaErKlzYMTJIm6Efk9IjQDd8qOhp1MTTowFCaIx2VMVTG04HUyD6QTnsfDUi/K42kKUYOIOkGo9EGqJF1OVadRg5Oo/ZxOGy2r2kaZreNfdt3sqfqAMGuPo4bM5bm7g6ULDtdVXU89PijHOho4mcXXUV2UQFOkjK2dvdmtnz2GWt3ryHQ20KDKCAOwLKX32Tths2s2bGHgTjMmTufnTt3crCjmv7aSkpmTOOyW6/nR1ddA10Bqqu2jdqW/3awdmcUEwlFMBvkZLQYmeRUUdRBUBCkowUvlfQhh6brw0bqdCNwmhi8R+U7SqsRBQKxGB+uXs7jDz3C+RddyPycCeRNK6Nx9TYe/+xtvnzpTQD8UY1zbruZH934Uzq7/YT0GEqgB6WrHV/ET1Z0gFyjji0aIkuIU2zVKRBD6C0HyFT9lHvNZIa6yAn2MDnDTpYewR0LU+4yM8EqkW0SsUsKZotKwhAjrscR9BjaQACzkMBhMRJXYhjNIrG+KP3xOJoo8OOzzqTPE2NfpJVtb37AxTecR5lR4em33mP/X3/LH3/1NO89eidvP/0Up59yOgBjymdwSZZKZXUHT9x+AW98sJbVT97GvDE53L1oNut37OHdh2+ifdM+vv7TzWz6chNfPvdLGrZXcv6p0zH5E4wfN5FZPjdrvtnMry85ifseepd//vlu/vOWp7ju0ums/Nsy6lWVhQXjePL5Zbz9/EPc99sXuem6k5GjEu///gYq129n6x/uRgx088jNN+Pv9tMbjVHd34vBaCPR3ovU3c2M4izyQ7VcEvczv3Ef+pov+ensyQA4QmEyTXDe6YtZetNN3Hf/b7j0qqt55533aW9qoScUwGA2IAvw85//nNaOdowOM6qqDvoXObKVb5hbXgEYdPiU8iV9TEAbaSI9eC65TVQdfk4/Wrse+v/Q38fa2pbu2tA+kk5bHfmMkRTJUf40RuT5vpzxaAPId93zfco91mxgaFJUNWl0rOlJ/BEFTjrrTGYuXMC5sxezv7aBe++9l/mlU9myZSvX3n4rxVNm8dMf3EiP3cAppYsAqN+9n4Ky8WTYnOSNHcPeNXtobGogYtLo7unEYXXR0ttBVXMNjz7xEH0WGzt21vDqi8+g5NmRwwqBpk7sNuuo7yXdf//993/n2/8fTF+t/RSzNUxFyQJCCOiCiqSbkq7OtaQ3Mv2wb5DROSkBEMRBgR8cLo/QfYchPXluyP7T1O/DR+p3ciUTc0xFzXJz+eWXUzxuLM+/+BI5i4+jtrqBzz/9AlnXCBkVll55NW+9+gZiuB+DEKXCZsUt62h9fnLtNgzxCAZJBYOMSRDQIgMYUHBZrEQDfaCoqJqKKEsktDhGUcGoakhIhIUEFklACYbxyApeoxExqiMpAi5HBpLBgEuGtv5WfIKFCrODjBlzqI+ZiBHhy69WcflFV3Fgfz27OrqQSsfz5YF93Hvptbz40h/wlsxgYpaFdQfquO+aC3HMPInFkT089vzfeOnx23nw5ddYeM4ZtKz7glt+fCbX/vIVXnz55zx+32ucv/Q8Pnp3FVNOmkff7j00KiZOnVTEp8tX8+QvruWOB57m5ece5JZbH+ThvzzIG0+/QG5pEZljjHz66SF+e+9FXHfHS7z+2q/40fUP8JOrz6NuTyv1jQ38c8VW/uv2K/jza2/y02uv4unn/8wvf3YLtfVVTJo2h6a2TjSDxJLFS9hRWUXZ2HH49D7sLhGvXcRj0cibdwbuupWcN286Gz7/kPq9e+jtDvDVt+vpVOLs3L+bTz/7lGUrvmTPwWo0iwHZ6+Gbb75m5pTpGAZNvkUGo8eQ0rKHypBweGH68DFETo+AhjDIdCf/JosQQE+5+RxyniNOilKHzWYhPBBOPhdt0CkRSSdV4qCcD5H7oQeCPrgkmv566trh6yLD/H4MvXbEJfCxFyzTpRQgm80m4vHE4fwjufPRqMl0Gnq656dmz8MwYhSlzyCLKPEYL7/2GuMnl+OyO/BkZeErzCPL6iKowM7dB/jRpVezftcmTp66iPa2GprjLVjjVhafeR5rdn2K12jnnffeBJvGoS1V2CaUYA4LKNE4DU1NdIU7yYuZWHjJ+ZRYCgj2h4hEg5Qvmku+NRuDaOS4c06hIKc4bdt9L3Pz/5Np9/59bNj3Hu0HOrn0jJuI6BoGoxVFjSMgJbVsTT/qw6T7UIKY8ngnHvNjDk3pLKSGfnSbYKIpEeaA2s0rt/yGTjVGzGggEgrhctvobu5gTEJnf1MNF86fQbCrCxkBo9tBfVsnDpMFLaEgGQ1oRgldTmBWwBiNYbUYEM0yomhEU8FitOAPBZFRcZhl1LiGLhoR7U5sJiP9Ax3IcRWjzcXBph4mTphAqLMWEgITJ0+mzATnXXkOz+2r5oEnXiELK0WixrgLT2Xbzl1onT2cdtFF9EV1OmSVuk/WcsapJ3J8sZu/vvYqv7zlLu75xe2sDej8ekouOVMKGKs5+UesmR8ddzJvvPAeEwtMTFk0hwObu+hIBCn0ZbO3cj9XnTGPvyz7kmvOO53nP17Js/feztIHnueuq5fwyeb9OIt8HNrfRG84weLjZ/LPl9/jubce4sfXPcCff38nv3ngMcbOmI3F5qLhwB4Wn3MWe2qb2bpxHbkzjyPSWI8m+fimfS83Lv01r/zlL0yZM5mGnXtRFAXdYuacyy/lJKGbeV6JqKQixgco/dmrNP3xZvwmkdqGFrKcHgyiRK1i5m/LV4FvDHvb/Ax0djNxcgVqQsFuNbNg4RyuvvByTAYjGjqyOLqRxWjUx2g7GIb+TrdwlpLLkXmzsr10dnYPA52hwHSsrpwOENNRBkNplu/7riPrfiywTpXrdNrx+4NH1W9keekW/Uc+b+QzR+Osdf1oq8akBq7S39bOunXrGF9eSmt3Jy1VB2mO+znOXUQw18Vp551LYMMBLGXZfP35F5jVIKZIPzUBPz//5eP86MrjCRDHbpHAoHFm8TmIi2fT/8FaVtTv48wzF/P404/gE13ccP/9CB09ZBjMVPkb6NQVHrzxPtQYvPT6syz92e3p227UVv2/lPbu+jsxf5RJFTnIooSmmhAl0DX5KIH+7umViCjKR4Qyjeym++gjp5WpfJqmEQqFsOZn8sFLr+MX48mAvjEFY5aPXlHAZjAxflo5APG4ipIAm8OFKaZQmOki02fB5THh8NlRjdDRG6K7P4zRZMdhzwDBQCAu0puQ6Y5p+MM6YcVIUzBBKCOfPls2dWGV1bXN+PJm0Sk6WFXbgqeinOkzJ3HSBedzzo1XIdsTGK86h6uW7+bNT3bi69MYY3WzK9rL18s+oKx8HLdfew3vPPUyM3Nyqf3nauactAhp8kR+/eKbnPaTn/L4mq/xnnIRACszJ6GfcBwPHVApv3opt7/5CXN+dSv7TRNZr05kr2imf8EZ/K1yLzPv+jkPf/oNs265hwc2HmDJrb/k2ne/Ztp/XMvjjX6EwsnsiNuoC0tMuuVGPtpQxw/++Ay3PvM586+/hntf/yeFF1xIYNw4NvfGKLj0Kp79aCN1uomBnLHkTJ3BmsYAXZMycUe9vP36K0wuyPr/JGoAACAASURBVKZz60aEUDcLKiqwiCKrVq2i3WDnkMlKt1aMrCataOOSikUxMKu0DIvHSXUggCHUyQ/nTmS+TeWy6eNZfMpxZBRlMnXBdGLRbvr6+gZ3iOjIgoiqK2mn/ymZGUkPjASe1Pa2tFzqoMVsyhIxaUWbOEyXHLaoHXLPyP+PJcej5T3Wuk06umPYe2rH3j43ss8NHSzScdGj0Tbp3u9YA166d0nlGcnz63pyEFZ1DY/byZisTHobm3jvw3fpqW3AYjKy61A1u/fs4PmXXyDqNhOsb2fBooWUzVqE2VzAx19+CYBBlfH7+zCZ7fTXdbN862f81+0/wVjopLO7kZ3bNvKbm+7i4t/czoIpc7nsvCvIyCxk586dXP6DK1AQMBiMXLDwlKPaL5X+7TTIc08vZfa4C/niizdp6qnEJPqwe7ORBZ2YpmCSZBRNRRKS+6VTn2W4diEBAqJ4JHKGruuHM6cD/e/i2FIdK2I1cNLJiwlGQsQaO9A0hVgkgks0YbXZ6AtF6Y0OcOdPbuTR197G4PEStznpDoZBMNMXiiGYvNS3diM7MhDsGYQNRuTsXPosNirb/HQkBDS3j/Y46HY3is2JbnCi2lwIbg+HYgPYy0pYXV3JaeedyoXnn8tpF53LvlA3QZubWG4mcy7/EReecAX9h9pwt1SSVVDIrtZm1ECMSVNnsmv/btbv2AmykcbmFsyd3Zz8o7P56KUXmXfieexu2MM3q/Zw2U/+gxNnzKLVLvPg75/h0pt/yjvPL2P6CUtYvmI1l918Ha++9BbX/uIOXn/lPW741X0896en+cFNv+DhPzzMaedewz/eehvf+GlEEwlWbK6kbM5cvlq5nItu/TVP/OFBrv3NYzzz/DOUn7iEb7dtI6OwkBZNpnJ/IwUnzOOtN5Zx80O/5ts1W5h14YX85ZEH+eEdd7Bz5bdk5+fhNWsEmtr44Y+voL2lnZqGQ8QCcd545L94a+Vy3PMWUh3VWG8wcsqs4/mwuotaTaI5KmLKLKElISEKPgTJgzqugs2xBJlWA1mFZfS0NnPB4gVs+HYjoUCAKVOmYjAY0LXvVhbSa3cgCCIC6bXaZN4j8qYfXqhM7ukeSuPZ7XZCofCwMr4LHIeeSzeIjCb/o91zpA+Nnn/UMvXktkRBEDCbTcRi8aPK/lfaON3v0dpk1LJ0HSMSYUmnKH8Mm9asweP14sz30trbzgWXXMIlp57N4mlzWLtqFe5xeeyu3s8JC0/GHjPSUlvFKedfTE3bQZT+fgpcWYQEmXAoRMJghH4/sUSIup42bv3pnZh0Aw67h5BB4G9PPMlPr/8J22sOMrZwPBbRgJhhxWg0pa3vvx2sP/37c4TpQRSzWTL3Ygrzy1HQUFUdzaBjUEEQZYTB2GnD4qWJ+iAHOMiniUdM0IFhmkO6NHL6l04oI70BZp0wn08/+YhFs+YQTwwQqK3DYBJQOnqIqFFEm4G7bryJ2x5+GNnmQsz20iqZaBMEDMXjaVJUYhlu+gwWLA4vCbeHsMFGbX8YwZeF1ZeBZjHRE4mjZXqJ2I20xgZwjSui8mANPg3efOqP1O3extwZUykvLeWCsy/m+iv+g3vvuIuDG3fy7p/fIsehMzfDQaNspTPcw+wF8+kbiLN/fyWnzZpFb0Cjva+L+sYWjl+8kOefeYkSwYBqt9C/v46S8ln01W7g8suu5Zc/uwWhV0HOCLCv5hBZxW6623vZvH0TNpeFjdV70Pt7UYw2qrevpTUcIdzbR2FZETV1NcycOZPK3bs465RTaGmo5Zz5p9DaWkO2lIUx0se+hjaszhh99QmyC8eyd/cuLrnwMr796ivGziyleW0lY6dO4dvln1E4ppwDq1YxPSef8tIitq7cSLfQS27heHpDEWpre5BRaWrcT+Wm7UwpXURl9U4MuaWcMWMWn27fSptkosXq5lBYR3TlUO1xsYsoTo+PbM1Jv0GjtauLAp+bLJeDv//9Q6648moyvN60lEA60BidhjgGPTHSjekQmRz6CF1PgnU4PHDUM0fyuCO536HlpqMr0tEjxyrv+4LhkefokAwtc/ic2WwiGo2lfZf/L+lY4J1uEJM0DVEW2bpjOxk+L2VTKhjo6adlx25q/K3sXb2BquYaDrU2opuNvPHkH6goGce2pma844s5d/HJGF1u2ppb6WhuYKCvl4PdHZy6+EyqDjbgMhhJKDFySoswKDq9aoxijw89GiEjN4/6gX6uPvtiOru6kBwWYmIMq5zekdO/nbO+6KwCcgsyuPGSZ4jbLchGM7pJRBcFBuJBArXNZI0pwijaQNLQBRUGo9cdiVoukPTXoTKS2fm+wjUaLyYi0JQI4i7M4vpTz+XA3j3ous5Ji47jwMFqVN2AweelZd8+rOMq8JgMWIwCHQmJXKNIp78PoyzisJiIDCgY7GYEQcAaVuiLDxA3ChS6vbQ0teKyubEaVTo7O3HY7KCKaIkoA3YBpTuC1+kgHB+gN9hNVlYOihJH0TVMiOR5XYhWB/6wQlSNcqimDrfXTa7VwOWXzeGDtftxWj3kGkS+3VuDzVRMsLsaVRY5aWoF1fW1xEWdse58Pt2yh/FWCwVlBfTHJBxmnUOddUzNmsqBql3MOH0B7XUN9AQFTAM9TJwxn71r1mMpzMUQiRMRVRiIkDd2DK3BIATijB2TS6ijizZBRoxGKJ47idYde0jEQTJL5BZk0lrvxy8GGDOmFEs8zIHaBuZNKWEgolDb7sc3ZiKV61fww5tOp3XAidbbzop11UiigtM1DmGghSvPnMM363Zw9rnnI2fnc+etv+APz/+ZkCBitTloqa9DNOjoEYWIoCHKAg6Hi77mQziNRrKsdq695gZMVjfBYBDJIB/2FTGUOhi6/z8J5iNNrrUhICENk6l0Gu/I/9PJb3Z2Ju3tnWlBdOizR3rZG628kRz50PodixNO/k4HrMN59iPlHL1Ly+Px0N8fGDHgpNnxNUr9vw9sfR8tXURCRGPv5k3cfe+dhAuz+ecLb9PwzQbW9NSSIcHe2v007D/Efc89z6UnLWbxCYvJmV/OmTMX0NPWxZLTzueRR+9m+4Zv6Q32YPTYyXGPYcXKNfz4ikvYt28X8+fMZG3ddhyCEdFuI7d0Nndc8Z9YIlG+/HI5bUqYseNLmVtShmdMfvr6/rvB+oqLCphVPomzLvsTSizpkChiSLB60zc4vTJFsp0JFVOJhEzoghmNOJpuRBJEkqGSjgiZbEhuoUqldAsMo1Eg6cBa0zSQBe58/jGqlq/FabXS0dfDQHcPqhJl9vTJ7D+wD6c3i8aGVryZebidMmqsD5OcTTTUBoKA1WpPhiOTTCTCMQSbGdkfJpQIETEK2BNJ4cvPziauSzhtJuLxOIH+ILHYAKGBMJrBTla2h5xsL6qg4veHCYeiiN0DZGTm0BapQRN8CAYTblOEoJRPrlOhN6Jy0sJxdO1qQst20uzvZcn049nbEKA4u589m2pQXUZi7RF0h5vexkMcaOmisKIY9BjR3hiFGU4O1gaYWlZAIBZAS8SxOtz0xAMYzDEiATBbHeiJAB6bj46eCLYMMxlBjQaDiqpHGWsQaIkOUCB4qREFsmJQ197CnNKJbD6wh7KS8Wh2EwViApvJjN/pQ0xAZmYOPcFW5pSXsquqhuaGQ0yecTyEexASflSPh1B7iIFAI3vbTDhNYYoyfBTmjkHXenj67W+49cqL0QgixnUSqkhQEhEiIXKKx2OwOjGKAhkOB3sP1vPQ40/iDwbQxKRfFkEFTVGRDabhMiWM2JmkD7fYSxnTDOVchy5uDQ3APLScdApDKuXmZtPR0ZUWqNKB8EgAHlneSFeuQ/8f6fZ0ZN1Skd6HJlGUh5VxpF5H2iBVhtvtpq/PPwR81bQgPHJWk47v/j5pVKpTB0WEYH0Tn/zjH3S4NPZs3cOtV/wHeKw4JRMfvv0K3x46wNWXXseAvwcDKq0HKjnYVkXB7DKeuPd17vr5xWzdvJ2KKVMJhAao2l+FO8OLz2PBZTPR1tWLc3wO0wvGsXP7JsjOpVCYwPGnnYno0aC7G9XqxhG1sODM9Lz1v93cfP5xJ9J7KIIe85NQbYgWH/5IJVlFIrtXrsCYm4HDmIsjewImyUxETyCKCWTBTELVEQfjwYmSiKKkNJlUhxoupN+lPRzVCQQBkwoP3XoPj4b+izZ/LweXV2E0G1ATJioP1jJu3BjsNg8AkurHJLtwu/MgFsaaV4hRlWnx96DJZvRonKi/k95+meJ8Dw5jFjYlgSAkI+B1hYNENY3mIEgkkCUbyALkeTAHJbqDKu3dLbhcJiaOG8/+rkrIzkK2yzisE4jHVIxWF6qUoNyUYMDrpVwIIQXiGDIMLJwzh0BLLRs7gxg8Jlp7HcwqLWRNqxPXBBMKZk4vTE7BKspmIQd6mTVPxB+LcM5xuXzb7MQi5+OM9NLerHD6RAc7/A58FgXV6GfJmJnsbo6SEMJk5Bg4fXIma8PddNVHiY4xcZnBS+VAlJL+EPlmO0vmltKu9XNl8SL29vUS6VSIuHMoHZdPQ2s7Fl2nvTvEqZPzeHtnDYVxN5OmTaStZi+TSooIZc5D6NlPVyKDhbNLaVu5knPOvohd6w5ices0dCTj4x1SBoj5YyQUnb6+biZMLKZ0bDHe/ExWbt5Itt3NZZf/gvO8OfSFI4iSGaOegISKLoiIsoSmK0lZEo7IynAZUgfPp0TnaMohtXg2GsgMXZwcTXtMB7yjacHpFuqGXh96Pp2GP7IOI8s4Vv9J94zR/k/NjDVNPer+Y+mS/4qeObRth5aviaAKMmOKx5OIxpF1HcVhwpSbTUddI57ifNZv3862Q1U8fH85+zduQsrxkdsVor6vj8xYJgDNLZ04PHbyc91Y2zQavS4Sgk5cj9MfUxmQTZw78VTW795KS0eYOXlZzD//FD574Vm2d1Vy9sx5vL1pMx8+9/ao7/BvB+uy6UUYCybx2fJ3Ofmaq9m57Rs+/vQZ8u0C470T6Q5GmGWxIOgqETUGugGDFCeaCGM0GlAUFV1Qkz6UDw/CKUEZPjX9LuEfmQQgEVdxCAJ3/OddeLLczJ48iTyHgZbeCDElQd2hHsyWbgAynCJFeWPoiam4dYX6Xg2nGABAtnoRpD6mTpnJ+qo9NAckcrMduMUEHX0BfHYLER0QBLxmF7oWxmfz0B3uJajHmFs+nZr2dkzSAHkZNox6gGnjCmhR4shGFXPciNFgxuAtQLBClh6k0eLBLat0B+N48wvIyrHi75oGxirKcivoGehCjijY23SyfRloGBCCycAS4/JLieb1EI0EmbUgm9bmXlxdCpm5Phz+PiLtjShOE4sqFuKnh117dhI3J3BnZ2AcV0xnXTWBXCNGUzFTMkz0qa2IcQt2l0aBK4OBaDatUjUnTJ3Fso92c/yiWQQFkZ7GQ2gWC8fNnIckeeiua0BXJM48tQg5KtLZEiTTk2DCpGzkzPG0b+3DbJYoLC6kJL+I8fkZrI1vIWHKw+NJehAsduahWEO0d/Qwe85cDrR3kJ+VictsZfK42dx43bXg8KBoGqKoI6oqOjKp2Inp6IaRnf6wzIxyfiRIH0vu/lWA+leogf/Os9KX/d2Uxci8o80kUj5FjjVADb3n+9Z/tOcNK08VENQIMZOZi5beyJqPP+f6X/2CQG+IWG+IQGsXRbOnULRkHroucMZ551Hf3kHpiWezuLEDh9EOQLc/is1jJxiIUF25n4FIAl1XKSufjNlsxhDRQEmQk5tBTVWc/sZW/vDcn7D19+C2eYjGVUqyMrntwdv55PXVad/n3w7Wf1/2JKfNuYfSsWP5fMUr9FXuw2VtZHz5bArksVTFenn2lfu5+c77sBmyCMQ1wqqAxWAlEvNjMmgImBGRUXVlsFRh0NfX6CCdSkd3mOFObFSziGYSMAUTxGva2bJjJx8+9yw33noHm3bu4sLLL8U5qNEvGD+HxlAUCwLjyiqIHOzDKnRhMLhw+nJRE1acdj/uZheiycKk3Hy06AAJIxSMH4ukGOnq6sHtyUSIhRGNFgx6JlN9Fnrb/IwtzwItQWe3H29eIRs27uL48klIkgVrZoR4PI7DMYDVoNMYcDHP5eWbtno8Lhce3U9jfz2PfNbKUz+voK6jl1mlEZq7xnFLeYS+gANnhon6wDwASgtbSFgNvPRGA3kVbp77cAN3XHsG0aiAwVXA2IoMJN1I2SQXByq7KJh7Pq7MSmYaHBzqlsmcvJCAGCEvaiXRZ+Bc6yT2DjQxTTER8/QSjoepCmRCoYGDwS4uH38cfn+MmSVFWDPGImgRgl0JppdXcLBDZn5mF6ubuigak02D1Iu3uAA5EWDslddQ7gqxs7GfKwp+TDxYz/PP3kB7o5EBuQGA45dMoTDDjSKKvPHaB5x38bmYIjGMVjNZuoOEaMSmmkkoUQwmEV1U0TUJRgHX0QD6WGnkPujRwCl17l9dbEvm19H1716o+75Ky0gaJzljPZpaGS1/Mg0PrjCyHY6Uc2QnV7p6jvwOx+LiU/nS1TE1MKSuiULSKCaOQkZuLpdedy1ffrsST14+M4+bwyt3/4ZTfnA2YwqKaN7Viq8wn0UVM+lNxCiZPJF4SycA/mgYp5yPaPYgmbzkCgMUz5pCy659ZBZk0ePvZV3fJ/R3tuOyJ9C9fZxdMZ+PN2zlt7+4h3XvfEWuMcHWA1uP+jap9G8Hay2h0B38DH8oiCk7l9b6g6i+CJWV+5hw2kykNj8Vk8cQFRN8+cUbnHH6hVjtOfQl4ny6/CPEUAfXXPxTAhpDNGttcPE5yREeaxFnqFCNTCLJUV8MBTGYDMgmMwOBENffcgctjU3kenJ494sv+M+LLgQgrhrJHjuGGDE6gp34fCYScj5yAkSTAcXj4WBlK8GIxJmLZ2JUGlD0AuZZMuj2D5AQNQoLC0gMKHhyMpFNTtp62zBbjJhtRqxeKxZRZSCuYLLZyfJ50ZExm4zImNHMGlaLBQMxotE4osvIRKuZmKASi5rRQ1lUeKqIK15URwyvPYtdh/qodhejuywEdCMD5mTwgYiQhyvew4QxGegxiRnlPkRTmETcguAYYIIzj4ZID2vW+9l3qI1wIIhs9GNMtNCrSBh0nYQSx2Lz0t/VwlZF4eorF2NEwKBbkWQzk0ti/O3tddxx7tkE/T1E/SJPv/Q+GVn5iEYdh+QkJofoaYWi284n35ZNiUtm9SdV/H3ffm797X/y9j/+gSQaWDR2AjG7gCy56OzXsPksqJZZAIypmEGsbi/eiWPIKy7EJUnMPuF42ns6sPsGsJpEbHYz4XCCeDSKKEuAAvrRId5G5T6/Y9DX0hh2QTpNcugOjaO95iUj3RxVhSH3Hj0DOCL7w/tAqk6HKYGj6nh4oyyCkKrD0Vz1kTT8WvI5w0F3uCfAFHU0OvUzfLBIt+0x3QBx9P2H80vJiPBHIk2BoulouoZZV4gbBDK8XmJaAr/fjyQbCfT2s76ljam+MkxxhY/XfsyC88+nYfd+6g9Wcda4IioqKnCbTKxft5p4KIrVYKa2roVMq5dNdU0UGp0cbGtj+qQJHOyuxqiorN+/lY69Vfzp8d8zLquCmvomDMpRr3A4/dvB2iIUU1/dT0nJOKo3dUDczdhxJTRsqWfbjm/p8ncyY85ctm1dSZ/QzD9e+S3nnnMX8WIb4ajK9NxiEj2dRFw5mFUrkiyjanFESUFDSZoJIyatIYFUJxrWwXSRpL/dEZvwBTAgJkPSqxoJ4hhUkbbuXkRRJhZXUY1g9yapAz3bhBLuZWrpFKpb4+RmuBiIx4j0hfA6nAwIJvzuAOML2/CZoS/gRrDIhLQEB+taEIx2UGIoCvT2diLKErFAgoTWxcXnXs7BfbuxCC7yih04jUYCgoIrI8qhQwcR+xxs33OQ3p5+TIKEyQKuefM41N7EjEnjWLliHXFPIb9Zej6WHCf71+yjudnNX198gYyMDCKhBAaDgbb2Vu65ZSkOP3RV7yUzGGPNPxq47Efn4rSbuO7e33Hz7UvZcmgL85cs5KUXnsduB4vDSbA7hlEUEJDoCQbp7u3BbDYTlzRyjVZKiscQ1gUcsonVy9didASpW78N78KFZOXMwj2tiFff/Zjm5lbyfHYCcpy6pmayrG5eeOIpbr/zbrI9KoV2C3m6jiUQZ8OqL4gY88hVFcrHemlubmbrt73UdEZZtXkvfa0NnHXOxbzx0H/x5UtvsfrTb5hw7684eKiVdSu/5LJTT+eZX91DydxFnH7+heiihKYLKU+mRwH0UAAcTdP+vmk4Z5vemCOVjsXhDge6o3dEHdG6Ry9z5KJ7urr8dzT2dM8brbyhzxxtUBxav+96droyUkAtCEkjGVFTsekSPRYZV0RHlQRamts48axTkKIaMX+ELz78nJMvvoCv3/0My4l+Pt72BdUDbZxWOhfJl6RBmuvrcY+fwKSps8gyuenUYuSYrfzjg7fJH1dCxGJA6DMQUyTKC6fT295KWAty3fVXc6i9nosv+QGnHX8uT/zh/lHb75j7rBOJBHfffTevvvoqb7/9NhkZGUiSxNKlS/nggw/YvXs3J554IoIg8Oc//5lHH32UDz74gIkTJ5KdnT3qQ4emlqZV7NpTTW1NLfOnlbJ1024C4SBTywqJ9/lx2WS2rNmEGGnFnVtAfu4pRKx7qFm9jPNOP5/9PXsQnBo+NRvdHEWUooOGBTK6nvI+nIzugqAOfkRI7c3msM+Q1Mf+rqmujq6ryIJORNVpjvYyxujgxCWLWfne6zQ1VBEKBwmpKu98vJwdO/ZSX1PHcXNnUle1kwEtSLjxIFbBy7Z9DUyZXEJ1cyd7KpsItHcST4QJhsOEIhGiMSUZXNdg4cDufWRlFbJo4WykaIC1/1xBSYYdZ1EJWb4c1q7bQiAcwWqxIRvAaDHRVHsIzBamVJTSsLOW3MwMZh83k8ruRu57+EUO1ncxYbwbm2SDaByTDnaDkdt++Uvmnnw6V/zoIiZWzKd683rmLphGU68ft8vHihVraa3vprF6E2OzPXjtMm6jQLZdwmrWsJsTZLhk3DYwiRFkKU7Y7+fdZZ+w/KNveO2D5UT8/Vxw+RUENzZQsWA+ry17iy1btuF2OHFZJJwmkfqag/T3BIj1BTjthIXU1zeBoLLiqw2cM24i9QMhdtc0I6oqwfZazjnjLLLaw9z/9qs0NPQwd3wZV113Da++8BcOVNYgawK5kpHC8gqmzj+VOXPm09/bxoLSIqaceCYY5CMhtb4HGKcFg8NA8v3AY+g96QBz6DmHw0EwGEx773DNc2Rdhu/ZTlef7xpwRrbFsXjkdOdGgqnVaiUajX7vwelfabtj1UVEQGdQETMaCcUjiAmFmtpqVq38kvq2JjZsWEdefi7Z2bkcjPZz5WnnoPZHOPmUs9i7aROrNq0gblApyPDxu9//lut/vBQhFuWrT74kY0YFkUM9PPDbB1n29/fIs3n5/NB2Ths3lcnzZtLc0YQQihONx5GMZrrbuunv87P8668pnTies88+leJxk9K+xzHNzT/++GPcbjdvvfUWL774Ir/73e946KGHuP3223nrrbfQdZ0VK1awb98+Nm/ezLvvvssTTzzBAw88cKxih6WGlkbKyiZiNYrIlgSnnDeH0oJCekLdNDa3EO2AeFxhT3OY4I4q6lrX8e2qdezYt4bnXvtf1Gz+gN7GLTjyLKCYUGI2UGwYJBlhMEhqcso3fPqY0kaGCriuHzGzHep3N5VSnUIkubJvMBq54/Jr+OtzfwFgrM/L5VfcyJPPv855F17GQDRGVmYmcRWeeP5lAgMCclxBiLkpnzId2S7z3vtfsGbFtxjiYSQthhkdi65i0UELBfF3tNHTEyFKhN07dvLw009jtI9lQDXQGNPZuGkPr732Dv1d/ciagkgMi0GmN+BnQNGobe7ki2+3IxqMDEgCT7z2Tz5+o4Zsm5uLTh9Ps1+guS9IdzRB1GgmbEi6qZXRuet3f+adz5bjTLjYv6eZNz/ZzZadLQTlOEKujd2HYnRGcvAbPQTULDbuakK2FzEg5OKPeqlpAYOjAq9ciM2cTzBmIsNpwq4rdDa188dnX8Ji1nj61b/QFZLZvreKUDCBUTNiM7ioKJmATTLjsVsxWT0cqm/lkUf/SiIuEolG+eqb9WzbWclJ5eOIRYwsvvZmSiaNZ0JJCaZYFAvJqBBTfDmE/VHu/l8PI8tGzjj5dJANmG1eLrnxRgyalJzyigaUROLwFrvU9x4qDyOn9KqqHqWFjgYcqfwjPTumfo/mKS51bWid0vHf6WR1NA112AxyxO+hsj+0DVK/U88c2T4j33Vom6XjopMeDdPXfzTvfunadLTr6b6fpmkkVAXQUBNxDm7cyq6uejZ99BFvfvw3dlZvZ9LcCu68ZSnf1u9hfvlkooJG0KAzdeZUMvMLmJs3gRVffMHTTz2Bv7MVgKf++BTTFi+kcuUaXGV5LP3JDQSkBNlj8plTNIll677lsy/+SVtLN9VVlVjddqaPK6etuoVgl58efysrt37MC+8+m/Y94TvA+owzzuC22247/FuSJPbt28fcuXMBOOGEE1i/fj3btm1j0aJFCIJAXl4eqqrS29t7rKKPVMBoJeSPMH3WTNZv3kNUNrNnWxsHDw0gxFwoUSe2nBK6q9oonVNKb8sWGqu3EVcVHG4Vgxtqm/bx8NPn09j5KSZ7HYrWQDwRBn14NPPkx1WHHSnn7yOnYKkjXQfSSIZ4khFYv2Mr+e6kD4qNW3Zz4pIzaeuIsnDJ+aCAV7RCDESjieraKjp2d9InSPzxtZdoqTuEJEl4zFYsgowgaLS192AxmXFb7VhNImWlY8nLzMJlsVBWPo4xRWN4//23cRmtNFc309vfhcttx242YVDBIZpwGyzoMcjOzsVgMBAPK4iijFeJ0Nl2CIdNx2n0YJHHMcYIE31e8qwSmRaJ4qzku9hliSw9q+nmpgAAIABJREFUF3QJs96L3Zl0sl6S7+D4+fMwyXnosShWcxCvy4iuaRhFEYMaJ9NkxKJE8Uki+Q4nvhwLGbl2cn052JxmzLIdyR6jPLMIdAsFmRXkWX2I0RgeVx4mpw8VEy6bC4ck4XXLOE1GjEoMs8FKPOgnw2GibPw4DGY7+T4rs6aXk2d2YzcbsCTMmBMwZXL54EdLYDEKGBWdYEjBbDajKHEMsThtrV2YoiY8Hg/RaBRhRDDdlBwMtYYdLYp2OmBIuVkdmv8IZ6wNe8ZIcB56PfWcoXlHA6OR/O7QPEP54VS9hg4iqXdNvedIPnnooDK0LiPLG63NhrZtarFxtPqP1iZDj+Gce/prw9tMQJZl1IRCIhgkNyOTTz9ext8+e4eBcC9btq5j185tOO0Wtq34hh41jMMgUW5z8MSLf8RVVoQrMxfjgEQsEWfuKckFefvYPPpaG3G5JRpq9iIbVCxeJ+PnTCO6v5sf3vZLFi88g0f/8AynnXgWncEAW3dsQbSIBIIR9IiOXXTT1xgdFSu/l1FMKBRi6dKlXHbZZTzyyCOsXbsWgA0bNvD+++8zduxY3G43V155JQBXXXUVv//97ykqKvquov8n/U/6n/Q/6X/S90jfucDY1tbGzTffzJVXXsm5557LY489dvhaOBzG6XQO+iwIDzvvcDi+VwV+ckMZGXY3/dEEdTu6KZ29AIfFwp7GQ9i6Wlm0+CxOPf86NlVtJ8spU999gEce+y1lxSVMmVnAhpV7KBvrw+czYxLH09S/hdIx05g19Szs2fMQNAmjZEzG0RtcfBFFUBHQBQ0BCUlMRiYXRBkZAUUERVcwqiAYjIdHe1EUESQRVA1VSCBpZvrMCkvPOJPKxmbOLS/ngb+9jS7ZsedlML9oLFMrStiztxK304PFCmJvANHm4lBPL16vG6vZQCgYxmw0EgwGUCWRTF82mgCB3h4sFjOqKBAJhbEYLAxoMaSERq7BSlxUMWVmEAgEGOgPYBQlnC4v/fEYHb3tlJSU0NPSSXZuFubeINMLcmkOxvHkWmltCHLeJTNZuz9OWIzTtXs7eVNmEVdlvvj6A/K9DmyyhfkVZZg6epg4p4AdHTL2fB+hpgZMmT7q1mxkyqmn4Pd34M7MZ8PnXzN15mTigo7BZGbPpt2MH5PHgMuCS9BZt6WSyXlONh2op8yXzdiJxYgtvUR8NiTfGA5u2ED+pLGYbF4G/LWYNZ3N2+sZl53BmScsZH3lQeoOVGMqyOTuomL2uZ289MEXnHXCLPwDAs3+Tt6/4UYufOUFIg09HDd7Kk8u/4qfnXsedQcP8fQzD/PTG27mzdWfE4p5ECSV009ewod3LiX3/Ivpi8eJiRJGJRmEWBTlpJanA4KGpgsgiWiqDoKORGrLWTKlgjQLgjDMK11yNpYKDzbUFFxFEmR0AeKqgiwnn2eSDShJz1FJ96+D+ccXFdDY1Iau68iyjKYpDJ8cD2qYAMLR1EBKkxXFZBjgkVaDh7VncdC0Xk9v3KIPrr6K+vDteOLgrCRVjiiKKIoCJLVsVU9acWqaRkGuj5b2nsPaujSomRsMBlRVPWzer2vSkDJVks06fEYyVIsWRRERHVUHVdeQdAZnM8l3UBCRRB1UHbtsZP/2tbz+yd9ZvWENbo+NccUlrN+xlddee4evvlnLnT+8kad+/ys27dvImDElNAZUxJ4BMsqy2bF+Bf0GmZq1zaxc+TF/evgBJlaMZ+f+BuZMm4GmN6LpOi898SX5c0rJyrCzcNpUpK5+Xt+9inOnzmXq3CX0RQKs/exTTv75DVi2+bn+N79Mi5XHBOvu7m5+/OMfc99993HccccBUFFRwaZNm5g3bx6rV69m/vz5FBYW8thjj3HdddfR3t6Opml4vd5jFX04ZXmy2be/ihmlxcRmFjFtko8tu9dzwSnnMqmilM0rN/Ll+mUEY/uQtLPo6xaYOuVsTjtuOg19XzKxJJ/MrBK6Q7XIgSrCupH2rka6u+qRM45HUjUkIelDQ5QtyFIyurKuSWgCJBIxJElCFk2omgaaim5IdgBROhKBRhTFpBCJImggyjKpGZ08uGdQQUCUJFQ0RBRECZRYHKMsYZJEUGKHBVkSwWoxYTUaiYQGDseRRAC3y4EuybTU11M6cQLdfX3YTRaUaAyLzY4JAWMwiiyKyCYTRqORuCxjEAXQFQRNRdc0EvE4RlFAlsTD3KoBEZQEVqMBk8mE0ZRA1Y0kBhLYTVZiavJdrLKMEREtHkcWJURRxGG30t7axLyyMpoiA/j7+tHiCWRRo7e7k3h0AJ/XRU+4n/bOVpxOOx6nl4TWR1xTcLtsZLo8GOVGBF1NOs9XFVx2K/ZMO1WJAVwOC7oImkEm0+FAFusxCCJOs4XGQw3IdjsJNem0K9fnwWGxEtUkFIeLAhPoSGh9IazRODPKkzSIw2ZBVVVC0QTBmEo0ohJTVGJEsbvcxKNRYnGVSFRBNYroiQSSyUBcSSAM7luWZZmEqqEOgmcKjERA05TkGsnhMF3aYVe9ye1woGkCkkFGVROoqorRaERRdXQ1gWSQ0ZBQEtogaGkkEgkEWeLIQmXybyyR3NuVUDVSnhUEJJLm74N0npCMZarEh4T8EkDTIohIyLKcVDwG3bZqmjYI/gwaBiVlJQlyg/SLdqQfaMKRckVRBHXw2uBWwyRlMxjJXE96y0QFQRDREonDfT+S0EgOXkm6UtRlNCXZVsJgwBFFVwcDMmikvBdqqTqRVLY0NERdT24mUEUkSUDVVFRdQkRHGtwOp4s6uiYgkOzze6v28e6H79IX6iGhROjojdDW2UyGO5vb7riTPzzxAh++8zFvvPc5/bEA1Y1+Tp52PJPuupr3H3iYiMmM0JWke9965TVsTgct7S1Y87zs2V2NKvdj1ePk5GTx+F9f5IvX3qCqrYXq1VuxeSzEoipbv/4GcXwWFVPKmFkyga8+XTYqVh4TrJ9//nkCgQDPPvsszz6bJL7vueceHnzwQZ544gnGjh3L6aefjiRJzJ79v4l772i7zvLq9/euvuvpRTpHXVaxLduyjRsmmGobMKEFSAwptBBKkktwwk0cEkjyfbnkhhFIQkIoIfkIhI4DBoxwwV22JcsqVjlq5xzp9LbrKm+7f6wtCZI4f17vMfYYp4111l57r+ed73zmnM/VvOUtb8EYw0c/+tH/6bA/9/ja9x/n2h3rmWmN44QVnjq0wMzsGc6eGscv93N6bIJSuEJoAk6eeJCrrrqN5eYCP911F1ffeDmX33w1D993iE2X3cixff/GjVdeybP7jtHV+2KUbmKdEiupzMfeZwbXtUgpwQYkWubIQRui0Cfyg3xydeDgCIFVmjiLc37a83JUQM5/Nxstim7p515LpjQLK02EZ/ES1SmeEtdakBpjJQH5PAVXgNUGD4svcqmY54ASgjiOSVKJtZZmrZ4XBcfFSkW5p4vG3CKBMagso9Wo5ze/5+FZjSMsNkkIHZfVQ8OcacQopSh4DgZL5PlkzSYeIaXQI0sWSCx4ToxKFxAi/0ioJCVwBTLOdce9QwPI5Qa93V00aouE1SqlQkRvtYSyknri4GhL5AeYLCbwHZaSJo4jsPU6bSFYqbXwhwfAahxtkc02BcdBCJcsbSGERWUZ5Z5eWkuKLEnzhUJols7OEHg+TcAXeUPwog1ryFp1qtUqKg4YWbuGWZUQrRmk1o6hlae6iSxFxm1arQaNVhPVbhOrIiowFIsRqt1CSUOqNNYBm1ky8iHJfhihtEJnOZcYRRFKShzHQXbQs+dGyJ/hoa0FZHaenxYCrDKkOuvwwR7a5p8lI0yO1J28sNtzf++4nUTJnx+koYWXc7KdAdH5jNH/1Pxz8mk0+TcX0Kfr5o5OqQxCGITjnu/BaN1RezguVggQuf74vDbbCKxwkfqclPEcer7Q73E6+d/o/04ffkFTfY4TlybXfmsErgVHaHA8tLXnrefnr5/VeTG2Dpwrvp3jWmtRXEDtnnUwhvMLlxF59KyDS8UL2PP0boZGV6NQpI7g6T17KZYDeob6mJ2ZIm4uMhg5fOiPf5Mv//mn+Y13/wq7dt3DC2+7lfmnT7DnkQfYvv1ainGTl77uMgAOHz2FJsYvCUShzY6rf4G/uOMT3Pnxj3Bqz/d57IlH2Hv0DO95x29wbNuN3HDpDu679y581WTPgcP09ZepPf00J2affM5a+T8W6zvvvJM777zzv/z8y1/+8n/52Qc/+EE++MEP/k+H+28fv3jbLTz64D3cevHVnFmaohiNIlRMv+0jXDtMf38fp2fPsHPoIvxSwmOP38PY2Pe58toXsPOFv8r0gVN4osieRx7hxa+7nZnHH2HHJS8g61lk8tgx1q69CuUWCIVLnMUUXEG3X0QXNb1uTiVUdEhqFCutGl1dA4wvz+NZwUBXD2EErTQja6VEpSLLS3V6qz24It/yTUychY643zgu2isilWWmluCJ3PTj+z7CdXAFoDRogwNEhRDf91Eqw7Uu4OBYQRRFRMUCU5M5glg7uoaTx0/QasZsHh7G1puIRouC4zA0uoaJiUmUY1Fphpd5uNogAp9KsYjQGuELHAue46ORmFaCUIJCZ2exbvUGZuM9GBVT7B4EIG4lVAou7XabQlTF1xZrNJ5SdA9UmZhdJqu3WFleZnSgyKmJ00gMaTMmCkKKylCplHA9SyBCMt8yPDRCFPoEWiHimCRpU7EuhcDlxOwiBS9gcHiApZam2W7TX+5CO4as2UZndVZ5lqeWl9m8eQ3dvV30dZUxOsXxLFFPiS6gVp/DWoFMJJOz4wBoY7BJm6QWI3WGJwyZSil0FamWysi4SZJkKDzilSa9A/1YY0hiibIZQjiAi1EarfPF3sUnReM7LlmSQYc60Gg84WB0RrlczndrFlzXQWuFVBbPdTu5I+TD5IzOUaMjUDrPsFZaY+2FBty5BmMiVUebZ0C4mLyyY6zCE52EQGGxVuWUnbHQ8Q9kWb6AWG1wEAg/L9bnUgW1VjhCgMmlbeJ8Zz5Hx47NfcFCiHxQiNLnbTDWWpSy5xE5XDD2/CzN4pCjd4BMXWgGek7uhbCk54+X71zyCIB8gbBgVH5u5zwR9lxz02KURRmJygL80OtcRwEyptzbQ622QiBryKzN3icewa8U2X/wMNdf/0Ke2LOHovW56ZoX0Dfcx8nJowz5Fd7z9l9iOl5kYGQVXVZwz/xRBuMKN77kjVy/aj2XXXYxAKenJrjs8u1kboN4YYEZG3N2ocXL3v4GTjy+h+996zv84Uc+RqMes2n75Wzcdhm1+jzrrtpJ/W/+D3/1qU/w9tffiDHP3WB83ifFTJ88TrXcTRIrlhtNJubGOXj0NGONJziy66fsPfkEQx6MzR7h4eO7KHVNsW5ggMRb4J5dn6F3XYmRyxxe8foX8m//+in6hvoIo14efvirNGeOMFKMcDwf5cJwfx/CUXiBpGAyrI7xEo/J+iSYImgH1ZhnqDegUC1j0wYrSzE9pQraBvQXC7iuSysztDOD1OCFQQfJQGoyZFthcDEWlOMgpcZkEpklZNKg6CB7Y7Fwnp7Q2pC0Y4LAxekkvnm+i0VirSEIfaxNEbIT2O75KDRZkuEKB9/xsRqyRKJEPt1kdm4eiYdNJJnOcIUi9CyuMGQmY7keUwgqLDQkYVgg8gq0ydPl8hvE4KJROkWkLvPz4wShYN+hWaRZpFQYIGWJ/WOn6R8cpFjymVlq0DCKvkoPdZmwsLxAV28fg+UKyytzuKUAx/GQjiBU0FQtfCWodHchhI+RBjfyMHFKV1RECBenkXDTjkuwXkQofIoOaBXwyMN7GegaJD4zR3e1jPQcFpo12mmC20x5yfaLAPB8h6ZMadVqNKSEhkPLaKbrS4SlMqopkK2YupIIz6PRbrO8soKwDrazxU+lRilD0spQGmIpsUagZT5BxsEirMHHwbUCz/GJ45QsU8RJRitJSZQhUYa21NTiNs00JZaSWGuaSUorzWhnkrbKSI1CGYXBoIxC6pw+kFJhEaSZJss0WapI05QsVbSzlERpmjqj1vlaCkE7U8RS05KGRFlSK2hpQzuNybTE8V2U1SgjsI6PcgSmw/Wej4TVoK2DtgKFJbMa4ztoT6BcSNEYo84/c7pHnleaKGVIU0lmLFLmJV4plf/OaJIsQ6qUTBkyZZBaILXAWBepIM0MaWaItaWtDM1U05aWlpS0lSLDEFuDtGAcQ6IkUmoKok3cnOQf/vF/8fVdX2M+dHns0X0E1V6W9x3mha+8gdPjE6xZPcyJmbNEXpnv/OAunKjKq295PXf+1d9w8yt/iYs3X8Zd3/wql11yJbpdYt3WS3joK1/jve/7VQD8wCUzdS7deTEVVaVqQvbtv4/HH3yYO377DsZPzfCFL3+esYkTFEs+n/3837P74AGmT81x8UVb+clP72XrpVdy7a1vfM5a+bw7GI/OznDrTRvYNrqFpfkYW0z4xddcz5N7Znjfb9/Gv3zpMBvWV6kULHHVsv/wAapWUFncyX33/QAd+/QPV8iaTW64ZDVOYugpDXF59SY87TI1/Rj+6E7iVkCYlUA7oF2Eo4mNS+RIpDOA9JYRToRAEaeSxSUXNwKtHFTWRmFx0iahJ1haaVEquTgWHNclVR1kbS19hYjFuE0g8wjXHDkojMoT3Kzr4rsuwhq8wEd0trMojevkW0GZJdRq9RyVhyXOTE0R+iHWCKYmzqBShVUaH8vczPTPNXgcx0Fam9MqnkdYqtJdLdNoJCil81ENGkqFiOWVRQYGemiKAo32It3VAvORD4Brcu7U04IssDiZoVAqsnHDBow+S3lwlLPJPqrFPoRNGeofIm0vExQDAselJSWRdZArTVo9JSJrEBjidhthHZQ1RMbS9iy1+WWC3i4yHJbm59CDfWAEru9jXJ++rgIV1yLSnJoIykUc3cJVAZmMeeHOnZx1PCqUMDNnKAqHS4YG2CRylDIQt+jKJOlCE5llpMkMTmkLJA7Vci8NU2eN6yMdC5kibedcbqol1kpw88XQwyWTGuPmyBOtcB0PbTUGyJQi8Py8wSVlh+/Kd2CO8FAml1DmTTcL+py3+Oe5YFTe+NOd/f7PSvEyrUgyhdYW188bnOddlgZwHVRmAJdUG4SxyFQihIvruiSZOi+fc12XdirROskLswVlO8MNjMERGkd45ynAnL/ucOXmQrZ3zl27GCenIbQ5x2nn/0OT27kRYPQFS7oyFmMU1hEILAqBEOo8ItcWtP5vLOM/443QunMMa3F9H1cIEpWipSF04Ic/+nceffxeNmy9iHWr1tE/GNI30MuV113Pt/btpd3K2Hrrm6g8fZKtl17BzOwE67ds4PgzB7Dt3Fm8vn8ts9MTXH/9Thatw/Uvup6nfvpjTjVPU5H1zrWEX/+1d/Knf/kJRotFnvjhFzn66P30bruMvV/4Nj1KsEpqtl6ykSd+8A1WDfg4bsgff/KP2FkZZf+uaW65/Frc/uHnrJXPe7F+z5u38tBjxxguhVQHXOYmmpgUpk6P8bnPfIGLLh6hvnwS63XRTl1mppYZWXcxF28Z5S23f5m/+6f3Mz3tkUbdrF+/hcbkMvuf2sfLXv1GjEpYMzTClHaRNqWerqC8EFcZlsVZ3LCH1C0TpKArAa7NSLAEYZmCAUO+5dJ4SNlAEyGlJPCKSJ2gvAIYgWPPfXgEOmvhO4od61fhuudCazoKAK1xhDjf9c+tr+SFyXVwsURRRBgWcL0CRgtQLkEQYLXMt82Ox8BANwtLdayArq4KWabQKiONNS4+SZYSlUsYo1GyQbOZo8NIC6xwaFpDoDTD3X2cmKjTTNu4TkBiFG6Sq3pC4eLoXNWgnQBXNOnqXs/YiSmCgqXVSmnJOuVCL6VCm1MnxvnlX/0VmsDK2BlavqC/p5/VhRJNLMVikcGhVfR39Z0vPoHr0YotlahIPUkIopCiCyutJu04wZBTRjrMqGSGKNT0DBSpUED5KavLRbZsGKG7q8CZTKOVQ4uU2uIila4Qt5G/lpfdeBU/fHIPs3qFa3dcSltqjAIpFMVyiKMzZmfP4q8aRBgXx3NIkgwvDHJ6Q+e7C2kAHDwctJQoIF8OHHQaI4SgnaVgO3MbuUALCJN1CrL8ORUDgNsZdXVOBaHPNxWd8wqLc9RCK82pDGNBZhl0iptjQWMR2uVctrZK8t/lnzfR2cHpC0oNoztz0i1YgxsEIC2ec+7ccv4YYdFWY426oGnWwHmFy4WpTP9ZDaLtOeVGfv65giV/NJM4V9o4IqdlRN6Q/DmTjFD/pVj/7PcuAusIjAGdda4FCqRFtuZ59vB+JBm33fwK/uN7d7H7/h+hpw13Pfx93v3e93N9NEjDg1OJYk7Ok8YZPSOriCdrLMwvI2XK+OQpql1Fdu/dz2Lm0dywjePTE7RnziLK+bms37qaL33pO2wavYLRkRBzQpGkLne+93f4t+QfmHriAR7as4d6K+PE9Cluu/Vmxh5+lI0Fj6NLR+kOezHVMg/d82M+dPu7/9ta+bzTIOOzDYpeL0cOtxl0BlCJT6E7412/dQtvfdP19EbLXH39jTgFj3ZNcNONL2JiaoqzZ07w1l9+NeMLZ9iwaS0nJ8c5OPEQBxcOsm3HAGmtQXd5HS21ilbDoHWK5xpit8GBo09zYnwfmLMcPvwIvlok0x6udEi9DK0lpe4SmWygECgcpBBI4ZP5BTIlsV6EsRLrXbiECktqJZkw2FTiOgHKzflFx3HQWdrh9hSucCiXCsisnYfbOy5YWD3QA16eSQIg4zauYyn5AuUajICldgOroegFSCkxOkWn7c6WU+KjiFyBr3NeNIsbhGHI5pF++oMSjjTgtLDWMjc5AyT41scsJoQyR1ZSWAwe1rH4ukiiJH4as2pgmNE1/WzsX4XwBdVyhcGowIaNoyxPLdJbCOjpivBlRrqyTL22wqmpaeZOnUW2ljF+pwBjEZ5LlkgCYTi6/2mKwmX5xBT7H3qKsrBUTIifamTSIm42+YPXv4GbL91EbfwMTanYfNFWFmfnKEcCb7kFNubSrhGGowJdGuaaOXWwWF/CT1aYPTrOjlIPK4f3w2N307X3J9yypp/FpXmG+rtIkzaDvYOoNGPV4BA6a4PUiI46QwlLM4mp1Wqdiee5vC2L2wgjMWlMICzGSIyTS0O1zXdBRuTNRKWy/PVnssNLazKt8vminpt/fd4YYsiyGNBImS8LWktSmZCplEwmJElClmUkMkNrTRy30EaidILSGQiDH7gkaZN2XEfKFKUypIrRpo1UTYSTYWxMO6lhdIxUcc79Go3qNFqzLEFpfeFpOhSHNfnfKUUqFVIbpDYoY0mlwiiL1jlfnlNGORoGUMoglUXJjtROWzIlSbIUZTSZkmRSI5U5/9TKomQuo9QGlAGtBMpYpFYoo5GpwkFw4ugxjNK4XsQf/uFHEYlk2+hqTk4cpd1YYP/TT7Fca/Ljz36av/3up9n/5IOcmZpk6ewSjdijUBkGt0xL1mmLlFYmaDTbPP300yw1G1xzyZWcmJwHwHMD5ubm6auUKPSMILy1/OM37uIPf+uPiFdXWd2zmmhghJNHT3Ll697KjS/5NY4cXmGgEFGIPP7yC5+ldmCcpw7se85a+bwj63azTtSVUNYNFpM20ZBipe6wNHuQbvckV2zezNmlo8R6EWu6SOVZbn399Tz640f5hetGefqZWR5W97Nu8xrSBHxvjsmZScoD13Ai1mTxQTzt4poyZ9pNZuU81j/O9METLJ2Zpu7PULU1fBOhggppzRA0Wsg0IwxAKMXU7BKJ9Th8aoFm4BA6Ac12k7YRzNHA83LUoFJJgotSHgcmZtDKAynACLIsI/QDAuHiuR6O6zE9s0iXG2C0wAiJ40B/dxcn52u0EkUY+TgFH0cZRNGhEvpomxLXU6JIoy202yukWhG5HolwcK2DUQLfCo4ePUroB9QURGGZqiMZ3L6JZ6b2UXCHKWcO+8cO0jM7QF+1xBNH9+MViwDIwEEREyjL6nJC3woc2f0gmbiCHluE3mXWd23i2IGfsnPDCxjsKXH3D35CT1DmDe97G1sSwd0TP2F2ZooPvO+9rMwc518f3IsjU7TvoJShmWWUAhDtOre/+Y380yf+gaBc4A2vfjn3/vg7tGnSExWZrC0jEolTsXRXivRoQ1DpopBK3vm6WyiZNjfv3IqrA5685zv8zYc+jIfCDfL3pa93iM997ON8e26GG51rGLxkEye7hplIW2xMLctPPcjy/AJppcjS8jyQMnbyBF1BQOg6ZEKgU0OCQgpLIXDoLpdYmJsn8Av0lSvoNMEvhPQPD1Frtji7uJQjYA3atdg0yfXLOKQqydGjsUhyRYSUOVWRI89cmuiJHK3K9IIFXliV97OtxbouOKBMrmXGKFzPReo0R+MOJGlCkrQ6umWD4+Zpe1jQWQdhZxfQqxDgOS4WRaYsrudhztEwmcTzchmo4zsdOqSDdF0BBOd3AucOZqCjGSdfkH7WHfqzTkltz4eanAM0P+sedsQ55Uq+gzWyQ7U4nZAqwDoOcZbRFRVwM8PM0iKbtl3Cvff8iHf9+gf4s7/4CG974xtACAIjefreXZjEpThUpbAvYez0Eaq9azh19iwjYS/DQTeib4mh7TsolUr89KE9DK3q4pptL+CBBx7koqsv5zd6cz/JpVuvwwmO0dXlc6Yec/2VL+Fb//Z1Dj/5GIenxliZm6N39RCf+8bXeOSx3Zw9cYyXvfLV7Dn0OFe87Hoe+t69vPK1b6H09HP7U573Yn3djkuYWlwmceDhQ8/iqipRU/PSF19Gb1+F73/9KfQqWJ6WbN2omT4+ydKZZTQx1e7tXL6lwNT0JJPBCZTbj0ubBXWMWmxpqTGm4r+lP7mJrtJmWr7i6NEzuO0VBgY2U/RjDhxbYPdjf8eHf/PVSNMGcmNE7BkcG+C5GY5rcR2Ndgy+9TDOtVugAAAgAElEQVQqJhAuKIHT2VoCLCYN/vHvPkHfpvXMT8zQUDWKWRHrWoQHsUroLvQB4GjJpZvXcejIYYSnsY7IGcogIklmGSz3cqa0SNhICNf2Uk9WEEJQDXtYDOqUagnSs0SlKqFU1Bo5d9a2GY5jaScNLtqxg2f376c7CBBOhk+Bgm3SbqQot8HRRouROGQxaDHRbLCmd4h0KUejWkpWfI8kSQilIQpCRjePoBbaLPiadPIAl/zC5UStjNa+wzR7C7z9Nb9If7HI2H0PM9A7wG0vfgH91qO9Zz8bNq3iA9dcQ31sjI+++heZXV5hbOosMRlrh8pc2jb89u1vRqGJjeC2S64h8Crc/LIbWJ49xbfW9PAq4xGs3kKw636cnWsp9Hbz0te/h0cf383kQspaN8Y3Hn//3a/yhlvfwGB3zv89dv8j9PWv4miywlU7h1nafQhTPM2WYkBX21CyJZLlOqZYYmGhTmUgIhQOK2kbP/TwnADjXTB4aKmYnJvBcRzSNKaRtfPF3XeZXV7OC4cHuD6OEKgkxXomJ1CEh1VpribqyOSMNbiOwXU1nsmpBI0g04ogCJBSkar8fWnF2Xl9s0kyhHeh4Lmuh0wzvHOZ0Z3CadDn6Q8pc2rPGoHzM+O2zvU7NKBN/j+Ekxd1Vwi0BetaMp2P78qkxXXtebOPVoZV3Xmh+c/UzTkTjVS6E97UoVLSBH3ec+N0FpT8Xjp3T7nC6ZhjJHSolLxHY8BajHUQWoMjcK3ANQrdXmFiZZmbXv9avvX5v+cV193EiUaNj/3xn3Lf/XczummUsKef7Vt3MnP0LMtxi+HyaqbjRUS3oSvzCYVh8sxpov4ywUAXB48epdTTw1ytyez0GDe98Ep+vOt77Nicu7TPjB1kefYUsRnhbb/z+zz5ma9z9kyDP7/rc5x6eIpXvuYGXv7Km/nO3sd53y23cvCRp3jdL93K4aUnmJ5fYOvqNj85dDej4XP7U573Yn3XD/ewaXQL/YNFPvKuO3jfb/85N129BdeTPLrvIdZeXWElLnD15p0cefY+ntq9ws2/cCUjV13JXd+8i1fsuBLdSGD1VuYOTNHX38WG/kF2ff8Otl09SLkoiNnNibHvMNRzCVes2cqn//prvPQ16zhytsWgX+G6W36N5WZKq1pCNHwcx8GXNVyTkfohFonvekgtSKxG+AaVJGjH0tRNZEdX2mugL4NsfhZ3qcH6gVU0F2qsHh6hVqsRRRWiKCJOE3r7BxmfnMZ1IkoF6A6LOK7P5NgUWTPj+PI4l1cGmW/WcA8soXRGoxjhHRtjwU8o226yLMNbajO5Ms+GobUs24Bev0CjFNPb34OnPC7ffjGuttRmzrKSpXTVNO95wxtJom6c5XlufsuLGVgyxMMRgRuweCCXu/3yldfhhCWO1xZYwOK122x3HS5+x1v5xw/9GV53mY23vYKv//adJK7lfV/4Sz7zO3/OgOrimjt+nUOHjuJ9/xkal61m6y/dyoMf+1uWMbzuYx/kW3/yGWaTZW7eeAk9WYvN3atJ1gzxt1/4DBtGNjDnuRSnZlgKxwiKJZqnJ7j7fXdw36c+yapLr2APSzhC02gvsvzTB5Gn50nOLrD1up0cNpY/euktTMs6/qE6vAFeuO4iHCkZbXTjHj1G/7oBJsZOkkUhC2kTUZVETkYiEzwRcWphgYigg/BA2Iwsy1A6w/NcUpMbNFy3ww87Ft93kQ65O8510BKUVDg2T0A0VqF1Cq7CdR2UsrTqdYJiCce1aJUipcQTHp4XYJ08DyRJkp9DmFJmaG0IgjxwS3WoNd/1OpHAF/Te57lfrTuIFDzPRWqFIwIg53h9388VSp2/+ZkYbQwaY3PHL50saLTJzWJaEgQeFkO1UqJSyt2HaZpQCAOkTPB9/zztFfguBT84z0evW92LdT0M9rzBzBUXGqqO42D1BX021kE4FqPB8b3O+eW9H2s1kfZQosSPvvpFWNvPd3/0DVZOTfLRO+/knoP7eOIrd3HprTfyzKEjdMcltq7bTMkN+Nd/3sXHP/1JPv6eDxAvWxZlC69LIJtNQlWlLhQf/cin+PI/fplL/QbPHn6GqeUW1q0y1cg5+LmVCaKCx+VX7WB5fIIdN1xH++AD7H92NwVZZbl9gtHhHrob83zoA+/gVa+8jT/6kw+x7dIRthQ28IV//yfe9OKXs/fUnueslc97sb5sx4uYPjOOP7/MEz/5Jq946Tou3baD/XsOEnZdzt0/uoey38t4OMPLbrmMVnKM3v4RTh4+y4aRjSy05undvom6yHC7HIY3X8FEOoHpMUR+laU5l7PZPqKwytKZsxx68HE2bdvMNZe+nC89+1l8pUnlEU5M3YOcWGTrmheRpkVE1MOKaJM1M2KrwGQEjkugHYzJ2NQV0EKxbt1GvtO5jA3fo7xmiKH1G5kfXOG+fbv5tbe9ma985Su89rbbWFmqMXbvvVS6qoTVKqfn5unpH6Dvos1MPrGPW95/Oz/58jcZXb8OM1Tm2D2P8JJ3387YI7s5Oz3Fzhe9mE2tJcSgz/13H6bqu/SvWctlr3oZD3zr+1z/6tfw3X//Frf/3vv5yl9/inf/7gf4h09+ine8/S088sMZXNcyFJRZf/VV3Px7H6Fo4E8+9xn+4Q/+HLm6m/Ezpxm0IX8MfHvP48wYeMfNt9IzdZY+DaMb1uJJh9PWEp+d5ve8IRZFmS7XRx5OybKAZo/l4F0/oLWyQmFDH9mZSfoOHuWF60eJPJ+e3c9y2xXbmDkxTiFLqfseor5MbdduPvzSVyFFgNEtgnUbsUpTdwzZpou4+1OfYtvqXqZPHiMYKFPJLK2xk5yqLxDVDFcUu5h5Yi/Wz1g8uI+WtQibvy8zi+MUqt3UhhyGC0XiVOJlGb7wUIColpmrzZNWK1i/AtKQJi2U8Al8TSXyCSMfz+boulSssLRQA0eDdfAcjzYxYeDguy4GRZykuG6IVhbRblMpFnI+2IIXlajXGlSiMq12HdcLEUbj+yFJpnC1xA9y/bOxFvdnApEc95wCQuO44LoC8DDC5uFUojOOzDooe6EhaC3nqYUgCmm1mkRBiJSSLMsIgqDDJ2tyk67B8wudgp8R2lx14nouxmgCx8ULIrSWRKGPqzKkyheWQugDlmbcIvSrSJVRKlU6zdc8ygFA6xiMmy98HU05QlzQepPL8PJF0eSKEiPynUOaLy4CB8cTSEfn8xOTBlNnT/PoY3fz4ds/QOumANs9wAdf+yau+cgf8+rrd3D9zbfgzEueffwZzCWrueMv/18e+96j/NZHP84X/+ZTrFiHOLNsvXgDtVadU+NTfPXf/wODy959z1Dp6eXMzBK//3/fwRf/6pMANIWLawVLZ+cIsiqIkDCD+dNHEIsRX/vAZwnDkO/83d9S6enlTz/xJ4wUezFBmS9/4V+odFdY2LrM+NzR56yV/2Oe9f8fj0ceeIDb3/7bfH/Xd3GqLYZXDSOyLk5OTjPbmmZ932asWKBUCOnpL1Ao9LP3qacYGVzH7OIUcbVNYTBg6sAZ1vT1ky6D9RN8Y9lcuJKxxf0Mj+7kyYcmuXbntSw2Zjh8dAVPrzDcO8LC7DyD1WF+uvtRrty2Hc8GjBQGac/N4QcBhSyj4vlUlWAgDCloRVdXRHtlHp0oFtMWD/3H93jf79/BF//XX7H/0H4C3+fgE0/iZxmt6XlUM2Z+coqFiTOUvDxRQhpLq93O7cPtjAHpcuLUSUqZIWu08JspRVzU6bOEBkItmJueQS/Pc/DUKUQc4rkSXc+YXpqn0FLUszYV5ZK1mxRSxdLsLBXHY/Hkabo0XDk6THe7zWwSs33bZbz5xhdgTh1h29a13FLt52WXbOGqjau48k2/QvfRI7xu+w5OHTqGoMm6rj6m6gsc/N697FwzyOVdZQ7u+iEjPYIt3WXMs/sYdiV9CtYmDsWVZXp96J2v0T59Bt9RzJ04TlnDwScfRboC2glaLXBxd5mVZp0lXaeuWoRGk2YN6s0FKl7A6ckzbFvdQ0NkDFSKTE0njAwOsXbjKGFQQfgRCkO7USM2GcPdPTSSmPHaIhe/9deoPfRTRkdX0+2UsIkk6upmSiacaNYpXrKeuZJlZONFbNu6hd4owrYayJoldX2szsDTyFSiFSSpIkmyXEWhBY4I0NrmaNQI4kSRpJYsVWjh4XouOsuwWYLjlLC2jMwskedgvYRKIcBo8LwQx/WRWufmGJU7IZUxGAtYwY7NG9g/doJisZS7HY3FdVwEAi3leW5bdfhvbfR5hYnSecPOcd18FIcjzitFzqk4/NDPlSnG4neC+QWawIdyuUi5WMBxHLq6q4RRSFe1QLVYoBgGufKJvNgK10FqhR+GIMB3PebmZkmTlGKhgOv59Hd3sbCckjtcfHKnpttxHgosufPFMz5COxjt4OAjcHE6BjLTMZGlWYbxfVA5FXL44DOsu+JSfvLNb3AiqTE8MMLxhx/lw3/9Z+x6/Cc0lhZ4/a2v4YF7H+eW33oHycwSL9x+BXvHj/OJ//0X7L77PnQx4PiJ4yhrUDpG6iU2bxym1vLYvu1Sxk48w/LUCRpzC7zzN3+Hz3320zRbMcYYxk+fwXgJp0+OERQtR/ZNsWXn9ZwaO0amBYmSvOfDf8B1O67jgf/4AX45YqUGutyDrjd51zs/8N/WyucdWS9qy7d3PUAjbvPijdvYe3CcVmueOGnQ1zeKV6+zYdVWjp6cYGHZ4Ptluvo2MzF9mGLdsPOqGxjc2M328lme2HOQY88ucOPgLUxPTbCyapFD+6YYHi+RnGnxo/ufYPJkk6s2r2J4JGLi0Cy3vfzF7Js8zGtuvJZuXOaXxzENxbb1O3DCMq1uxeTMFI7VrB8cxVeGoVVDHN+7jHSK3HfsEEFHjRQUIgoqpeAK6jNn0UnK1MoyfhSStJYQgO8HaCWJkyZdfpQPKkjbgEu6qLGORyPNiBp1Es+QNhU1meBkGbbqc2YppFGAUVknDTyask48t4KQGm88RktF9uwyvnWpLyqyNMHXitBzaKUJs1XL5MkDOCMbGJ9eoDndoOZnTGYGk2qancTPdmMFk2m04xJJjW4uM1oqc7o/Q9RbZNUIpQU9YQnjJdigQKxTgnIJlWrKQ4Mk7ZhiX5Ge1WsZP32S7ovWsW92kqDST9jXhZxbIRMBJenjhgavFRMGRVCWuCRQiWEmXaG7a4g0jak6XazQxjWGQCgmxydoKEkhKHDF9dcSB3DgX78Na0eZPnmGdprvrfeKjE0jQ3SNrGL4kovo6+tjTdiLVTF2aQanPk+rOcfc+FMs1WtUdMAi/TjREJ7J5WZSC7LM5EOFXaej7RVYqzBIyoUSUmYYLNYJCaIIbQztOGO4p4+eKCLNYpbTjCRuUyqVcHUn+8Ia2krjSRfXB2vyDAulBY7jo6RGdwZnKGlpyDZYJ5eo+QZHuBRCn9DLG3zadRA4uMIhSRL8MGRuavZ8sFLbWEqFCByXKIpoNhtEUYGVpWUqpSJr146SyAxhNEmSkaUxvaUBJsdPkijF2WSa1atXc2D/Ua6/5gXILMNzBb7wWa7X6OnpwXeDzrxDB4RgeNUIGku7HZ/nWYxIscZgO3k0CJPnxZyPMwVpLsgDEQKtcrf5OWu7cP0OFy8pOQFeOWR5aYFV/RGZV6OUtSilMcG2tfzyrb/E7374nXz+u99h3R3r2Xr9JawcHmNi/zM0Vw0wdt9P+d8TY6SRYFV1CM9IJs6eYdVgP93FAoeffZCRtTvY/dCP2bZumMmzxwmqPfm9T8JlO7ey+8knkYQMVj2S5iJxVueyKzdy6eUX8+xjj3I2m+JF267g+P4n6Pd6KHeH3Pi2N7P9spsoTjf4u49/+Dlr5fNerFdtuoHTE4cIuvs5cGiJxx4+S6W3RKOuqE4dY/1IN2XTR+/AKk4c17h6hekzM/zCtTu49qY+AptycmGMidPz1PyMN33gXfz4G7uQbsjH/p97eduvbkO0NdVwPQ/tOU5rVvJsq01XMaC8KmJloc7G0Ss4eeIkTy7fz0pN8O43vYtql4+jUrpdn96Nq5mSTVaWl0C2eWRijNFgiOnWEnffcw+LOp9bmFoJoc+9999LV1ggVYKwUEAaSaoUjrFIBNoawkKBtpS4roMAYpMSeT5pmhJ4ARkaIS2UfHS7QVCM0ElG5igC6ZF6Ep1ItBcik3wbq7VCO4ZuNyI1lnbaRlqF75cQ1lApFHGdRaoXr2Xq7DJd2qV7wxA9Wcrk/BRbLtpI3LmRWitt6lGApxPcMEQUfZIkoTW3QFAdpjk9R7W7i7Mrc3T3dRHIFBO4JNqgSXFqKYVyNy3ZpNiao1rwKfseg1WPYk+RdiZoV7oIahnFSoUeqzDlCkJU0apFb0FQGRklTixuscLug3uZPnWWF19/LU8vPsbAiYirX/FiHpk9xpo1Q8RRxrYdV/PCL72K6tAatgQuxmkCcNu73koriVk8PcHef3+Ksu8SOA1ssw3DvSx6CYFXwLcFiq0Mqxyc7iGyLHfGlRwHVwiiCBxXgHXJZEqxGOXmGeWhTUKx6NNuqFxm5wUIkzsaG802zWaTkufiO4JiGBG3YsIwRDsWzyhcbbDCkkmLJzziOGZwcJBysUDSbnJuHmM5ili/fgPj4+MkicYXkGQpnOO6URQiB4FPqVIlCAIOHDqSzx50HGSm6equ0Gq2cFyfVtwmyzLiRJJlimazyeL8XN63CQq0kpio5OEHEcfHZ4nKFQyC05NzeEE3zx49zZYt67FoMg1dvf0ok8v9hMmzQyamZ1mqN9iybSt00vQApFJ5gomlE+EHjg1y3rxTH8y5gKZOsFPSjimWS6hM5uFnPrjWRdtzsr+MG19yM3seu4fKyBoe/dEDtOKAX3zRC7n99rdw9OAzZAuTfOA9r2Fg2zZG+qts3rSWZccy0NXDvJIMhWW+sesHrOvvB6Pp6+mjp1DhzPwcY0cOkZgM1V7h4qu3c/+P9gMwsGqYJ3c/RRpFrOldxYmxZ1m7dpDBSpXGfIyRGdPzc4ysX8OxZ4/RNT3Fy9/7f/HUgcfIFqfZMVLi/3z1s2y7rv85a+XzXqybSxlDA+vpKdzAk4//kGrUj7EO2i7SPdxDo6/IwSfGGOhzWDNwEes2bWTX4hRTS6eYPwOLapkVPUHF9LK2MMrcM7u4cccafnJoHx++44187bvf4PJNF1Grx6xbtw730jJlXyOjlJqxFHTE4p4Vtt70Gg49/B1uuHo7/eURjs83ueklN3Bg9262XH4pC61l1vVFZFkbG1nu//ou5uM2pXI3Xm9+gb3UQWSWnlI/idL0lUukOsVzCkShxVUWF4svOg6/ICIMPVKZdEwF4Pm5Xrvkesg0wWiXYljGFx4pPr4vcIwls4LA83CFR9HzsNriBxFZEiO9gJVkhTCM2H7pxRw5+CweHnOxhlI3WUViuzNKxdW0M8WSWWLtFWWUP0epK29cVdauZtivMD09TXOpRtLTInEcBoe7GFeS2R6XSuijTJXFukshW2GxtYIyAp00EUGRZETRbVzWrhqh3R9RdODya65ioLsfKxxK/T1s2raBHpmwefUQ1ncJu1ehpYP0agRBQNwUBIVuXh85JO3cRPGOz38a2V4iWYnZurLM0f3PsGbdRiKvzczRHzO5N2Z5foalpSVe+5EvMbH3ETCWLG5QKgtUOSBREbHOqLQNXUSsQnOktYgtuSi/SJqmEBUIbEqW+rg6N71YNwWTEPgOfsepVyxE1OsrlIKQ3qEqgV8kU5Zms0kcp/ngP2GxRlEMAzyp8SMPz8vVIH29A7kOWXs58nQdgig/h6TdIgo8dEcNkrYzxo6M4TgOgRtitKRareK4Hr4fAoZyFIHv8uSefUhlqFR78hS+DqWyXE/QGlyZJ0MWohJSWoIwYtuqPkTSINMZkXDJKkVkGJLUFaViN6lwyLKMtJM+mGnLk08fw+gU4eYFtVwu067XqJSLBMLF4iGs5vjx46hME0Q+V23egLUuthMWZQ14XoDpGG+E7UgA3Tzw7FzTNAgiZmfnGRwcxAiDcPOkRd9AZjOKQcD2y67ku9/+F5JSxG9+/C8Qy4s8dHAvV5XXUO4e4oarr2H3gz8iLFV5aGEX9z74AL/7R3ey98QxPvn5v+d33vwb7Lz8ChYWZ8laLQ4e2Mfwqn5qjQaOKXNmcYHXv/JWDswdYcOazrCO6hCZO4mVltb0Ausv3UrWqrGwJDl5cpE9B75Epceh1WgjXOjvH+SLn/sSL7jyKr76z9+kPZOy9/HdbNkx+py18nkv1r7xmJp+kLFjexjsGWTjlVV+cP9jvP61mxGiwImFWZxKizWjW9k4tJlGY4m1G6pUil1UV23j1PgT7H6sTa+XsP2KtVy+/RKWmxkbsg0cfnaOqDTKra+8ndNTCxzYO0Hvlas48PiP6XYdbGEDfRuuY+tFKeOhZF3vMJsufwkr3X3Mnllh/NQRxqYXKW9OePjAEWZaBabmxhncsYHa0ix9q4d46Yuup3HZZgBu/uDbERayLB8+K5OUWrsJrpObKuIUoTICN2Cp0QAclAGlM4TIE+tkktubtdZkOqMQFFBZPoFD6gxM3nAyMkN3ojld16XerAEQGoMX+WytbiJLJAOb1uJViqzvG2B5qUFDaVaPhFR6Q04l6xgKYXv/DrA+XlCif00e5PSmj32Etmt4Y+UOnEJEobWELXQjyl0gXdCCrDWLZx1sfwVXNpBpii8dskaNuXoDp2WYz5boypo8e/wkgRFYX1BbnKClQuZPHWLXP3+WbidisZnhaE3SXiYTGUWR4QA6KKHLIa3lFTasGSVbXqBbaGYas/StHqUSukid8cp3/hZzJ2r4wmKVJSi49A3lg4xrySxB1Eu0dhi5NE+fUqw0BU0JbZFQ9ALGCxEmLNBOYjLtURoYJGspAitwBTSVwXVcjFZEvoc0GpvkiYa24SDQLM0u0tvby8zUPNZaCoUCgeeircEKB6My0tSijCYslWg0aljXpX4mxnF9tFIEfkjoBlSqJbJ2TLud0Tc4wMSp0/kNYx1WDY8wMTHB0OohxscnEV6JhcVpCoUCadzGcTyMI3DcENA0pe4EeblkmcL3XYwR4JNHF2gQVqATyTPHTtPOJJWgQlvXKPge1mh842A8B130sE7eDM2zPgRGafwgIHBzF2azlVAsV4kzSVNkGJMQei42UwjXpd7Ox60pqwj9vIejtUFm7Ry04OI5uf4cm+uxz1EhzWYzj5dVef63VilYhzAsMDu3RNQ/AMbyype8jGWVsiaMSEKX2mXDjD96msNzU4h0mdWrRzl6fIxXvPYKVj87yPd+9H3e/6H38v5f/1VWbe5nzkhedc1tPHzvDxkYGGB8apKegWHGDo2xce0Qrdoyi9M1Sl4ObnY/9gTFahddfvD/sfeeUXad5dn/b/e9Tz8zZ3pXHXVZkuUiuTewwcYY7GATgnFeUwwhlAAxgZBAHMCBrH8gGFPy0kIzNhgwrrjKlq1eLY2kGY00vZzedt/vh31GcrJwki//xZc8a52lKWfOmXM0+97Pvu/run54hRyu65KvmBi6xHy2QG9vP8F8Hdv3aevIUKoUceouU2NDbNq4mueeeZJkJMHRw5OvWyv/6MX61JHttK2wiKZrHDl4HFXtZs3KxYwNnWblonWIMxpr1w0wPzKKW2hi6doka5s7kL1Ovr/jUbJHp/g/t3+WvcOPU4lN8dTOx1i2ajl6rE6T49Bz/gaeeeIlOlesZc3mLSjNMhPSLpLYqEKeXdt/xmUXvoGiu4xMZpynfvt/6WpW6V5zHdtfEahi8fgLz+N6AVPVEldddQXTFNm343lKdpWoIEKlBv/4ZXb/9CEs30GPGNiei+15RDUjvGxzXfAD4oZG3bIIxHAnVJqbp6urC0mEKCJ+YxLvqRI+OrIsYSRT1GwLX3SoFEv4nodiqESNOJKmh73B1iSO4+AFPjXXRXJ8Ort7qBWKeMUCf/reP+dEYQrdkVGoUj19mjZfJsh4VMZyONlpFNti1nK58Ip38+MP3EqtGpCdq5BpD4jEFIK6SV2VmD6VpbPJYLJs05xZxJ6DJ2htiSK0NlHLlYkaIrKoUdQkVkcMBMOgJoKCS1wGkwiWpKBWpmnvbCMbWCgRG81TiCSWMF/IE5EkZE3HURTq1QJNbRHceISaHKU1ncAIupgsVliWyTA2fIJyqYbuQ+BWkCMigq0SjYTkadMMD3hxvoqmxwhiTcyYM5TkgGWpOF1SEzlHxBE97GQHqpFgYnSclsXLqLh18AKaUgaiIFOrmRhGtGH8CJ2pgSuhyDKSFMrN2lJJrLqDYRjU6vXQui5J2FZALBknHYmEkQCE8jzDMHAdhwARx7IxrRqlXO1MzvTIyAiGEcabutQp13PYfhXbqSNIoGoyhqGhKTLtrQOMjIzS3d/PfL6AV61heT6iF4AUoKoqltXIVfd8As8Nr8oUDV+RUCWN3sX9VGs1krZGIqJRzIfF5uT4KDXcMDI2CNBUOcyDV2Qcz8HzQq22LIgUCvUwDx5Q9DALxtAjCJJIUyYFQFRTQoMOArquIvgitmchIiDL4eOEmfMOruugGTqzc9MM9C9uyPsCZEklCAQsq057ezue6dLS0sIL27czU5hlxeZ5MqLEoZH9XNJ1DlL/ZnY+9SjThTx1KeB7P/4RSUulXJrjE3u3c/UVF3Pk4B7O3XoV65evJjt2lOUr1zL20IPM5Yqk29IocsCL24+y5PzzOPBM6Djsae2m5jv093Tx/KljpCqt1OoCNdPiuuuv49UDB0klmihUqmiahuXP8MZrL2dmdIqx0ydZs24RxZkcspp+3Vr5Ry/WE1MH8CM68XiExYuX0dPSyqGh/SSkKKInsWTRYiZqE7R3NNHT2smug89yzrIO8CWq9RqClGF+epp0fzelYg7V0Gnv6mTeGmNRtI2nd+2nuXk1Zr1MzZco7Bth7aIm2lI1TiV2sLIAACAASURBVJU9rJpPMp2kxZX4+c8fY3lPEzOn81x584cY2VcPrbOOR9wKMD2XF597mvlYQLFSplzPExgGXj20aOecKpoqU7NqBEFAvVajXC4hZAWsuommhlnPruti+j6eD5lUM4VKHs92wHXwhJBEEihKOMAShfDgEkWiMY1SLo8iSMQiBrqqoCR0ysVSaLJAwHFdJElB13WmpiawvRqqIvDgT3+Am51FRWRerKHOWcxaHrW6RJMho/tZmpIhGQcgiFToiUWItut4AgTlGtF0HMd12XTuRszaJHp/ivmqx5veuoVCNceUY9Le0xU61aJR9JrJXDWP4Au4hkEpm6dTT+DEbaazs7QKJcy5CmqmD6tUIKJFKJZccAvoRoqqVULVm+lIa1SyU8g4pFXwzAJNgkFMlnGLWVoTKoZTwBYFfFnArYERUyjYYc/algU0QSTQdEpeGcWu06Ql6I3GmRqb5MmdB3BEm87N6+laP0igRmiOBog4pKMRIqqOLwTIkkJHezu1qonjNTI6fPDdRoC/KKMoCjW7jh8EVKqhpV9WQNNkmtItYTxAI6s5nU4T+C6yIGLEovhBgGVZ6FoTlUqlYcKBRDx25njRdAXbMYnFI9TrYQa4aVZwHRMCj5MnhxEEkZnZWQRJDnXRjocoCbi2hSRo4LvIioIqa2hqOBhFELFsm/ZkiqkTI8iGQjqeYOL0CE2ZNoaHj6PqKoqgoGgavu+Hed+ihGmHxV9U5DAMynaQ5DCDXQhcbNPBiMh4Vg0tYhBrnHg8x0VRVEQ5DIBaCIgyohFcy8ZyrdBgszCQ9H26urrw3fBKE0UOJX5BgCiFw1RVUJibm2PT5vPYe3gnN9x0I6MHhzhXhGefeIEbPvhhfnF6Ek13sWUBz/Sp+gHZfJHevg5s32NR/2JGDhxkzzPPc/7569m1dxfVapV1m8+nkJthbmqEvtYW9h44gu+Gka63vPVmfvXkI+zdu5uBFcso5PKY9QAjGkNXVbacdx4TU7Ps2LePmZkZrrr8HOrVCs8+s43Wpf0UiwXMWpHqfP51a+UfvViPjQ7xznd+hRdP/Jodjz5DdWSYq9+ymm3bRjkys5+KX6FUirKkq5X5+h66V7eSG8uzZI3GldYKIldezfyhXzJUrHH65Vm2LG7nwR/uI7mok7FXj7B56wWYsShmqUZVjiAXj9O1MsUzh17l8LDJ599+FznrENUTJ7nxjWsp1NKUqmVOn5okqgnUtTS1SpbLe5cylvQpnR7nqFnE0iI4Vpm8VUeXGsHtuoblOOiGQb5URElEqdfrSIKIrMdwXI/Ak0CVEBWJwIeK3kAhyRK6rCKJPoIb4AZhepokBugRFU8AX5cRhEgY5qvqIEvMui5yPE61buO6LnoyjYOHG/iU8zkMKcDRDYqOR6QrStku4RYUpKhJ7xIBb6pCZqCPcg60uISthn8SVSVGPnBoSmfYcWqUwXQzOdXD0wzm58aQRI+EZFEo5lFlj2qtQF0APbCp1+vEUhm8qsWiZJJJc56piVFWLOnHKWURgwyZqkLckUg2aew+OsT6/r4wEdExCdCol23SqRYmSg5yMo4Y7aLgOjhqwInCLK5dIxlJUVNdAkniIr2ZkltGEGUcW8atmOhK+P/S2dTBzFwFs5xnKjvDyISJnGomkhZJtnRxOIhCPUvh5DjnXLKM/FwBSbExdAkTAcUDMIjqEeanJ9FjkTBZz3UgEJAFD00VkSUBWZMRAglXBjn0kaAIAp5r45gFktF4SD2xLbBtLMdGNKLYro/ke6TjUU6fGqejo4NCoUA8GbZyFoZyXZ19WJbVmHEENLeEX880N+HaJopmAAKFqkk+WyCmySSNKJZTIwjkUE+thzG4nuUiLgBzsdHjUSbyWbREGs+XmJwpEok2Uar5OPUAx6ljSzp23UMQHRRNR1EUNDFM4/MshwDo7exkemYG17NQDA3Vl1na3ooqeIzOzuBUwpOoIcrYthkOVTWdI0cPs2LpIOVyGb2xaRACwvag74WmIVlGBGRFDmWJsojn+Xi+GxZ2ROr1OnMzsyRUjX3b99C7biNNEyVaNqzj5aeepT3TRs0rh22pUpXo4lZMu45bg86uQV564ikKM6P4XpmhUwkmJqYIRIHRkSlMq4JvifheBCPiUXbKAOzY9hKlQhlV1Zmu5VmeytDf28n2l3fQfl0zP//R93B9iWI5R7olxtxYnu6Wfppb+wmCCIm4xPmr13P81YnXrZV/9CAnlyg//MV9zI6/zFuvP5fFm5cyc6BETfO5pH0FQnQJrUQZmZonIrcTsRMcPl2gXkkikcFTkoxWPKyj81y4eSmvzhXJmh6d51xFSmrDSw3wzEtPY9tZtj31E9YuWcrLuTh9qQG2nL+OfeU8eW8l08Vh9u3fQ09Tgo7uzZzMKRybK1AtVCjNVXni5BGOv3qCI9OztMgGpCQ03yGl6mdS0hzTwnHDibosh8lsCz3lQBCwTQstEiVQDWQxSlxOYlVN4pJCRIqio+D5NSRRIfB8DCkk1BhaBCUQMItlEpqBLumhpMp2iSFh5YtEJRUlEJAtE8MLEF2PmG5gqDpRQcSrmmRqJv7YDKvaWujqSdPpC3SmYowcP0BLa4J2PUK/Eb4WJaiSFKMcnx5jQ3+Gkh4jgk5ptIaLQDmI41giM2WZXGBSz/Ri+FCRdMbxmQwE6rLCadNC1ptpWbqc+YpATmkKQ3JSMUbauhkuuiw5ZxBTlJhy62SlNJLejpvso5zswhQVCp5EUY0ixwysXImBVDsxScX3JJZF2umyauh2nr6EiF2IkcvlGNo1wqF9rwKw79AUQnMfuZZ20qvXoXR30bdpK22Lb8K1NtHbupgaGovWn8/Owy/Q2h4jHY1y5JWdbH/0dwydPIGguWTNArFYLAzKF0R01UBXQrlcMt2MLwScOjWCEVGJKRJS4CMjIIk6hw8PM3JiDEmQUCSZaDRKJBIhHo/jWjUUMUAzNCzHorW9BTtwSLWkkaXQFi43bOXHjuwnHpExVIjqEjFFIiIJRDSBdCpGRIFkRKItrZCKikS0gECooUo+iuiBZyL4dQS/DtQIsFAlj5gKsaCOLLtk4hIdaYmmpIwsekiSQzStoccNdAVSSYW+7jYySQNDEdBUAUkM0HQVUQwYPz2ChktLIkKTIbK4LUq9VCCbK6HrEaRGDK+oKpTLZSQEVKCntQOJBqgh9E5imia1Wg3BD5CQGxprQlOQKIYYskBAlhVEQQF8Mq0Zmto7GTFrJH2ZfXte5Ic/+zZKvYrYlOb6d95GUoxx+/v+gnPXnctHPvgp/v5r36S/v58TQ8c4duAg5155GZqRYPbkGCnVQHE88lPj1MtFPvG5v2fFuZdxwfIL6F0eDgR3jx5iZnYSx/FICjpVv8rU+DxvfcubOTLyJD2rE6xdvRyqHp3pVg4dP8iB43sx0lEWL16M7els27sfTz3LvPzP649uijk+fJjFi1bguTUKc/Nk9AilksmOV8fYsmoDP39iG+euXIqRVqjkTAStl66+jUzMDXH85D5SkkDnqlZEtcq2Z45wztaLicZFirN5Np9/MUXJZ/GyFfh+kpVr+zg58ipKrIsDzx/g3HWrmC3OM+f3Usq+wNGhSUpFl7b+QRwxzAMpl6sIuoId+Exl53n8kcdYM7iWyXKB+VNjBLZN3bH46Mfv5p++8hUsL8DyCK25mhRKpFARPRlNVMAtY3k2vufiuhaqHsUhoC6B69vgeliSSBDIOFJ4KWnaDo7tIak6XiBTLNeIJ2OIiNiyiB5PUPUD6q6PZEQxa3VkWcVIRCi5Nq5tsmzJInzRJC8JTNtl5qnhqRFmqhKx7sUcGx2jFmhM2grX3vReHvrxtzHFCFpHK/VCFdkVKCgSajKFRZXm5nZmrXFEXWdRJINnmRwvSxiSTjSWIpA0pqcmae3tRLGrlJwwWrQ5piIoCqof0EkVwxPozCyjki2QruYQBAPDq+Lmpyjl85SqBcy6x/ToJMWpLNPHR6jPVVD1OPtfHWem5KGku8jRwdGST0/HIE0ddbxIN9GWPjZsvoSxSo56NEbNFNi39xj5qkHZ9ZFSUdpWSay7fDFXbLyUrkwzzx/fTU9rM8MToxwfPsTx0RHm56YZGFhMRNYaCCwPTVaQxZD+E9E0ggbBJZlMIKsqlmOjGhpIoaxM1hR6F/cRCCDJoYFElsLhXdSIhHpjgoZd3EdXZUSE0E4th0CK7tZWLN9HUWSkxokcQseh2NDAKYqE4AYNmK2PLEGAhyz7SJKPooAshzdNl5BlIVQYSQGyoqCpIuDhey6yKqFpMqoiIUoSiiIhKeGOHtEBwUZVRWRFIGpoqIZEJKYTNVSMiIaggKAI1F0LQVbC4WkkQiDCQEcH8/kchm4gKwqeG/bTDcPAtEJGouu6iEoYJOWLC++bgCiEppgFa7wfQGijCfAQ8V2f9p4+NixexMM//AVFyUE0ZEb3DlFpTvKxv/xLtj/2DDNOnZ6ufqyRaR598WnSAZSCOjOnx5icHOOyLRczPTGNEEjosoYgC9ScKrv27ubk0AnSsSiHjxzk/e//GF+69zP0dXTh1x1qdYdisUqtnqNWLjNXGKOzJ8rs2Bx3vO0tSNSZrzlYooZVr1EoWpyzfh1rV20gcASuf+stf7BW/pdtEMdxuPvuu5mYmMC2bd7//vfT3t7O+973Pvr7+wF4xzvewbXXXsvXv/51nn32WWRZ5u6772bt2rX/o2I9evo5kuk3suOVY5y/Js6BnUfou3wrWzSZJ46cJKmAlPJJV5LEliwiiEfZvaPG2JTFCjXF4KaN7DtykPXLr6G7y0fXZPbunuDcgTbmfIW6L1KYsLF9HbVSpmX5Zkb27We0UufeL/+Ed96xFdk/xMjheW5+6wewvRiuq+K6DoGgIOsSvuMiN0XZ9/iTjB4eQq+6XH3dm/nCzx9l+UAnlfw0AKbrhAHVfoAiGsxkczS3tGCbJoosI0hgBDKu55BqbqFYriEqAporEdgBgeejE8P1A4qygO2LaLqBXS8hSxquFyAKIoqmU66YqAtON6GO64Esyugo1HwFwReplU0s10IIRE5PzNPaIiFaUabHx2hPJ1ElG9m1qY/kWdGURJEEirVQIrbGcfGkgNpMjkQ0ICO7VKQIomxRzQXo9QmiRjMjiTST4+OMlUwUO8KkJzFn17H9AMENOHZqP1Ykhlso4AgCuhFFlC0U0SDZvYhDJ4e4Or+NJ14d49pLrgQ9oEnSycZ6yZV8WmUTvb0NmnU6+myyk3DsxIts2jDIymWLkGUbQ4cNb9jE5ESWeFMXxyfHyGmtzOZnAfjxy4fYfM55DHSuYWvPImpejUqlRFMiSpPYSuVYhe2leWbNAgOZQT7+l5+jq7UDo54lHtfwRJXRY8fI9PbQ0ZShubkZRVGYm8timiZyw1wiSRKSJOM7bghI9lxEQcL3HDLpRBi8Lws4foh18z0bXVbP5loLYSGUGpT0mZlxWtvb8DwHt9EGkUSwrZAL6nsigh/2b2UtzDIR/ABBBEGUUTUJRdWRJHDckHW4YDhZyJxeoMD4vh9qsT0XDw9Jaux+Az/MYG/MUEzHxrIsNE1CklQgQJLkM1nevu+jRRVUOVRDOXj4ooQjBCiajKIqRBq5HoEXRvr67tnM93KliKIq+H6YOrl3z37WnbMe23awXTucWAavaQj44eA0CAQQQ0RazbYRJZUkUXYdOkBaWsSalUsRKxJ/+t734Y9kyXT3Mlssctsn7+bAk8/R+swpXnhpO6ZZZ/PmTWRLOR59+vkwFVTXARFPEjC9KlrdYN2qlbz40tM0lOI4kshMMUfdtHBdmQCZ/kUJDF0mFsuwor+HlqDIDx74NY6ugQER6phOnViqmenZKYaHR9i0ccvr1sr/slj/+te/JpVKce+995LP57nxxhu56667uP3223nPe95z5n6HDx9mx44dPPDAA0xNTfGhD32IBx988H9UrJuak/zsV1+jJdPB3gNZVgws4uT+YTTDoGf9Rpr1BELMoc1PUHB9pqdGWTa4leXn9BL1TQ6PzhCpu9RMG72lg1/923dYtmYjU9kqUlyjUq8gi50QsRHsgKKncf6aFeyZyiGWAhxRRPYttl56HSYtWOiIvoLjuxRqDrJXRm9J8/d3fohIdytiROWJ557ksU8/zgc+/gl27XiBeCOCXvBtVDWBJIIqy6TTSeqlOkZUp+SFeciFqSLNHW3MTs+FhoZyHSGQEXUVt15Gd3VMyQr7t8CcEwIJLNs8G1CvqLiehWcGyGoYKGQ3ThIzs1Oosohn+ygNLFNEg5nmKtnZAqVcFS+okJ+qsidvUwxEiETxA5tABEHQ+ALwjyfzuLgg+EwNz3Hh1gF+/9welq1bimFlaOuKYLsQ6G0MdjahDajMmyID2jitno4RSaD4Mr6gcsxsZUl9GjeeZKKs0CZUGOgWGJkUGamUOG99By8j0L1mI1Z9BF3IUvV60IyA1tg06XaVYq4VLT5Ksr2N2QM5XtlxnPbufqJxg03nb+Ff7/sFs67Omu7jbFnRxtql7SQ3hlivq8/fiCZBJOngqwlqkyaHD/uMzb/E4KZ2evuWM5hq5aL2ZWT37KZVtXCCMslEFDkS4err38SWiy7mc3/7BW5797vYvWsHO3bt5G033czKlSuZnp7Etm0ECVw3hAxIjt+AyIIkhm5CXxLOAAnwAzzPQZBpaKYlZEHAdBzi8TimadLW1hEaQggt3BBSUhZAvIooETTs3J7XYCSKNIAZ3hkreYCI6kmI4kJIUthKcAL+Q+gTgOh7SLqIZbvIkoQY+Phe2HKwHQdVEtFj0QZ5PQyGEhonEk1TkeWQhKNIEo4cktQDL0BWQ+KMLMs0RjwN9YwCYtCw1zciYRu4ryAIGFyxDM8JPw+5wAKNZFUCIQhT+USx8X664IkIuojkCBTzFa674Xq0niaee+lZ4l6UlBbh8CsHODIxynkXb+XBb34XpSnGzseewkpBWo7wzLMvsOmiC/D1Mkk1QrVep2aaJFMJgnKV8YksTu0VFF1leCTcqPmBRM222HLRVrwAEnorD/3y+/QPdKLZInv3FXDmx2nqXsWh08NcsHYthZGjpJd0UMgWOTFaYunyJaQ7ul63Vv6XxfoNb3gD11xzzZnPJUni0KFDnDx5kt///vf09fVx9913s3v3brZu3YogCHR2duJ5Hrlcjqam14/7W1gjJ6YZHFhCKt7PkfxRtm8/xRVb1jKHw+k9Q2xdew7z8jTNzRFkGaSOPupTOQ7tn6B1cANevUhtdC8bey/k1JTJO/7sLo6PlShYZcxygGprqPEqnu2i2RKnT5zgogubuWLrbRzpfg5X7UVye8g7LsX5ApF4OwgWkm8RUTQm50sUJyb43sO/5I53vIN0c4rn9ryIqmo88fzzCGmFQmNqr2XaUYmhyQpaVCYpzqNpGrajULV1fLQwha+rmXKlRiIawXcqCKIBkoddTZEwUtiBhSqKBJaAKwR4vokvKbhujYguYznhrsep11D1BK7v4PgeqiqiyA5V30fyY4i+hFPMsuqcxZyemaKpJUPVDUj1tmI0tbIm1kXMG2Hc7icZNZC8KiMjoc7z4i2dVKo9OFoMa+wR1ixNsuOAwpoVfbTK7QwsSjB+bAapeRHtiRlMV0Lz03REEnhqhNlcAVGO4wcqS+QMRrmPw6dOsW7VIHPjE7xUyJNMqrQsWcFcwmX1olVY2TpD9Qhi6RSF2CylusCJuTr+KRO5MosaNRHKc6jaKg5MHCNrC3z4ztuJxgwuvOJyPvuJT/O5Bx5BzZSYqwjUGxGgw/kq2ZEJ4q1tyMko5/dnuHhDiTb9PI7vfBVv+Fl8e4rBNauYGTrEV7YuJaeK/G7C43S+zi9//SAP/P4R2jLtPPfMkxCIpJtT/OY3v+HI6RG2vfQcH37vXyAihhr4IMCTQtSVIEn4gYuLjySIiE5o+piamqK9vR1FEnEbRbNmm6iqyrYXX2TDpnPxHBdNlFEl7cyA8bWIL6ehKpHgTMH1G4DagNAF6PsgilI4qSNkni/Q0BWR1yT6hRRxwRcQkdA0CUEMED0ZPaIhKyKlcrkxjDwLxZUFEUkOg5YCAQgCNEVBkWQUQUIWRJDCQCZRADFwzwBzTTtsd3AGSBD+jrIoIiphRY6qegh6EBoqJSk4424UGu+H54XWf9EPs8MVQcQXPXJeje379rK0PkCqtZ29v96Gpoo8uftFbrjhBl554Tk2rljJww89yNa3Xs3jzz9FQotx57vfzf33/jNKQialJ7EcE8n3mB6boL2ni7n8JJWaiSpKPPLY4wAs7RxAUgS62/s4MDrEhnMWcXvyg/zyF99G92QmT1VZvamX7b/aRqIlTiye5HCuyuJMhK62VqbzMygqzNvZ162VQvCfmTl/YFUqFd7//vdz8803Y9s2y5cvZ/Xq1dx3332USiXi8TipVIpbb70VgNtuu4177rmHvr6+/+6h/3f97/rf9b/rf9f/YP230r2pqSnuuusubr31Vt785jdTKpVIJEI50VVXXcXnP/95rrjiCqrV6pmfqVarxOOvTzx47br4ik4GejpJaTK5OZeq2EVct5kdfYbVm9ZiGytRNZG4W2P9YJwfPnGMtSmZjjUrmcq2YUZsUnrAzLHHWbHsMo6eqhFJduAKDul0CtcJyM0UcTyXjpY2cEr4ySi5uRzWnEhTVwxFdpkVHH731e/RvmUDTqnG0MlhBNvBqVkEkkws3UKitYVafp4lng+yRzXRxOEDrxJEZGqjp+lftwHPC3C8ButOlAlk0DUVz67jY2FXRFLxBPlSaKcW8XC8EMCqqho1s4ahJnHqs2iRNE69jkwZ1BSeExCNCBSLAYmkhmmVEQQZPxCQlTiO6RP4DpLvI8geKRRmxAI3dvfScuUlPLP9IJroUTaLiFEdvxo6IcVoJ1ZpHl0RCGSd/U/+inPedAXlWoCuauTyJQh8XEGhJZbEaG5mcmqMle0dDOemMSJx0ql2BM1maPwEzVoSu2qSK1bp6u2nWs3S39VHrpSlb6AbWdTIZDqJtuoMRCJYcoqoX0WJJelKwamshVCymCtXCPJZiraFVSqRFWrkZ0tMVudxqwYtSRkxqnDy8V2UvDx6IoYeiaEEDt/91v3kRoa54e2385MHvox9eoQNgkapepqOrhXs2buP9SuXoQcmgV3DdJNMzY+wurcDWdaZzk9ja3FsVOamp0j7Lq4G/vwsM31X8hNfITE2zqTpohYsPvfFv6VSrSKJIiLhrnNsbIxkMolhGKHkTDy7M3acMCFPURQC92xm80KO8+HDR1ixKiSUlCplJEli8+pV7Dx08OzBs5DvHAT/iVO40DIJznIXBfHMfV7bs4Zwdy2KYf/EF0JajBgI+EKAdCZitfFYjbxpVZTO/L5SI+/DaWRPy422j0j4WkT8s/nUDfbo6sHlDA2FtnkIc7N5jVPx7KvgjKY7vN/Zr4V3Es7ADmRRAkHBkz0itsfRl3dQWNXOlpYV1AUBAQ8ngIQvEIvrfOYDH2L/iQMsv+V6ru1Zx+EDu/mnn3+HOz78SW5acR4nx0f5h7/7W9z+FpZoaebtKlrE4K9uuZ0v/uS7fP2L93L/F/+Z7/z0p6zoa6XjnBVE/YDJqXnWrFnDK8++wLL15xAxJF7c9TIXXXoBLz32HGo0GYa71bMk4wmcWo14okxm/SrufNdnuO786/9grfwvi/X8/Dzvec97+OxnP8sFF1wAwB133MFnPvMZ1q5dy/bt21m1ahUbNmzg3nvv5Y477mB6ehrf9/9HLRAARTV49fAEzWmblt44bbJLd/JiLl1zLkVnHhI9xJIZ/v2BT6KqG9CiMeYrBxloWgV1h4jkMj/nYisdTBZdFF1B1ATSsQi+U6NeKNLa2YwvCnjVEgEBdiGLFpVoEmPkckVM22PX2GFyqoz13F7mJAehVidpuyzqaoGYwZRVxZp2sPCoKwZZE+b9EqKqoTZOTJPjMwiaiiDKWGYNXddRdbVBcQZBiRFv18jNF7EFHyMRwa5a+IKPJktIokgyEcGy6/QtXczUbBkZGVGKoilhiluxbtPan2F86hTJVIpqtkAsEcWyyxipKOVyDVER8WsuE7qMXTJJ3Hgte7ITnJ4bIRpJ0dTcjO3UsdHJdLaTLeXIdBkImoaVDw+KYqKdxX09KJZF7yqZ5kAkkGwy0SRCOoGgbaFZ0FiUzyMloli1PKtXr+ClR1+iNnmSiBynedEgh4eP4wg2x0aGkKpl6sdH0BSdId+nIku09WWYcyP06iIdbc1svuBSdu7bQ6ypiYJpk44FtMdjRDpb6UKitNTmKi1BOT+Ja9k8+puHqYoicSVGV0cHB4dGuOS8NeS2P09bLbyk3DAzRzXaiirUsafqVPQx1qxbxvDpedasWcrE6eOoUZEusZ1svoCoq0ixLkyrQNzzUBNNWNjUJ3LkOrbw/YkJpKCd2ck6d37wRh79+W+plfLUXBddj6D5YW+6p6fnTCH0fZ8AiUDwkEQRVZLxBXAtG1lUGpSWkLEpBAKLFy1FEjz27d9F/6IBggZSxX1NGL8oCKFN3PcRJSAQGxGjbqiUCMLWQojD8s4UxoWfX1iSJNHU1MT8/DySQKhIEQMUUQ776oKAJAg02LYE0Ci+hJRzAgIElEZrZAGA4IkgNJQcvu/hu+EJKtJAx1UDFzEQUQQRIRDAC4noQRAgIeAuDED94MwJUBQWTigLLSAJx3OQJBDEABcHAg+3Vufff/Q9kpdu4oqPnk95fI4IErrgcHjHLpatGuS3LzzFvd/6V758z1d4yXyUt7zham698WbSra30LOpnYN0guaERlly7lRO/f5GXhg5jdLTwiU/djbCojSYjTmUmjHmwkjGO7DtGvVbm8vM3c/XlF1OaGuPAyBBNvoaoadTNErZlUahMomkakqRSLeTo6+uhVq8Rl5MUT8/C+X+4Vv6Xxfqb5U0MJAAAIABJREFU3/wmpVKJb3zjG3zjG98A4FOf+hT33HMPiqKQyWT4/Oc/TywWY9OmTdxyyy34vs9nP/vZ/65Gn1mlKuixNF2dAt09IrXhbuSYRCzRTna2yMSpF2lLr6att5d6PY0sztG3cgkTMxHqFQ/LMUknOmhvilGsVhBUETEwyc5UiUeaEYlSytaJxiN4tk9KkVgzeC6v7DuGXQvo68hgWTWOzuuoBkhSBAWLLlFkcUTHVl2qVpWo7SNEDTRJxYnFmB+dRxVERMfCDTX+NLWlw6l0IOIaMtJCWpjrYNerBJJMU7KNnFlCcF28qojoiQi2S61SQ5IU6pUyQRBQKZUxXRcEAV2LYtbGwXUQ5CTF3EkQVOr1OskgyuzxLIqhUNFsWjIZ7LqJ6BVJxeI0ix6ZdBMr/Bpv+MynePn55xFti46+jex57AiV2UkUzyFXNij4WaRE2BucHNqP5R8imopi+AqTKFR8j8AwiDZFiBgGPXqUQJHwRiCaTKANili6S2rNIKJnIqY0Nq7cSpuRIpAC4vEoViBDpUxgemSzWYqORK42RqwqU5gY4vGfjSDUy5RESHplHhkaY2NXB7sOjNLT1k5Hq85Te47x+Bc/zGV/9VVufdMGDhXmGWhVOHL4JOetGoTA5Z5v/xv/eNe7AZirmxQNiObzDC5dRkSKMnz6OItjKQqnxqk7Et1tOrV6DUVPk63WkIMyEdGgKy1TnDlJhQTFjZfxm6yP5RfY0JLm3w69yMZdB9l94BA7//JjfP3rXw93jwQIwVl6+ZldYBDS7T3Pww98BAQCAazAQ2wM+kQhDEeSVBXLE+jrX9qQqjVWcLb3HPamRUAM6SmiiCQtqDxeu9s+Sxl/7e56oc9dr9c5cuQIHR0dsBBJGgTge+EOuVHkgzOOAmlh7x4qMIIgTAVsMCPDcCYB3/PDgu35Z4g0giBw8uRJBvv6z/TLg1BnGLofGz33BVq64J292hARwpxtwUcQ5DPvrSaFpJt0Kk2uVMaQJFqbU5wys/D0s3y/uYvz16/nxNFjiOkYEyPH2XfqGB2dPXzxC/eyemARO48fY/HqFcxXckSqNruPHaW7Kc2pyTGcF1/h6ace5YZrr+ULP/0RCUPmR//+A97yhhvwGuqpdEsbX/rVv1D+/uN8+qF/45VPf5r16xYzMzpBx6ZNpGYsdmw7gNYcpzY+jx5VQFFRNTg2dIplK5s5/OIRXnj4H7j15j//g7Xyf9Sz/v9zrdrQTDQKAx1RDC3CB675CEpbhEhPB6PTE5w+sZe9258i0BMsX7qISLKTOA6nijaOP0cq3o1tCaTiBrKaZHwuS0wzqJbK2DWN9nQXK5YtZ2Z2jFJxnuVdfTSn0shRFS0qMjdbZM/QMD/73VMMj5zESyVIuT6l7ASr+7sJSmXyVZ9YRy8zxRJFTHzTIi6qeHaF3qjKSHaG4ZNzrFjVz9jpafp6+hkePh7Kp6IGvixj2zaSAIKgoEmhFCgQBGzRQhSU8HuShKpqBLZP3TKJJiIElovki+hxHVtwsRwVx5onEOKIsowcCShVyniOS1Q1iDRlKEznkGIStfFp3nz9tUye2M8V73kXzc0d/OyHP8MXbD5y1wd4du8QlfIMy5cOUs7XsGyRJtXmCx/+BN/+7jfBVyiZJeIiVGZO48RbEF2f6XoFa3ac3LzJVMTEc3zK8zZzZoFm22R0Pkdv91Imhg6zdHAVo3v2cdWbtvK7bdtZs3g1g4bL8xPH+cIbL+OW/++XVH9zL/FrPoL1y8+z+G1/x7M/+hz/5yNf4ssfezvv+PzP2Pb5m3jjx3/IK7/7Z66++uP8+Ed/w93v+xxdF1/I0PQYk2aKiljj1bFZWlKtSHqMolkiIUucHDrOxM5fc+snv0S7JvCpqzbgCdN0pZL4VRtZjHLgxDDdqoSfytAcTxJERGbnp/E9j3jrAPurSY5oBqfqVTJSnGwxx7yZZYnt0drUwQXX34IMVIuFUKIpigSvAcOGxedsK+EMbgsaW1URv1EGz2irhbC1EHhn8VwXbdzAi3v3nHlcAUJl9mtaB+HHZ9Ud4eG9wGL8j8/tuu6ZQijL4S5aEs4+3tkc6bNYsdcOOAn+43Ofbb+cbU2Az8I9XisX3LhyFYePHw/fGy8IGaSNIaj/n96jIAgQAAkBSRAB/0x7xnZdZFEEX2DXrl2093bT3ZKmND7HX/3DRzk1NYVqJPirj30EZ2KCckonaktURJkT2/YxWi3w8rbnWHX+BrpsCXX9Ita2L+bS87bS25ThTX92C7LnMFfL41dr5LNVrn/3zbzy66dJZVrYfOlF3Pcv95MaaMOwJWKJBG+79DKO1Sc4fvhlYlILU+U6LbpOtpInXy9y55/8Gb95+NekutpojyrkyyUSGRUv51GwUrzw4rY/WCv/6HbzjN7MkkXd6BmwiuM8/NB3WX75On701QcxdLhk2RbeetOt/Oo32yhWyiSSHkcPnmLNxh4efGQHywYFBCmFY8lEtHFiagRZBENV0KIRRLHKfO5VbLvCusElzGaz7N5xHNOG5kyMrGlycHKKA8MnkQIJPVfnZKlAW2cXr9rQ0TpAxXM5NTOL5HlEnRpL0xGUWoHmgTb2HxoiJYZ/ZKlCgVgmRX5smI29bfhAvlRDjyhE25JUc/OUbZPe7jbq9TrHjo9y1SXnsXPfIcyaxfIVg0xXyoxPnqKlJUNa1zh56iTRRJJaEKVSKBOJynQ2NTN0/BjdPV0EeRcx8JFkHX++hOd5dLgOE6eq/NmfvJmDB45x2Z++hRubNGbbU/gH9nHT9Wv55sc+RTxaojKTZyazGM2tc3JynqWpAfjwJ3j5+9/lsFjhmkQbv5g4zq0b13L/wz/DLIk8ed9f8/Y7/y+f/OSf8/wDL9C8roO5oRfpvWgz4ok5tisGV3UG/K7YxGdWS3xPWMQn10iMPmXxg7uu5r1v/xyvPvtl/uxtn+CVe/6cf/6bf+Y7H7yRB37wXd57/VKKe7bR3avwr/c9xDOfu4Vvfe37/O2dV/PNf/oal79pkNLxp5hWBL585QB3fu0EN13WwcNPvsQdl2xl7tQxnho5itDcRToTwokveudfoMWiTNpVjvTdTnJvkSYEjKBOJQIr1i0lHhF44vkhEoJFy7pBJlYsZf9omUpZRlBjiHqEhCQyMjLN0o2b0Ide5Y2XbcFRIzhmFdsPd5mOG9qeEV7DQBRC8nZYbs6qNQTA97yGQqMh0mvkYwhyQBC4SIqI/5oAfsfxkKTXFDL+YxENXtM6WAjwD4npjSU0boAQ+Ciygqqq6LpONp8LsVqigCAscBH9EJwLIew3CB8kfF3+2V13ECDgveZpRCTxLCNxYafseT5yQ/Zn2yYiQpiqFwhIQQMcEwRhnKvvAyISAqIU5ofQoJn7nD35uX7IpFy/aWNoEBJ9Ovv7uWDtBrKl54llMkQTcX76/d+z9ppLOXR4mERfL1dcfDm33/NJXnngd1z73rfjxZO4O7O86eNXsOvpZ3jTl7/IXf/waR77yQOk8yJzrQlUy2H01SFKvo1fK3PkwH4ALth6Ifd/9X7u/5svEmxo5VbtXB5LdTK8byfmzAzj9SK2ZRJVBB599veUrTr9sSiq4rN6+SKyhSn2Tg+RSrW+bq38o9vNc8UC0VYolceQhQyxfolqeRJPs1jev5R4Br7zrX9iTW8bzarOb3/+KIeGj7L74DCZtl6WLF9Ge3srejQMPJfkKPP5GjYSlm8yV60ynM0z79rsHj7MeGWG9sUp9k+f4Cjwq0dfoDxXI4bIynPWIq0dYP2VlxFPpHGVCCfz8xTGhun2yiRnRrmgu53i+AmWL+7j+JExOjoGWLF0NQDdHT20NKfp6OjADVxSiRibFveypjODWMmTVg2uvvBCpHoZKgVuv/UKThw+QFyVuPLc9bTJHpH8HJcu7WVNKooxOc0Va5bRJ3qkinNc2NdKryyj50/z9os3k/HynBOPkalVaZdE+lvjiLVZ+nsNbrvhOp565rdcdPF5uILHrmyOFdnd3LxhI58+RyVaPsVPP/U2jHzAUx+9FHfvaZ79yy34I+EA68T+PTz/9T9heOdBfvCPH+Xgzx7jyD3X8/Y2G2fvNjrxeUd8nKmXXuAz56c5NjLJ31x3Kd9+chv3fewN/OgXO/jS397Jff/2DB+4cQPf+fbjfOxdl/HYt37EnR94Ey/d/wO6l65kdWKUx0/O8q43r+AHD4/yyQ9+jG889Bj3f+Y2kkmPpD5JVpS4dlMzu3af4uN/fgv3/Otu3nP75Tz52HPoFLlszSJabIlPnKMxdPgkt563hnbbpDZ6DABfiBBT0+hqkt9852ucuyrDjFllXG9lZEplZlJkaFIjedEtPLJmHd+e9Tk+10Q5nkLp6iCbz1GqVog3daKLLqWxUd54xVWgKQRygO/4uLYZMg4J2xm+R+Mm4rlCGHPreuAL4AsEjWK0QBUPa3SA5/sghmRz3wfX9UM24gJQVpH/w873tTvesFA3nH0B+H5w5rbw/YWb7/v4gYgfiLz8yi5++8hjLJSDhd241NDpC+GeFi9w8f2Q47jwr/saTbTXkO/5QtjO8AVYGAkuPKckSbgLkkNJAlEIc298H9EPkMLob+RAQG1ENSCF6XsLWnPf9/GC8EoBMYR5mLaFoIrYjkktcHFth1YhQrq1FV02+P3Tz5OIt1A6McXe7bvYd2KIx3Zu4ytf/SoHjh7jb/7hi9z39fv5+qe/zOLly3jh5ed535c+RfnYCLG4Tn1Rik5fIZ1KMnH6NN1L+qjXSgwNHwEg6QvccOkVLL5kM4/97nFm5kaYOrybpuYudENB8Bx0WSCRjiEHFoJiMTc+w4HjRzl4bARFbkH0ohTL9detlX/0Yr14+TKG9p6kr7mPSMKj4slYCFx1wVW0dvaixCUG1wxy7PRuDh0ZZXDzebQtHWRitsjiJWvJ5SpMz87huQGSLlOq1bDdAFWLMpfPUfcs5nN5ZmdylKo21bpDbm6SL91+Bz/+l/vR2lMcPX2Ypp42ZianaJkvkT24g2D8AFdJdWLHj3Bde5ru8hS3Xnoex17dyZJFA+za+QpG1CcVkanMTAEgKwJOdp72phgtepTAsehqTlKcOEVGl9m4agW1wmkEM89gXzsHXzmBJiZZ3r8Ms1LGrJm0tqQoVQocPT1Gy7I+joyfwk0lWHTOIIVSBVXVWLZiNdlTp1m+fAVFz6WnoxNfDRiv5Lmya4D5oQn2736BP73tGmark6zYtIq4V+W+v/4xt129kYeefpIPXLKE/FM7efO1aykdfJbu9QNEzBxd65cBkGhO4jzzCn1pi6VmnVO6jnhiiPdev4bvPbyLv/vATdz34AGufvvl7Dg6xk03X8PJVw9z5aZltB7ZxbKeBKlijVFBoSvWxq5ZnWsu3MqjLx/j+osNHth2lPfe2su3/vUV/vQty3j2Oz/l3C0rGHn8mxSSGbwRk7/+i4/ywx/v523vfRe/e2SU+MpBTGcWLx7wljds4hePjfPFj97MN791P7e9cwsPPbOdm/8fcecdHVd55v/Pe9v0olHv3XLBDXfAGEzvEHpPNpBkfwESkpAl2YTspsAmIdkUEmpgAyF0YjAdDMa9Gxt32VaXrC5Nn1t/f9yRbHKWs39yz5kjWT66c2f0znOf9/t8y7JazvINEPMKInVlAAymsyQ8XlpbW7n9O/dydEgw7qvisL+JTS0z+GUkxKOWj62Gh+JcGSWhMvrH+8iMGwgTAgGLsd0biLUfoUTPcMGChUiOhKGoOLbrPS4LKc/4kCZxXLfDNYlGw9i2TSanU1JWjuZ11ai2IzAtC8My3eKG48aCOQ5SHmP+LJzhdsmWY2NYJrphYdkOhmlhWTam5RbPiWBcYPJmcGLxtiw7/9VB101mzJjJ4sWLwRFYtltULcvCNE0MwzVQMgwD25wANI5f24nXJwvX4lXYDo5tY2Rzbj7lBJPEca/Pyd95DNty7122jY0bumFZFsJxkXhsB8VxwDJxTAPHcR1DJoo9MHmjsAXouo6iqvhsDUuy2X7wAK2HjlAyZzr3/+LX/Ncf/0Qym6Nx/hwWLVjMFV++kfmzTsYMqcydMZu+Q518uPJt7rj5NnZ27eflZ55g756ddPW2YY0n6E8ncYSJL+jDTCWJFhWQyLiCuN2HWjn/svMYat/LstIahgcHONLXTeu+rQwPDJNNZamtrybgD1FZWU5A1lC8JqeedgayCHKwbQ9zFsykseXzLVK/8GJ93hnLKYk2EBYxxsYSVDZ52L+vna6DrXQO7uONVR9hJiRKKosob5qH5anBF6xi+vTT0XM+slmFSLQUWfVjmQ6hsB9/QMWwMxQEo4yOpJHlCKbjI5GwsPQctmawYvVb3PvIb4jvOoAvnkZP2hSVlbLnaBvDuQwLly3m6P4dXHDWArZ+spkZpy7m7c3raJw7m9HRcUpLKlhw8iJGxnoIBd230TATBAsChAIe4mOjnDRlCiOJceK2TXFZBUeOHqT7UJyahnl0HUsynOrhrEWlZMfbGR8exOdViapBTMfmpnPPInmkl2nFNcyvqWXo01ZqiwLMmT0FSdeY0tKAZNikkiMUFXkZ6+3kivlzaKqN8PTDP6Ms5CdrCc648DwifQOcpOs0NDiUxo6xqV3jvBu/x6ptPXx1eT0rPxrlW7efyfurB7jjyoUA/NvtC/n7Gzu45l/O4dXXVnPF+afy5kcHKa4o4tDRdmbVCV766CCXXLCYRx9ex+3nzOeHdz/GvfdcxZ/f3Mx118zi6ede4qKvXMRHa1Zy8il1vPDmWgJ1U+joztA3ahKuquaT3iRfvvEi3t00yPf//SL++8ltPHzX9/jd04/j9e5j1d4+Ztb6+e+Vm/nP713Gw796hquvPYuP//gGC766hKwoYaxH5eIzw+xoH+La+37BNzZH+dp999HR7sat6WqGmQ1NLJy7iMtv/jq/H7d4QQQ56CtGzhVSmitGTyt0ZvsJ1gcJNpfSOtqKrfdQHh+mpC+HGLXoHx3m9LOXY8YTSJaE7QiQbBRxArUNJz8kE5Phrx0dXTiOQJFVHn38CY62d+LkMemJwmhZVr5jtY8P3hxr8jEBZRi2lYcnJGyHyd9zcK/BtJzJrt7QLXJZA8s8PlQ88VAVFyEX2Di2iy1LwsVJJgq7EBKSpEz6dZ9YnI8Laph87RNUOtM03YGicwL1zrA+8/wTilzTsTGF4z5wYQ0nX7BtrEkzNEsCM69anLBGlYWCIskojsCDhIKr7nz1jde56bbbuOamm/l/3/oWmz5cxx8efYjzr76K+cuWcft1tzCjrJY62cv6d99l08b1+Gc2cdZlFxELBjFlBX/Spjs5gBz04JNVsqZFKpljLJGkraOTrq4+wsEIAGHVyxtvruCZF5/kk9XreOqVl0nldIaHRwlHCzh96ZnkMhZezc+BA62YSAwMDdG6byep8TGi4TI+3rCG9q7PDx/4wov1K2++TH1VjP6hbmY3z6P3mGuifrCjk1xXITWNl1Bd3EQwUI4/2Eh3n46wCzB0iVRWkMzA4FCC4ZEEegbS4ylMPYOezSLJGgWlFSQth6wto0YKGDVMhuM5MmqIVW9+wKjiYIWj7I8PsmvXLqqaZ5JzIny4pZNk2Rw27DjItNmnsn3dfmZWTyMwlETJjLPk1Lm0th8ipQsC4WIAerv7CIeDtB05TGNTPR2HW2lt72PG/KV09SeRTA+zmn2kUkcpioZZdspSRg4eozAQYMbUeoSlIwcFy5ecxsZ9O4lVR6iujbF160YWL56JLySRPXaYsDKAkRgimhph7swawuODHH7tWZbNKSQ1mOGJ3/6JaadM499uuIu+4SxN0SgrH3qcliXT6Fy3joJIOb4jf2e9nUItCbO6b4ipU6fz2pEO5hS6/ryLmj18sr2f5voIuz78mC/ftJCtB9JYmWH8IY34zr3Mn1tJlbcHu1BjZHQIa2qA6P5NfLBrlIsXVrN13T7uuuwU/v7GQW65+Uo++XALt37lPJ58Ziu3/eA6Pn56PWde3czhV9cwWpHFGs7RG1Hw6bvYPxzijZff45wLT6Z10x5azpmGte9TDh5ROeuSy/j56v3ctPAy/vjEM8y5+05mPzRC9+xL+d4j/6CmpYC7f/crIlPr3NfSNIXBg9v5ZOde5i48hSZfJbXFJeSyCTKajqdIpjjoIbNrP3rPETLbtzJz2Ca3r5O9h48gV1VwwXVXcuaM2bz17Evg8SFLeSxWuDxnO6/CE7Zww25NCyeP1Xq9Xrcw2SZnnLGMWCzm0swcGwuBI0vYkowzAQkITijYn53/S447hBRCICQmOc6mZeQLtzl5A5jori3bmPT/OLHATqSbu/Q+t/BPGP2bpomu65Odek7P/9s28nDIZ4eYQsiojsAnqSiOwOv14gv4J69vAs6Y6ISByR2Ajdt127Y9ucMw8w8pTzt0HAvHMlEB29RxLAPJtjDMjAu9IJBlAbb7Hlxy2aUcGxhlyVnLKR43iGgSy5Yu5fR5S7jxki+RSiXIOlk0G7bv2EJiYIB3PlnPfff8ADPg5bLzr2BgKAFCJa1bHBscRcpZqFE/Xp8fWfa475/mqol//sv76TvUxqhss9caZnwwQ7CgGDkQxJEcXn/tPfr6RimMlTE8mmJweJxYYZTuzja6egZYv34vZcUtXHTuNZ9bK7/wYu3VFGrmT6W6qpyy6maQ4zTObSDlmAjFIWI51Mw9k1y2iqa6RirKi5F0FdURmMJDMmNjCy+SHCIzLOGXojSUNFKgFFNgeogaCk0Fxcytb0IeyxCLlBDylpLQAjSW1jI2NEzPtk2cW1tGUayIQ+2f4tMsDDOHXlrAQV8hmyyVweJqOiNRDqTitJxyPjv39TPcPciCpun0HzoCwMnTW5DSOgunziCTHEbIKS44bTrZeDuxIsHi+dXoGT8ZU9Awq46R1g6yWo7GWbPoG06QyWQYtXO0HjrKlJaZhEJB1q/bysUXXcno2CBjA2lqmmuIdw9RFxJ8+4ZFXDJ1Lrff/CX+9OSjbFqzjTLnGL978V3+9vpGrrruqzREYOX7m+k/42TOnlrNk+v2csPFC9myv4NZJ89g7/pPKJszlfiePQQaKlwTZkBN2fREIvgHsrSaxXiPdbCxb5zNK9v51h2XsHf3KOcsbWLHO7u46NIZvPnGZi6+YAGfHMzSdN7FHP1oNcUzC+nZ1Uq0JEZtfB/703HOiGns7u5lYaiQV1bt4YYvXcrv/7ae733z+/zqLxu47t7v88jzrTRcvIRHNsZZduPX+M5bn3LxHXdzxXOHOOO3P+W0H/8Va+ky7mlr43W9kNCsORzbsp2zv3IDb364iut/+SMYyKAfbQNgrKuNoJ1C9wboPNLN1n+sYJ5hMr5rJ6Md7VSbDlUk0MYzvP/E0/g1H+2DvZx88kKmN81gVnMt69e+Q4FfYtnc+aQsG8tMI9kuXp2zTTfxxBFYpg2W4w7EsDFty8VaJdfm1KspqLIAyw0dkBEIy8Vrhe0g2S47QpMV5DyWLeWHjwCSsEDoOHYWgYmDjaxIbherSMiKK5SRFXH8Icsg3LALlxUtYdlgGqDnbAwdTMNlpbhmVDKKIqNpKpLk0gstwSTNL5PJ4Nimm25j2zgWWIZF1rTIWhZj6TT7jh5lV+shdMvdEViWBdJnxTgnmkoJxx2/Ko77vWS7TBgssISFiYkiuXL1gaEO4okOkulOVq1+GdQcQhFkDBNDOBiYOLLExRdfztziZiRZpqy6gpKSUp595Xkef+JRogEfg7v3c+4l51PZUo+lp3n5Nw9R3lhF65EDLGiaTlVjI1lLUBkoAsshmc2QTIwyHh/D51MJRwOEoi5nfMvWTRSWl5McTKKkbPwxlZlN1aiyRv+xUS6/8lwWL53LuvUbKY5ECAZ8DAyN0TJrPv6QREWdB0OHpx597nNr5RderIP+YnrbdVStkLFEL0bcwdYlqsvmMDq+D+ERdO4ZIhSpIpsepTaoUhaQiRTGKPZHsOM6pYFCxjuHKQuHCdoOEY+PRXNnU1ZURFNVGbFAADObwZYd9KEUifE0SsDDi6++gCMrVNTVsWXdFoyeQ5QpMulEmoKmKew52km4IMbIaJrhmkZWDVp01szmkW0H+cuRPg42zeav6RyrohUArLEq+USt4U0zymD1aXgXXc077Rk6ewX15YtZ1SfosHMsPe18ujvThMJlVBZMY93ba6iO+Pn3736Nyt5hli8u4pTptbTtauXf7roVMdZOOJfmhZd/z773NvP3lf/Dl284n+GMwvJzm1i3fxtS1yFuenAF8rxlxJadytmz5vHHp//EW6+t5OP2Ns679j4W/2kXY8sv5qBVxpsftXLHhaexo7WLmy4/m50b3+XsBadw4OU1AOx6fxtnnnEyW9a9z+mLa9i7fT1XXbOc5zZ3Mm/mXJ7ZdoD5p53JC6v3cvY51/PWjp0svfwrPLy+jX+783zu+zjFDff8mO8+/w5Tv3MPC/+6hsXf+y5znnub0Jeu5/aNR9i1bAHfXDHIjkWncWPfKC9l4ZMBjedGTZqX3krsyn/h5y+uJdMwn7d2DrMjK5EqrGbb3i5Ovel2Vj67gplXXMLj9/6ESy68gPdXvE6RCPPQLd+kvrqCyxe7kE6wpJSk10NNtQ+PmaK5UuPhR/5A6wcfMfzRZl577GlSrUOEQ8Vc/ZVrCcSCKI6CJBvMmr8QQwtw+13fY09HH/rAIF6vH0eSkXATjlVJRcgCSRYoqoyiyjiYroOeLFCU42rCCROmCTqd41hIwkGgg2MghIOQ8sO6fxoKwvGBpWPLOLbAsUw3OcW0kGwrrxoUefqcwDB0TNPAsjkBhzbyXa17jS6t8Dg2beYxcMMwMU0L2QSPKXB0h5KCSgrCpTi2W9QnOvYJ8Y3rTS1RXVJGdVkFzonIh/1PtEXLdtWbhoVjHt8JuHxx2TU2sUxUQ0bO2mi6wfq17zHgA85UAAAgAElEQVScyHLsmEJfN1x92ddID7kQi2VmkWwLRWgoNozZCdJyFtMwODYwjEdROfe005nTOIWd76zmHx++z3k3X0d8YJy31m9kyZyFLLvoAv7lW9/ijNOWcuTIEf7wy99jZU0CQkEEPcQKS4mEo6TiKXeXkBfrBJIm0eJCLj/7QiSvn/FkjnXrNiErGjMaGtm8eTOb1mwjVhBGt7OoGvi8Cjt37KO4tJLTliynp7sdT1D73Fr5hVP3Gmcspqs/xaCtURsq4dDB9xjojqFEHBbOvolstoHBsSTpRD/OgARpD0PjKex+i0LJz9JFy6hqKKWxqgy/RyZnmnT0dtM5NMLg4CCOJIPfjyNLxBUbv0dDs0BKG3zr699EFjY4Og/914M48WHGDh+lvr6RwW0baAiHGOpqw19eRMfurZTX1KCPxEn7A1TXt5DNphnoGWfOvPkA7E+PUT+9heENnzI+p5hDbUPYcilySQHr+7rwZjWoXsTqzR3ktBw3n3IGL37wFm+ueI7evbvY1tPFb1d/xIuP/gG5qIx3dqzh+/96C9+47U5sM8eKZ57lhe0refPx/2FKSzPNi5o41NbP0i99nZHKGr7y25+htw+hjsDu4U6eevSvDHZmmDlvDn/cth7ZHyQx/Qrufed5Flz0DX7QF+KA3oJP8vE/+2T+cPeXuPySFRwA5n1g8ZMX7uOc6y7lqVd/xDWX3crDz97CmwdMvr4jx8Y58/leu80HjUuwDgwQP+9mvrPyU/obTuLBbePsUGqZEo+xwyzm+poZHOv20XL6ZRx+9EXuuv0q7rzry9zx0JM8/717ueSef2XtH/7KObdfx4NP/4Vrr7+B7/32t1z1H3fz8I13cv8r/8M9l97MA289zQP//p+cccGZdB7Yjy9YSjyZZntmBLatQSmtoaowyFD/MZrrS9iZaAWgoawE4fMzTQ6ybeOnbGs/RmNtPWWNFcjFUZaU1hIZVAkH/eza/iZlZRVENYW5s+dwLDGKEH56BxJ8/99/yoYnn+CQkDFNCywTXQPZcSEBxzzOo5YRk8XYNVgycWyBEDInsjnyBL4TJOBuUXWFKVKeKjdB9MtT2060OM0bMJGnA7rYtjQJh5zId56g2U3Q6LCdz3Ctj1P/OK66dFy4xrYtVK+XD9atRddNli6e5zoNIh8/n7BxbAdVFsiSgm4aKCJv1MRxaqEi3OtQ8q6BE4ck5ZmFtoWTz2E09AwKCrKkIpQcsZIIc045ifSIzWBfH/v3r6Wn7yASARoaTuLT7m7OPPMc+jo6QBIEI4XItkN6cJTE6Ag/eeA/efBXv+ZP9/+GbjPF9WefwsKeebRmhmlqbGHVJ1v511v+hf968NfMnTaLn9/3nxzu7MSWHcIhH7mMTmJ8nGhBgMqaajZv2gPAeyvf4Uh3F17Vi5E2OO+cC0klRikIl3DVlVfw9a9/FVX1MDY2Rk1tGedesJwNH2+lMKgzlkwzdGwcxzIRnsDn1sovvLOuqptJ45RqImXVfLRhB5dedznNi6YQLp3PSFYwTCeGlsbx+LGFn/KyGr509aUUVJSTEzZdg/0cGexl454dfLB5G592dNEznmIsZzOeMkkYNpbpYKZMIrYXv6kS8AUp8IUJylreT8DLnT+9nx888Vf+svItll9/DX39vXR3taFKAQIpQWEwTG93D8PxQU6OFuAbOoZzrJOqYj+Z8X4AosEQxz49RLrIj3lsADObwCJHRnPI9vQRqQ5SOdrL1EKJFT/5OU6un1d/cz8bN27go4PdXHjzt/j5089y1FdO7JSL+O7P/8it9z7CB706T29q5+zv/55rv/lbtkem0d5yFo9uHuaeje2s84W595ePM/rhbnL7DuExh/nlT36F1VLNnk+30hAI4/9gA6csm8O+lX8gMTDM4rMu5dO3NiFPm0+7aGC4uoU1yRDpqrnua5k6janhEFJIpb19jH6/j9f2fsL0225m0/sfMH3pWax5cQXnX3AZrzz1GnWnzOXDj1fjv2gJf3rqKaZcv5yHHvsdi75yCY899GeaF8zh4Wf/RlVtGR9uXk9D8yxGPtmEUhbhk/27iMsOq9Zuxtdrs3/zHtK9o7xxx/1cfuPNvPSDB1m8dBn7Hn0dK5Hl5LJ61r/+EkVhjdTfX2ZpRR01aoiykI+6xpkUlJYxMthLdV5kMdCWoqN9jKbqKEXVFVx62W0YngJmVTYz11vCnLoZVC6aRllzI0MDGfZs2kNTYz0BLYQi2SiOjsBCyCq50XHi8TFy2FiWO2QzLQvLdNxijJz3W5byhVnGcdyf/3OhdnFugWnJWLaS/yrh2LLbQf/T0BFcXHyCxWEYFpbDJLXPshz3Z5ZFLpdj1qxZeS/mCQrg8WHmhGfIiQIWNx/SxjTch224jBVbgK0pZA2dWXNnMW/hXGRFwbFPEM3kOdauNF1y6XeSjCLJyELCp2jINmhCdlW9MCn6wXbxfxwXCnK76vz/SQKPVyCpDv0jo/gixcSHc5ARSIZDLBQhMTRGZXGMtWtW09PTwysvvsCuXTsJhwN0drXjVVTGhgbpaD1E30AfK19/nVW7t3LNbbci6Q7n33AN8+taME2ThXPmsvrFFXiLozQ0NiEUlZDfDUiOD48iGRZVFeVEIj5yqSTnX+j6T3+wcwsio7N93y4U2Wbbxo/ZunULq9e+z8233IJhmcSKCkFyCEcj/Pcvn2Dfrv1k4zqV1bVcdfUlRENFSJrvc2vlF1+si6pRnRGEE6emvJCOgzHiup+UrTKS8pKyZXRHQtJUfJbAOzZMcs9GCksEoUKV0cw4O1s7sL2FCF8Q1ACBUDE+T5RFp5zFlNknY/h9SOEQkieAHAoiRUMoQkHVQbYEPgd8WZCGMmT700yZPp/HV7zL8+9twGP3MdS6GX3PTsqHhwgO9rF5y3sMdx1AiQ8xvHM7w9u3ACC1HSQ00ktxfJRYahSz8xDp7nbUoXGmVFYSf+dDXn3gP7h8yWz+8fyf+dal1/P6hrWM6xJXXX4pb73yAtOaG/nSslN5/vlHmLJ0Oq9veo1DnXu45wff5Bu33sCS6y7kvPPO4aVX/sK5p8+nEQ9HX3uXrlVr8I4eobxUoaGmlJuuuZTi4mIqimt57OknSKaHyKzdB4eSFKhRfvy1b3IgnqFoShlPPPYQl333X/nNAw8w9ezTAaidNYMnfvkzzvryt/n43bdYtPw6Pv10DzMLwhxZv4tli06he38/WfpJjY7SvvpjlGODjLy1mWAqQPf7OxkdGIL2IQ73fsLMxcvZu/Zdrrr+Clp37uKS089i1ZOvcONFy7Fe+ohTZ0wl/d4azjhzOurOXZSIONPDEnvffoXu3RvQju4jm+pmumTz7jN/piGlM6cugLJ8JqmhYQxvGePZIGN6hrm+CMd6x/F7XHhK8oVZcs5F+GIFXHTd5TSWh4h3tXNsoI9trQcoKa7GzubwCw1/qIiRsTi//N0fyOgCRVLRJBB2Bq+qYWUyyHkMOi/5wP0YiUl+s8iHHpuGNVlUDcP1bNbzg7pcLoNpZjFNfXKYZxgGumliWDqmqX9mIGjlPUEmoAy3q5ZwbDAt0G3ImjaG6ZDN5hBCYsWK1xgdHcMw8iZIJwwtJ84xUaQnjZJsO+8t7eAI96Yj2ZIbGYdANrL4hY2dzSHLSp4W6A4ATcuazEqcHIzaNrIDtm64svM8Pj/xXBMPyzYmb06mbky+Tms8R+/hQxza8ymRQAHF0VL6O3vxaR7q6prIpCRCnlo2rN1GcayQs5adz+WXXcvcOYvYtXs/jdNaEB6VaHGMx558BMsr8fjjj/Llu7+JFc8ytaIWraKQsxecQrqjj7efe5FtmzYy3jvAMy+/xLxFC4knEpSGwlAcRvg9eLwyup6lra2dxLgbcBGqKkBIFqoqE/B5cLJZhAPJdBpZlUCCRCaJ8Lg3tO98+zaqSqvo6uxj+44t3P9fDzJ79mw3X/Vzji8cBmnr7UANTCGgpbGCfnIZGwsPsYCMKiR8qoYkZDyBIPhVBnNxcpLASQP+EDnbQrHdSCLF50fSvHmaj4ePduwhHFQpLixwk49DfiTVQbO92MgYqoMm1Dx538IEbCGQdRvdsenNZYkKm2h5M6l4F35VxRRpJNuDSI0hh/w0SYIR2/UHKFIlNDtHT+9B4kkbRZUIqCrZgS5MTSVa4OfkS84noAaQVIW163djZLIMWjZvv/4OiZ4xOpP9fOPrt7HhlY/Yvm4rv/rFL7n7u9/BGNH595/9mkeeeoxHfv8Xkh0jvPjoq1TECkmOjFGleSie0kxnPAFaMYtPXczvv/MfFEQ0hodSDI53su9AO7Om1iMl2hj1GZjDR4kdHWCsP86+D9Yz1jPMlOmu+VF1oJCuxC4K+8fZuaudM6+ZwuoVgj/1vUTlydN54/HnaFwyhyMvvUfLwnkMbd3E8iUL2bFzIwunN3N4z1Yaysvp/fA9TsrYrPjZt1m+eAYf//E3xEfH2RtvY2pNIa//5W/U1BXR33+UqrIIrfs+ISRBVayAfQMjDCaStDSWURArYPv2NqSATcRTR2Wzj4GUh1ioiHblCDXRHKHZU8nlHLKOQaYvSFmty7P+xu03ITwGD/3pdWJGmkuvvRSfk+WUxady5eXXccut92DaASQxigiEIJHBZypoIp/QIhQs0wTHJJ1JktMEEdMBzUFBypvp20hS3q/CcvMDBSfgs3nIwwUrJrw2wLItwGU8WI5AwXbl046Y9Iy2bQfy2GjegsMt1jLoOX3S+c40DSShoGheTBwChYWYioJs2i5UI0+oCB0EMrajH++sHcdVSjogSQqmaSNJIAuBJVwvbkc4CFnBsF2WhuRiFgjZQtiqi8vjoMsOtmUiWzIZcKFI8iIe25r0NzEmZOOOky9SMpZwEMINjdY0L0PpfiKlQXLSOLlULx+v3sCU5mmsObSSsupyXvzHi1xzyeUkrWP0D++lr8Nh1aqDSP4oZ5x6LkpqDEf4KCgq5Myzz+EvrzxPaVUtL/zlr3z3pz9m9RvvUVxZyoUXnIeu62zf+wkeycvQlg0UVZbT391JdXkF8UyC9NA4sRKVbFYnmzGYMb2Fwb4h97WMpTFtA1sHI+AhbUPWtghqYQw9C4qBNyghx70c3NfB1KoGAt4cKcnCo0vUNtSz5dMtyNoJPjD/dHzhxVr2aOg6yEoQn1/DUjI4yQReW0Lz+/AE/Miq5mJjsoahWhiyhOw4qHIOOS81BVBlL0I4eDw+HCSmT2lAt9zuRVY0vJqWn4WLSfzOwpnwPM9/eGwkWUY3TQpihShFBRzaO0hLUz0+b4xU137saBTVzGHbOpZskTVcYnxfKolmJKmWfIyG8kMi3STgdYjEvGQyDi2xIjBt+gcHycRHcbI5igoK0Qd7KfR7CHuLePPpvzHYP0yRnuUr19xESTjKW2+/y8oXVhCNFWB5FKRshrAjUMbGmd3QQE9ylE3H2hCGRMCTZmBgkAVzw/T0ZqlviRESJoe6HfZ1DRD2ZjFGZIqqVQ6+8wEN+gg7H36c5qk1pN95B4CeV56mpNbH1mceo76xiOG171Ha38rM8mrUzFH2v7mJOUvnIfn9bP5oFaeVF3Bs73sEAlUY2QQB1Ud93GD7UI5jkVEqmurxplNk0kEKy6rIxGKUpceobJlFt6eYoOwgZQ5T60mjl/lQcSitK0WXmtBybWgemdPqFpL2mIR8NnIiSyFZkAqoP/dCaopHGOMwiv8kYudNJblviFDEXd5qepT7H/g1/UNxrvzqLewZ6OeMZcu54xt3M7O6HEXOp31bFj6P13WRM3IoTGQZOgjLxqdqqALXmc7JY8WTpp1gWQYn+mNMHBMdLBw3TzqRFYHjmhNJch4acRy8qvYZ1sTEIQDLMJnS1Mz4+DjHMoOTUnVXAJP35hCCSCTiMkzyRdU9n5wfdhqT8IeVT4Jxu2mwHDOfVZA3pZIFwibvwCfn5fQGtu0mvwgUHGFjWUr+et1oMQvLhTycCd8TThiwTlxnPuVl0i3Qfa88mg+BTM+xHkZyGqPjI8QHEzTV1JFIDLBp8wbKeqpZvPRMPt13hBWvrUJTJWY3nkFFcYSalln4vBI793yCblgcPdjGl668ms50iqZoOQeGuti6YxeZXR3cumQB37z7LqZW1pExTEaTQ1xw9uls2baV6qoS9u/O4fOqFIoA6FmSVo6saXKkrYt8p+eGV4f96EYW1aOQHMySsR1uvO1y3n/3DYSmkB7LUBIr4WjHYd58ZxWFERVZyFi2xfY9e4lGIxQXBT+3Vn7hMIhu2XkjGRVZUvB4g8yYMZPLLr6csrIKNK+L4XhkBVmR3ORv4Q5qPB4fqupBkhRkWcWybTSPz12AhgmWjiq7C2oCu3OHP+6CmUycEK6JrxACj8cz2W0AZM0MXlWhMBIloPkpq2+ipKGJaH0lkfIYWk0FsdoaAAq8AXzhCI5HoaaljpQF47aNFAyQzJl09w8xMjpELpclndaRZZcelY3HSSTGGU/EUSQZI5VGDasEZZOZ0+ooLgnT1FzNlNJC1PQY8a4OoqpFgWqgaDLHhgc4NjyIMG3KwgX4tAjkJGpi1XgcP0baIKwFqCquJlZYTAIvxRVh2o/1MSDGUIpKCAc91EbCzJl1MgDhkhDlRUUEi0vJCQE5DWQ/B/Z1oWlhasor6DzQyc72TgpKyhk7lqCwpJT+UYMdB4+QifoIxiQKT4pSWT+XolgZRixE5SkzKWpoIGdlsD1hevrGcIw4mcQoe4WfnF/i03SCkhlnEyisJFgxjdjUaqL1Hnx+ibKARlVYpbi0iulNs5jaWIttm0iGl9RwEMtqJT3czoJCwUcr3gQgUlaNBwfL1BkZH+H0JcvYufMgQ4ODOI5M1sxgOgYIB08+y1CWyCvxzEnoQJUVsukM2HZePHLC+skfnzE64rhY5n/lTE9ap7oQh5HL4VG1ybBYhPPZByAkicKCQjat30g8nphUTU6uWSGwsPIqyLzCLz8wdJ/fHTY6zmcFKidetyQBkoMjjottJpzwJuXqWFi2wDJx5fTWiS6DEyeV8q6A7mH/L0KaSUfCE95LWZbRjSxZPUXznOlkhuJsenM102fOZc3O7bz//gd0d/bw4erV9B8bIZ1L8Nijj7Bo4Rzauvby0GM/RfL309fXRyxWQThShGFJrHjlDYLRCL2HjlI2tZGbL7+Ka2+4nsqSYr761a9y0kkz8CoqTVMa2b9/P5YA3TSYOXsm5RUVqJJMcVERmqwhKxo502IiYywaLcA0TbxeL4lEAkeSicbCDA0NMG3WVOLJJMKSaW9vJxT2YkkWwXAATVHI5XKUlpbTMm0aPt/nY9ZfeGetSjK2CeQVVAiJzZt2sM+zj5PmTCc+eAyv0FAcd1Cjah7XJ8C2sR0HTfHkBQKSS11yQEgq4OJyWDaqUPMLzEERMrZwt5PgGsJwQgdjWRa2ZWELhz//+c8ku4cpUCLY44MoKhQVFXH68ssYNcdY9+LfKQj6KRFuZ9YYLUKOyPSbY8QzaTCznDyzBVkDwzKprm+ga7AfG42ScNT9EJk2ZiJF1uuQdTQGjw2ihDRKvBGyiTitHR1EfSHa24eJmjL1VWWM2X0QDJIYHQefH9s2KCoux5PK0NfdT1FTPYotsWv9Zgoboyw4eQn73t9GIFRPQblJSXoK+p4PWVA3m7Wd7Xg8QZpKoxgDcV46MMhvACftI9SbonLWDJJDCSyPQVU4wpgVY8q0AgZ9KUaCFRTnHOI4yGqITLCSeVMdMmUqRbpBgZKh1vJSEPZQFVYYTenEPEWM9ApmtxQz6MRQSweoKUzTOWgjKX2Yws/ylnqCood0bgAn0UXf6BCp3CiH93cyNNpOKpMi5PMQ0kJkjCSObCHSBvfdfztWTmE87ZDT4vj63S3lj7/zbTweE1m38KRNpjQ0s2njTsJFUYJeD4bHVdtZjkTIH0AWDooEApdWJ+FKoYuLi5EQKHm2hWNZWJICjp0vZK4PnuM4k14eE+vqxGI46c1h264oxbLwehSioSDj8TSK5nVDc8VnfT/ckzn0Dw2SMnLsO3iAqqoqPB7PZACBJElYlo0j8jQ+OS8+cSSEJDDz7BRkkb9u8s9hM/kUQsaRXH73hN8I5EU/+c+IIhQ8iuwG9OKgIyEwsEx356Bb5AU3xy1iXWsPCcNwYUPTNBGSDAgkWXLfOwsUCY607SMS9TM8OkRGMqlsbuDVV//B9OaprDx4lIaWGcRKi9m08UOuuOhS/vzwT9mzZzcLFi/g2quvYvXb7zCt5SQcx0NJRTUzZ86gqqiQR555jpH4CLnMCDFbULZ0Fr1rdtA+0MG21WuoKSumZ2SQsMeL5FF56vlnOW3BIto7O/n5T3/B7372C8KhKIZIks0YZLPZ/GtxOefJRJpYSYSIBJbisHHjenSyeH0BTp02l3CRlw/WfEiOLM0zWliz8R2Ky2McOdTKkaMH+PGPvv25tfILL9a2bYJsoTgyXkcmJWyq65qQhEE6DiFZxnI82B4HO89rdMz8VhSXKkTe2UsIy+U2Oe7AZ0Jd5PIhyUdg5C0oTzCYEUIGx0IGDNNE0zQ8sszXvvY1frjlYzy2ScCrUNJyEudcfCFJ3aLAW85j+46QJkvU/XvxaWaU0rFRKquqGPIGqAhEUW0wTJeTm8ml8Rle6uoaXKkyNh7JxtYtdOL0MUaxWYhpJzDMKK2ZLLU1dcQTaerDZWiqRS4+RkF5Nbpt4wsVYRkC06uRUTVUn4IUTuAvrEBOGnhjFoMjCvERL0sWzuKj/jIUn40nVEjPfps+o4+l19xK2EjTfnAjSmUDiy1XPmvPW0Kaj5kS0OmoqKbIUhk6qZSP/7aOipSPl/Zu4447rsVzaAs9ikpZkRencQHlwqE/reHxWHjNEZRUgkRS41AuQ7Krg4GufXQM7uIDZG6/4nQODh7jk/hJqBzAn5DYvm8np115FcOWgS4XMWd6Eb19B3nk0c3MbXan83q/n0Kfl4DPpLZoCnsPdBCNevj1T19GEgoXX72Y8upazm52/8axkJ+BtIFuGoz3DvHqG68zbsRprqlGSSXx5Gziug5yBq/Hg5y1UXULQ7JQkdExEbaFPxomlx7Pd7E2OLIrf3bsvFXpcTvSCYzZXWPAhCG/NVG0RT5c1sbIpRkZiaOpgki0CMcyMOEzYQEThV43TYSmUFRZ7nah+cSWCSjDsqxJ12nnn24OgDscFW6RFpaNJJTJ808E7bpBtBqyIsjlctiWiiCNcGQUzY9jp5EcP5aew7GHMB0ToRZgWwap8RSxWAwHG8MQ7gBWWEjIboCCyKLmMWwXnwHTtlAnchodh2jYz8wZNaxc8RxVdQ3MPnU+oXCY7ECcN155GRErorSymkw2SUFBhLf+8Tq19aU0njSN0lgJmzZs4a6772HXprX4ispY98oOliyYx44N2xnr7mb70T2cM7+Kl959ha/ceiNte3ezJ9XLQGIASbUIFQVxRnSy41l8Xj8hX4Caykpa6qawbOGZPPH8cxRWFRGNhNHzxktCCDzeILaeQsgKDc1VdHd3UlpcRltXFwvmLSQghXj/jX/QOGcafUNpPnx/N4qA8ZEEFbWV1DbESMQ/P4PxCy/WOUlQbAQZUJOojoxfsxDoSLJKwhhD4EOWwTZsZOF1F4aY2Mr9E6bnqLim5ALHcdVPE6blE+kWE4bvMDHtdk3VZcmlKcmOgW5Y2IbMUP8gkqQhB72M6QoXLl7GWC6Hg4IiKeiyhJM1sRQXM//JTx/gp//vG9QpYYZ37iLm9RIIKSjBCPHxJLt37SWesdh15AiapqHncvnrdl0QUOCM2TPI5XT8ms7sukoGdZOQYlEU9GAKi2hJE32pDFXVdWhGDjM1TMYRmJ4A40MJpjTOQAeSviSDZg019iDhIi8PPLKWO++qJCNmkhNHmHfDco5aEpWxGhJ9rcw66zziiQyBcCkALZURwqnpaEkTv+5gmiMUDHWxuE4Hb5R/PftU1P6N9BngaEUMG8dY+ehvSQ0PolsmvmDAjQxT/HiFhS07IAwyKfB6BKbh8OyrbxGLFTBzVpQ506dypHcYKVlLV087Sy6/jqNvvMevX3uQZFLHJ3wEtQhG5hhFIYFP1bDtLLGCCKrXplQBM+wh7vVxaN1Odh/tZsFSN93Io3hQxw1ChkNibJAZtkMymSQSihEwbNa+/w5lpsG0JbPokjLYqgx+BY/I+3zYCoolUCQFM51FwnTd7bCRJU9+bHhCYZVkJPHZ9el6XQhkAbZw3fUcoSCrEooSIBDwIUmQM92mAdvt9oHPQAUTSeIKbqHWZGWSTw355mNSFDMBW5zgrY2FwMXGERKmY+VhFBf2URUVO++RbRsmkq2iaim3M8fBNjOYKMi+DI4xTny4k8GhflqmLSQ9OMLIYCeqp45AsA7JzmHLXoRQkGUDxRI4tgc7D8F4ZRnDkvNOgjYaYGbiPPn8syxaNJ+Fc8+hva+dtu376dl0gCvvuZv//M2vadIc4uP9VJTXsGv3HjZs3s7dy++i6+N1rE3uo7q4gq4Du0D10d/bxhWXns+bb3zAwtnzmTojxeZDe2g70k798jp6Dvaw4OxlPPWjuyiuKKX/6CCz582hUIqyZed2RkbHWbnyPRwHlp1zHhrgi3oZHRtBERJKfqjcNK2Rndu2Ey2MMj6SwEwfYNmZ89m9+yjCltm9Yydj/eP4gzJd/cMsO+ciCivruP/OH1Lg95NJpCjy1fP23//B/T/66/9aK7/wYq16NB555T+4+9qv0G54Ua12JCtOYbiasVw5ppnBlkF2vAhHPyGRWeL4oH1iYCHn6VPHLRhPNHuf3I46LukqD4IgAZYFslCwcJBsnfKAzILaOp5RFXp6OjFELf5gkLRkY+k2sm5jCYHigJWXaCtqgKHBOFt37KbSF6DfztFcUM+Kt9/C1g1MJx9NZLtDIlmSsW0LWbXdWm1Cd98w06Y1YCSyGKMDJJrnJeQAACAASURBVEYcAsIkoVuEKysYyebYsGkTxpqNKICkaQgHTEdCdRyuPud8hJJBaKAaFuFYDAmdk6aU4WQGSVhdjI6NYhhJ3l7bSiL5LjlDd/FT3cX67/vhv/HMg79FpNP8/EffxEpqHNx+mPKKIOUhQcd7GznlkrPBMamsbab1cBuxiIqsJzCEScTvx5EchNdLKj6OrUl4dBXbK2EaWbzeAAN2Di2joo7meOudt1n1jpflp53K8FAKn1rA7d/6DrItiIQ9kLUQPgWPLOHXwowmR1D8EsJQ6O/qQkg6AV8xEgY5IZgyZTq7O3opK3LZIKpfxpYsvJIgEAiw6YMPiXhUKqMlZNo7uHDmbLaN7ODJB3/Ljq5e7LRBMOdFNSx8CFJ2lqyWwwiqpM0stul6KpMvhrZjY2N+Zi1KyJMzERwXa8Z2U1EEE4M2UCQlb1fqkDWMvJ+0hBAqtj0hgJFPGEYKBE5+Z+lgGMYkzmtZlkuqcEcw2Hl3ugl/bTsvnJnEr213ECkm8HDhTA5AHWEje3JYkoEmSrGkFMlsAI0cippE5LwkRg2EXsPUullk43Hiw0MURn3IPgdJ9WKbOVRZw7FsJNnBMW1Ur00uT0NUNRkrDZJtIkgyMjLKUO8hAmGDVevfYuHcxRR6/Bxq30/RSdXcdf0tRApj5FI2q9Zt5sLzC2iqbqS1/DA7Nu6korEFO56krLGeaKwUXVhMm72I3//377n3299n47r1/M9fnubiq6+isC6IPnyYfjlHt1yAEc/RZnSQiqfZsG4jlaFixsfHQNiUlxchSRIj6QRZK4vq80JOIpfOYUju37316EFiJQUkEylkVMpLy1izai1ZI4zlQH+ij2zO4fzLLqe96wheW3DeWcvpvvQy3l33IbGiGK297QRKyz+3Vv6fxdqyLH70ox/R1taGLMs88MADOI7DvffeixCC5uZmfvKTnyBJEg899BCrV69GURR++MMfMmvWrP/r9Pj8Os3lYfqHNqMEg4StNGODXST1oxj+CoR5OmghTDJIQseyZSQ7TwX6p/SNCbzwxC7jxKHOpKEM7mK28r+nivwWVhhYih8FD2NjQ0jZUWRVI+wROLqr4nIUiZxkIVkWkYpism3pycikYCBCMBKmLz1GmewnlTF57tXXUFQZjxCo2OjImDh4NNVVgDkyKg6W42BKCu39/fTHx8glde742nWMrHiXmuZmtNJSVqx8HQBZkpAlB9128Hk8rlJMBq8Duw/vZMa0WgYP94GWJjcUoHdvGxfPnUUiG2Ggz2TanBDebAPjb65H1STIgCQkMoZOJOB6HTi2jO14+eEvHkaSFKKSw8/+/DOO7OjE2HOQxpoGIjNmcuWV11JTXEgooJDWc8iyhuZVcTCRhEpO9qJqMpqkgcfByDpIqsCTU7H0DJIviISMpqiMd3VimjlsSVBWUsbI2AheR0NydCQcPBaoAhAaXs2DbeqoNqiyQsgfYDQxhKp6sDM6qiZj5Ny8tXBAI4lOVrUZ7O3n4ltv5MkPPiIYChEtLOCBe+8hKMlMmT8db30ju19fw14th75qO+rsKRTU1xDv7yQaDJNNpRFGFlS/a6ok5YO1JjZ3E/FZeXrb8V2fgyVJSCIf7mrnedKWkd/pufi141guHc6ZgDfsz6xhADtvO0p+fWseFctycxddz2drMkTAXeuuAlI+4VqOO+yBbbo4tiNZSDjIksBCwTEjhH0y61b/HssqYcGpS/BrpRgJFcW3Bz07TDRQzcBQN0c7D1AcjJBKjFMWDKBIWVRZwcomSIwPEKlsxMTETOfQPG7Z2bN3Oye1zES2LD7dtQWvVsCuHUcoqCjmwosv5uihNt5+41myfjCObaDlvLNpHk7wxNNPc92tN7P1k22InERmLE1paS0SMDI6xJEPW9m3tYSK8hIOHz5ISayATRs3EI74mLdkNls2rWWG08SclnpKS0v5f/fch4UPnzeKzydhkGNgZAhLVjh9ySns3r2brq4eHMchEAwgHAVdz2LaEo51fK6Qy+XQNA0hQf9wH81N0+g+FieezJLVFaKxKDu37KC8rIhnX3ia2ooaZta38OmR/bT1t1FY4Scaq/ncWvl/FuuPPvoIgOeff57NmzdPFutvf/vbLFq0iPvuu49Vq1ZRUVHBli1beOmll+jr6+POO+/klVde+b9Oz/oPX+D06dPolDsZ2LKJo5/0MW9BISqVRPxzsT1ZEqaMV1GxkJEE2LKGTI7JxI38h8LGQclTg4TtbmHhswOeyWm0Ih9fvJaDY+WQhEEqmwNNRdUC5CakwpbM+OgYHlkhm8ngLQxS5vioKamgo6uXpOnCGS889RSpbAbn/7P33mFyXfX9/+vcOn177+q9W+5ywZZxwxCMbWxjg00L4ITwJQkkgUBw4AuEahOCAxhsqnuvcpVkS7K6tNKqbd+d3dkyszv11vP7444k+5v4R/7jn9zn0aN57u5z587OuZ9zzufdbB9XVyBn8+Hr/4JHHnscU+jg2uArOIiAreJL0AS29APHMM/H9QJkXdEEP/jF7zijpZadBw4x/MY2VA1CQkU4Hr6iAT5uLouqKViWj62ZHDsxxuH+JBWmydnnLWZm+xA14QgVLR20tczlwed/zqaXR/FzJUQ8RBiDEiUUXUNzBagne/kFHNVDFWAoHkVP8vGPf4k48IElC/jX7/6AVDiKkAZ6LI5l20Tj9RRSoxhVVQgCr2Vdg5Bq4mkuCSOKpRbQhI7m5ohoGoZXQLMkpunguhJTBDscHYWIFsM0wNNsDCIoSLBdDB10IdHNGPPmLWBg2wiuPUNFKM60VaJ/fIyIp+B7AZAV1U0WNjRzeLqbaFUVj+/YjSIhFo2CAnPrG+n3c7y56QD7Uv00RqrRFJe5l57Hc8U0h7ZsITqSIpK3qGmow9JNfMUFxUTxQUfFKz+warkfG/hSny7WQgb3YxgGjucHOAl+AKNIiZTllbCU+L4NilYe30Fbwyq3zFzpoZZTa/3y+HRsl5MufPh+ANrJty1mfIHnOO+wOA04z0HyiqoQhN0q5clCkegSFNXFzeSJGV00tiY4uH0/ETNGKGpQmMnTUFPD/qNvYYRqaa1fz8Cxl9jXs5MPzOnCdyWlYoaQbpBzpqiiFVOXhHUTxw7uoaOlhUce+j1nnrGakbFhug+/gF8q0bH0Ip5/5UlueP+HUPIXE4pE6UmmsDIlbFVh9aplNDQ0MDUxwsToIN/93r+gzaljzx8eZfUH3sNbb7zCLbd8ipnMGH/43b1s3LiRJ57ewmQ2z+TkILlsntf3dHNo33F613qY0Qpm8i6lqSmkoWF4UFAkVZrGq6+/iqKohCImtuUGKlHbw3PBcVw0owyiOkEggqL6qJ5KZX09hw4lMRMGs5kpFDSqKmL09fVhuVm8ksudd96JU3QpWQ6NzVXUxeIc3nvoXWvln6TuXXLJJXzjG98AYHR0lNraWrq7u1m/PjDJ2bBhA2+88Qa7du3ivPPOQwhBc3MznucxPT39py5PojJOcnyYTH+RXbv7WX/uRTTUdBGJ5BhObqKYHSccyoOYQZMqmmeg+vng5k+mbJQHoCqU00j721okJ1fbb+e4nkTjHcsG6dPePoezz7mEFUuWons+uqri+ApOGYCZyU3jWLO8tfkVHv3tffz8P+5mMDWIISVaJKAFrrvgTGzPQtEEuqEiPI9MtgSqgeOBVHWEIgPRQ/nelPKKR/gSVQh04aNKF1wPUw1oPKqUxGNxFEXDdzzCauA6qErwdQGagqaDUFwQNr50yVgFDhzqw1UdLFxe3HmIf/zaN9GtLL4lMYSG6qpoaBi6StgU6IpPRAmYLSElGLSa1FEIWiwdZoTWaCWuL2iqqqdCD6HjIj2bcFUNcc0jpohA4uEFHsmKpqMiSIRjqKqKaYQxDaOcP6kgTBNN09BQiZT9jxW7dMrDGLygmAgfQ0gst0h1TQWhUAinWCQaDqEpGhHDRPVcwrqB5kl0Q6EiFGAJE8lhzlq2HKskMeNRPnHbTSSAuGJQcF0uvPQiVq44n/jCNladuR4roRGWAn//cXbe+zuWVMc569Iz+PxfXc/x3gPoAYkY33NRPBtVBowIRQSyF2QgoVYJQgSE76GpEte2qK+vpbm5GccuvYPCJkQQKGsYBkZZ2HXawpRTlC5V+uAHK2BdV1FVgaqBqp0c7xKheEicgLEig9eKHvzM85yyX4lEUwWK8NBNBUc6pzyzg351jhBpXK+bWFglN+aieRO4pXE0RcWMRhB6CITK+EQ/3Ydfo72+giXzu5iYGGdo6CjF9DSWlSUUaqI0Y5HqO0j3oc28tetlAKzZaZYvW0FuOkMxV2LO0lXEa2pwnCLVEZMnHvwjXjGPJkocGehhy+Y32LtjB+/74FVkx1Pkhyc5sq+Xn/zn79jxwnbWrT+TX/74RzS0NPKlr/wj9/7qAVq75jNvwVI+/KEPURVS6Wrs4FMf/yzz6zpwFJ1XN7/AyGiOWF2CkK4SCoVwbYfKSAK75GBbEt00sV2XeDxORDdJZ9IsWDA/oPmWW5uaYaKoAun5hI0wIyNJHMchN5NDV82yitOlq3Mul77nGhbNW8zll1/JvDkdCFXDVQqcGOxj2dpz37VW/o8Dc//+7/+eF198kR//+Md86UtfYsuWINTxzTff5OGHH2bOnDlUVlZy4403AnDTTTfxzW9+k46Ojv/J5f/3+N/jf4//Pf73+P85/scA47e//W2++MUvct11153akgHk83kSiQSxWIx8Pv+O8/F4/E9e9+57f8RQ8gWmDk2hm2kmZYEqdFauXkByagCcCtKzlaw94yPknBiOWsBwDRQFfMV5x7WEUE6vVjgdhST4r6R+t4zfa6qOLT1cxwfpEVE1QqaOI32K0uf+e+5itucw4zM5rr3hJl7t3st1V76f+35xL81VMcaGBpgsWjiOw6fv+Dz3/Oyn6AKW1dYylc1S0FQKJRvVsokZ4HiCGc9B6Ca+6wVAjqIg/CBpw/d9TNNE2i4lPM5qbmW6UGAgXwgc14olqkwdT8CU7RAKlUUcZVVmsVRC9aAEVKnQFo/TaUYZl3lKRZ9wZRX7x8YJ+YHpfaIiStFyidTWkUqO0RCPcWI8SYVhIvAoqZIqV+ApUB+OUh2N0FlZRbJQZMgM03f0MCsWLsLxJelSkexUmobqBEq0mpjq0j+eIaJKDC2Mbzg4rkGVkmd4eBZpurRFYkyUSlRrYRY11TKcmmRJcxO7fA3PFqh2kpFkioimcXHrfPbnpyi6Ll0tLciRMaJ11ewdSXF2fTWqLxiLRajTdfb2D/K3a1Zy87Mv8Kv3X8rxsRT1SoQ9XoYLbriCv/vb7/Pdv/8yyace5i9/9T2e6+3ll/c9gTw4wlR6mLPa5/N3132YXXFBejJPJOLx3opKXn7yKYzPfZywkiCnW+i+xFOMd+zypCz7UpfHWwDYBTLvA90HaW1tJZFInI4BA6RXzi88aRMq1LeN62Cb+N7zL+WZzZtwy0GyQohTCsuTeMzp1bp42/ufVBCW1YMysEXV8EhNH2YmO0pn15nMZCAsXFS1yPDIEeZ2tDI2cpye3oO0t6+ns2M1b7y+mXPOW8W+vTvo7T1BPBxlYGgAqUjiFWFWLl9JYaZI31ASoUW55dZbmZqc4Nj+Q5SKOWobK0nnZvjbL95N9+HXePKBh1GkhfQdQhWV6KrPzrd2UywK5i5cjC8tli2ew8BIknkLl+MUVP7vv32Ly665mL7pcc5sXMQPvvNLrr75MiaHe1g8v4u6lhbG+lIUQgqVepxDR3oYHMriUKCyuoFccZLrr34/v/rDH0il86xoqWUqbzN/yRIOHNtDcbZIOp3FMAzqaiuZSKUJh8PomkEul8OTPiuWLUdDcLT7CH2DE9R1VqNoPnbWwS5IzLhgNlvg3DNWcWBvN0Xb4YM3bWTHm3u5+OzVpCczLF1/Ed/52ncp2R5SFzTVJvjwJ27iu//8k/+2Vv7JNshjjz3Gz372MyDYigkhWLZsGdu3bwfg9ddfZ926daxZs4YtW7bg+z6jo6P4vk91dfWfujwFNvPMI8/geCZ5I8fkVJJYVQO5qQkKeUnEjDORGkJSRFBAkzqOXsKW2f8ShxSALIG3riICxF35f6KITkpqtZNpy76LKgSarmCGQ3gKOELgSLBLReKxShQhcHARts1f3ngTvceOUVNXixkOIQGjvH184I9/DJJCHBfLt8Fzg4fC0BFq4JEghECT5UnjpNKs/KCrZRtMFYHtB5mSnueUrYJE+SEFQ9VASjQpsG2Xk+5ufllYpBJ4TWpCQQgFTWh4QiGq6EQUHQ8HVfqYIuiPaqo4lZp9yr5SC+iJQgiEquF7Aq/sZ+k7NjWJCIlYHEVoxCoTmKZJOBzFsx0UX0cTYOgmmuKWFapK0LKQAcfYpyzakKD5UMJC9zRwJYowMCIxVN1AxUP1FUJCRdMCWXZlRYyQbuD4DkvmzkWWXJJT42xcu4Kmmiid1bXYuVksEXwvhl0AXAqqT7ub4JabbibrCRpVl7QnSe0/Qk1xkq9+5C+48j3reOjub/LlT95IvuBQW2+yMeow99BhjuzZhu/7NJqhMoipIJUwMRFscxU/kKVLJ/CK9jynbMjkIqSHpiksWrSIWDyOaZ6WuPtuIOZSRUAzLZPkkPgIBVzPOcVccl0LBR98F9e1TxdhTia9eKiKgmloaKoESiiKjSHib1NSemiKj1Uosu+tE1RHluPnFWqigW/zxMAkpYJk394+pgdLREIG23a8yuFD3YT0KD+/52cUsgWOnxjG8TVWrTyD/EyJSy+7ikWLl2NETXw3T1tLgtdefpS9O14kl5lk6ZJ2dm9/iTntQc7gY08/xKEjexgfGUbD43DPDvbu3Y0ZqmDV2uWcOHqAqF7Bs09tZaB/igN799HTs5eOefOI69V8eMP1PPX089z+d5cTipZo7pjH5IzFjj3HqKyoZjY9jetMcfMNH+HTn72dNSvP4r2Xnsf1V1zLy6/t5kh3HxUxlcPJERpa6tj0zHPojobngGmaaIrADBtYVpHZdIaZmRnUsEAxPEaG+uk70ctUJg1AadYirKnUVMWQlFCET12VQX1DFZ0dDVx11Tk01s9naecqwqKJbVt7+M29D3HN1VehizCJqgjnXXAhm1/e9a618k8W640bN3Lo0CFuuukmbr/9dv7hH/6Br371q9x1111cf/31OI7DZZddxrJly1i3bh3XX389d9xxB1/96lf/ZKEG+P33n+S9l7TTXGfh+z71NRVccM4ZDBwex7dc+vqmaG5YgW9rIAMakHRsBEE/8h2+CapyKu3Y83w81wH/tCDg5P/iJAtKBvQrxfcwCBzWFKHiOR6aEgSghnQdU9FQFI0Tw8NsfnUzAhjsH2DgeC+OHUjaIShwdQ3NNDa3B/675T5kKBTC8cpm728rzpXxSgDCholrB7sE1Qfh+hiKSljV8cv5eZqiEDKMgJIty6u48rUMwzhN3SoLf4RQ8FHQhcTUDcJasBtRVRXdF4FJu6khbBdVQnVFIriGOO15rGlaAIL6El1IQpqKoegorofjqNiFIqqqI12F6ekMpmliRkwct4DtSwqFAqiRYNUUimDZReIVFUG2nuegEIggdEVFVxVU4aAKMBWJdIsowsMM6QhVA1WgeR61sQgttdVoQgbipViYop1ncUsT+bF+zj1/NXizCN9BMU+uWjWiGAgfMlUG2197A4kkKgWeahOVNksrOohNjrD2gvX01baS1cLExBCx7TvIjU5gGipttY1kDZ9V85vYeOEKFje1Mp0cYdYrngKuT3KeT34nuh6MA9cN3O1EuYdtl4qBf7EMpN+udPFdG7UcBqAg8V0Pz3FREEEyOsFEfdJ2NGRogRweL+hx46OpHr5nYZVyaEgGe48xMTqI705RsvYT1ktMp4/gilksRfLBD38ARc2Smeph4Ph2nnv+XnKlAYTtYxeK7DhwlISxko6axfQe3U4+N0wiXMne3ccozQjsrODY4REcS+PHP/wFTz7xAsnhJO3tncTDCYrZHH3HD3OwZx+vbd2GGQ7RvWMvANFsgfqKSpasXUXfeIo5cxbROW81vqfy5pYdmKbJieP9RCpqyOQKHDvWS0NdNbfc8lGOnDhIbVuEnbuPsGNbivmda2hrbGb9ugu47vrreXXHDppjNZxx1pns3vkm3lSaZWsWkToywPObd9Da1sBPfv9D5rd1ctUV13D4yAmkUBgdGsSzLYT00Q1BbjZPJBrCFz66ruNkbYozDmOTGfIlD0RQhyoq48F3owqq62qpqalDUXQO7D+EojpkJzO4k/2snlPFI48/QN5QWbQ8wbe/8y2UiIeHyZtbtvKeS85711r5J9sgkUiEH/3oR//l/G9+85v/cu6OO+7gjjvu+FOXfMfR0hZCOCohRcMrZlnQOYdXNz1JJKERrm0lLw3qOus5Ovga8+ZtoFBUiRhRSp79DvAwMHgPipUqAiF5EFj6dmHCO987ALACRzKkj+8ErRSkpFTIo2sCww88KBVVZWh4lOKJXnzXIxIKEVUl0i7hlFc4kXAU31dQpYvq+wESLmWQdi1O+5GomsCRgRxeEYJSqYSp6VhuYK7jncykcxy0iIoBeF4OhI6UoOkKnhvQwJzyBCSV01xy8BGyLP7xfHTTIGRpFKSNRhA7pQoNFYnvSmLxKJmpDCHDOGWKJQBFU6Hkn0o9EZ6LVt7Sq1oI1c0jvBLRkEprQw15KYiGTQwDNMPAKxRxbYkqLQr5LKG4QT4zQ1VYwdR0NClRZQAe+m6wywlee9TV1ZJN5/HyNtLxUA0FbItoSLCwvYXBgSSzjktIBKtZ33aojEXJmSr9E0la6toIl4e3E0sw5QkIaSy74Ewa58zhijWr0KdmuPqW63H1KDFPI3H5+1F2dqMe3o+7e4Co7lIKKRihCKkGg+LiJjZedha2r+BOJGkLabxv7Ty2jEzjSfOUx0zAC1URUilbm4ryqshHU08rBk8b/L89vdw7xQPU1SAIQEofcZIbiI8vg/Ho2i66oeH5OYSqIYSCa3soWghNV7HsAgsWtJNKjhA1Q4ymxrAycRobK8llZpkZLXJ45yY0E4TQGZ8cAT3M3gP7UaVPXcNSzJoGErXVpDNj3Hzj7VzzwetpamxHi1dTdGw2fuAavvPtO1FwmTuvhcamKoRfpPvgbmqqG1i/7hya6jsQikJP70HmtC/mwN4DAIwW04ymxhl/7nVuu/2T3Pm9f+M9l17Mxg9s5OjBHvp7RhibKWD4Lm2tDSzsrKelLUHviU2saq3nX7/+PdZecAbLl7QwPDjIEy9soi0ew6mJc/najSj1gh997y4+dP11/PKB33HjR25g7aUXMO94llfHttIRM7jy7Iv44T0/J5fPEjZDVFRWMjubQ9dUotEomcwsZiiEZbs4JYeIGcH389jSIld0SSQSANRWxwmFIJebxZrO0VBXjWflmJ2dJTPhMbejjnhMYce2vfhhaGnQ8GbTfP8HP6CpsZpULs9YKssP7/o5//qVf/tva+WfXRRz6TXn8/pTuwibR2lesITx8QHWLl7GsZ4JOlqu5NDgb9EGw9RVwqOP/AuXvPezWFYTQpEITS1v6ygb17zdzStQkfllA3PgVJ5cQGfyT7ExpDzJvQ4cxhwJu3fvxvBdZsdTSOnhuTZT42MIQyNmxvA1A/xyDFLZCMLxXDQEru9hOTaaVCkWi4hIAqFqSOnjI5CehyJ9HILUDOn5eH75tV/uJ2oaju+UJ5kyeq/rYKrBzF/+HYmP77hBgSf4XIoQhEQgwPAVjaxdlvhKD12UrXwUgeq7eIEej2iihmxmAsUJ5LPhSCT4/XJvVIiAj+v4JaQSJap4aNE4WiiO5UBmtkgoESMRi1ERM0BV0KP1OF6azEyBqppKVFUlrNng+KAqSOkCPqoGtqfieIFfRCIsEcVpFMsiGoviiAyG54OmcMbqVaSmUmRn84RrK4jHo0xpkhAqmh4nOTiJEqkk7io4ZXVZU1MDvZUV3HTb+/GtHHoxR0n3qDtjBVatyrZXXueSaz+AfPppqnr7mc0WEDpMaSEiNfUcitnMv+Qi1JJBQ1snpfFBonObeO3Hd9OnatScfXk5cxHwvTImclo1+HbZuFr++58swkE0ijjl/Kj+P37GAgHidDE3FFHOe5SEzRCe46Cj49kanmYRNk1c1yUaBjevkM2Mkp7qprFOoybqkxrfyd0PvsTtn/gCqjnB8qUrGEsWyWSnWbSwgUN7DqObtTiOw7bdb/DeqzawY+8W7Nkwf/Ol73Hjxz7BZLKb8y+6iWhVmO//8N8498x1VERC9Iwdoqm9mdxEhs62eUxNjjIxncYMVTMwdJSm+iYefep5rt74PgCObT9MXzbLxe+7gm/87EdcdtV7MU3BV/7+m7R0dLLmrPWc2LaDefOXc+kF57Jnx+vs3bOLkbTNmvltnH/eUpqr2xkdGaCisp5FTa30jYzwlb/9PFMn+kgWZmju7KKnr4eVK1dSpaj8/t7fMD07yxe+8lk2/fE5th8exFFVLnrPhbz44ouohofjucQiYTIzWbKlHBHPw7d84rE4U5NjuFKy7qxV7N27l4mpwCJ1MjWB69m0tTVRWwO2laW6Noqe9Vk8bwFv7ezBp4ptPcdQFIVsyiUjDH7x6/uIV8RYvaqdxsYEpWyedzv+7K57W7t7qG6JsPLCC+hPpbC8KPv7uulatITkwIsk5BhC6SGf9/nItV9HUUOgFVFV7R2c0cBgxsf3g+IdmKsHPdsgRiOwgjwVYV8GhU6CjZ4IUpMdxwmKihEG6yQoExRzU9VACDRTR9UMpBb0ktWTqx5H4lgWjmPhOh6ua6PpOsIXREJRHPd0O0YV8lTPPRBDyFNULV+6eK4kZAbCD0PTAYViMY+pa9jCRfWDnYGHRCoC3TCoiFUEfscSpBeAjrbnopoqBgqa1NGkSwnwy71kVfqUXmkxjAAAIABJREFUClkQDomITm11ECukaUawsg4+IYowkUKCsCi5PrqmUFsZx/N9NE3BiGhMTYxSzGWYnpzheM9BDh/cxczUILqbR3eLiGKaQnqcYi6H9IIJRkqBZoTwHXAcCzWkopVcTMumMhanqqoFXQ/hCZ0SKt0HDhE1YjTWVNIeCVEcT1GlquSMEkptLbndSbDCtHXNh1LQWhpXZqmsjfPQr3/Jzgeeo7R/P7/6wucpjCQZSk5yrr4QY/NhSnt7SDtFMrZFrGUuI8UZ8subqVm7htb2+bR3zkGvV9m341neuPc+RuUMxXDQKy75NkUcbEWiGBqu7wSudeV/ru/g+h52OS3c9/1ACIXE8bwAgJQS1/cC75ryrvFke+XkWPdcBY0wOhpCSlKTB+g59gBGJElECaNJjUSl4JXXnkKKGfZ376Whbj6+PcNkcoiGyhiNiTjdB15mbHyUopUjFtc5fvwo/ceH+P2DvyGXy5FIxLn8PRcx0T+IacSpjBvc9tHLqa6NENES7Nj8BMnePj74gQ/S09/HvqETxEO1fOMr3+atbXvIzGaxfZ2S7fHSSy8yMTmG41gsWrSI/7j/twDUL1nK1VddS6zgcOXSpeQG+3nztcOsOX8Jt37kKl57+BGu2ngxTfVV/Ps9/8m9Dz/BrG8wr72dR57dxI439oMaYTZvsf/gPvYc7GHOgkVse3Mn+dw0r256kdbODqYmZ2irr+TRJx5HKJLJ0WFyyQIPv/IaviKIJSp4afOLJCorAnqsppGayFPIWSSqKpnNFtE0jWQyGUjjVYPde/eRiMaIhQPa7kw2i6IajCTH6O9PETLC2CUdVQnz1s6jSCnZvWsPLY1tKIqG5ZToGxkAFGKxSsZGx1GkwoH9R9+1Vv7Zi3XESFNyh8lmwggZYfWas1iy8K8YT+skx2aorFjDnLnXEK1pYmjyCHgJdL2mnAt3OkXD87xgtaaIQAyjKrjSDVzGNIFU5KnBr+s6jmMF4A7lNoX08IVfViRJfOmiahKp2JScgBNrey6e4mM5JTyFUw/VSVWkXVaRnbwvTdNOtWJs2z41qZxs25zsaZ9E8r3yqgwClaKph1BQcRyPaDhCdVUVuqKXs/zEKVGQoqpYloXr2oGqDoGqBhORqQV975NqNSklhgiujxRIKTBVqEmEEdImGg34vDoepqqjeCpCCe4pmAxBERquayOLBWIGHNm3m/aqBItbmymkc4xNTGNGKwiH4lREE9TXVeO7Doaq0dXRSX1DI1J6QU4hAl9RwHcwEciiw5lnruT48aPkpjOkUinaGxtwXZuobiLyRazRMWL5NKGwStIqEJYhQrM+CdfF6qynasVCrAhgBwDjGXMWcM6yJdy89kJWXnIlYwtX0TeRonZqkvr9e5hN7eP4kd1It0QkXEE2lmB0TRvqVefRdeVlrO5YiszOMpPqZvu//ZixmSTDuo8uTRJaBCldpG+jSD8IpLW9d+AkJ02WArBWQahKGVfxTlmYejJgg5w0X3L94LyPPPVzAKl42J6F61s4nkdL43LSyVr6jk/hlJIkB3fTd+gITs5Bl1HWrVjG8MBW9ux7jp1bR9i65S0aa1ew+aXt9B49zKMPPsDRoz34SLa88Raf/dxfo2kalbE4vUeP0dY4j0Vz5tDcUkcsUoPqKtQ3NXLulRsYT48wZ95cLj73YkrJNDEzzmWXXkFX11wmp9I0trTy/PObCZsqdbUN7NjVQ99IhmuvvQGA9uoWHnjgAZq7unhh2y7ue/QFrrxsFRcv34DIq5yxYSG79x/goUefoLm9jeqqBja/+iZTMzk++vFPUNXQwNe/eScDyRT7Dh/lY7fdyrJlS2hsbiMcbmB0eJYffOu3XHf1R0lUeJxz1jri0sSobeBTn/siYeJEKxP87D+/i+JJPC9DyXWwrSCpxtDDFLMWETNCyIyg6gpG1KCxpY6KijjZQp6SFSwIHM8nb5UouQ5z5zczMTnD/r199B5L8dGP3kKxlENRDPqHBghFIyimSrHg0NVVx7GePpYtW8S27XtoLQdm/HfHn71Yt0eW42aibH9zG7V+G3ue3cOhQ29Q8lQKTgShNjM5WQtiPlV1nQjfxrZLgRHO20QFgS2lf+q867pB8rIEx5U4rjxF7bNLFgIPpIPnOfiuU75OoCjL57NYXg6pFCkWsoQNPWA6eEE7xC0VcQuFICOO0w9SIDg4vUI2TfNU4T4JAJ5E/aXnI8qvA9ZGYMR+SnXpS2bTaVzXxdAUMuk0qbEU+WyunEiilC0nwTAMKhIJEmWq5Mm/i+d5SM8npOqYmh5IkHmbchPwXImdK5AdTzE1NsnePd3B+9t5dOkApyOaVKEE7205mHYBZ3qM965fxUXnrKI2LGmIqKxcuoyOxlrq4hFU1yY3NU46nWFqaoaJ1DRD/UNkp2eZ29YW2GuqGpNTM6CrOPj4mkaFoiEsl9z4FPl0ipCTx8RDlRYR3cVws+heiERJJ5oXhBTBuOJBVYQhbZqN117AjJsnZwaTaDweJ2aaHNbyHBjvofKt3ahvdTOST1GrmhzIjxOf28pghcnI3Hq8NXNZtGY1Gy76IG5OIoxZnvvP79Hz8lPkGk00SxB1DcZmSuTUMAe7j/LKi5vQECh+AJgaITNQtGnqO4uxPE0jVTQVRVPfMYFDOe6q/P2cZjIFAKOme3iiyOjEAB6zIIpcuHEVkVicoyeGSacyTAwdY8WCWrZtfhrdcznaPYrndNA0bw6He2c4eOI4a9a+h7bWubS0tOG4wcKloroGRQ1hWRbHDx8hHq3klZffYMurryC0MD+/53eE9UosV2do31Faqut4+dlNvLl1OxddcBGH9h9m784DTExk6Zq3lKNH+7ntE7dx4MAgC+aeSVvHEo4dGiQzkQGgpa0e35Ps3XeUhppGLr7gLL7yjV/wk9/eQzLXz85dA5gJk5Jb5KILN1BXGcFUQrz08mvce+8vSCZHQAgufe8VjKemmJiYwPdhaGiIcFU151+4ga1vvcCXv/aPTM7OMp5Lc9bG8wi5LqvXrqVnqJ9EyOCLn/8yXXM7MGPVaIaOa3v4rk0hW8DLO2QzhaCdEzEJRw0KxSymqVNbXXlqIRM2DXzLQ7gKSJOB/klq66tYuHAhd931E+bPn4sZitLQ1ACqgu/4vO/q9zDSN8iCRW2MJHupb6hiNld411r5Z+9Zz2ZHmJ6QtK50cVNJzl28lslwmv7RbpqjtSyYN5+jkykGJ/IUiy3IWYuGuc2ooTiu807znJPOThIC/wPh45eLnyoUhO+jqSqlYhbD8HEtH9OMBCbqnsQXfqD+K6Y51nuAoSP7iRQNTNsjHooQjkUoiiJRDGY98Cy7vIUNvjBT11GFgicl0nUJhaMIu4iqaVhv53mXV+TCl3i2w0kb+CBeieC+PZdozEAhCEU1VEEsGiE/m8cnACF1oaD5kElNYoRM0o6FaqgoHvgiyH0TqoIignaD67rIMkPm7S2kaETDyk/R0lhNNBoU/K62FkYnp5D4aFqQ1OO6NtJz0SNw7uqVvLTpZYZmHRJRJZj1XVAcBy8/i6OoKL6NYahIy8HyXVwhcRQVWzNQFYmwAxaD7TqouoanhRAiz/TIMFdtvIRnXniTRCTwFckJwZRlk5pIYocjRKt8iFWSWDuX2xYuZE4lxEI1/EXR4ehPf0vhWJJU2YPihf07aVywmqpcluj2HtINGloohKmqaJ31IKaYqYyjz2mhbckKujrn4ts5iFqMP/QQmbEjqGqO/pJJg6FTbEqQn03TXtNAXq/ECsUZHhvHkQqedLF9G8tyME2Tkm2jaVqgUhUCoZStUmWApyiIQK0pg/guVSiB+lacpmoGlqbBzDwykqS+ph7btgMKp5CMT6Soa+7EtU3CeokdO5/Bk82Ew6088MAWPKkxfMJlPN3NymWrmC0N8tpLb2CaJpF4AskoqBrvvfJKDh7ooa21i6P791CorcfXYkTNBho7mqnrVIhUusypXkylYXKs5xjjkxOsP+9M6mtruOLqK3jhhZeJV7Ywmhzilo98nKHhAW6+5RZO9I9SKvp89Wv/yLNPPwOALVXWnb2S+Uvb6Dx/Pa+9sp3L3n8+x0/0UV+3lDPPnCY/k6GhuoKnn36Wq66+hsqa7VTV1bBm1QLS6Rned3mY9PgEn/jYbSRHJ7jvvvv49B2f5P4/3M9F77mQp595jKvfdwnHj04QqTRZuKKKCy++kIM9R9m87TXu+uYPGB4dwkyYTE1PYBfBRMcIqdi2g6GFKEgXWfYeD+sa+WyJWFWM/EyekBo4I7q2A1LD1MMcPthHc3Mdk+kJFCWH5/lk0lks22G2kKeiMoauuXTveouKhMnYyCRTaUlFbTXj06l3rZV/9pV1fVsrk7kM2rTCnJY4Q4P76X52N62OT2Mizp2/voeBmTSPPvYyxwYOk0hUYXoqnlXCVzWkYuD7OiFdJ2q4KI6GdMNB7zNkYc8OIbLD2JkBNDVPWPUImw6ZyX1MHttOSPWwZRhTC6GaCVB1ZjJZfM8JUp5dHw0Nt1TAi0f4yM23c3xynIUrFqHGKmloa6GtoR6AkKIS1nUiCAwJBauALgW6IjBCOvgSAw2VgCInhECq5ZWWEGg+hHyJ5oqyWZSg4PrgaehCwZMengDH8QMwUhUoeEQMjbpoFF1RsG0PKQUh0yQUjqE7Hp2xOB21ClnhElIMXB2kq6AqgkoFsiWfXL7ETCrN0FQwWKYnS+giTk1rJ4rlIrUiqqoSssJUhaLUKiFKqQz+6Bi55ASl0RxOskRh2iHvGxg5FaSOH9YDF7aIJGxohMNhfE1DU8NE4wlmsnlUJcSk55EpFPFtqApXEgnrdDU1ML+tnTs/czOP3PlPfPVjN3H7Zz/BhSvPpPasc8gtWkjv/l7EhpX84Nv3kpxbx7apNP7yxbieChXBg3RpUzuJnd3UjI3TlIjgpaaxRZiJrM5jM1vJLLI4ama45NoP0BSrRboumX27ee5bXyalz1BgDLMksdQoMWMad3gas6KFguvx6PE+9h8/wqLFy8hn86hSQXoupmaC72JoQY6hQAuoer6HKgWmDGEoJrqp4fshClYW8Hl101YMwy4Hx5oIxcWxp8APhGgdzRHCyl7cqd0MHXmQvbtfpqF+Ab4FzY0VjA+N0Fm7jJbWeezYuhlD+Cyev4ye4b1UVMQ5cbyHPdsGmDu/mfXnX8D4ZJpdBw5iS8nTz7zET3/2c5JTeXJumKIrmZmdZP6aubz08hssXnwW+3qOIr1RJvLHuerqcznvojNI9Q7x0CNP8oOf/JSjAwO8+car7D98mJ/fdS8vvbGd1ze9yGPPPkbXnLn88z98jeO9IwDs2tvNwvZF7N66l+nJLPc9/iS5CZcNZ63g0MBbrLj4ckq2iaoqTKdHuOvff8Fvf/M0khL/8Hdf5z9+9GuG+47z2MNP87Of30cJyBZ9eg8nuf22G7ji4mu4+99/QSRRReu8BroaTXoP7yAmdM68YA3Dh/cxOpkirFQz0pdCKBF0U0eqPooKkagGwiUSVhEu2HmL2UyefKHIdKGE44JZ9gZBU7Fdj1Ixxydvv5CWpgghNcR7L7uMc8+ZT9+RFDP5HIrwed9VGyjlLaQ0AndCP2jVpnPTRKJV71or/+wr6/pYjLPP6GRZ1xJef/lZOirnMHdpO8s3LKd//CBz5qXpaBnl6osX4hWmmZp6nvq65cwmi1Q3tKJGqnD0MPsPbkcUcnR1LSReVUt6KsW+vdsI6wquE0RoITRq61qwbR9ZmqKpQmP46A6qGuYTilYwPDDF0pWrePWVbhy3gFCD3qFr+cQTEaYmUrz84isIz2ByYoapmRla6muRWlAUVq1awZu73qK9rYXB3btoqqslFjKxEdTWN5LVDepVjVmrQKaYxy1a5DyJoeuUpIMWLnNybQfDCOEVbbRQNNhKez5u0UKXEFZVdOmhOhLbB00TFEpWQOsTwZa5UCxi+x4NsRh1NVW0VDfz2M5eNMdHqqCVJK6nUJASqRpYnovvSIzxIElhPDtKwgIjYSKFR05GKakGRVUjPZvFc/KsWL6C8f4BNNPA1Xwyfo6IJfCKNv2KwPEUwpMO4co4XR2tVFdGWVjXREd9DV1dHZiRMMeHhqmMx5npHWMw6vHMrx8hbc9izRTpmjuHbYUpHs8USU71ox0aY+O3/g87Ug+hmpI1rXOpWHcWO5/aTOuKRSjHBrli6SqePryH1qiOUQg+y1jvABEfijM2U3mVxPJlJO08b2WPUVtRzV99+F6wLZgEI1rk+X/6JPXL2lE0B7wKZuwWsnqKmFnB1p4Q4fmdFNUYT3m9GOEzSA7sQdr7WL92HVEjhG37aLqPqmq4nkM4ZCAA2xM4rouuC4reDEgtoCUqFn3He1gwbyndx3dydmEVTqlIJtdPRdxgdGSKZUuWA7DlpT+COktTzZlYvs+CBW2MDHeTy/novg0UaJyzgB3b97Dh8ot44onHmXYnSVRFyExKJiczlFwdRU9QEYlTzBVJRBLYBYvzL1hHIm4wt7OJrtYEpZJF8/lnUMqNY8oCP/v+T/n0Z77Izlf2I8w0MWLolqSmpobX//h7zthwAVO9Q3TMWYyXK3LzV7/AxHSKsT3d3Dp/IY8+/Ai33vJhensHAMiMJbn//t9wxtnreO7VV5i/oIP9+/ch3blcdtE67vnSP7H64uUYkRoK9gyf+dztlHLjeCWX393/C37ys59yJDnCDA6di+fR2ZbgK5+/HonJrn0n+O4//oTzVl7A+JFBxqZSVJg6S89exUhqGsOoYkwZ5FMfv4nP/OXfsHDhAoaSo3iqikRgaEbQRpQSVQVNk4EALe9ihE1yhTymq+BYwe6+MhHFDUFudoaduw7S0tKCVAIyQv/ADM2dVaQLBeKVKnv3H8IFBkcmA5xNVQIJlOuh6IJ3O/7sxXrflqPU1MaZzaa56sYrmB0r8sq2XRi5BmYzU7TKItesXML9RzaRaGonrFYwnuohMz2BJQdIFw3SOZNcbpqQ9Dl8bAIjFGMmmyUejpLJpnH8HFWVFThOjqk0pFNFivlZKpY2EooXGBk8QUXdPKxsiE/fdjPv/9DlDLtFookoru2gqyYlx0IXKr3799FVVcfs8BCNURMln6dYbsec6DlOSNcYGx4KwgUKRbRoDEtKpmdmOX/DBp568lGuuPJKNr34ItW1FXRGq8C2yZSymJEwLg7ZQoGC46MoLp50sXI5fCkD3q4AG5+QKlAVBdcK1I5eyTrlYywIWiwhIIxKKZvGrE9ghRXGNIsKW0VRVSK2QwGPSquIAhR0UEPBYIkTQoRBNTRk1KDadimW8mS9Eppv4A+N01RZhbKuno7GSubXxmiuqaCmsoZKSzDc209+dIxUJkmqsQnX8Xj2mRfZaep85a57uO6jH6WmtYof//AHfOnb38A72MtN11+LKWzqjCihkUGq1rYTHZxgeNdhViyeh7kshPro46zVZikdH6I43Yfn6cQmppljZcn39FJzznpuuOx87tz0Hbrqygpar0DKtlh43lJ27jzGeesX4KiV/MsFdzIzeBhn7BB6QyM9j97N7NBxnLpmJpMOxBvJOyaVFTay8gKeG81jLmllRYPJls3b+cLG67ln8x6KmXF2HBhh/64D3PbpTxKJJbByFuGIiaZoFAuFoCeNhusLLNtGaAJd0TE0jemJblrqYxRLJ2hti9G9fzsrly9jeGQcv1RJa8Nc7v/1r7jovBsJVzRRysHBA29SKlgUCheycs1KcpM9JMcyRGMVPPjUU0S1KKFUlHA0ge04TKemWNSxgnAkwpH+FEJXOHSom0Q8wvDoKOPJUfIzOea1tZIaHqa2LkQYh7HkEGvXrWLFokVcfN55/N+7f0xDZYhCyWLJ4pX4kyVmLMk5689ncXUHA5E42zZv5Y5vfhWt5OANpXn4D4/Q3tXKRHKUD11zCSsWLQKgrq6OH9/1fZpbWrj1ppu54abrePHVbbywfQsLz+jgqlvPZ8tLe/jLz36G3/z2UR575GEu/8BGXDS2Hujm5ts+xtNPPU9TTQP79+zFtnV++cDrNNZUE6kw+PTX/g+fvP2zfP5TH2PnaztprEuweo1H7ZwGwn6cvd27GXjyMZ566iE+94UvBaZuisCxHKQDtm1j6iE86aPqBrgB391xXWKRKIah4ThZACLhMJ4ODXW1jI8Ps3/vPtAUBkeSVFUkmMxYKFqI1HieSCRD17wuenuHsaUHrk8+Y1FXH6OQzb5rrfyzF+tQLEptayuTs6Ps3z9Iha5w7jnrePL1l7lw8RIqq2vZtvVl6jts8vkJ0vlhYglJUfVJxBycUpZc1kNxQjiGRqmYR7E9kiMZ6uvdQNPva5Qsj2gkiu9J1p+3hp27dmCHqhnsO8CiJS1sev1ldr8xSk1NNYN9x1CExLIcUCU5XCzXwjQiJJpryKfTSN9B+AaOlBSzwRZ1dmIcC4twyEQti2DsYgmhmZi6zv7tOzAVg9HhcWxXYcKxqO6oYmY0xaoNF7Jnzx5iiRrmr+lk186drFq+nJGeg2ghk9aKdhTDRLgKNZEws6lBEnqIrJLDc128kk3J89FEkOFp+z4RTSPrW1TUVeClJlhcVUUNJrtDgoKiUFAd8jMu6ZCBYZokQhFUGXTGLt2wnpqqWtojVRiGT3tTB1sOHeLAwT1og4MUE2HOymbRL76EH/7zj9gzt5J//vLX+dRtnyRkhPj+Mw9zywevpz4Ux56cIT05RUVNNSoCZ3yIaz90NdVSIb31Lf75zEtQL9Lp3tuD4erYbpYLFy3gYHKc9WoUJjLkc2+yYNVahiYmyA2M0tzWwvBwPzNFh1VLl2IuNKlbt5COJUvY+vCT1NbWktcCLKFu2SL29+yjtb2aFfOuYuHZ7yE9NEgp2U20uoPD93wFZ/wYVkMXBbeICHmkzRoSyjgFczG7kis5kkxy0a230l7McCSbYvll7+fv7voWzqSHzExRWVdPwbZIjoyyenUDx4eGaGtrQ9E07GIJU5fM5mw0I45mRHFdH0W1GUn2U1Vp0VrfxAMP3s/crlpGk1sYTxXxrAwDA2Okoj00Nwb824mRETwRJ9RgY6VzFArHeeGFfbR3LWHNWWv4zvf+nXnzO5k/bx7PPv0069auopC3GB/LMDGVoWBPUddYwZOPvci5556Nr6k0trbw6c98mk/95V/R3lSF50k+99efJp+dZexgH68+vT1I+jE0Vi9bzT33P8DffOE2vvu9n3LO8jOon9PJ1jdeZ29iD10L5nDjrdeR7T/OY5teorWpk8cff5jf/PY+Fi+6gZmpFAO9wwDUL5jHgw8+yLyWdiqj1Rw9NkhjfQWLOltYVDOX7Vue5tzzz+Hun95N55wu+oZyvPjMI3zi5tt5dtNu/vOuu7n44qt46PEn+Nxff5RD3Qex7EEWLJ5PU+dSJlJJvvvjr1MlHcan5tHR2EAhHMMfK/DL537F1VdewtJFnXz2U7dzw62f4bU3X+O117YGFq4K2Db4fglFUdB1E8uyQagoOkQNndmp9Kk2yP/H3ntFSXaWZ9tX7apdOccOVZ27p6dnpidrsiZJGkkog4QkBDJRGEQwIIExGGxswGBjywYbDBYIEJKQRiggaZTj5Dzd093TOXdXV867du3wHTS/j+DH/7/Wtzjxc1x1VGvde793vc91aTVYmF9c/n9JNCOrItH6EEYxR6GQwuawo+kaqiSQTlZJKFUsNiuKpCOaTVRKOSSpilLjD47x61//+tf/78Tw/2z+7VefJxTy88qrx8jkyoiCyOjUBOu2tDMxcJ7GTi+TM4so7hqKbKEhHCQ+m8BtCBLxuXGFzGSzElJVWYa5iwZS8QLlsglZSlCoFBBtDoyCnUpVIp2eQjMUcJn9jE8uYNPsTE0vkpUFQtE6QCQaq0Muy1RKFQSziCRXcDhdKLpCpSIjOmxkC0VEhwVFEzFZzfzFF7/Ifz7wHTSDAUWR0Go1RINIVa1hREAtyajlPF7VRDqRxGYz4bM5KVQkFCFPpZAmL1dQkiqGmkJVlvDZrYxNTOA0m/C1rODc/Cg7bryJ5998kavecwNHhy+yZd16ulvbKSRSdLS04fR6sJntBH0+vEYjTquJJpcVn2YksK6DprYu7r7hvexes5oP7tzFgbVt7IpF2WGwcNPaVbTs7WTXle9mqrjIc8+/ypGpSdo//2H+4i/+mv03vpuhmQnM84usa62jrBj4/ugIhpKJtE1l9ZqdVI0iHZtX4E1LbOzs5IqNa9lrFXlXfZTLzEb2upzkBs9RHLhIZnaRTDbLub7zjE5nODMwADWNPc1+dIOKrMis3tjJ5juvpGlDD93tXYz0DXL9XXdginjp2nIZzcEQM9UK3asbqczN8KtXD7K1rYs3Tlwk4nKx52P3kBo+h6t7NcdH+rniiqvRqhWczQ1cevJFzj3xn9TsKhnVjSgtkHIEqZm8eFQTE5KboSkVa32A1bfux12sIFUkjp1fpLu9md2uBHmDDZtgJNTYyOreVThEEUNZoXP1Cl555WUcVhdzs+NMTQ+jVyv4m2PUCtrylq2hTDo+glpx8fWvfZUrr+lGUI2MD06wEF+kLlCHLMVZv6mb6eEF9h/4IIMzr1Ar1Aj5NOyih5mZBVav6eW5Z18iVy2yefM2Hn/iCSbHJ7E5PFy6NEH/uUssLCWpVqtMzsZJJpLs3rmNhdkF6sNhxoaH0as1Mpk0n7/nz6jzB3jo54+wd/tOxgeO0OAT6Kmro5wbo0icA1ftpO/oKHWBOqaLaV557mU+97dfYnJukRUtbTTGQrz53Kv0XL6NUl5i+NJ5nnzyKRLlJA11zaTmk+w9cC1/+w9fI7WU4fzZ89xyx52cOH6Ksl4hky0wshSnYX0Xz/zibbyBMEahjkuXhujo7GTk6CliK9s4cMVV2E0StcwMgk3Abs6yZdNK1veu4MKZadZv2sGLT/6GTCHJtg3beeEv/jJXAAAgAElEQVTgy/z6V4d4913XU98SpDib4q2zp+ju6OHhXz5MsSpTKUpgNFKTdSxWEY1lebbJLKIoKpqiY7OImE1GRFHALJr47Ge+zAP/8n0sJgOKKiPXTNg9BpLJLHfedgPJpSzJeIqOrmaymSyaCvl8CYNRx+JbNvis31JPIKZgMFW596Nf+71Z+ScP6x/96q8waxXMiDjFIOMzJfx1LqqGJNGGGFUhTl3Qz3w8w4rOdhbiI2RLSeob6zEaZKbnFhiZMuM2e+hq7eDMxQRm1YTPq3PZGj+3XLmNmpZjdvESGztbcDtcTIzN8daR47z/7huoKCIHH3sdn6eRpbk8sXAj2USWXLGCpILdKCwbm00mLHYHJU3FZBYxmY3oooAkSNTQuf++v+SBH/7TslVaFECvgi5Rk01YbA58AT/oMpqpgmy14ahWSFcrRAJmVta3MDE2RmGpTGPIx1TuEpf1NLPuQAM71rWyff+1nDl8CrPoxFCVSczOsLK1jaGzg+gmO41NbSxmc6zfuoWLQ5fYd9WVzGfS9G5Zz8zYMC1mCz6zgfLUPKGP3MKtn/8C993/Be797j8xPrvIuutv4udHjvH0iXNcykt84uOf5MEHn+S1E2dxGd3sCrbgizjIXxzAnEpSymTo9XtxoNHpC9AsyNRRI33sBNX4PIbELPLsLOXRcbTFRSqqSlFKsyjKFF0uWsONqJqBWGuUSnGRlpZGfGoVf6wV1BKxBi92l4tS0M2YXScxMMOlU2dZnJxGK+YpnB9gqX8QOTvD0bNvksqVePSds1hbHTgbNYpOB+27rqRj1zpWrN3OaHqW7jWX0du6HqfoppzM0ve9L7FkS1NTnVi1JfJWjbJeh10y4nPEeCXjZJQAl91yIy1BG+rcEkbBwjtLca5eFYLDT9Ek63TbZF4YmEE0FZhbkAh11HH+5CBDfQPMZhcwZuLL6+myiUd+c5C9V2+hYCrhE+w4rHaWEkNYXXkctgiTkxkWstOkyks01a9CkwTqIzGOHj9NY1Mn27bdyCM//zKyLOOPtHL4zZOYBDMWi4VgOEjU34IZG2+88SaKasFg0Dh/ro/OjhVsvGwrJ0+dplyR0VSdYNDHxOwMyUwGQbRSKEnURepJJpb45WPPcO31V9E/3E99Qyu5qsxcpsC2Xdv5xUOHMNsbWYzP0xBtZ/T8RTbv3kV6foFAd4xdV15BYSHJyXOXiIZjNDU08q3vfIstmzZh1ipcGBzlPx96lK/99deZnJgi4vXidNg4dvYM195wA+8ceh233Ua9P8C6ljZu/rPbWRzrY2JxgVuuvZGJkQlaupuJtXYST03zwI8e5spdm3jqpbMEGxoJYGd2KMfDhw/REqujo6UBuWbi5RcP4wrW0b2mlfm5JeLxErqus379en71yBNs3bmX6YU5qpUKlWp1uacWl92ockX/HTBgeQFOM2hYrUY0VcVmtfKpe7/I57/wF5itAoouUJUkPD4vmqLQd6GfdLpAQ2MTswsTaJqJas2IYDDTGA0jqzUCIXGZQeJRsZka+fD7f7/h/H/Ms/6/Nfd9ey2psQxOWxBTyc7gyDSbru2impzjtbcnuH5LhOj6ZqaGJ/H7G+kfGqJ7bTMjIwv0NF2GZEwxUxBQM1liHTYOHVGpLYBZgI5WK0ZVRwg1MJXLoy/oREON/PKxJ9izbTNpeZaRySTFkgOHQ8eswg0HrqQ+WEcNBUmrsZBJIlqtiFYnCEZmZqaYmJqklEiiF0pougGr1crM1Cz1MfeyAdpoQ8qVcRiNqIYSBhOs7OllfHQOLVUkK+ex6Wbuu/fjPHrsZwRaPHSvbMddsVFJVzAb6ygXJnljfJzdHSt46rfnMBUVFIcXVSoBGqLRhMVkQRCdqIKOwWrE6bQjl0sEInXMxOMEg3YK/Ze4YVUn60QLI1KRI3YPyWSZD95+M2fPnsZogmvWXcaMUkSZSaDbTXziBz/mu7vWk04toRSKlIp5ZLObiXQVq9eFVs3xuQ2bkYwm+tUaFsVKyW2AaoWAy4lWrLCQiuPwOsFqQpc0vBip+ET8HWHy44tQM1KplOhsa6VaLmExmhly5JkaneSaWButlgAXBgbJB4L0Xxwi1NyCpzmKoyFEy8ounA0REoVFNkdbiVi81Dx+xseGkQrjXLbrKrSkAbWSQOy6EunYb1nIJ1hIzKHFR1BqObIVnQapSs6poJm9WCSdfE2jaG3nVFUj2lxHfbQZR32UvFTm4tnzeAJhXOP97Km3curF39LctYrFcopQrkDDytUM5XOci9foOzNAQjRRsboI1DUhKWDSDYRNXhpDObauaKWSS5FijophkdFLi2zZcoAnDz7H7iv3oyoVCrMFTKKfvgvHuPzyjSwlknzngdf50v034vf6eOf4Wd5943VMTJwmGPIAdt555yyFks7sXBXJqFNJFela0c7F0VE8TjdjQ8Ps23cFqeQS0cYgs7PzNDbG0DUBg8HIqdOn6eyOEJ9PsXHTOoq1IkGng87OTk6ePMnS0iR1vhbePHGOK/bv4bdPvUhHTxeqqqOaa3zl0/chGgQWRieZK87w2uE3WMgpXLVxOw2RRhwugYuDg2zZspdPfOp+7DaR6669hlR6HtFhR60pRHwhPCEf45MTLE7O8Lm/+ixLxTizx4d5+ugZJi+O8+rRJ7jnznvZe6CbOtFHHpXp8fPMzih0rd/I2PQIV2/bRCFe5tzoEGZ/mAt9/Xz8Q3dx6LVXGDw7zCc+9Wmee+kpRgdHcHkD3PCuGxCMoGsGfvLgzwkFPOTyGWqqAU01YDFZqNWqGAQNo9WEw2HEZbchGkQGBxapC1uwWkSKVZVsTmLbtq0MD10iuZTBKBqwO+1UNYmmRj+Cyc7I4BRWq8i6nU2oyjyVtAPZUKA+0sKrzw793qz8k3fWC7NlqOokiiU8VRsBv5OJyXPsWtfNbDJDUzRKrSYg5WsY/DIOixW3aKWrvW0ZSanZqGaT+MxOZEkgEvRQrFaolpZvO4RDPmaLCaRSifmZAg6Hj2jbavqGp6kYFigXRMzoVONLREINjJ3rQ4vmcXtc6EYBv8WJ1xNEtNgoFEoEO3ppb1mBLMtkEkkWlxaRq8t9oslqJV9SMKEjeCyUJAm1bKMlZqOh1UBsxRqe+vk7BOr8WIpG+o8cpat1Bb5WC9FQgGyixvn+C+xar3FxfIC16xppbI+Sffht6sx+DCYBoygs35muVlGrVZwuAaVaw2nzsjgzidVsplKpIFit5FJJnG43qDpmq06Dy8Id11yJXTIxfuEIe71R5guTLDz6JNPGIrlchlm1wCd+8GMGMhWMVTPetm5Q8oQFgWqiRElTURM5LAYDpZKE2+OmqKewWBzYLA4y1SJOtw2tYMBkMmIy6JjMgG7FGbBhECtYI148di/VWp6LU8MIZY1sNk9kbYRQNMCYXKbWFKNWv5aS3crNd9+ALxCgvaGFQrWKx2bDqJmwuLuxikZKUhGnQ6SncwV62sv822fwCjYWp8do67qSxx7/DYlMDls5xwafRq7BiEcTKGFCK4mUDRX0WhBjsI1Tcws46sz07uhGSRhZmO9DLvuoWRvosUoEagmEmTLNPW0YjA7aEck44Ng7p1jd3oyztRG3kmfd2k387NkjHDvfhy1ahzY3xZuz0+Ref4TEhdNYAi7GSqt45kia6/bs4/jh87z35pv41RMHuXr/AapWOH1qgK7u1cQXkzhdy27M0fEJ2lo03AEL45ODDA/O8spvj3PzLTdg0MwklpaoD7Zi9jjIe7NYRRMhn5dsucy6jRs4fvwoggESyVmqRQmlqtDV1c3U5DQLC3E2buplXl5icnqQeKrMLVe+i4mJCS5emKIxFuLi8DidnR3kshLdq9egaDViLXUUSgkO/fZphi9OIWsSjWEve3fswOR2ISVyHDz4MLGmMDa3j5888gs+8an78fq99A+eI5fLoRnMBN1evBY7v37sRT533318/8z3qWQzLMQXyco5bjpwOY0fuYW//NzX2LFvNVbRjjNcx8k3D6PWTOy/dS+BhnpWlVvZtnITP/z3h/B5g+zcdwVnz/TzlW/+M++/fT8WbJw4cQqXy85VBw5w6NWXuXD+DNt27GBqYhpd16lIJZSahqqDCRFRFJGkMlaziKZqKIqOomgUy3kA7A4rBl1DMIDNauDUiZOoiko4HCKRTCOrMkYrFPJVLA6FaDRCoVDC7bZj0huQdLg0nmMoM/IHs/JPHtbBkBtd8DI9OY1T1Ymt8SDJdQyNpCkvlXl47hy9TQES+SJlNclof462aAmrx0o5N086b2ZdWz1SXmFkLkk1YaCcrRIMeVmxukZtoUBXOMTqxlamXbOcG7pA2Ofj6GwGv+Cl2W3DZ6phbWykpBmRdImXjrxEVakhywpOZZkFLQomonVRIg1h2htiODxuOhvqyYbD4FzelLvltjvwO4OoqgGDTaFSSlPM95NPTRBrMyEbqrzvmzfx0689Sc+GRkI9EerNsDSSplSoIHph47XdZAxFlLINo2Sltlhm384O+mdrLC0tEPa60WUNm9OB1SoiyypO0Uq1nMFiNiKYDOhqDaGkYDVUETQLKjoG2YDZ7+DZHz9CCy7MTTYG4uMUIzXqHQLehih1WiOd8jJBUMnPsm/VZma0Cp68iFAzEHX5ySUSBHo6UUwSjV0hTsQnCDm9uB1G3LIbh92MapKItoeoua2cGx5iPp5HKdmIqSY2Na7BEooyuZBgxdoONm/dRrgpRk6o0eRswFPnJuhtQtBNYJBQqlWExXlUBSjVqM7McnFqnHDQw+DRU3icIabPjdI/eIo0JpbiNZranSxVyrjq/Dz4oa9yfHgUs9mE11QkHnDiV+3M6Ar2Riv2hBXBupFztVGW5gxc/b73UiseQ5xPkRR9LE44ibW6WD/0NO01OzhzyDUjxoKG0V7CaDWjJKGts41QdzP5E+N4rHba9DJSbo49+/Zgd9mZ9Cl85e8+w1tvvUV6fBJ/1Mf03BL3HHgPernKoinBwtEp7t5/I88++TqbrtlHrnCaeCFLNZ9AlpZ/F6vZiFaT2bx6FbMLk1isRlpb6qhVkqzb1EVJMdHZ2sihF0+gaVlW79qJ2WoiUZC4fNtO5mdnQFMp5WXWbVzL6PAYTz3zLKvWrGL9hsvIZFO86+r9jM5cwF9nZXDgArOpLGaPE7VqZXSqRLA4TT7bx47tu1FrFY4feZtnn/gZn7nv73F1tRAfGmZvzwqavQGOXDhLSarQvXolQY+Lqdk5jNLysk84Uke5UCSVyrN2Qy9Bt5XDJ46xb9du0okFXnvzJe695885fuwMN92wlxavg6GT5wk0hPjo+z5Og8fBpmtvIRJoZVVrF0//+iUESWLPqo287+9/yQPfvo+/f+CnzD7xM/bs6uHafdfwXz//PtmsgsniZH5qjlQoi81qZmZ2iiMPnKct2sDKlSuZGB/EZDJDbXn7uFIu0tLaTCKdwIyBgN+NzW4hsTQBQKlSpVqsEm2Nks0UcNk9GASZoUuLmC0iLe1RiuUqHpuBVSvWcPrsGaRqkaV4FqFcIGh1sGv9ASbyL/3BrPyT1yAf+vNGcrMScsGAXiqxcv9WRvve4PI9exF0iccPHaMtEiJbhCuu7eLM23Gi9WCxxNBNNVAijI2dZ9XGBnJqjpingfMzc1QSBkqFESKBrcQXM8wllwi3dtIcbOOp3x7CoLgRjUWsUg1dKyKKIkLVhGI2kshlMCOgKzoVvYrF6kDVZEwiaEoNXTNiE63IRQWbAzJZE0u1Aju62ulq7URVa4SbTeARkVUjBjFDe2cd01PDWAU3bnMzS+oSmgqFeJJ8aoKr33MFb778MiajCylvxuosEjWtIuQNc/7cS0j2BtqbNvLQQ4/hdnpQpWVAUM2k/o6PbUKS5GWokm5G0HTMgk64MUK0kuaqYICqJjFvd2IoCARWNXJp8BJtN+8h88jrVEQTzkiAUqLAXx89w3+8ZzsOnxtZtODUbSQXKvQl5+nuaealV1/hsy2dLLhk4vU+BDOMFpZIJk00t0eIxiKYqiq93T24/T7Cbe2Egg1IpRxmsxmb0Q5GG1o5R2VsAewOKCkszU0xcqmfQroAapVsMkEmmWMyPku2oiNVy+gllZVdIUyGGhanSE00Y3T6GEwsYUShmirjDNZjqUJVLvKTd8b5xIFWcoUyMZvANdu2MVEy4vGZMMo2Lqp25tFxu93cdsU60rLEaNlIsWRgsSJwXUc96Zd/REvQSnwmS9uKMLmlHKpgRy3L2Jw+cpqMXa4ip5dILeYI+uox1HfwV8+8w2Uf+whvP/EETaEwsUg9J956ngM9XVy9po2nTj1LuytK1VZHUioSW72ScxdPEw7X8erLR7DazOiU6V3Xy5Ejfbz21gh7tq3AarRQ1+xi/bpeTpw4xaaN28gsjeOJuDhzcpFMtsDApWk+8eE/46Gf/ginP8yZi6N4Qn6cDgsmk5lcLsdtN27n+MlhNm7r5tSxAXZv2ong8CKVkpw4cwiDyYGxIlDWZXz+EJs37eBn//lTbr/9dp585lncHj89q9rZur6bsNfP6ZF+7rj9fbzywis8f/BRdmxdi8tbz7/98Be89+67MAnw6K+foKmugxNn+2hsaUCradisZtAVfC4747Oz9PZ0YTKoHDs7xr2f/gjGQh4ZmbnZRTrqQxw6MUCxVCEXX+DAjQc4cN12HGYrD//oMfKqiNNjRqkWyaUXWLd2PWvXdJPKFvjxT55g49oOBMHLi6+8jmgXUVWNmq6hKQrV8jKUzOQw4XE4iM8lMQgiFouZQiGP1W6mItcwm8BsFpFUGYvFTGa+SmOTi0KuiKAtL7yJJgFJqtLZ1sXw2DC969dx9vR5mmMxWtpE8rUaS8kyfo/E6p5O4pMpFNXPzNI5LvX9/kj+k4f1PZ9rZ0toO32DMwQcDpK1MYzOEOr4DPNynN4tOxmevsjll1/OpZFzlLJuXNYp7LY6fC0GXn5qCUNNQxaydPSuwCobKGpAxcqGbfWcOzGPjojT4eXtMwOMT0sYVAdV0UhA1XHqOfxOOx5szEsFjG4bQxMzWIwmNF1GVEwYjSKavrxgofwOvOPzepBKMqWKRg2FbLFCxO9B1fM0R5u44eYepqZmWFwokcjk8fmtdHV4iPicHJ9bxJUUiUQ8ZEU74fowU+MThD0qTXV+BLvCUHqa3EmBNdtW0NjoITOb5FcvjDE6lEGualjMJkSzAYcggmaAioxFXz4qqS4Rg6aiVSpc9e530Vgt0To1iWZwMmWz4Ql4ISRSHVui8UALuVdHOTkyjcnlpFau8p/9E/zgL29BcqnMTc7QFGpC9URY29pFRSkS6lpNzBVhKZ4jbS2xq96HwWlBVN0gKFCroctQSi2hVhX0uSTJWpLs1BJTcxny8WnmFhIMZVJ0hLycPTZK0GMjY4O6tmZsJjvOgIN4Lo+uGgkUsmTUIkavm9L8HNhtOEx2TGIJO6BVrcxmakTdIRx2kcH4LDafA91s4cFXhnj/7k5qBgObt22g3qwhqgr17Rt4cmIer9/FgR1rseFgolhCMniZnV5A8gTYWDqDcz5BYXYBS70Ln+ijmBgn0NGBuVpCl4sUihqZInSuamVx7BKaWWTGHWVwOs3zizmu3HkFD/3gW/zrD/+DQ795ngPrNjE1dQlHLU2LplCwVpFrKpHYGg4dPc1cIo7fb2UxmaGlLsSGDd1ML5R46Y1TvPL6BfZftYLkXJz1G3pIZ5JsWLuVx598hiuu3kB8Is7Jo6NEOtbS2RRBkWv0XxjBF3ExPj1LYinDN7/7tzx28Akam2I0UGWuPE++oiCUa1hlL1OFRdKpAuvXrmXPNVs49MRBypJAsC5C38AY+/bt5clfP0ZDfSvvvetWTl88QXtThAtHjrP92u3YBQtKVmbnu67i4mg/Y8cH6e9boKGzhUvDI8hSDamq0XdhkA2be0gsJpDLNYwWgZpSZsOmLUxcukjA42Y2mWbdim56tm3i+cd+w9RimpUbV7F/7+VUczOYwyGE5DzPPf4SH773bs5dOI8iCAS9IqNLZbRMFtHiQDYo9G7bwYqONQy9c5RfPPI0BUnFaBXIZ4t4gh5yiTyiYCJc50HRaxg0nZpk5K733c1DP/8JsaZ65hbmyWVLhIIe5GqJmsGAbhDILkh0rW7CYlaZGEuhaBoGVaG9rYVMJkUqnmfb7i14fQ6SiVk2bbicx1/4L6wOK6vb2kjl5ijkQVErBHxB3nxp9vdm5Z+8BnGYQ2SUKayxPIlUkVOzw8xNTVMfEdjS2kxuNk0k1M7MhWFcoomqkMBiCpMvp3DXGti8qReHNYdgEUhVVebndELuDk72H6ZnVTcjYxfo7GzHIFoI+bykVYX4bJZqzUTWDDFcgEpKlbCZTciSAaUsY7KC1WZmMVciEC1js4XwuQNMDs8TDDqJNVsYG1rg8t1tjI0vH4VMBoFaVWBqfI4f/TCJ2WimpmW58ZZ1yEqWfFVm7uwIhZKFkbkstpk49c0RBvoH2dbbyJnzczTWBxkbWiAcjuAI1qiv8zExPUXyzCR337gT6ToJuzeKKvqpKj7GRuYpFipMz8epyDlS2Qz5XIVqqYgv6OfM9AzBLg/DHiNNZhtzQolMwEwwFMQSWYnRKtJ0+zVs8tdjqPegSssPpXfd+UFsViORRj+6EsFgtoJJoJYvIuYlVK1CqE5EzQv8/Hs/xWE1kEknKSdylOZSzKUUKnIGyaAxa4TusA+Dx0yXx8+MnsdgqhCrC5OpSohrIugWJ+5ahapNoJJKki4uUdYVLFKNit+OrQSDs0nWeTyULBGS+XkEg5lsUsbltqK4YUopYSqLmCIhCgYz6MvqLMUuoCSrLF1YpG7rFjJOFy9enOPmq3bRP3aaZLXC7Hye5sZ2jgxMsqq1juyhH9OyppGp1CShJidSCapClqyoEExUuECeraEYKJOocpnU5AJGk5WaIJE1CsxHOrGNHCc9PUVL73pOvvYC4YCX4YUJGld20Pf2OaYtIj6HnfODZ/GPLzGfSKOZRHLpANGoj8GJcywpModfH+Qv7/sEAB53HfWRCE899TbXXruFN14/RTjchs0ZoFiZ4103XM/g1CTVcoHJkTEsVguZShGbzUes1c13/vEfyaez7Otdz+GzR0ini8SidbicLk4OjFEXtLL1pq38/McHuf++DxON1POVv/5nHBaZjoYoz/76Uf7m/q/ymb/6BvPZIqN945x48S0+9Nm7eeulMwiofPxjd/LGCy/R3boSccMKDtz6HhYuTHH6tbcpmwXm4osAbFrbyYhgoH9whJKsEwr6OX3qBFfu3c3ZM0Pc9/nP8K1v/ojZmQW8TQE++LE/45HHHqel0cdjrzyD3xfGbK2x/upeFhOzlCQDUipNo6+VsFih4/IrOXbqJC67iaDZxYPf/h7X37CFj3zwevouLfDK62+zb892Dp86idvnwG610dQaZXpsCtFqIZWM8+hjv8Af8BKJRJidi2O1OJDLFSxmE7KkgLBc6VRKJQplhZIiYxctoGuksnEy6Sqf/Nyn+ekvf0apWMFk1DGaAmRnTGzY1sLZixc5sG8Hb73UhyVoZduB1X8wK//om7WqqnzlK19hYmICo9HIt771LQqFAh//+MdpaWkB4I477uDaa6/l+9//Pm+88QYmk4kvf/nL9Pb2/tGw/tBfW2jUm9C0MOXUJLO5PGOTsKYpSldDAI/TwzN9x+nwe2ht8zGTm6a9KcrpEyVS6TQOh5G1K7aQyI7gDjgIhzdwbjCBxaQyOv4OPu8aQlYPDR0RnC6J08NLPPrTYxgjTTgdVdZWBKpCCVH0UCtLKIKAbhaQqiVuvf0W2roWePzg23i9MURRpFjJ0dysUc5byRQy+EMCdc0x7vvz1zEZDdz3uStJZVL4YiJz4wrnjw9y5fWdGAUr87MZTFYDTmeITMbKYnyYdCZHUTWzKqRQLdtoiNkIr3QSH1GItIhIipm3XjvFh/a8i8FUPxvWrGQ2nQWDEUfFx/7Lb8IfasDq8uFtdCHarAhoVApVqrJM39EXsVgsrO3ZyNj8OCbdSpPPitXrpyTYMBWLCLkiZXSKS3FKmRxdt3+eV//+XkSrwODIMFVFprKU5FJfhbjwOypYZp62WD1yTaU96CJTzNIca2BBrqKHzEylRIxKBUelgmYwoxslIm4LFdFOdWQKU70fNZ9lqiQRidXjqeikKxoz5TzNgTrsLjczM5MILhAKFmxumVx9HQ2LWXI2A3pRIObUMCoVKoqd8WSVcNiO12UjuSCi57I4fEYeeOcSX7iqDcOqHtoPvJdTb44QCFi4cmOMkk2mtmgEXzsjcxcxeRqolxbomR4iuzCOYq6xfmU30/3DWPwuNM1KKRWnajAQ8taTc1YIYUEul7HX15iYqDLTvJczyRp2g4mCNMv6HTvIHb/EIz/6IZ2tYQq1MrOLCRqa28nLJRrsAk2rOzl69Chrt2xgfi6FXbYTa7fx4qEjiCY3mVyBjRu28+gTz3H7+w9g1GS0isabb79F96ouvN4oJ0+8w77LtyBJkMjILC6OsnbdJoYvXaKjez3jc5Oo+TJ6tYogmDA47ISjfuyKiTdeeYvoqihWm5FCRqLRH+P4+Qt8/NMf4bVnnkSXJTrXrkUVRbp626kmC4RcIQ4+/Sh3ffD9PPzTxzBoRt5z1y385ukn2bFtO8mZRepjzfS/8TadXVGCGzbyq4d/TWtXG1s7N/Olr/09JqOBO+64jXMD/RRKJZxmK+VSGkwCJsGFiQpTwwmsdQ7++Rv3ceTMUTy48ESjfO8736VWE7nrjpuQCjOkpir0jfXTurIDi8PFpb4xPvnZD3LixAny2TSlmoGqJBKwLRKJtnDq7CTZvIYk5clWJBBMuBwW8tkidtFKsSSjqhpf/cqX+N73vocgQH1DhEKxiKpUUeUymmBDtIrEZ3JYnYZl+basADqBgB1FNpLKFGRpougAACAASURBVPD4PNS0KrIscc1Vuzl7op+mtgCXhsewGAOsWe3k4sA4qsHAdTdu50cPvPP/L6xfeeUVXn31Vb71rW9x/Phxfvazn7Fv3z4KhQIf+tCH/vtzFy9e5B/+4R946KGHWFhY4FOf+hQHDx78o2H97w9spiRZUA0pgnKIwmSNgy8NE11hxWa2IThEqsYqyFkO7L+Moal+HAEXkzNF/HY7umplYWkBQQtTHzRS19rGTK5MJh8nPV9CzrjYs307+dwCfYPn2H3tJozuOj7zxQeJBWJ0ajo1cwWHbKCgmrHbBRSq5Eop2lc0snpDPR2dzcRTccYmp1hcSLCqo5FwtJHJmQkyiSJd0Ubu/+phPvmJFdTkAna7DYPTzNzUNJdt3cGFk0PURyOUpBIdDe089MQherp6yCUqFPJZzG4vpVSKuo4wtkKBD995N0cHnuYXbw9xx5Z9mJzzZLNOvH4XpbklrIKBxq7VHHz+FSKh1bz83FvU2cPUOYzU+Zy4XB6ikSBdHb1Emr3Ud7VSmkmhGXNIkoXxC2cYGh0nPz3HVFUil6mhVHJ0toTJTc3yq5TOPb0NKEYIBkIUi0sUdBVvKEA5U8RlNFMTJIzUcHicJJJx7KEwVblEoxKgWEtSUWNglNFVicV0ni0busjKS1g1J+eHJnD6veRLCnUOL6lSEYPNjiyYCYsaZd2EZrZSMpRZSk6y3hfFUymAL4AjW8NGjqzdw0w+R14qk5GdlLz15IpxQjWFtMMHqTw3Xd7CN/7rNT75wcuxWurY8/4PohokhIJEW9jPRFphslwjUyjTXB8htHCMlZV5Fs+PMCpD2C0S7mmmPF4kGvJwavgCtlgTYU3D7nCTUEQa3GYyC2N43AGe0/3E7RFKtRqiWkMQZBbOjzB7YpBsMo7mUKivD+Pzu1i9dhWHXniKxrowdrsVSTcgaRW8rgD5xQLpbIpcykxDq49zZ/tY0dnD62+dp73Lh66IbOpdTSodx+qwgWYnn8myFJ8lUl+HKxDitTePcP8XPskzTz1OY7iZnft3MdY/wtpV3Xzv3/6DiYUsqy9bi1at4HAHyFUW+PLdH+D00aPo7gC/euY17nz3fkIOB86AnVAkzDf+9j9oXxEm5m3g9LFTrF/fjqsuyLnTo9x6w2384le/ZNWaHvZft4WnnznC0aNH2XvDLnZs3UjEHyIzMMUD//IzFkSZhdEUDRE3qWyNrs4mFClO5+aVrO5eic3q4Cf/+htKWoXN2zu5bPVuJGkWKV9mcHCKvoExRIfOnr27mJ9NU5xNU9QkFpMpVrY0s5hJUDTqqLUam1d209HWyfMvPM2td97F0089Tk0WaIw1cPjkOQwsM+UVWeVjH/4IP/vpL5BlGVlWsJhNCIKBWq1GIOgh6PeRSc+xccM6Bgcv4XL7GBiaolLWcXssVKsKNVkjEPRQlfNIko6qGnB4zIh2M5WcjMNqIJ+rsmvXZi6NnkcqC3jcDnSSrFwVY8vOGF/9wuHfm5V/tAa54oor2LNnDwDz8/MEg0H6+/uZmJjg1Vdfpbm5mS9/+cucPn2anTt3YjAYaGhoQFVV0un0HzWcnz0zytBijgMr2plNLXJVTzPH3BnWdV9GoWSgr28MR1DA63VQy1VZEe1iMr+I0+lEzmWxG0XqYp0k5ovEF/KkisM4PGYEVUMXPRSULEdPHsYIxCKrePGJV9h49Sq+83c38txvRzFd0tEVIwVTDeQyc4spPvO5jyPrEtlihsWJUfyWHGrZQrM7iFbOU6mamJmuYrPGqLmXyFSWDceiOYBu9DE2PIAsiIQbmzh5+iQru6Jg0MgWamRTI2zf1sbLr/TjsXuwWWTMFh9FxUVV1ZgdE3jkJ09gvczOvbfs5fzEJRqUDkxmicYWN0cnhjFqIlODZ2nvrOfY64OYRIGqWma2ZGA0t0RVFzEa7NjfOs8aW42gMYPmsFDW/aiSQsTvwWSqgMeGUdZZGQuhGILIok5RW0akzhcWaO3uZWFmFq/TRjqVp6YtX5nTlQqi1Y3NU8RiVOhobmVgeB63z8OinkfWKxQoYbCZKGcrmNqjjJcz6EYDBlWiUAGvYMBtVZAqOYqKRrfFTEoqoricCJPTWJ1Gsgs5VvnbScoi1ZzEuFzE2N6LoTyCwyJisgdxOc0YBQtm2UtgsZl6m4k5v8j5w69zrrLsk5zLWPnoZ+/irTee49ar93J+KYVPjHF06BSd6zsoD7xKNGuls1ZhXoNBEbo8OjZTGEdZJ1FMcnp6hJ616yhW81jdTuIjg6xqr+fgkTn2XL6bgwU7s6KTqJKjPLdIz+6dXHrjGC888jw7d25lPpvm8gMdzC8kGF8aR5xwkMzrSGWV6elBGtpi3POBG/nHb/8E1WwkGgwQba5jdLSfWEPnsu0IqGFGMJXwBQPooo1yVcGgVEDTsdqczMwtIc/Eed+d13DwqWeYHM/Su2oTP/juDzFbdPov9HHdzTdy7EIfQ+cH2bn7MsweG001C9//lx+xbf92zpw5RVPIy/bt23j6sUeYmZnBUFP5m0/fwVe/82NGLCluvPl6EkvzSCUNpyByeOAsRp+TkqHEyy+9w/TYLJfv3IBXVlCTBb7+hW9y570f4G/+/Zvc/8kvAXD51bs5dfgsC3MzrF3Tyvz4FM31jbz17GHWbGzgzrtu5p/++V94vG+aPTs388TTL7NxSy93fvB6nnriMMcPj7N+1UoSliV279/DiUNnaGiNcqZvjE/95Z+RTCaZGBjm/MA5LC7bMmfeGuT6m3bxzNMvsHZ1L+cHLiCVarznxvfw4H89hKZpmM1mQqEQmWwaQdew+8xIUolSUcFts3LxwkUSyTLpdAWnw/rfWSYIBixWEy63jcpSjrq6IMlEDkmSkTQFTTfgMztwuXWOnzhNQzSMRo2pmSS33raTyfHTlLORP5iV/yNEqslk4otf/CLf+MY3OHDgAL29vdx///08/PDDxGIxfvCDH1AsFnE6nf/9HYfDQeH/BUry/8z6tZsI1zk4N1qkli1RTcRpb++hvr2Fs68PcNuuHXQ2xdBrAmaDmYmL85h0D06zmYE387RFGxB1C7nKFEZfFrNhiT1b1rOirQmPFVZvDBNpC2PxeZhcuEiscyMvPp7kpUcW2NwZYecNHgr6DFoFOre04atzMTh6kXh6mqHRC5QsNd4+c5iR+X6WckVWr7mMkdkp+s+cYuR8H363E8n0O4u2qDKZmqJ1cw8iFlQ9j0HwMzUpcOStIRxOM1gEwlE/1966EcGq4PK1gTHE5OQUQTGI1VthVE0w3jfBwEQcXbJiMlcZOLPIialxWts78VuDdLgiuJ2t7L5uK3d+4EZkoYZKFdFowu4yYXUoVIUqg1IZob0Ha10DBo9AJWxjUS8ymc1SVnKYVRmzUsRbKVCenKPTsXyft6Wzg+mpWYL1McCERbRiNOpgh4LBwLiSQowFMDqC5HSJUHMTBtECdh8Zxb1MLMsJNJib6UirRFQ/ptEylsk822NhXNkKbpObpM1B2mtkwqqwiJWzOQ95czP27Zu4ZPeSCK5Djm3D0XM1NncPPu8GVkf34LbtJqldS398HZMzYTJJBymhxOliHCk5z0pvmEXTsnVj5w37mFQyWD0Rbrjl02xp3MDFkye5evMaXC8cZHduHk+lQrEsEz9xlg6rBaUMtkYXilxE0FXCUR9Zu4bdFaRcqWKs93DmbIL123bwa4ubkxOjeI01Bubn2XbgAP2HHqfFaiLaFiVjKLB+ewg9LSAkQzT72zl+7E0u372VaGsD3b0riafjPP/i66zoaWfjph2MzcwyOHwOXbNz7twwurDcv/eu6aGtYwVvHHmHvsE+NFVnaHB4WRgtitx8622oco10PM3lO3q452PX8sbbJ9AdNhR7iJlckldfO0R3oI5t67qZm5iiWTRDsoBU56AgQ3wmw9Z1a3jf7Z9m56593Pae93L3+2/jzMAIt3/gNoKxDoxOD/MJiSdfeocFqYSsSTR7Gzn16ii+QAtHT/cxn85g9oQ51X+Bte/Zz1tHjvHCky8xMrWM4X3jxdfJF0p4wlGwh4lPSbz+/HlODw2zf8dGBs8cpbdtNdt3bOLQiy9y3S17kGoaLz1/nK72MBvWNjG+MIAnECEm2CirWcYm54m2NtAguvjNT57CYPBSzIvURTp59JGDBPwa//h3P6anK8bAuQv4rV68ThePP/E4NrsZwWjA7rCRTiXRVY26hhBKTcPnC7IYL1GUdJZyEoLNSkVXUU2/MwIZdYzismc1mUrh9rpZWkqiKjqKtCz9MAgKuXIOSVEwmqyMjSzg8ZkRTWYe+eU7rF63gTNn/jDP+v/TbZBEIsFtt93Go48+SiSy/AQYHR3lG9/4Bvv376darfLRj34UgJtuuokHH3zwj75Z/+/87/zv/O/87/zx+aM1yFNPPUU8Hueee+7BZls+Stx777189atfpbe3l6NHj7Jq1So2bNjAd7/7XT784Q+zuLiIpmn/o6C+4wNONm28hV/88Bfs23EV87lLzE3rKLLKZVtjeMwuyuocXrcfUTcwNT7I9v176B8YxogRu6eRZCmNriWxWsxIqSI9vStJVMu4jAEGL04jZcuYbU6GJiZobm/D7RQQjSImzcb83Aydna2oApRyWTq7ephZnELWDRitZuJDizQ212EyV5kfnWXV6pXMa/PMT+Ro8YWZSMeJ1Xn59reP8NGPdFBX34nVb2Do3ACSJFHXICAXvbS0NzC7uIRBNmB1mDC7BM6cHKDO2cG5gSlW9K4CytgtJgy6wPR0mvHBCa66rpeLA9OYjRYcMuQNOa7Y8S4efPAQNaWMblQwq2YMdhuCYl7uZI0KgminognUea20+lUK+RTNDc34mlZzqf8CIjVsVgflrIbVY8UtFlErXuRchv86M8rXrmjDV9/E+PgkDcYqyaUUpYZmXAsKg2KVqKaTjDVwakCmLioRDQapGYvka220mGeQbAIF8UqM6fM4bQ7yqp36hgA5tUrZ6KI4NYTFMo+SbUIxSZhk47Lf0mAgVUwh6DL+QCOjeRmjXMJm91KxmjDbLZg1kNQiIY8PoyaTFQz4HV4aHQoOk4vO3lW0u2N858F/5ewTz3Lg/TchVBROHz/+f4h7z3BJqzpd/66cc9hVtXft2jl3zrnphqahmwytNAoiZgdlRHRMow6ekVEGBxVRQVQEBCRD03Sic9iddu/eOafaoXLOb1WdD30+nOs64zX/L//L9XF9ft97/a5nred5qDLpKCeL/Ojxb9FUmic738fodJSl2xs5/vogJouUjesaSGTSTAXFZGcnkMo02OzVSNJByrIc8zEtcyuXMRxPEpwv4LQY8Pv9LG9sJBeN8qPv/yst7c2QzVIoKhicnMBit2DWpaisdFMs6rBYnQwN9iASQKaQMzkziV5vxON24p0dw+8rYTEZyOUyJJNplBo109N+lrfXctOu23jy6WdYvmIJyWQKimk2r1/L3956n5qmJhb8QcILAe775B2kkzF6Lg+Tyma55fbbeObXz/Lw1z/HS6++xs5tG3lv38cYzCZamhdR6TCgtxgJzk0hKcno7OzFbLVgdOjRK1RcOneKe+79FENTXq5e6uaH//IIowuj/P7ZV1m8xM2itnpGRqcYHYshiBIUMzoK6SA/+8F3eO3D93BVVqDQ5JHJVfyvJ/7GslYzv/nts7x+aB9E82xZ0kLWVsmDe7+C212Fc7GbiYtXuOfez+Pz9mHRavnra++gMzlYu64DjVLFK699RGt7He3N7aTCYVKlAgMXBq7FCOvlzM36cbrciKQinHYD4aiXG7dt4eWX3qQokhOP5UEGMrEIQSijUCjJZnJIJWK0Wh1yuYxAIEBjUy0T4zNUVZnx+YMIpRJFAVRaNRFfCoNFdm2KFkrIZBLKZRH5bAGjSUu+UCAnFCnkSyApIVdJKWdKZNMltDoJcqWYYvFa5VuVR0Pf5eh/y8r/cbJOp9N85zvfIRgMIggCn//853E6nTz++OPIZDKsViuPP/44Wq2WX/3qV5w4cYJSqcR3vvMdVq5c+T/Ces/dTqJhAZkkTUtbIwc+HMHu1GGvUGPSWRGJcph0Embnw+hMejw2O9PTk/jCXtpbN9LQsoKTnWdprDUyPT9Fe30HI8P96CvMTIzP4qmpwWWtYHraS9fAFMlchqVNHbS0tBMLRtEZdQwNDTHYt4ConKe63oJGr0IpU1MqlIimw5Q1ZVR6JT1nuxGSKZpWu5kaDbFt7ToKeiWnPzrER/tjfPdbiyiWDYxPTHBpeI7rt9dRSEnIpkXEUkkCiQVkqQrq6pS46/SE40ms1sWcOHqVRDyDVlciEU2QSYmRSIu0NddgMBa52DvFzbuuJxecJ1qOokwqOXN+FmNOQqqYJ1+QQElAptSSTIfI5EVo9RoyIhHKbJoVLQ4UmRJyg5SK1jVcOnCQpXozGXGJ0WSKbFFMRpanWJKik4XZfyXH7q12/CI3payTZRoV0UyCaVcFld4os6oUdfIFzFXVnD6fp7YuT02lh7m5OQoV9ahKEUqxELFEHYHiLLlMEk1JiUghI6ooks5l0cltCIUAxZKCcjFPWWPBm8hiEorkFUX0EjUGpYywJoNCZcOgKFJX5UAqEqh1OrBaq7DbTMxP+vAOjxAYH2d//zQVsgwJg4Gl1W14Nq/lmU/fj2vtUjJTQUw6HYayAFIR2fk59r34Y3xXzzM1v8CKhg4mghfQlo04WpcROt2NvFnH3GicpatqWYgm6DnUy9Ibt/KWxEJcbkIyEyUiWcBhqsQgjiFPZNj/wWHal66mZ7CXdCSOu1FEXUctxw+PohTnSSRL6IyV9PeNoNPLScQi2OzVJDNR0skUTbVNjI17sTksTE9P43A4KBayVFV6OHz0HJ+570aOHDpNtaedS1cv0Na2CItVS6XLjFpj4tjx0xSKJewWM1qJnHyhiFKp5ONjJ1nU0UA8mUYoipnz+zAYVOj0RtRaBYODo6hVGq7ftgWDBZpqq+kbmKVnbJYrVy+xbukSLEopZlsV+48c5JFvfJmXXnqDTCCD3qHH7aygrb2KuhoPl0/34nK5ePb5F/nWjx6nZ+Ay6rIWsTTOspZaugYv8evfHOGJR77Af/z5j2y/YQfHDx3C6GlALxXzqU9/gtdff5F8OsPWHdfzysvvs3vHZqRFMc//5S1+9NPvcunSKUaHR2lqWEIoGiGVyjAxOkMml6bRU4vOaKC7pxuL1UahUMRut7Mw76W61kk+6aPG7WB0OkwkIjDnX0AmEQHia12phSISyTV4tjS1MuOdoL2jhcH+SZTqEolkjmKpRLaQRalUEvFlUOvEmE0GRCIRcrmSTKqEVi8mm0sTCMfIF8SUhDIKhRSlSk4mmUIkkiBVSFFqr4VFpaJlqqvNTIz991LI/zhZq9Vqnn766f9n/9VXX/1/9h5++GEefvjh/xHQ//fqGSqSCEUxGQXMNWK0SjE3bV1H/9gVUuEA11+3lkB4AkfleoZHppmdDzIfDWGxV3D29DkQyal2OpCX9ZSLAd754CQbVq5kYWYOg85OIS8hHI5it1jZtE6HXm9maOAq6cIs7x24SGNjIxptiM9/aTv/8cRrFKfzJJNRJEUR2zZdR2jGh8KkITQbppQvsGRxG+l0lM1rdnDp6nkkqiKti6+FqY/OBskV4/RcnkNrN5NJabGaqrgydYXe3jn2fnYXv396HyWJHb2ljMHkIVuW4YsUEYQoOqODVDJPfX09Gk0RsSyN1iFmg70e39AsUmucbFSEp7Ya53yM9JTAfQ9sJIeC6b5xClIVY0OSa0W3eZhO5lGplWSiWdKpJARLhJMnyVe6uCjRUCFJMCbocDW6qRR6sYgUXE5eu3fwOFdRYfOQEFSEImFSE36qFTKS6hJak5x0To1LbUJjzjGe9rMwFCIjFlEcHUQitTOZDCHNpCmJSuhMDgSJmHA8gVZrJp0TUVKWkGrsmHRikvECTpuaRmMlNosdpTxPvdmFSaMmMT3D5NgcUX+YQGiY0/0jVLsqQVZi0YZ1SCUlzA4bhloDn1u2FolUzsSZDwnMTDH8m/3w6ftRe/2EhSS2lIx5dRF3XsLf/vJr/MMniQe9FLIC095ZIpPT1K12U04K5OttDPVNsWjDYlJlKYFoHKHawb6cBJO9n7DfTVGrxWpwExybYMVaE4mYn/aWGt58Zz+f+tRuUvEJTpy8QqlcSVN9HYlEgETeRyIdwmzT0tLchndmgqpqNxcuhtBoDezctY23336b0ZEgZpuVKk815XKO/9P9S2OTm5PH5chlRSqdFczNT+ELSInHK/D5QhiMRqx2PeGAn4EZP0q1joaGOrbv3gBCjuRYkunpOUQSkCgt+CIh9t5wF7WuKjLhOT7cv4+Hv7CHN155nUJZTk7Q8ePHvsHJk6eRqpRMzY/xX0/9hNGRcbTyEj988lv88IfPsL//AoN9U+z5ZAWvfnCYN97/JcPzs6g1OQZ7u1HrDVRYXHx07AizM9dMH66WCtobrMQi0yxeuhiZRU2TxcnxSxexm6pxtrg5efACHS2LeOutg1RXNVJZ7eHPf3oOq1GPzVLFkSNHqa1rYHhqmJr6OpbVt3Fx+AqRhVks1grKEjFqlRZ/MEwylSWegUIuQ32rmzNdoyTiAgqFAqlYTCadpSiUKJdKWK02otEo/sAcLoeNjrYmzpy4hNGqIpXKoNGpkCEjk7vmS1Ap1KTTadLpPIU8mC0aJidTyGVS7A4HPv+1fBCH3Yp/wYdCJieTyyMTyZBJJKh0MoRkhmg49ndZ+Q83xWy/sxUhO0tdRTU9fTFsFS5eevkgX3jgNsrlIHODXjSVGqYnppEVNNQ02LA3mOnq6WfluiUEQ6OojQaGRvNUeexEE3FGxidYu3op56524qpy4hv34q5yMTnspcKeRa9wMjGRxlxVhUip4pU/XWRF/Q1YdRLu3rOZZAEOH7rA6EIQhcFGJi6wdOVK9BsAsZiFkQnkSHHVeghPzuH1XeuUG11IUN/aglwTJu4TGOyepNqTp5SF1UvaOfj2aYw2JwWRmmhESzqb5lLvaYqiFCa7FblSTTqdYGCon0qzAXGpQDScZs9dt/PG2wfw3FqHQVLmTHcfZo+LS8NDbEjNoHfbaVpsYTQQ5/bVG5gfXsBjqObZdw/irq9lcGAIp02PUFaypN6BXN/E/FQSi7ZERUmJtVRPqFjD1EKRiPbax3d4WIxisg+1qoBCZmRBJSI/PoZapSXjEygXjBxNT6LWWBDSSvJ5ORpNjqigwG2Wo83YKXhMiPJRzBYDYr2YVaYWLHI1rgojKpuKXM5EeKaL2dEYcwMXWIgJnBSyLKtr44r2CjhtNGjrMNbW0r5+JUIxTeOG1dhEEua7u/F1naend5jahmo+3H+ShZ4XcS+6n9zAD7lr89O88pNVAOxaZmT/xQwRAwizcSIOPbu//D3e+t336e6awKIQo9ZkUGo9KF1GgsUQ6rAIhUWNLJMnLIKsXMHH5sUIiSoapSmIzdC+ZgPjJw+zttWBLzBB/6kFhqaiFGQpKhrK+MetVNc5Ueihr3cSrbZEa3sT6TT094xz+NAplq9cjFQuZvetdyESlRkZm8JoMSCfyeAL+JHrC5TzUpKxa9kgr/71Y5yVHiKxJNlslr333cObb3xEIJBDpzUiCAIqtYJ77rmHZ595iV033c7ZziPMBQWkMohk0qh1avR6JRqtjVQ8Rdfpc6xf0kqwEKTeaiIUinHPns8wNDREVpxjqH8AcVHMsRM97P3UbsYHRhGVSjQ53SjkYmrq9DQ1uWle2c7ASD9vvfVrus6c5YZt7Zw7c44vPXA77x3o5PK5U9yz9w7KumsDwbmr3bSvXkP/2DxtVWZGvV5KlVZ2L2nmj8++Tr6Yp6QQWLutHatTzoGPzvCTJ35MeG6asD/A8VPnaWlpQaXW0iyuRSxScrbzMiJpFhlSZryzuGvrGBkaxl3ppLrKjUwlp8mzjo/29SCSSiiQRSqRkk6loSxCpVSSTGRQq7XEolH0BhXxWISX//JXzCYNdmsFQsFLPp8DsQSR6NopWiqVKJYEdDoNuVwGERIkIjmUC6QySaTKMjKZBKW6TLFUQlwCMWI0CjkqqQz/QgijQUMqkfq7rPyH281v2K3k/ge38fb7U8j9WbIy8M9kcTtL6HUaauuWkspME0mJKOeVGJUxDG4p3tkcekWKaEKKNxliSeNajh26xNr1jVhtLj46cB6ns4qeS/3cfNMaikKeSreLk2cu0dvvJZ8Xk03nqXR5aGqrYnpuBkUZaiwSJBYT3WML5HxJMvkcn/nU7UhUWYSCmN6+y1TYHLTUNaExKznf1UlTjZGHH9nP6k2VqFQi/EEfq1aup/dqD1qVFYNZw/RsEK3ahl4Vp1DIU8jJmPcHWLvJg1yl5NCBcVxuPcmYFItZQ3NbDf19fVQ7ZVQ4RVTa6+gbucripmWE0zP0DWcp5fO4zDoMRiVWGyRCZUp5C7/49T7KZQdGs5jWlkbGRoZYumI5JakWCmUWoj7yRRCKEmorK5lf6MHskNNgd/NhTzdjx0Zp3nkbyXyeoljApDeRyWcoiiBHkZJMQW2liWIhhyQPdrsdo15DVXMtkukA4tomVOUy0dkBonMhxkNpoiPjZApJxsUaNjgUIDWirDdj0a+hWj1PXAvFfAGJt0QgNkEuWcI/vUAp6We6e5i9n93Di8++wOsvf4envv8Ct337brx/OM3ex5bx80df4Wf/+RCvPPUyS/bcwfSl8xiqlsBsNzv/2McDO6pZemcrp8572XXzdh57+K888OCtZMsnqbJUM9o3xga3G2fOSI1WxURynnwigbvSTkXFGg6WEpzqH8fe0Iq1oorC3ElWtFTRPTJIfDzN+csjrNuwnCuXL3DL7hu40tdDeqFIQWJm1itgriiAOkUmXuLY0QHcbg92h5kF3zTJqIjW5iqEYoa1KxZz3bKSTwAAIABJREFU+uwFmjqWEk7kOHXwEJUtcpRSC6uXLuVXv36P+vZq7CIZJaFETqLAF/AhUchJJDPYXRY+fe8eju0/yPnL3Wzceh3zCzMIQp4vfekrPPPr32EwmkgkEjidThrXLOXShwdQiaSkRWXqa830XJ0kkpxHJzUSl4lorLJRaTbgjwu4Kuvwz07QVGuCksChq4M88aNH+eNv32CJu5ackOdcVz8Fg4I7b91KbNLL8y98yKoti/ANzWJyVBCNhVixsoHfPHeAr312N/54mebaWsq5BEfPdmIwKTFXGLBrLGgrazh78gS2KjO9l/rwzSZwuCpo7ahkdi5OdXUDI3PT6KQy+nr68QXC1FVWUJbLmVsI4KqoIp5OkExmcdltaBUiFrV7ePedw0gVCgRxibJCjJApYDEYmZsNYzQpsdtcBILzNNTXMDkxRS6XQyqVk84UEIuhVBZTEPIo1DJkMgkRXwaXy0gimaZYLqFWKSiWBQrFaxq2Vq+iUChg0ukolwTclS76+oaQylVs2LqR/e99RGW1jFxKhVJaYGwi/d+y8h9ePnDq5J/QSgU0BjOnT3ch0UmJxYoYbDIESQm9Q07nqSEWL3NhqZRy5tQwtY12JoYm0IhNSHQ6LI1uDn/chSCyksmo6TrXT9KfRq8ssnH1avR6GVV1LjqvdFNRbadlfTW33fcZFqb9zE0OIc2raWnVMzA+jtZk5sSJizz85U9zZXCMu3atwGW1c/bCFfzxCPU1i5gJj3Pmg4+xNThwWi2cPDHInk/8E//yrcew2tRIJUpC4QhjIwHKZMnk51i9qg3vTC+5VJ6ZySy1DQ0EQwtMDkUw24uUxRFisSTXX3cTg0O9iEVpEuEItZ465ueiVHmqSedgdGyMMg56egKoFUZ0zjB6DZw9MoKp7npe+dshHnhwMR0tWTZt3cLZ3iHKGgPz8QgSWRCTXY3DmWPDBiMWR5ZccYE1q+pxWiDnn8FoMXD3bZ/jo67jVDhMWM0anFVa2tuaubV5EetXd7C+polFZj3bVregUYo5PzzI5OFOBk9e4MLwAPNzU0QC4/h1i5GqweNpxdNhoamyCretiop8gWI6wuDZCxQmL3DwxXdokkZ46+nX+dxNLfj2H2PrjhZOvnqEM99vI1TU8KVmKYmykvs9AfZfXOBftpr43buX+cKujfz2tZPc94lt/OTJY3z7sa387D/f56tf/QSP//wd7nrsB/j8Z5gcGMeBhuTYAqmYQC4Ww2FTEJFaqTGJiKrt1DbV8+NXjnDXPZ+DZBlFTSWdZRnjRR2JZAB7GSKhIbIRP4UYJIMFZganWbd+DWqVDrtdi0KqwqhpIK/WkCxP0rxKhLupjt7eQVa0XMf61U0sXVyLQuGkUJSwdn0d+XSC1saVlOVlrnZ34R2aIB6Os3r1ckTpKPMz86xev5brrruD3quXUEqlDE6MoLPYSCQzxOIJFAoZL/3przz26GOs3bSV8ak5Zn2zzM7P4qx0IxHEXDx3nnQiSTgQZnxkDNFskFQxTsO6VrRyFdJgip6RQW5etxtveAqr0kwgHKSltYWp4UlCIR/ZYJCO+mY6e8fYvelGLpw+wIYVK5FVG/ClUyg0BkTREpcuXmLdrrVsX78arUlLVZOD4blR7rxxF0LEz47b7uejwy/wqb13cbbrAimRgLuqCmOlAYtYgUQnJ5HKMDA0SWhsGo1Uxecf/gpKkYSzR84yE/TR0tFOKZXCXVHB7Tfdim9mhjXr19PV3cv6dRsIBX1UVrj450f+iYKQpbenj+H5BVoXL2HKO0u120M+lSKTKhCLZXA5LaSSWXK5PIVClmQqQS4vIJPLrzVEyRQkkzmKQhGZVHLNMJPL8/3v/oif/+LfyRcFxCJQKtVkM1lKlFGrVZTII5PLyWZy5NI5fL4wuaxAsVxkyjtKhcVMPq/E6tQTjsZ57Bs/+G9Z+Q+XQWYSc6in5Hinhli/toHmpav59ZP7yGZKCNk8Qb8Si8VESVAy651BbhRRRIREK2MumGHTehd5hYRVi11UL63jvVcukxJSfPaLO3jznX2sqdjO8MAl/MEYLkMFLfU1RCNeCPZQ325nfFZLqhQg5dPRVlPDuN/PllWbqda7qKwy8v5HfezYboKyiqGubqrMVuxGO4s/0YxOqeGj/Ufo7r0mg1TYNJTLaqRSgYW5GI2NVUjEZRrqKum+0I9aqkVnMiGXCxz46Czf+dE9HDv2MQNXo9xw43K06npefOUvhGNlXB4FWquCDw+c5+69q7nQc4K25vUkAmVCES/2CjWVbhWx+TKpmRiLmhzUVyZZut5GqqSlZ0KgRT7G3k82ohJpCSeDDHmvYJKmUalBWSigykZYUmMgFBxGqzPitNlxORsB2FhTYGA4xJXL04Tm80gVJU5ZdOQdVjZUVNJUZ0c9qcZtq+L2NRrKO29hbuAKFokYkVTD/MwkY+/+mnwhw3v94zz1+Jf4wvd/SfjCL7Ct/DnCke+x6I4DvPGrb/LAvzzDb++uRzoe4l7zZV5PxtlpUbHu3x/ixT89x/966Hp+9+cjfOuBB3nxvTd49Gt7OXf4Cttvu47X3zjL8uu20HWil8YtTUS7J5kqQUmexry8AYDX9p/gGzet4q2Pr/DHRx/hrT98m/pb3Tz9++Pcs0nJpDiFf/Qcf8gcRSFy8NUfPM2W+3ZTVXByOjhPwpdm0aotDJw4hEVTyVDvAMZNCrQmLWqrks6eC8x7wWo1s2KVA4Uqynj/AFXOZjKxCGOj/Sh1Dromr6BXWkklQ2SSIqo8ZsKBErmMGO/8DMFsBGtlLQ1VVUxPBjh98RzXrb2RhHCGqdlRAOLhIF1XrxLL5cl4ZyjkS9jtdqKxED/5yXfJZWP4g3Pkcik6lixFVCyQy+R499ABdNUV/NNXvsq3Hn2M6vpq6rdtRJgeoraUQ9fRxtvvfcj6NZv567H32LhyGV5fiBVrN+OpXcRb7xwjnE5w/caNvH3gOMmiwN8+PssyS5mmihrigRn2dY9w6vQVbrxxBzXWGn753efRVGpZ1NpBU62TdnMzh051khMlAegZHmHiN79g9869FLNFnnj61+y5cycLXi9DyQg2QYwol8flaCGWCvKzJ39DNBjBoJVhMJpxGxwsrWrGpdbxn394jq6RMSb8QX7wg39lbnyK9YuXolJp+I/Hf4pCo2bR0uVc7DzHcO8ARr0GX3CeXKaAXKVEIYJYIo5coSSZTKLXa7BYLIwMTbF9+xoWfCGSyRRabYZEIolKpcIfDONyWYFrRdNmjZ5wMEIkEkOrVSMUs9gsJkLRMPlcAY1KSywdQ1QWI5PLEcvK1DfVMj8RZGEhSiAWwl2p+bus/IfLII/8dAnxmTni80GMahvecIQqVwsjgwusXlXL5Nw89Z5azh2fYPlWHaePj+C2W7FVyZmdCnDfg7cwPBamEE2RKMyh13qYnPAhQk5VbR1DwwvMz4yTTafZuGo98XicZCiJwZxFXVdF71AE0kmmz4ex1CqRyHXMTs7R3rIIrUXGoZNXyMTTNDXb0MulLK6v52/vH+eOr9zEyJHT5ItS1t60gSf//T2Wr7WTEQSmpyNYzWraWppIxkJMjM/hcFqQlKUsW2XnQucEwWgMuVxJhbmOdNLPimU1nDs9yNKlLZTVSRrq6vEFFgil8lgsEkQUOXt8jCXtjTg9SqRyCzMzwwxf9LK0o5KWtg4mI6OIBSlK+bVQH6VCC0U5M95R3B47iOTkU2XMej3ZTI4z57uora5BazQwPDXCzRtWMpvL8cQPTvDtX36RmWARtc6GSAC33kVobo5oNoYsnUMZ97L/6jjLXGbePHiZD/7zM+z+5h9JvfNjPA/+G73/uotHD03x7dVGJixLKR17Cs/9/8bCOy/RvO4GBt99l8Li9ZiHupgyO1naoKas0DN08Cj2VTeSEea48fq93H3f53jj+Se5+f7v8uGL32HV3T/mwqlf8Ildj/GX577DN//5cb73sy/x3E9e5f6vP8Dzz7/Bonu20VFdw7Erx/nyvx7n+Xe+jDMR5XRPlJagj2MzCVwNanoXfKhlyznX00UgUsAk0pDV6rAYVWzethbj2i0ERsZJyRZoNXloa21h7OM3SETCmCuMhPxTeKqbOHz2j2y97gYuX5ohGs7hcTs4c2mcYkJCNhVneYcRUamSy0Mhbt7bjsPm4INX3sdmbeLsmfM01NcgV2uRSrLMh8KI5Srmpqaoq17EXHgGuTxIlX4dB48d5vrrFlNhqWJoep6pqSmUCgUKpRKJVIxRp6SxtpZ39h1AJdVQXVeB3W7nSlc/MoUCkaSMzWJFiohwMMJsMIBJJ2PPltVU1DTz82efI5YWOPDGn7n3oS+wbvla3j10HJfbzarli1DJ5HQNdrNy+VJWLGvjo4PHuG7nEuZGAnRd7WJuMolebySRjlEuydm0eS3dZ8+w+5N7yBYi3H/7Hr70lUdpW17Jc78/QGujky2bGukdmkCqVbF5zWb+/MyfWbR8PYlMmKlRH/F4jJxITDYHze11TE9P8rmHP8u5A2dwGMycPnuGFAXWr9tA18VLtHW0Mzk6ik2np62jnZ7efuLpLNFkCre7ku2btvDMb36D1qRCrJQSSSbQqlRkMxnkMjmikgSlQoVaJUcQBBZ1dNDZ2Uk2k0colbBVWFBr5AT8IQqlAhKpmHigQEW9jkgwiUYuRylXoNMZCPgjJNMpZDIJMrWcXC6HXKpALVMQiUWxVpiJZcKQUpDJ5tCZVDidZQa6M/8tK/8/ORj//1wTPaMEIgL2unqU1ZW0ddTirjWSK2qYWphBSCuY8vp4+Ov/zOXOMaqcNVg1EhprWmivWsnUUIKZsSSpuIxUpJmR/iAkBLwTKRYGvVRWmGle3Mae+++ms7+XS6MzOJY3E5W46fxwhsjoFDUVHWz7RBsLoTSRcBpXrQdkYi73XuGRL9/DjhvXEIhmmfSnmfBnueehe+k/M4BYr2XH7uXEYlMANLfV8ui/fZF/+vqNrFxVw8DgFRbmI4jEcmyWakwWBTMzuWu6tNGDVm1ibGqC6lobIyN93Hn7dkZ6+xm4PMN7r+4jsRCmsVpFxl9gvH+K7VtXIMpkCfumkJVEFOYL1DXZWIj4SRUzvPTnPsQKG5PzvSAREwjFSAkZlq1ax+hkkEggxcjMJIGMn5nQBBtuXomhRsKprgts2bKGrsuXcWr+z8m+cJH8wH6yx9/m/LMvELjyFvv/9F/8fEWR04de4lFDhh/fXceaWBfzR36I/4MXmd73TZ7/z19y9ecbeOGl9/n+rhpeefVjdlWXOXwKlonnOXpxFFlFivOdAa5bI+XtzhFu3bWe3zxzlJblK3jrdIhNK90c2X+SnL8Pi6cJ3+w+tqxrxH/6bW7btZni5R6ighaZ/xIhQU9FGS7PZqj2yOgeX+D2Vjtf+drTSJ1tAFjGBS696+WubR2cMWoQu6yMzwf59K49/PXAUVJWA1aDiahKSjLuJRPOUNIpkEr9FIQgJbGNjMHEyJ9eovtoH8s7dhCYSDAzFmRyZBq7op6xniCZRIQH77+HieEYt2zfgdkssOuWjRw/P4raUcHde9s4+W4nv3viORwOBeFYmFy2hNPgwDsySm19GzKRhJA3REdrC1JNmPG+aaKzOYZHegFQKKX0jQ6TzoiQq5RksnlsNhvhUIzrtu1gPhChpqYeV001Y5OzdPcO0NDSQEFUpijA6Ngwcz4vgriE3qJkUUsjoooqfvPaX3nqB19l75ZWnn3lZRZVN3DTzdfjqasj4PPhqnWhk6TQlZN84wsPEgxMUVVTh5Ap4vf7WZjLEQktIBGXEJfFLG6vY9+b77G8fRG/ffIZfve713jhtReZC3tJlK5doj341buodjezcUMLseAC06NXueP6zWQiYVJCnLvuuBmD2Uw6m6V5aR0jA/389qffZmXCx1cfvIu6GhW33byRH3xiL77JHh65/3PMD/byL48+zE23bGb7lmWsXN6BWiWnutaBLzjLh0c/vBZGVcwgCAJ6uRpJuUBjQz2ichGJuIC9woJIIiYcDtPbe5VctkBeKKJQyUikY8wHfWSLOVQaNVLlNWepVCrG6TBQFMrEoil8CwF0Oh0Ws5VMqkgxXUKrMZLJZK69ApEpQCwik702lZfFkM9myKT+vtjxD4f1quYlmOV6IlEfl8/2EJmPE0wt0LxcScQvRynVUOup5Z+/+Sg2nYvlTU2sXtHGu/sPoq80kAqWEYJ5rI5KtKYoOoMYdYWVNevaicYEcnnIxDPMTQeYG/NT59LRd/4kn73rdqraLKg1Njq7TzA0HKWqSk8qASJFlv6eiwS9ad547WNCwTgicZ5kMoaQzzEyO4p3KIHe4CA4G8BmuObmHB5Y4Fc/eAZf3zieRj12q43lSxajkogYujhObCGJ1WxAb5EikYJQzLN1+3LOnpzAZNGTLaVISOSoK40oXS5MNZVcujRAbZ2VDetu4PixbuL5DLm0gqHeK8jLYoqlLBKVCl/Kz6btVcxOzNFYvYS+nknMUj0VGisLgRit9R2o9SYal7STz8nJJCT09vYSjxZob6kmlyxhMFq50nUNCm6ZjiatlBe+tgFr0M9/bZWwUabhV9/7Mwe/exNvHT/DnnYz5yYKOGLDXLgq4DbIODMaw9m4jN4FCc3OEtGylry3i4LCSX5imnnBgHtqnhmHA20wzYzBgDjQSUyhRdZ/AGdzFcWx/WgNDp574hc89pWtHHq5j9sevI2jR7x8clcVL7y8j29+6w6OfHSOe7+1k0Nv97Js52ouv32UmpZWAp3nMGmtXP7wMADtWgVDE37ef+UAl0+eZ8eNd3Pxgp8/HfqAd97+AltXb2TtHbuQCWUeevhrFMt51le7iRz7mM0mO3OXjuIZ8xGKhymV0iytkbN5dQdvvdlNfGiUDctvp61mPbfeeC+9VwZQqJN0Xj6OXuMiFVRR62jg7MnDvPriOeIJOTpDK9PjSjrP9lAWizjb20NYKNN5+iLhuThFiYh8AVTiClpaGjBbXKxe3wpAKi5hctKLTBEgGAxgtFroHRgkFonz1JP/xeDQKDPeOYbHhrDaKwiGY8wt+An4/KSjcYxGI55aD6VSmvv23MdAzwQ2hR6XVsNirYZtzcvpuzSNTCnm9889w5YbNmC16SjlUhw8fQF3rZu1m+/k+V+8xf5X3+S1373H4JURxsdnWLNqHTaLlng0wYkz3YQLeV7ef4KUSMxDD34ap8mAUW7hrT9cCyryVCno7D7KR4cXmF+QkwnKiUWyjC2E2bX+ZiZ6JhBkOTq2trO92cEjn7qTKd8c2iUbuH6JkX+9bRNPfW4Pc+ExFCUTQ4E+lq5oIRtJMDCU4NS5SYbG5oimSwxNzFMoFJjzzuP1elGqtQilMul0Gk+VG0mxTLFQwuFw0tczQjwWwWa34PF4EElAoZIjiMoIIhFSqRSD2UhGyFEoCgBIBQkhX5xstoC9sgKlRkw05qNUzOPxmCkKORLhFCa9jkJeIJPOk00XkJRAb9RRLoCoJEOttvxdVv7DYR1P54jEQ7Q3N3P33esRo2OsbwKdJo9GV4nJUkHP1SFWLPNw3eZFDPR2cXmglxtuuoETpzqRKw2sXreRQwc/JheQIhI5MRtcTI6Ns3HL6mvPdqobSEeybNu0nk2rN3P/Fz/DCx/s59zRXjKlInXNi5kcHkVtNLHz7gYi0TwSiYRcVkGunGdybJDbbtxOW2sl9YuNrGpx8Omv3Y1Gr+CP711Fq7qmW9VXG/nkdTcgEmUwqc1sWL0RcgXCvjRWiwK9UcqZU1eZnvZRFiUJh7Okk2VEKDCaKrh8ZRSr1Y6knMZhUmJXGwmOCUxMRnn/zQNIShKkajO1za3kS3nUi0xUuRZTzIFBLqHSoaKslfHX9z7GU92BoFTz7ulT5HNlVCUJ2UiSqeFBCtE0Fo2KKouDKp0Hh85OMDCFzmbB5XECECjFUVaaOHW8kx33LWdqOsEDn91GdzSH2+3kZFxOyTeFvX0p+a5JxC0m4pe91K5bztyBPlbd3Ep0sMj2e25hdizIJx/bQveZq9z3wA4+PNjNvXvXcu7oBR55cC/nj3Syc1Mrg5cn2fnQDew7PMnaFTWcH5fRIBriaLefFrWMvwwkaFjUwYkhH9c3y3nmozC7O1bw9PunePihezl4sI//+PoefvVmH9979HOkUioA/rbvA9o3LaN3MoBcWcHzH/6EJXdv4vr1S3jqp69SWyMgrxbIm+Ic+N2fMFcqMZkqEaUkKO1K3BmB9//0K+QigUQ0wVe+/kMk+QrMKgWGkAynUgxCjqmJaY4ePotKXkXIB6FYihOdp4llSlgtddx08/UMDQ0zPjHMxMQUa9euxWBV42mrp7a5EZVBQ027A3d9BcPTA6gNYkqiPG1LWnh/30kAzpy5yIY1qxESIqx6E/NTMxg0asRikKuUaDV6igWBRx7+MjqVimWLFmPUqVjc2ki1p/JaTIRQpNZs4/nf/AlvPMqV8SlyyLnjp3/mtG+WQMSHzFbB0FgAcVaCxewikwKz1YlRU8GmLZtZuW0TBWmZULrI6NQ8ep2cuVCIi92jGKxahFKMm3auwO4ok09leenFv/K7F/dRUse5/pZrZrk//OFvJONRKIa5Y08brcsdjOWGuX/nRiKBSe7e0IIip6JKaca6eAmyRgejA9387s03+dKTz/PbY51IgwGOHDrFzTc0csOyNq7O+Hjj5GHSgQH6+i4QTaaQyORIpVIyBYGCUKRQKBKLJRHyBURyKWUR+Hw+EImIxqO0ttUgl0qIhIIMDw+j1eop5AXKggg5CvK5ArF4FKlEhOT/ImipCEaLDl9wHqNdT4XLTFmUJR5LoFar0el0hAIJnBUuFDIlLfXNaKQqSrkySqUEuVzO5MT032XlPxzW/QNX6Vi+moW5eQxaDUvX1rJu8UrigRRyyyivvHocmUxHPl7m8tkh1GorM6EIx45dQq3Tc+bCGSa9XqrdzQgi6LvYTSQWQm9VM+8LsXzDMhbCk/hSPnrH+1Br5Ixf7cNKFGOHm43rWrDKxWzaczvx2STpNIyPz9HY2MKW210UJEE2rVtJ54kL5JIChbQSIVAgGB6io6WZHTsX8dyf/wZAKJQmkcozHC3y3htX+Pj9ThKBMJu3tDI1N09bRytNzXVo9CqSySxSeYGLl7spCGKmvV4mpkJc7Rmmod5DQZTi8OnzOOvcjPf5yRXlxDMpkqkQwYUMZouBQiTHobMXCeXKnOkcJxMpUWNxsXJZBxOTfi70TLCkdRUB3zzTU+NkMhlSqRzTY0lcVdUEIwlEJSmhYJwKq4VUNspQ/zVJJ69WI5YYGI9l0TsLfByH/ae6ePSJJzn/8mtUN5oYmPLz4Kev50zvBLtvuZe+wX4evHsDx06c5Mv3bOCldy5w3fat/OWtfjZ5TLxwaIZVbY28fjXI6lVLeLcryobFBd44mGbPFzfxzMF5trd4+HOfhB2rmrnlod34++bwbG4jc7qbtXeuY/7ISRYt3c65c1PomttJnnsTR60DUeA8h8J5lIYiZyeSuDpsDJzvAeAP5+OYb7ubi7MBFi9rZ8Pi3VSLZBw6c4q165Yh8npZ6h/jqW98GaPFynzfFF/e+xCNZjtPfeExCmoThINMnu5DLZMhVso5fuU8er2UWXGWqYiXzq7TDI14cVY7WZhPksqkMJosiOVK5n0BpubGOXb0JCuWLKHe48HjqaGn9wqZTIbx4RGWN9djkomo0DjRZLLcunknV85fQsimERVj3HFrOwA//8UPOHjoJMFIknAsTjafI5nPoLbrKStlZEWQymZ47bW/MjY+wezcAvMLC8x5p8nnkriqnCSzGRKJFOVyma3bbyAQ8WIxu9BVajjePUW920BZpWRxayWpvBe/d5QjHx+g68oo7x0+za0713Lh1ElyuQKxTAKUKlRKPZMT04Qjccan5jAaqrnaPcDW6zZQFEmJxXKoZFme+ulPCfiuXTD6Fgqk0lXEIxANF/jb629y+y076OsdJhHP84cDF5gPznPufD+//d4vGO+7iNIoQlLK8d47XUxO5Hi/u4ve/R/RUdvOua6zyLJplIUcY9E03kiMTddvodpTiVIqQsgLyDUqSiIwGPSsX7MekajM7Mw0+XQam9WKxWJhbGwSUblEc2MjmWyeUChMU1MTFMrkUhnUSg1qhQKlVIRMdI1jhXIOkbiMRFZCoZBRyJdIZwVESCiLIJctcOvt21m8pJHx8UkKQoZwKICQLZCIXkvtzOULCAXR32XlPxzW0azApXMXKcrC9IxcZXxyivG+cZKBEmqTlPZ1NeTTCuaCMDSXZToixqCvZnlHK+FIhptuv5cPDx/k6pUh2uqXkshnmJ+dQGtQYDXLkaeKdF06g6QQ4rr1TVy9fAK1SUtWWaRVJObqVS+Xh68iHr3K7XfW43CqWLy0gZpqCUpZklJOwdy8l7xYwvjUHCODY0wHFmiqchOJzeIw2WlvqQVAbrZxeXqGFY3NbNjSRLwoJpUr0dMzhkJj4sDh48z4kzhslTiqXSjkEloaW9Cqi0hkMiSyLC67mUgoyPqVW0kEYGHWj85koK7aRn21HavVxpx3gnFvlHwoQ71HjoI0DqONucksMiLoRXIsGjVN1jpGhoeRytVI9XYUBj1CSkRNtQlRQYJepWfMN0aRPL3dwwTnktQ31gDgn48iSMT0lUpIVDp6olmaVjejs0Xpmpbw2Odv4eKMiDoG+eWxAB1WMa8e7KKpBj4clKBYmOL0cAjj6JtMpk1I/WMs2LQYImGCOhXKgoi8WEl6bAih1oHCO8GYAOKhy9gba4j2d7FrpZJ9Byd5aO8SXvzwHT6xvYpnf3+Az379Lt7tPMHnvn4f/3Uqxp3f+gZf/H03e77/bT75+05uePgz7Hz2CI51WwAotNdw6eSr1LRv4OpkhHefeRens5ZGZzvuBg+xQIiezgEO/vYkru4kAAAgAElEQVQltu+o54/vP83D37yTUiZNSKQm0N/Nurt2oF/bhi9cwlJZTUYUoHmli7xBzpELl9BY6ymKNXRe8KI3a4gHiqxYtZKJuVEkaik2mwfvbIDWRR0EImGUOiX5Yp62la20r2xl147NRIQ8b+//kEhazgt/eRONSo9GZyQWCNN35doPff78MXbetIFlSxZTLOWwOpw01HuQ5FXkEhni4QCfuu92Jsb8aDUa8pk0CrkcoVRCLdPS39WL3xchkM5jtNs5c/QotTVtDA8P4tBpqK2r4tN770BTliCRShFlYxisGha1tKHRlGmodfHEk78gmRORiAt4XA7clUZaF1Vhtsu5/sa1rF2zAovVRKGg4PWXDyMWCUjEYNRb+PyXv4ZMcU06uHPPVozmBBPeOdJRLd9+4BOIZQt4PBau9A1SUVPF+uvWY66xUNNST6uljchFP5UuJbfctJGRaIRerZvP/+xHnDlwCLethqIIZjIwOjuJVismn01xpbsToVjE5nZx/bZt3H37beQSccZG+6lze8hlQSRWkEjFGR2axGRU8+g3v8+ly8NodFqaGhqZnZ5hSUcrEnGZQjFPvlQkmxFIx3MAlPJilEoFuWyRRLpAQciSyaYoUSaeKCCSiNn3wQGGBybQKhVIxAqmvVOURCWKxTLFYhFEZQwGw99l5T8c1k21Zor5AlaLB6NexrzXx8aNGzDbRMSSBQx6AaNZQFNlJC2VcrGnmxtv2EksksBmruDjjz/GU9vKvC/KiXPH+OynP8HixUsJL+Q4cLSPmpoabti4BautgumpfnQVVk4fPY7T3cQHZ4aZ6RqmTWOiJKnmygkfUwP9mI15Mskcy5wr2H3bKipqbbQtdXLDzmbuvnUV7tZKDu8/QLYER491MjJ27cJkY6uGNcts7NjUwtSECKk4RSjzv9k7ryBH7vPa/9DoRgYGGGAwmJzjzuxO2Jwjl2GXQSQlRlEUqWtFW/K1RVlWoCRbMi05SpR1lZklLnNacsldcnOcuJNzDsg5NYC+D6OXW2W7VPfFfvD3hEIVqlCFf/3669MH5yTIKzJRWOHgwMHdyLEYKk0Qu0FFfqENf2yW2toCamorcZpK0Ov1VJWWc/rds+QbU5TlW7BokvjdHjo7WzEJAqp8K46CUkKKikpdOXfuuRXfnJu29hJC4TSjw8MUF5ZjdaqRshqudE3wwdvdGHUSLc3NRD1BhHQGJRZDpc1iLyohElPT2NDK2NiaZq1KxUkkvaSMWpYCUKA1kcc8LFxjyFhAkS7GuauzONRRpEItltjbiPpKIhdep6K6ltXhQbbdeRD/QpCtd25kcT7ArQ9sY6znKkf2HqHrrV/xic/uovetZW6/ewOvPjPAp776OU68c51HP3c3z3w0hCxJnIrmI9bs41chHUvl23hXMfNzT46fX0yyUNrA356fZ6muhVMTM6QqivCR5paHP47ce40bPn4nAEduupfUaoqF5SEIjPLQD+4knhqg230Vk6zH40nw2ullmjbtpag0gqf7SUwOLWXlTqqK9TR3NjM7McBje+6mumUdH713ieTCAnnmDAV5JvzeND//5UvMzi0jpzIEvT4KSyw89evn+OLnHuWOW4/i93hpaqznlVdeRtKKOJx2Cpx2HnzwAQrtGi6du4x3doU8qxO13oCrsoyJ5VWCAYXZJYlIfM201ffBFTQpNUNDA9x04wFqKippb2jkyP5WHvnEnSTCaZ59+jU+/ejHCYVCJNMpkrJCJqtmdGqcUCyOOd9CRUMzvqVZPnb7bfz26efQ2Vz0Xlvk5KkrPPbVf2JydoXy6nqWPRlq6lro6R3jlsM3kpUztK7bQl1zPvsO1XPDoXZCXj9d14YotBWyvrme/v5e9Fpobqgnlc4hqHOIooa+gXHy7Pkof6hbe+ZXpxmbzFFS7WLzlg1cGfWzsuDD6lLz4H130j18jp7FMVxZEY1ex49++1v0+UXUyU7WCwGO7q7ghZ/+ihl/ii6Tlr/+199TXlLKyuwYx374Ddprijl94l0sOiOSaISsxPEP3kdR5dCKOnzLMaanpzEazcQSaWw2Gyq1SCwm8xdf/ToGox45lWF+fg5BUNHa2oqSySLLMmq1hFarRxDWEBoOJYjHUqSSGRw2C+FoDK1WSzabxWTSoShZGmprWNfUQColA5BKKsgZhUwWBEFNLpMjFPz3Q5zgvwGslbiR+ppazJYc4cUIVeWFhGIxRLOEzaKBuBtJm6a1s4CZ2Sn+4YePcuKD42RkIwHfCutbGlAUiVhCxuvP8uLTrzM5OIFRUtixewMvvvwSly+do7K8ivbGHXiXPBzYf4iQe4ntNzWzbVctsiVHMDRFkS2f5uottNRUM5cI4pXm8fo9WEw6HDaF6fFRxqYGWFmcYN9NO/D7pxgejHPTHQcB6Lo2QdKj4s1XrlBd7USr0mMyS0xNBqiuauDNY12UFutpXefEaE6xo7WZmTkvo2NzVBSX4sxTE40nGRsK4XRUUlRcRTqVJRBK4Cot4dzlq5gEPTZRhzqTwmDRoMqlMOt1zM8FmZ6bZWlphdrmeuJShqGAj8LqMuqqitnYWoIrD5YWV7A67KjUatoqq7CZ7ExPzeH1BZlbWkRnkgCQNBZyKh1FpjIUs5Gh69O483RMZbIsXF9E9M0jqrUkJ5YRjHYio2mK1jcQXgiw8egO/EGwlpkIB1RsbS9jdSHOxo3VnDo1QuveOuaHliisdtE95WFvYzlXhwO0d9bzymKAXHEJ/2cowZMjMYLVVTx1dRRr6y7mJ0OoCkvRznoobKqhb3SAvZ1b8S2tUFhWQSaRYkdTGcdODjA7uoBiXLulPNzp4tyJLh757H00bG8gNDqOy5nHA0fuRWMoJD+/EleJkYIGgd6LQwTTBRSWSJwZ66Jzz0beffMKntk06ZUpPrF/HW172hgY9pDJOhC1FpyuCkS1RGlpKTpDFl8oyPx8kInxZURBwr+6yLYtGykudLJlUydyKoNeZ2ZXZzvuoSGSbh96LczPraIRVLQ31tDocvHD732SkpIUm5vrScTXsuGd1bW8deoMsTSUVhSgswskUj6O7N3D9OQUgkpGFAWOvfgmJosJtaQmEYsRDccpLWuhtnoDkhqi4Xn++W8eZ2RmlltuuxFRAL1ey+bWcnbfvJ2uS910d3fTVFyDVRYoMKiZGJvk/vsfwOt1U15hx6DNY3nOjyzn2LZ9IxrJxpvvnKTAmY8kpBjq76GqqgKVCjrb20ClZm46jNa4Zk1zrybZfUMV5bUZjp94AcUpMNCbRO8oxBOeZXEhTXo5wYW+IZq2dOCwGTl17TxvjFxlfXUL8oIfo8WMqBhRaUSaK/M539XP3/7NX/CLp17lTN8M6ZxCTBEwlpSQX1xGZV0zvQPDWPMdlJU7kSSJXC6DRiMRj8dRqdWoJA2WPCOiRiIWiZHLKYiixG+eeQ5ECVEUyWazpDMy6dxannU6m0WtEiGTJZvOohW1RCNxREFEyWUoKXYxPHgdt89NRslhMBjYv+8AmYyC2WxGFEUymSzZ7H/spP4vh3Vzsw6FVeanAiRiIoG4zNhKH1qdgGdWYKhbQSFJ75URvva1vZw7eRa7tQi9TsJis3Lm2nt0D1xhy+4NrK6uUl7fxonjw4yMJdCLOYoLS7Gky3BKLhSTkdqiCrzuOCPd46QWfHRu2k1eXhE33LKLucx1LGUC7skV1lUWM3IiSV1BAyfPzHD+ygitTVsZnooQmI/w2ttv09bUxAsvfI5M3A9ATGXmvdMnWfEvodXqiWfCtG5yUl5t4sSJ4xw9up3q5iaGeldwFtXw0ocf0LGpCZU2j+OvfkhajqDOSWTSMsND45w83UtSY2d0McSVgWE0BjPhXJrCgI/DlYVUKAJqVZLTH37Iph07WPbKaMwWAuEQ2UyO9ZVl9PT1sbXegFmSYCWLSaXHncowPLeCSszD50tgMdgoKiklh8LmzVsAyChJ0pkEQxdOU6RLUNdcw+pwgOz8FJ2fPcxCl5+bHr2Fa1MJvvTYfXSN+Pn0YROvnPZzS5OH3745zUO7yvjhS9doririhycmKdfpeH9FS1uhwL8NSAhKLU/6ZL5rLOIX3ihP9kzy/FKak8NBVh3V/Pj1QR75k8/y/u/fon33Rk4dP8Udjz7M8++9zaY77+cX//BL8jbX8/w3n6CxbQPvvHWOS/0+YtPTlNfp+MFja40kUxOn2LpDQ15mEvfMGIurkGdupdNYQyYygKHRyye/2YoQctPQ1EYyUcji4DSNdcX8/rdvsb6yjk89fohAlZmAapXP7Gtl332tmK1Zgiov7350CqvNyPlzF/H7ozQ1b+Cmm/dR4BJ47pnnCAXjTE9PsrA4i9PpoK6+CpVKoa+nHyUp8OHxy6h1Or7/+Ff5u7/7O5554VlKqh28fryHG3ffwNtX3ue2u7cBcG1skc9/+WF+/P3/zUsvvEOjpZhHP34Tl69d4MKlq4iCGUVRiEQSZNIZJLWIJGowmw2MjA+spdUNL6AXHJy5eJ6BsTGqayrQSCoUFXT1jfPuO1c4euseZhZiPP/qG7x+8jQTS8tMTk/xjW/8DdevDzE2NM/kxBxXunvQG810d10nmQ4hqvW0traCnKC2qohkIkpOELlw6TxlJaXccOMGeq6sBTnVNJUx0j+FKivj9iaw6pPc+/BdTM5Mc/xkD1va2lAyCkUVDi5c6cLnjrFhywE0aZGPuub4l3c/QmeTuDo8SHzcy9b9t9BY4sAgqFhSpdm2YzfbNm/CpE3R1lGGs6CYbFqmwJ6PkosSCPiwO2wIUg6DRUBvEEilU0RCMTZt7mBdUy0qlYpDhw6RktMcOLifbE5FVs6Ry+RQMgpKdo1jeo0GWc4gpxUURUUmJ5CW116rJYHp+QVkJctqwIdGLxIMBnj//ZNodRKRSIRkMoVaLaDV/je27kXDErPTfoJ+GWerlpIKkUJHLaWlG7l+McEtRw4zMuDjy1/aS2hVoHH9OipLa5mfGODhTx6lorKOrbs7mBwfQdInudDbi1Tk4nzXBP/03eeJJwX6FiL85Hcn8A2mGFtcwWEooL6+hkOHDrAUD7Cw6MHrC+CbFLl27RpaoxmtLo/yDYVEkn6qHFnWNdei5Cc4evMebr3rY5i1ZQwMTXDhgy4M0poGlxX83HzPbdz8ibt44fljBCMpXni6l3BAS01FGy+/cRaXoYSAaGJuYJX7HryL0pJKUrEc3/jmP+GPa5FRMTAySzqbI5kVWAi7qVtXxL7NHRTY83BYdTgLHBg9Xu7ec4Bc1kwknmZoZpxoWks4nqB5fQWldg3qmIcv3n0LviURlcaIRD7ZUBZPwovfm+Cn73yEd97L0kqQWDBDNq6wNLEWz1hV6EQviVRuW8dSMozNoaeyvhRHMsOnG6v43ofD3FYocilsoDUZ5mc9CSxlbTy3qsJNA+dMZt70OrhokPhWvxp/cQ1/0ZdhtK6I//XWCKl97fzpayfYd/9tuE8nuP2eI3j6F/nSA/fw3lMvc+iGvawMTvDhQg+90yvUFdXzm7Mf0X7LHnqv9HN4Vzu6SAbVSpxIOIzVn+Xy6+/RUFjJxNAV7t2zmf2HNgDwT3/zCkurYQa6g9x1x52oDGnUcpIrQ++TEAwoUwXkJly8faIHb1ymJC+BrbqZVSHKxge3kdKGGOxb5r2L7+FLyLz+wSWaHB24tBVMDC5zw827kVUJ9EYdZpODt944yRuvvseh/bdQW9NM78Agc0vLTC/McK3/CpeuXOSZ515gbNbLX3/vX/jMF++kpELglbde4RP3PczNOw9xqfsKo5eWmB8Z5msPfIJf/vxdAG4/2sKtNxxgaGaGW2/ehKMxjydfvcSPnnqLtFokmgiSVRRATcAfJhiI4XWHOHrTLWza1IojT2JdTSlHbr+JZ1/7gNLSYl789TGmpyeIpHwIVgOmfAMfnrxEOhEnrs6QV2zHUexCUauRVQpGm4XFxQTLyyHWtbWQziRBpSYYBkmXYGkxRCyTxeksRJI0KMksWp3Irj2bmZr38b+/vtbdmkrHmJsLUFHaxq7dLYwtrvLya7+hd2CJ5aUwTR2FrGuqZnXOSy4ZZ8cNhzl+6iRtW3fy1vQQtrxSllZl5HSMppZ2RnuvM+NOkkmmaWusJyfEqKzJR6exMtrdz9Ur7/LZhz6ORAarNZ9IPEY8FiaRSKAoMrPTASqrCskvNDPU24tBq0MliHx05hTJdIzTZz5CyYJBq0PI5VCrVKjWFmsychqzVY/GokbOpglH4kiSSCyVRKXWojVKpAQw2S2o9GoENTz6yEMIgkAuCzkF0nIOrV7HfzT/5dkgH55/koBHpqq6hqqqWsb7shw/cZ2zJwbZvaMKnWYRo9mMZ8XNQP80r7zTRVWhmp1b2/D6l6msdLGh1cqG1iKu9wcx5GUpq7URTcXRWUoYHB+iubYKSaNgq66ip28YId9BOLnEwMw0pXlaLl7ow7MgUVajYXgyCqEI8biB4WsTjE0tsn3PfgrUGoRghmTGxFRggvePj1FepKPIVYQi6jl48B5++5vvk+9w8btjx9m2eRvr2yu4/a4D5BSZYHCFT9y3jRQJnFIe3lCSskIDlYXlhJPwyiuvc9tdH6N76BIPP3oP6UyWG2/fz/jYLPpsAiEscMM6K4sZFc9e6Gc5mCAZ8tO/NItkysNot7KyGkYQRSqcZgZn5snKUazoMdcXcOnkdSJqNQabilBEQa/VkpdvZX7JSyqeRdBmMJo0JJJZ7vjYF3nxd39PYZGTaDaJM6ND1KfwO0Rm3V7CIT9CRSWj9iwXemaYr83iVnL0mUSqK+1Mr1oobCzhxeVJXM0HSC3OYtneQf+pD/nCn36ds8eOsePj93Ht1TMc+PTtvPTj33Dgsbt5+rs/4/Fvf41/+/UxHvriIwwPTuDtnWbjtp289eZb1NbW8dzX/hFXvZPo5BgxVRajnEYsMqOTU7hKnUwPX2WqZw5bmx53b4QHHvkCzz/1b2TSWQJ+DemQF606wmw8yfOnuri9bT8//MeXuXyxm/ayDja2NjEZTlFTuRnP+CxFJjf9Q7OMDiTYW99GPBdFEqyYUzlOT4/iQ2BybAZJkohHMiTiGRQFjAY9Wp3I/Pw80USC8vIqsrkc83OLyLKCXq9HLehoaKqjr8fDr379BnqjjTyTjmgqQX1DMx0HO7j7pqP86Mcv8NnH/pI9O3ay0HUNi16NuDAPCTVvv/khH53pIZcRqK2pxecLIkoq1Oq1MCElt5bNPDoxQTQa4tChw2RVOUwmkeXFRVKxHId37mDj1k6SqTgbOjvRiDq8Kx7kbIZcJkciniLPkofXGwUEclmBZFJGVKtZWfaSTueIRmLUVRUjaQWsNgOimCabgp7+SVpa1/Gd73yb3/z6l1RU2rly+TKf/cxf8pun/oVCp5NoIMPY6DzL7gg6jY1ISMWqO8DMwiQORx4FDgubOto49ru3cBXlceLDy2zctp3F+RkcFgP7NmxmLrDE+MoSFovE5MwUD3zyTl56+Q18ngw37D/IqROn+NJXvsT1a10UlRRy9tx5DCYziXgCk8mMApSVVbI0v4BGayCbyTI7t0AsmaJj43riiTBmk4FcLkMsnkAtCpBTkclk+eZfP84Pf/R9EvEUoiQiqNXoRC0ZWV67cCoZckqOTDaHgoKcSFFSVMzZs+f49Kcf4srlbrQaCZ1ORC0qfO2r3/h3Wflfvln3D01S0VqF2zdFf38v1YWV7N1XjJLToZOhv2+OWCCGTi5nQ2szu7Z3EIsKLHtFLl2Y59TbAzz/63dY9cdxVBjZsq2Nmd5F/vrz9/CZR2rZsbkevU1ieW6JdQ4Dt96xBbs6iv/aHLeVr6OsqY6DHbWkrQuIqTxu37ebmaUcxwcHCWsF7nv4U/zuN69xbXSS1y+Mk4hH8U7FqKuy0lDfQmVpGeWlFgBu3LWPSDjIhvW1DHVdJbns4/KJi0x1z7C5qQaXoQitwUxJmQVNXo6XX+ji4ge9jI2NkCPOyXffoaasgLMfnifoC/J/fvw0eq2MJ5rAWlnCS8MTxOMC7Y56cjYXr4/5SFodXJmcx15UgNftIRoOc21sEr2mEE9YxfVAgN7rveRVFzK7HOJidxD3qA97ZQnBQIya6hYEtQ6704zVZSStWnu6XWEuZXXeS2llJd0r08z6UtimkxRZa3D7cnRPDDK9EGLHoe3oggbsDg3B66dZX9/AqjCBUm9HfaGb2zet583n3+OGnVvpfX8Bc0c+5y7NccPRQwwNDJALiJS32Hnzqz8jptfxD7/6JUFfgNzAZbZ2NmKRlyE0j8mziNNs4Oat5bRIBQy/04crHGPozDX0F1Zoq9mEwbiNnvEQtkI78lSYrQfWPONtu+tJZgxklTBCrJRIuJikO8ynDt7Gd376OmVVFfzpw59j2w01dBa2caBlN1tK9WzraMYTNXD73qNcG5qgSqvj/OgMy1KMKVWC0KKH1ZEZ8u1FJGI5VH8AZF1jBVqDQDQWJxyJotMauHq1h9mZZTZt3kF1TS0ajQazVQJJJq6aYdvejfjiIUrryugeG2X66ijt+cUcuPtzZO06Wk3TADjLKvn8X/6AN+biTJsMbL3zZsobLTz+7c8QjXmwO40Uu4rQ6RSqy+2UOo2sqyukqb4GXzDGsy+8Ru/QAPHwAoY8I7F4nDMXT/P0759lx4YO3nnlTaanxjGYtajFHFpJpLG+AZ/fzbpWFy0txQiqGIqSJhqNEw4lSCZkSktLmJtboqWllWtXrzM6OsE9H78XAUjLSf7yK1+mpqqK1SUvy7Nrd6Ied5jZqQUuXerGZM6nqqKDi5dHmZicRpQk4ikBvz9JcXEpUzMLtLU2sLwQotRqxuuZQdQa2NDSyro9LYyMjBBYXKa5bTM6rZnvPPZz3MtBBicHyDLBp++9hbPH32N5eZXf/f4lBJ0WbyBMnjWfWDIMKjU+/wq5rEA4ECSdzZHOZdCaRbwBLxabFZ8/SDqdJYOKtKIQTaVBWotITaZkJI2I6g8ySSIcRxIk0lkFSa1GRIVOLaITRfIsBsLhIA1Ndbz88suIooBaVKHVasll/htr1pl4KVfOjaERLayOuDG7ArhD0+y9o4gPBgZJ62B+JsLVsR7G54M4bBpGJmZIySnat6/n+sAMdx39JHrBw/oONRp9nNKqHKdOvQExBQ2r+Mfn+daffYq54DDB0T5ieh8VG9sYiMW4/HY3TU3bqTXlY6sqZWB8nv0729hod6K163j7rd9jcRioKqjikx+/iVN9l/GGBGYm/Zj1OhYWFliYWQvZGRq7TkVxKbXllTiKXcwtxZmeWeDOe7aj0clcvHKOEy8dZ3FpiODcMtva13O1/ypFViO33ngAjRRCjxFRFWdubow/+ZMHWF2Is2XfAa5Oj1BgaUCVTTIWmWE+6Gc15CcWTXDw0D68vmU6N5VCJkSxw0U2uURJiZ1APEzWXwSChvI6O40dlSi2PC5cG6bQVUY2nSIYTrC6IPPmKz3ojU4AvCRJiGom+mbZYKvCLuvxatS4jQJjqwE6OjcQDiW4ODLH9eVlSisKQJ/P5NIk/VPj6NMhdm+tpvfsK2zauIWvf+bPWNdUxJ+1H2Hz/ia+sPEmNnS289ozP6Ewl2Z9cz6PHGmjwrvKIweaefNnv6U0vcLYkhd3/wyLqTDpQAQlkuX09HUiBUaMeQZSmRRRg4I2KuM0SKSiKSZHZolrXUycWtNGJ7rGiabcqHIpVuVVDBmJhvp9XBtaZdO6Dgb6rvPMUy/yi79/hVHVCvMzXVy82Mfx4yOUlpVzYryfH3zvK3yUGMNlNjN4cYjX3juF1m6jrrWRiYkJjhy5FVESkHRquvqu40sEmZibQavXEQlFsZos2PJsnD1zHp/XTy6XwWTUopKz1NrXUaKzU2nLJ7ISxJVXiOhy8fWf/IKPzj/J/bdsprffA8Cjf/kNgukUKVsInSFKZLaH7W31PHvsDZaWfIjqLD7fMt/6+p+SjEaR03EcVonaUjN3H9zOoQNbCHnS/OLpU3jcYWLxADanAxSBl997l72HdlBTXUI0HMbldGE06ommwtjteSzMrrA0v4DTYaZ5XTkOpx1BLZFVcszNzaOIOQLhEF5fgm0bD/OD7z1BDti6sYNSp4OlpUWGBpfRadciDVLpMC2tbVhtLvp6hzn34Rny9Dbuv/9+iksKSYST5Oe5uHSxh57rY4xNzbBrQyOeWIywF5zGPNK5BP/09/+KyW6ktLMMQV4mkPBz970baa4p5sH7Psa//eRd/JEEc/OLlFRW0965iWRcRpWDSCRGkasEXyBKMpkCUUMqrSArORDVqFQqMpkMbrcbrU5CVnKolLUScbUa/mAGWcv+yGXIooKsgCTpyGYVdJKEnFVIxLOQBTmVRs5kufXoTeRbbQTDESSNmmw2SywWJxlP/4es/C+Hdcy/gkmXRZYlZmcEfv/KB6giBsqromzeXc0nPvEQNTtq+OTHdmLSLhPFQ1WlikJXikBykE89ugGdPsDydJKD7Z1UWlXcdngPNXW1TK2Ms6O9gl03t3JxepUYTl6/OEHWo0dlNGC0OTl9+hIruRyLUzHG+wc5eaafZ994n9bGDQQGF/nKn32FbbvWc/bqJeREHCMZ8vJVHLqjE29slmQ2Drk110HH9haWFq8zOTHIgSPrKa3TkFWrmVuM4/atgjrL/Q/dQTggomSMzK6OsHvPYYb6Z3j33Xepqq9A0OUorHRRWl3Jh2ffp6q1gP7uKxRYTVzu7iYmh6gqy6O6WkdNTQVBT5jlhSl6L49SXuaiutqFxaSlYUM9Pu8CaV+Y19+9hDcQZMXjxhtIsv3gDlLRLNFIkpyQZePWdpLpODfdtJehsSEA1CQoMGmJ+ldIq9NkrVq6p8aJylF233iISAwkjcDevTfgGw/hnQuTi0E6I9Na4mT4w7dxFDkoKtRTXBtmV10xR27Zy6biVsCN0EAAACAASURBVMrVJm7Y34DdFMRmimG0RWnflk/IG+bE+ABPXbmAdn0enoyb3UfacRRqeO2Jv+f0h5f54OIkBTGIrHqQBDXanApFhLiSRKXVohYUBAFefekCGt+a/tfXt0iB1sb04AoBX5pU1sbKQgpkA4JBSwoVoUSKB4/uxqFWqC2rpbBsF+PzqxBWc6SgmhdefYmYO0yJKw9Bo2FdUwvTs/OEQhEK8x0c+90zFFgNZJNJNre1I6o1VJSVE48maGpqIpFK4g8EqKouAZJkUkn2bG3HbFCjL7Zy6XoXlwdGqd7dSSCyypUrF/nOVz7HzKmLjF27yg+ffguAaFRm/7Zm2oU0Ox2lhMMyRkcRmXQaZ6kTnd7I1s11PPfMrxG1Fto3bcZeVELAH2Uh5qOioID17Y1UN9QQikVx2O0Egz5uvekwN918hMmxcaKROIcP3kg8EkelFsikZBLxODU1VUTjWXz+FGMjC2TSMigyJaVOduzvoLS0mKGhEbZsaWBmdhFLYQEAV7sHWfAsoihQVFKMx7sCgJAxc+7MeZwFFiS1RDKeIpXM8ctf/paFxRls+UaScppoLEYgFOO7j/05ybSfQwc2ku/SkF+WxxvvvI+k1xONBQgvLjMzN4t3PspTv32DWCpAS0M12/ds4rkX3yEUS9E72M/E1CT5efloNBKxdJJwLI4oQDIhk1NktGYNgkohlUgjKmpmZpYwGC3I2SwGvQpJoyBlQZWBTGptE05m04haNbFECllOoZZUqDUSVqsVJadGUqlRsmu6tEav49XXX6OrqwclJ5DJZchlFFLJLIqg/g9Z+UfB2ufzsWfPHiYnJ5mdneXee+/lvvvu49vf/ja5P1hXfvKTn3DXXXdxzz330N/f/0fDurC+gDy7iEWQKWt24gsJbNu3noVrMUy6GP1zJzGgZtof4NCNh2l21bO+sY3FyRmKdZVcPdeNvVSicrOVqSUPC3MJoj6BEmc+jS0uZpUAZr0fURmmOj/I0Qd3UNZcxMj8JL9/63XMLjPPHn+fvtUIZ/pGqSurYktbK78+9iYVLUUce+sYH/X20nlwI6FEBJexgV2d7WxoddK0vhidXoP8h3KH1XkfLc2ttDTVsDK7THV9IXv21FBVoqfMWcSNNx9mcmYCb9BH25Zq5haXCCf9PPyl7VS0aBieniAh5zh1YoBQSCabUeNeduNe9JNKhCko1GHIM1FS6iQn59i2tRaXywq5LDpdjng0TFFhJR5fkNmZcUirOLBnL51bChHVRhSVirQcwqS3UFVdTHGxi/mFSTyhaXQmNfMLy6wurx2W2WkPJsHO+oYtJIU8gukALS3ryclWwrkcl0Z6WAqs0Ndzhn037WZueQ5Jl2B4ZADP0gh5DhPvXTvJ7PQImxvV5JOm1J7BYBV5/71r9FxaIRXO0NmxAVVW5Hr3HMVihD/52C3cvbuUres3MT3gRllWsDXZ+LtffoUj97Zy9P5bUBQFmzWfrLx29iQEoskEgqhBpUBOpUIgRyy8FuLeXFyMJq3GrjNRU1XFwOQY01NLSJiQMxlyaoW2+lJs2gw/ffUUL42M0iX3cuf/uhG1UMzTfRf5/G276ZlbIJkUifgCXL7Qg15rY9UdZnZ+hfx8F0pOpLmplaHBEWw6E0szC1SWVHC9vx9bvh2NqGV+ZpEil527P3ET0WQCURS58NE5VKKR7//VF6gNy/zgL77A3bfu4ns/+hd+9Mp5Bj0K3//+XwFQ31yAYNPz5LPnOT+fwVncyctPv4lOk0fUH+TGGw6wc9cWtm1vpa7MhErRceL9Lj7oHaSpsoF3PvqIwIIbm1aHq9yBopKxOwp58fXjxJNZwtEEHrebi5fOUODIJx1LUFtVRVlJKSp0ZDICKlGLyWRl38FNVNU7mJtbxu12MzY8g2cxytjQAt29A4yMLaHRGRgZHcJktHPLbUdZWlgk/QfARRIh1GqJyclp4rHkmkVOEmhtXYfFbCUSidHX243RlIfOqMFkSJOnzbJtXTXe4QnC/nmMmjzSagVBlUWvNjM5FkGjB58/iclk4Wtf+xuudXdjc0E8GSMU9qDTiqRSMbLICIJAIBBAp1MjaQQEIYekVrBYDOi0oFYrWIwGwr4QsqygliRAQJaz5DKQSa6dQaNGi6RWY8szYjDoQJ1Dpc4RCPgQBUilsqglDYqiQlAEUnKWnbs3kEllUCkqFEGFqFFTV1f3/w9rWZb51re+hU63tqX84Ac/4Mtf/jLPP/88iqJw8uRJBgcHuXLlCseOHeMf//Ef+c53vvNHw7reJLF7eysLwiq+wAI7thUxeG2VUNqEz5tCLeeYWZjFqE5hMuZ4/vXjnL06QVItce7yGLV1++k9PUeDrZy0KKGzGxmbm8YTSBIN6CnU2yjNb0YtmZiZibC3eSNR7xRF6gQ337yLktp2PCPz5FnNJBI5VLo4I8E59h+uJRnzss5po9Rp5nrfAOXllUwuL5FLZrj83hSvv/gh/oU0M2NLAEQDIj0987x67AJXPhhGmzag1alBl6GwQuJnTz5N95V5quuLmVjo4ujth0lEV8nLkzCbbGj0Gkz5Gipr8nEUpUjKSQpsDgod+RQV21HIsLy8Qp7FQTJswLcawO/3olIEdu08SDZtZmBgADnlR0mp0UsmZobD7GjfRr41iUHSU2RxculEL6HFAKKSY9f2TjzLfgry7TgLSrDZ1qxDaquOWfcSo+NjnDl3ldh8hMSkl5w3weUzVyhzlWAUXSAL/Pznz7MSSpFM6tjQuZGtB/dRWd5AY1UrsXga77KOtCrKqu8qjdvtaEwpIrlpKivL6O/ppcDqAFFBXWvnzfGPmPIGCGqWKd9fTtQ2j2TJsPvwXbQ1FWJRwizEEvj9fkRBjZLLIaIiGIxQkm9GEdTIaMgCMmt9dkoySWglRL5oJOwPIeoNaPUSqGTyzAKPfe0RZuQsyVCcvZsq+Ny2dhbfeomn/u0pbt2xlZqSIq52d3FH/U4mx+eoatnAth1b2LhxI0athWRcoaqmkRV3gIUlD6Kkw7PqRxBEBgeHSacyJJNpcjmBjvXt1FU1szS/ihxXOH11hPatW/nUI/fx5K+ep/aGLfzzz57CUpBHZ1sDuzuaOXSgmVMX1zbrvdv2cqixg3sf+ARCQsVw1yBHj9yBZ9mHSoGnnz7G08+c4NSJfnqGVxA1atZtqKXCbmPJ7WH/5k1s3r0Oq0aEWApXYQGeQJDCggJ+/+KLbNq4jS2bd6LVG3F7V8hkMly4eBEQWFhaJJPLEImHicZDDI70UFbhoKSsDKPeQXNrDRqtwD//6w8orXcQTSeoa3Shyokk4ynOnr7Iwb272NK51llqM7kQBDXpVBa1Wo1er6O5uYmJiQlSKZlIOENdUyNZJU6RNZ+T71xmd1s7Z46fp9BlZH7WT4YEKTmNmLMSC+cwGPUEQzFKq+w0NFaj1WrRGiQsViMuh4NIwEcqnsRsMWLU6dHpdBQXF2PUm3A5C9dAmpLxewKYTWYEQSCdzqBCJJNSiESSOO1OREFCrVYjiWvLTTyaIh5NE4/HkWWZVDqJQg6NqEaWU2h0aqKROEpORcgfRaWGS5d7EUVQcioy2SwZJcvs7PT/P6yfeOIJ7rnnHpzONS1zcHCQzZs3A7B7924uXLhAV1cXO3fuRKVSUVxcTDabxe/3/1GwdjUXkE7nMEkWFElhdsXN9ckJ0pokmbSOlopOIh4NwcUsJNR89a8e5ejHOigtNPLxhzup32xjcHSeucE0hfo4VeUCzR2lVDc20n3ZzdKIkfPXelBFY+TEFOOXh1FSana2VKNEI1iUKGWNlRiDKR6+bQcP3radz9y+E6crj70b9yIKEns617OxoZCl8S4OHarg1IXj9A/NU+bowC9LaJxrSVl5eTZSGZmP3XUrt9y4B6MoUVLQjtcn0DM4z+4DrTgKkjjybHSs34nBAEZDmsXZZWpra7GY7cwvrJKMx/C70+zY0UJBgYqWDicag4q29ipMBpmzH13GH/Jy7nw/6ZSRwaEFXnrpFIsrAWzOfNJpEXuhi0AyxHJ0jq7uq5QUmulY30oi7EcnZiguKOHYMyfQYMAiWZGyZq539WA1rF2U9flGMkqKZCTGoR07qatuxzsformumVhMxJDVI7rVaJMRbtm/gS9/5XPY7Rrmp6d4+oVXSIVTlGULaaiqYVFaoGxbJQtBPzqbzDcff4hAUub1E9eIRkUuXxpjqH8cU1ZDLqjQ4CjBqtZjVMH6gkZGBhfpGxxlctjDxY/6Ka9ykZCTKIqCRi2STaTIeAL8/u++SUaOI6jVgIioWfunnC4vD6vVTjaVZWVxBUd+AalcErUug3tpkbNvf8iBzftYHZ7EpMnwD6++wPbDt/HFO3fx5e9+iip3iLamNv723WMokpaZqTnOnDvL1atXWZifo8iZj14U0IgqGuqq2bt/G4FonEwWTJY88vPzyTNbKC8vJhj3MzIxzN7de4hGUrRWVWELx3nyiX/lLx79JMPHL2KwOHj3+BWGxydQcmkm+kbZ0rQGuOmefoYW3Uye78EdDeAmyhsXTlBYW0JtcwPGfAOrITcWh4a64nxOvnOKYmsR6zZ1MDkwyZjbzWvHPmR8dByDRos1z0Lbhg0kw2GMBh0D/b2cO3eWorJC4iT5wp//KXqjhNZgxOqwU+gq4Lvf+mvsDhP7d97F++8MsHV7C0aLxPTqLCabxM9+8RNsLgslDRa27uwgJ2XIAH19o5w6c4GllbXlJpHyk0gkqKysRNKoqauv5vz58+j1BsLhCAaDBjkrY7fouOnQfkJphZ/+7iQrmSxenZGopCUjQaHNhmfFg6SBRDyIKEosLIb46MMucrkcckqk0FZMc70LV4EFQcmSk1MYjFoioRjxSAyvJ8T8nBs5pSAoEukMRONpYskUWSFHMpdEpQKNoiYcjKBSqUAAQbuGUEEBjSSiUqnJZCCbUJFNKKjUInJOIasCUQJJyJBKyCg5iaxKRNBpyKiy6HUaBAUSidR/yMr/tHzglVdeYWVlhc9//vM8+OCDPP744zz00EOcO3cOgIsXL/Lyyy9TXV2N1WrlvvvuA+D+++/n+9//PhUVFX8UsP9n/mf+Z/5n/mf+8/lPa71efvllVCoVFy9eZHh4mMcee+z/2ZhjsRgWiwWTyUQsFvt/3jebzX/UF/j5k+10Tc4iJkwUFOpYjfpRlARSpgizM0UyaGO0P45db+PM8gA3bNrC5hILl4aniIQU1rdvwG4vxeeep6CilNjcFHk6Le9OXSMwlWLn1jqmVr3kciYKLXpmh0fYeeNBUvEQFy6PsX1DE9/+55d46IGjZJIpPKtz1JUUIZoUfHKOuaFetm3bQTIYwGS3omhtnPxgDDkdpdRZSUtDBaPTwzz74hluv3EX6dgMletKsBS7SLmjVDdV4PEuo04J9A5fZnAozO4dHQQ8cxS68hF1Oty+BYqLXBitJi72DFPlKsW9ssqufVtYXZ3GaDWSigr455cRzQb8qRhRt5GJqXGam5sxGvNYXFlEFMM4nRaiwQB5eiuZlIggJvCuGKlv12DPK8VgiKNSZxifDaLVFzHYe42i/AqCwSyGPBjsWuD6oJcjR2uwmrXkaR1ICKzfuJFAYhqHqxT3UoBz53twCU4y5hy+aR9iYZAFb4SODbWsuNPEgh7y7TJFZes5ffYKm7c0UFzsYHBghMC8DUlnJxwZ5cKZBUpLCykv06LWpahqKCORziAJMHBlnCNHD/PO+bOkA2Fu3teJrINVj5+BMZnN9nxeOzdJcTqGWmdEp1Jxye1j75bNxN1+iqsrePn9D7j98H4yKwG06TRCjYuoVotWsmHW6Ignlrl6+RoGg4G7NVkOfuUBREspqXiKn/76WcJqmVTAjyyaESQdJpOV8aERImIOUjkK7fksLS0hSTpuvPFGzp4+QzaVwmiwMD07Q21DDZu2beHUqRMc3L6LQNDNwPAAtVX1eAJ+vvj5L/DE49+jsqmBvuFhtrZvJr+0nNvvvJmfP/sLrFoT2qiMZBb48ZMv0t5QRnVjEx6vn4ykY16J0WKw4g0sUddYgbO4CP+qnyJXAadOvM/E2CJ5+VV4g26ayp14oxGcxjzGJ2coqC1BTiQxWzQoaQ1LngBGvYjd5mRhdZGKqkqGR8fZ2N5BLBRkbHSS6qpKpqYmSOUyOFxFqHIKvqUl5KxIR2cVNVW1XDt3hfaNm/CuehmYmKC+uoprV7pJK/DZzz3Cr3/1KxIJhbLaUhamFpE0IhqNiMGox+sJoAIqK8uZn58nI4NKpWCzWxFFkYAvSFadAXHtM9lcEpvZggoBSZBwOM309UzhcFhQchmS8QQFhU6WF1cxGg3EU0mKXSXMzM4jSWC12XG7fQiCimxWIZcFFNDq1ahE1VrAUlYBVIhqCTmdRqVSYTDoiUTiqNUq0qkcGqtENptFIwqYTXrC/gQpOYdaUqFirZDBlGciEQuj1euIxVKoBQlZliGzVgyhVq+5T2RZ/ndZ+Z/KIM899xzPPvsszzzzDE1NTTzxxBPs3r2by5cvA3DmzBk2btxIR0cH586dI5fLsbS0RC6XIz8//4+C9XIwTtYocWlgHrXWhEKO8LId33SSalsri26BnErC7ChjXUEnN+05xPmuMbrOTrBj23bKahXUjqs0bxVxBy4zNrFC7/UFdm7pYPuhCpxVZjSCjWpXDV2XL7Hz0G6uHH+fubFl1jU2ENdF+d737yAWWyWdTnPl2gwBT4yakkKKrVluufE20tkkWq0Nh6OUWCiMww6VLZU4C/Ukoov4Y2u2Kp0jR/PWzZit5az0BRnvnWJyZJYT755l2ROnsaGTRz+5HaMxgdZqZjmQJt9ZiDsUIRTx4/MvYzaqWFhYobKmlGQ8jk6nY2kmSjoB4WCYRCZN19VpJhZnyMuzc+liD9eu9rGxs576hiLmJ+OIuQJWprII6XzUoo54ZoWV5WXOnT1JMq4wMjxLkcsBKpmVUBRfdJjSSi1zMx4qa9Z+tw0bWnGveFheXiad1vD+690UGZoZ757Gu+KneV0rjspqwu4oA7MjrITSFDnrmJ4NEwiGcDrtlLs6iET9rF/fiN4gMzjUgyRJVDaW4V6dpqiwmLbWDWRTSTa3tLBl23pWVhcwKDKVJWa27qpgaLCP+HwcW5kVXWUF8kyEtsoWdq3LUNsu8ue3d/Avj32RhM+PkEpz196dhN0z7Nuzg5Xo2gNGGYHJpUVWVhepsZgxZNJYjGGMygrlKgl9QsaQUePzJHl38Dr3fuWL/PLMe5yZnCYVyiDm2enrGaWxYT3bD99A1mkjlZKJhyMIubVgn61bt/Dqq69SU1dLLBFnbG6SrTu24LJa6Tt9htaiMk68dwpvMIyoM3JtZISNGzfxjW99i4jBwPWpWXQmMx87cgRjNsNz//oEBysauHnLHh64+XZayioByOgklj1uaqorMYhQZLSwsLDA3bffxcLoJHImxsTsIL964U2Ghlepq2nHs7rIvu0dbFnfiWi1cG1ggrL6JsJuGYvZSmdxHe11VQiZHIlEgr6+AUySmfmhSSocRcyMT7EwN095uRO3Z4H6hmo6NrQR8y6zpb2Bzt2b6NhZx/rGDvq6BgiG41RW1fDR/23vvKPjKs99/ey9pzeNRhr1Xi1ZllzkXsCmGEKHUAOkkUNJSCWHQEKAGwIhJCeFnJwQTkISIAkBQseAqcZFtizLktV7HY00o9Foetnl/iHie7Juzr3n3rtuHK+l57+995rR9+5vz0/ffr+3HGrFabNz4NAxKlfV0VRfgxxLoH70Lp9KyFiddtwFdiqqc8jKyaKguABndiajYxPY7BlokkZ9Uz2Li4v4Zv2AiIRIeVEJgqIiiHrmFkIEAkEWQwH6ekdwOGwoahpJBwoCk7OzZLhdIOmw2JwshHzodSJmkx1R0IMooAKCADoJnE7rkkgDgiAgigImk4l0Oo2cBlXVSKVSiCLo9fqTWiZoGpKgIxqMIIriUnKM2YDL5UAv6di4bgtmg510WsPpdCCISzdCkkREERRFQWf4f4wG+Y/ceeedPProo1x99dWk02l2795NQ0MDzc3NXH311dx+++18+9vf/i9/X2ebF70HzmheTVvHMGZzLhUlhUTCAXweHZpqxTPQR1vH+5Trbbz6/GusaNjI52/+JB09eygprCQwnc2eN9pIqyGyVmi4GxXE+Sks9hRtb7WyYZOJvukXuPj86xg86qN+w9kc6RxmVVkeYlzljRcP0dY7iE3VaFpdh5ph4s03T6B6dVglkVx3GYuhOC898y7xxSQ2s0RJfiYe/wioVsyWJX/+CkcupjhkCga0jCglW+sIhOfZfOY6RuZ6mFkY4oVXWhken6e4opDNZ5XjLoxT05CHICWw2gUaVlWzYXMVyQREF1Mkw0ZEQSYYDJJKmajLq+NTHz+H/CI7iaSVyuqVmC0wMd7LaP80O3bUklL8ZBdAbqmeRDqFI8NMljOT2pqV9PUPUlhUQ2DORzI8jcPqoKKwjCybGYtkREku7W63HD7I5u2bSBLEURRAsBvZ8/Z7xBIJ9IKfE60fEHOMkpBDXHLVenJLRJo3WlCIMjU1RTptwjM3jMOlY352hHQ8QYE7l0x7NoH5OVwOC3ZrLu48I9m5Ii+/shc9CfJz7CRJMBeexR9bQO8SOf/Sbdj0Rl797R9JqSrjMx6M7grmFmxMJud5/IX9GFw5ZLvcBHtHuOLCy3j16EEMytLjnUxrqEqaKoeVaocBi0tG51D41Beu564ffYNzbruYLZc30l9qRxZNfPHaT9P5+lHOXruOzpEhPnvVpzlr6yYK3U6e/MXPyTGZKch0gk7CGw1jNpnw+/2YzWaOdx5n94XnUlZWweDgIDfddhsTwQCl62s464Jz6RzoY8w7jSyr7D98lI3bt/Lpa67HhI7hnjEe/OmjHB/uYvvG7cT0MQK+SRQ1iSuxFB5amVuBL7DA6/veZz4dITI8Sa07n2DrCfrHvLz0+l421TZx+VlbufVz11G7Ipvisly0eJKinDy2b91AZU0RwXCQdCpMd8cApWuaeOdwG5VVNfhmw1xw/scILi4yFwqRVBIUFhSQTMrodDYsZif5+YUEA7N85tZPse/AYbavqcGlt2J3OekZnKCkqojX33qFkppiKtY2YjbrGOnqY2o+xC+feBr9Ry/0okGPwagjKyuTqqpyxoZHMJkl7A4LWTkukDSq6yrwer0YjUZEUcRgMKDKKkG/D0GRMSAiCRKKLJBMKJgMJs466ywMehOKCkaLFbvdTjgcZXExRDC4yEIggU6nI5lOEAyHEKSl79Xr9Oh0EolEAkmSEEURTdMQRRFB0JDTS2GhmgqyLCNJS6niACigKGA0GtFUHfFYGk2VUdIpouEwmqZw+PAhYrEIekSC8yEkUY9OFEmlVSRJYilEW+E/478s1k8++SSVlZWUl5fz1FNP8cwzz/DQQw8t/RHg9ttv59lnn+X555+nubn5vyzWathBiauBZNBLnsVN95E+EoIHo0smGB7D6+snr6SCaNRMaaWFYGiBp597n4eeeBmfP4sf3vcWaa+eVeUVVOlWsjAdpKK6kWgyl5DHRm7lOg7smWZ2UGByoQ9faITtl2XR1FhC7+wkQX+YinI3t1y9C3ellaJqG7LOR11jLW8eG2R6dJaZAT+iBhfs3kFHxySSYMA/7GHTmSto9bdDeuk26px2fvv6S7zy9nuUZrsoyzezemMuwYVBqhvzcTls7NxWj8OoUqC3gQfMyWIEVcOY6USv13Ps8HEicoBJzySammBhfpJ0MI6cDFFel4d/YoL5cS+rV7qxFSUwmlRqG8qYmpxlVUMNyWSM8opimtbX4g/78cymicZj+L1xUjod05MaB95rZ35mGodFpTjHQLGzikVvlKpqMzbL0nzmFeTwzvsHEAwWJLGItDSN1ekgrcHglI/uwSC//vkhivRZvP7cENvKdjLdY0ZN6jn3rIux2FRcOXbajo1RtbIURbDhC2hU1zcxMjHC8WNdnHnmCorLHXz8yt1sO2c9E1PT9I/4cRfkEJ/XEGd1rFDyqbfk0rpvgq1nXsY7w6NMBX0cbmknEJwgpyIHY50LVQgwkFokb90qXnzvEHZXDkvrJbBnOLELZpwGkWRsHs/iDImeACY1yUvPPMVgyxiP/euL9LaPEzvWzsqUncJzVhNNSShxmVePtNDW28Oif451RRWsKyij3l1CiTsTU1ojFA0xNztNJBJGEiRaDx9lemaWmek5bvvcLTSWVNJzaJCnfv8sTSsb0RIGfvDdR/DNL3Ds0DHuf+gh7Pl2brntJrIys0mmNaZjMrLHT5bDiN9pYMq99MZjKS5g9caNOB25VBaWsRgLcHi4ixcmj1JfVUOp4MY/vkh//xATXi/WHBf+UJAoIvc/+igt+z/EnZ9HTUkp0VCMvMpi7vvRT0koEE9EKCh388G7H7DuzNX8+JcPkFJSdHV2srAQpuPYCRJJGTkmMjg0y9O/e4WUKvDHF95hIbDIv/3sV2zZvIqyvCLERJxidx7dR45RXl1OTvlSfLWzIBNH7tKGvHdqCneOk6mROdoOD+DOysZqshMJx0ilFOw2F75pH66MTOxWB5JBIC2m2di8ipwcJ1dffRENtdVUFhYiCQIgIKsp3ntvLwDxmIyaTLMQCKOiYdabMJvNZLmySSZTCKJCUo5hMBhIyTIICpqgYjcbkCQJg9GMoGpIBom0qmCy6NA0cGRYWdrpE1C1pWxMvaTDaNQRDIVAENFLEka9DovViMNmIxZTWFxcBFEgGIwjKCKxxaV/CpIEggTmDBOC/h84gzHfbSMYmWV+0kCmw4pBZ2Z8eBGDw82hwQFys0Uat9eyfccmhk94qCiu4vrrNnLlxduZmJDpmxtivHcAQcvl52+2c2RfmN63FuicmMHv16GoAUpri/nkDdcTj8JEcpYH7t7D2saVxDxjeEJxMZ0IggAAIABJREFUMo35DPcNE4nEqXflsTLfijMvzNZd63hl/zGe23OY8cgI/QsTyHYdDWvKWb+6iKGD3WwsXkE6uNSKp+vEcc7fsY0sl5PWI8dZmB7HkWFiR91mcg359J2YAZObuAyyECchxOge6qa2qhan1U3viVlqa5y4jE5KG/IoXeVm1h9ANKnYrXbcVjvllTmYNSP1rgYU3wJ6q8Q7b7djsgsshFX2fTiF1zdF/9gIs/NeqiqyWF1dSnGhg55DnWTl6ahZUYKgL2JiWMZpyaZzuJeYkGZoeg6dcymCwjcfREZiaGKC7uEp0lIIWfCx6A/StW+OT92wi6aaOrBJbFuTy97WD9i7p43m+rW8+eI+3n/vBOPjfqpKyjAL4BtPEltwcfhQGxKZVK4o5djxdoKhWV544zV80QjDoxGMSj6xBZWuPpm+2TRD2iLvT7bzqZvOZWToBI3V9eRmuLBLGoX2QoKzFno7WzFbSrDY3ByY7sWcbWdtUwkFWUttvdBFcJyTh1STh9Vo4PF//j63f/0mnIsa8XgC5CRn71rHeRc0ctW2q3l476uU5xVCnsbA8aOEfXMspjRMqyspb67iRCKIe9NqxmZ8RJJp8soK8SUCFFfnUVTiIMfloLIoh1tv/RTzsTjeRJSF0BwvP/07pgcncRmMfPXOb6HFk3z9y7fx5L89zLbKGp599knWbGrka7fewgfvvEtENmNSjCgnhrFPLe0V+cf7MRnDuHN1TM1P8Ynrr+LaSy5B88t4Aj6qt66nJ+BHsZh46813iIdUkjGZvpFRzr7wYyQXEpAWONJymPKVZWSaLBBVEBSNszatRUpA7YoSMvQi4/v60KdA7zBQmJvBlo3NGHUSlaWFbFq3isL8bHbt2MKqujoGhsapqy9jbm6O0io3Hv8cnSe68fr8zC8s0lTfyI5Nm1hZVYNvdvbk739ocAxNkpid8zE/v8Do+DgIKgaTjjnfNIl0Cu/sDGlR5ooLP8bjd/wTwcUEiWQKySjS3duFP+BDFFV0koCIjlAwhm92gUQ8zWI0gcNuRk6lkDUNTUnh8/mx2awfrWx1KGoanV7AZLVgsZgQ9TqS8RTiR/4aJaVgkHSgCYg6kdBiFL1kAHXJXw2QUpJIBglNBFVTcDgcJBMyqaTCnC+89P1GI4ImYjAIyKoCqGiKiqqCpi09i6LwD1x1T5edgScQZ/O2OhxZFsKRJIFwGrNLorgsk7SWZHT4OC6bQkBNMh0KICcEZob6uPKWRjLNCo6iQtra+7GwgC7Tzm/ff4mQZxa3WYcShPD0PHNTHmpys/jMpdezrqaCF9/aS0HVauwmK1NDc6xq3oE+N5Ox2DgvvNnDnjfH6Ok5QUlRLUo8zo61Ozi2r4WMlMCRD6b5zdOHUA1ZlNZWkFVuAeBj529ncm6Ea649i7UbavEtJBn80MuhoXneeLmbpoYziSYW2LhxDeFwBAEzBQUVKAGB3PwKrr7xWvraF+lsH8YwrxCfjqAT9RQUVdJytJe55BxDMS+vtvXy4L/8ntFhmbkJLytXFJPtctHbNcOqNTkEfDYUTcdiKEo4vEhH7yjj/TLFxbXMjMt0DIyRk1WFYHJglKG7cwKzRY+qJXFlGgCwZBhpWFtLYN5CaZWNdEokO1/BYg9y85fO5Jnfv4U7G9re7CAp2bFaSskpyGY+5Ccz141mlcgqqSYYWuTEwALnXraV0YlxXnm+n3AoiqYTae84gcNupLiwgvb2XrwLaUbHPKi6FAXlcbbvLGfFShO5RU6m5/pYsbIcoz6Oxa5QWlZJTkM5OrNGRV0+5122g8JyI5esyaN5tUBGzMcCS91VKoyFBF8+zrHXjrKxrp7R/j6C817m4mEu23wmRv8EhVkZqNNBAqgMpeYon5Z5/KYvc+jFPeSb7Fy2+0zqoxZ2b9jO/Wfs5tkf/Cs1FVVEIxGqS4opy8lnRUkpWixNKh6maW0pHT1H2L1rEyM9A0RSGp+++VY0g5GYQeUrt38Bk9HKU889z7e+dhcftHbz8P3fpfXt9/jXR/+Fq66+jEO9Xdz7+L/ytX/9Mc/0dgBQtXELHUc7saeSjB3p5je//i1zsRApwQiyRvexdvq7JkgkYjRt3MrvnnqWbIeTbJebkalhkok0VZXlXHLZhWzashU1L4OaVQ3EpTgfHD2CI6eU7/ziEXzeRfa1HSIhpclxZLBt+xbu+fZXMZLk139+CsUgYbM5OdJylNmZWeobVpKf5+K8c8/g6affw2QtIJlMkefOJhwM0dbaRl9HF+2tbRTkFwIg6kREdAQXw5ht9qVsPlkhmU5gseoQxDRrV9VhQCQYitLR0c8/3/8o/oCPbc3r2PfOIcwWC7Kcxm6zIcsqSlrGYjGi0wmYzWbMZiPpZBw1DQoamrLUN1FW0yTkJAZJh6CpGAw6EokYmigQTcQ/+lwKvU5EkiRisTiJdBpFVdEZjKiqiqppGM1LvxdN1EjEU2gpEEWR+eACZpuZaCSF3mjEkWEnkUgip2QURUOnE9EbJJJxeWlTUVURATkp/6daecrF+q03OxgcHeOdo4cZmfJjyMhGFa1MDIcRUk50aTcj3RFmZ72cmArS3tvD9374AgqZvPP0B+w+5zz8qTEyM5PUNqwmFvFyy02fJWJw03nkOI6KVYRMdtJOK36ng+88/jhz8TRXX3U9C/MBXJlOZJONlnf6iLYNo4/osZoKWd+4HoNmZnB6imtu283B44NIDjuRtMyqxiyqqrJpP95JW2svHX1LZUWffPl5PnPlJZzoOoqjqJhYSiWzsJY1G9aybnUFMdUDignvdITMrFLm54K88cJbjE746Gnt4MMPP6BmzSoUnZ733+uh7dgEV+y+ggzByXXnXkKJM49st4tAOsqWszezdftqMqwaNeVFpCICudnFjA5Nk5Dn+eDtYbZsXU9haQEYLMzFIsTSEbz+AIEZmPAdZ/87fRztmKW2upbRoWlKC4tY9M0DcO72ZpKBRdautnBw7yCVhXn09fXgynXSPdDNJz5zHe++041QoBD1TlBYlMdCysf+A4NE5VE2rK9lZsxDdraLqupSfvi9Z7jq2vM54+xqNm8vxb84SaYrmxMdQwiKAbPOSSQaxzOTYLArxFhXgoF2HyM94yQiQXJzs8l0WcjJsRIIhWntmKR/8BiCIcHKugL+5ce/4WuXbsM/EqHn2Azvv3aYVYUFAMSzbfQIGvHifMyKgGQOMueZpqu1hZ996wGaVpVjNEg46qoZiPu44VO38Fzb24zNBrjvD7+hsKGWc7bt5IN39+AZn+bW3/+cL33/bjwzY2RlZDA9OcPE0BTzMwskE1Bdv4qug8MseFNMe+YQLTrGJ8aJxKJEY0GUhMyPf/QIHo+H9o4B5kIakqTni9/4JvbcQs7cfh4v/elFcsyZrF3ZzAUXnE/ioyis9n3vkJufw3wiRobTxM4zz2V/yzHyS8oIRJOMzcxhz5bIMhdQlVvIji0baD5zPZOzo3gnp7HnuAlFFnl73z58c5P0v3UYczhImc1NeXUxecVxTrz8Ehc2raStbxpDysLolB9ZlrnvG19j7ebV7Dx7J4mkTPeJLubnw0x7/CRicd7dewSvJ4Qzy4IsJijML6IgNxeL0UhjYyORdBJJr2f+o56DqqyBoqLXBMKBICASjy35k+d8s1jsFhZiQerrV6BTNCpW1VKxtYmzzlzHq2+/R0pNIIp6UgmZVCqFyWRA0VSsVutHpUdTFOcXkGF3IAkCoipjNhswmQxL/RKBeDyBgJ5wKIGok5AkPUlV/qgZwFLjbDmVxqg3ImqgkyRSqSSaUUDTaWRkLEW9GXXSUrsuQNAJiEZQUdG0pY3L4EIInU7C5rAhigJyWsVsNqNqS/dBUEFCh5b6z90g/8s4678HV17gpLyhGpMbjrScIBRXsIq5lJbnMNA2RGZuNWub6jnWeoyCsgb27T9Ahj6H/vFB6uuKkJI6sl06vFNj1K9YyUw8iUnvwDc7gN7t5t2X26lwmzljxwYUIYXH76M0J4fxiROs23UGcxPDSKZa3nj7XdasrcJqcdDW1sb21XUko35KS4tRySfBDAuhpfRvySgTmllgw7pm9KYsUqrC93/yFI0rnFxzwzm0HW2lMK8SDSsZxWW8+cQf+Oo9N7P/wF6qalcSmF1kcrgfo1WPXhWw1mRSYnWjyXFcpRIvvnuMtdaVzCdCBJNRRCFNKBJmVUEuJesqOLD/IO6cIjwzE2Tmi7z2ko/LLt3A3nfbMIvZ6DLTDB9bYN0ZGRTmF2CQVAaHZ5EVCysr82g9GsRgDRDw6LFaBWa8U9Q3FJHlshIOxXnmmWHu+tIOAvFunJmr6e6cZPOOPDyTfhz2bDRdgvHJAIF5A4Iis/HsMob6xxidVKkrK2Lvng+44vrtZGdlMtzdictlwJ23ku4TQ1RWZYLOzcEPh/AFvMx6gjhMFgLhKLKS4uordjMy1YfVbkNOq1SXZtLT3Y8r14rT6aKyspzDR/bRtGILkUiE2ZkIGQ6Zvu4ojRvyCadVqt15tE8mUSeS/O6lN7j1odt55bu/o6nJTX6DjRX5NayoqOK9Z/ZgMtrpnh1hYSGNL+jHYnExGk3w3BO/4J7vPEDcamRNYRl/fO4Ntm+rQ9HMfOHGG/nZz3/Jpg3NCGYXrR0HKcnPw+v1cvjIEWyOLL78hdv59jfvprSmBlkUycvMBTHBZy+/gu7BIeKROOeecw7WvCJu/fznaVjTwO9/9yeKivJRJch2OEkn04QWvWzduo5EPM1Lrx6guCqbAofA+pXrGBmaZWQxgi8wT0F5MQN9/exYv4nh8TFyXXZ6Oke55MrdxOZD7Gs7RCohkJRT5LlzUSWB6vpKylbW8YdfPMb6pjpuuPZaLJqD1rdeJ6wXGI6ESAR9eMb8uLLsVJS4aWsf5JobL+fVl1twZlppO3gcDDIWvYWSyhWMjPbhLhSwWPVsXH0WT/zqeTIybJgsFgQkZma9CDqJdCyF051JLBxBSctI0lI2qqgXsWQtpW4nY0lCMZmi3DzMRolYMsWG5krefu0QucVuAoEIsqySTqaQ9DrQFCSjiVg4hl6vRxC0pVV0GmQ5jTvHiT8YxmnKIq2EWEynMGgCgiCRkNOYJQlVpyOdSoAmIXwUnWE06kmlZJIJGavVitlmJxxdxG63k4pFCfgiZGTaWNFQTceJLgRBQEFBUAVQdAiqgpJWEUQNDdDrdMTjaXJzs5j1Li2OTCYDippGVUCW1b+plad8Za1JFuRFeOv3bbhjuVQUGRBMERpKN2G1NXDGxs288HoLvcNRGuvLmZmbpaN3iNwcM35PkEy3nkOdwwzMqrR0j/Daq0dJxqKUVZRTmVXML376zzSurGJuepHKihoaaiqpaCxgy84L8I0nSS3qaNySi0kvk4gskO/O4qLzNzETmad55yX4FtLYbDL79vahC4VwCCYMopXsrEqGp+fwBhd48tU/A3DDVRcjplNsWt+AYIgx4RshuRhGynTxb48/zcH9U3Qe6EWNpwnGUlRV11HVUA5hI8fauijMz8M7G8apWGnvP0pL6xGycpLE00nWb1mB15egq7WfaFzjRPsxHJkmBEsma3a4Od53nPl5A+s3VTPe46d6jZmpoUWSiylaD7RTlFNOVUUxv3p8PzvOqcEznMZVYEOU0lRV1oEiEI4EcHxUdW8yNEY8Xc6PfvQudWszOLS/m5rVTQyPjWFEpqwki8XYBO5SG0NH23BINlKRMEcODdC8uZauYxEGBk9gdxrQlFIGeybJyzdwqL2TN/YeZf/BEwQXIoiaiShQ21RBTV0JR1s6KC1yI4kKfu8soWgER1YmdStW4fUs0nqkg8UFleP9XtqOT/LOvm7GQimSNoH+QRGHqKPrWA/ZgpGsxvUAPLjybOwuO/VbNlIUsjDYOcOvfvAbWqYn+bBrhNrSLexs2opJdJCfU0qFq4j3X3yJSy++DNnjwzvlY9fHmhnxR3GV5nDrf/s20/EQz+7Zw2tvvElGhosPD7Zy5Ggvt3/hn/nDr37NvXffxQPfvZ94PIpneATv7CQXb9/GOy37KK0tobqmlKHRHva3HSCVjGHTBM6/6BzC4TCGlIzOqaOsrgLRbiaUVsnIW3IdlBcXEVNVwjqBA8PDeKNhFE1FS8hYTRbGfbO4s7IY7Bni1k9czGRPB2NDYxh0dlbVr8TlyCCRSLAwH2Rq3s/c0R4+//HruWj9du79zg/586t/Iv+c9fz7e3vxjI5z5HA/D//kAewZMkJOJjuaqznx9nvcd9ctXHvxx/iXHz0Mgg4py0mKFLmFBWxctxOHKY/W/S2cc952FkNRZrx+pmY8KGkVOb4URxwJBJfcCQgoGgg6CVXQUNMKyXgCTdbIdpiIRefRiXqM6Dl0oBPRqMc7M4cA6E0SGU47gqAhGvTIyRSgLUWNqCoqCoqWxmw1EgonkWMQTsdI6BJk2rJIpMFqtqLXBEQZDIgIqoCmaciiQCKtEE8llwRYhlQ8gc/ro7SkAkGTEIWlPZ40CsfaOtHrBRRNQxL1qKoCikwyKaMoKgISSlojHksjCjDvX6qNIggCqVQKTRVO9nT8W5xysS4qMzLnHaW6LI+kEKOiOJeK8nwe/tETDAwNMzHj5xOfuBAt5eeDowfItJspzHWSkgWGh2bxL8pk5mUSCIWJhuKctWs9IxOTvPLKh7z2fgt33vszjvYMMOqf5NlX36FzbJrh42Mc7GvB5BLom/ejzII1ZcDtyqAsS8/IaC+1uRVMdY1TXVXHoZ5hVqwqZiqyiMkGxasqqG6opWOyl/72Tq49/zwAWj7sprdjlAPvHME3E6Q4K5PEbCdbdq5AEw3EVdjzfgv7Dh0nI6+Y7t5BrCY77753GJ0Kz7z6BitMlcynFjn/kt00NtfSvKIZsz7OoTcP0jE2QldfP03lddTXNRFPKthDsDqrkLO3rcXuMHL04CSOYhsOax5lZQXIxgzC827G+hbIyrRTWpnPk//2Dus2NdLdPorBkcGbe3qZmpwjFpZJp5Z8cItemf1vdfGlL26n+3iAkqoCWt49SGF+Pj1DUwyOjrNuXROeCR8D03q8s2lqK/LwhWc43rFAMNVFRWU1Kk7GT4ywGAky4Y1hF8v49GfXcu+DlxBNaCQUjfKCAnZtX8XkRBxvOMXrL3ezOCezZuU6tASkQmZaDgxgt2aDmuLij53DG39oRU1mQFSHPmQkHAqyoIV4f2CQjE3b6B6aYv+xowDc+JMfg0mgq6WFrc1NXHn9mSRzBS674AKqCzKR6rOYLrPhtRk499ormI4H+P3r+/j9089wotvDJ7/8Jd5+/QhqOM7LT7xGZWYxeVmZlFSUMTY5QWd7D/H5GPN+P2+89Ty/+/VTuHOK+d4jP+Oqaz6JKJgZHRjnqddeZ6B/jO8/+AiEVQqtLma6BrnknLPJLSlhW2UT4YU4c8k4F27diW98FC2eIrUYY6izG4DCgixSMY3BYQ8bt2xhy/o1rG5YQd/xHuxGE+GZOYqcTupW1pOVn8uh1mFEvQGXM5e29jbSaRnJqqe4ogR/3wStY7388qXn2T/l4cKPnc/o5BzWKYG6LDehyQVWlGdRZ9bhtjg5eOgwh4f9vD/p44W9L9Ky/xBVTifZKyvJynBg1Amk43H8vjiJtJmCikoS4Xlu/MTFCLKChISk1yHplp4xRQWzyYAkaWiiQlqVMZlMpKJJDOKSXzgeVZFVibGJUaY8HoLhNDqTHpPJSjKRxqS3MO9bJB1XUJMaOnEp/E7T0iiKQiImYbHbkaUkeaUOrrnxUipX2dFFRRJKEld+NiaLgZRORbNAQkxgsJvJK83HaNOjs5qwODLILSxC0onoTUZsGRZ6u7rRNIXA/JJ7StZUTA47sqSRTskoKQVNAVESEFlKRU+nlsL9JN1SZxi9XkIQlhwbFpMVURBOxnf/LU65WDvsZspqqqiuryGjwEk67kBORfnBo3eQFuZ46dlXeOmFtzh75xYOtp5gbjKMK8OEqujZtrOZbTs3EonIGESB8uoKDh9sJy8vj8oVFXgnglSUZ3L2WedRWlrCP936KWxWB9NjsyQ8EWIzfkoLCvjT86+TWVSEiJm+I8e5ZPc1OHOLMdlUjrW2s3ptHZFoHMGkI5Uy89pTr7PvyFGyJBuRxRBHW/oBCMgLSHYrWUVF5BaVMzk5TY6jiKGpEcySyKaN5RRWlyGImXT09mOwmXjt9ReJRuKgszM6GeCttw+TYc3njb378QeiePvn0ExmAjGRhlVrMOgyef2N90grGk1NTSQWHBhiebz5ShuakMBo9FNdZ8CdZaAw18zQdBcbNlbSO9LHYJsHvQsKSg0MDfSzck053qkAV39iI35flOLCRv7wh30A5JQU8rk7LubD1jYKyh14fIvE4irTnkkybNnMzSTo7Owip8BFYb6Tjt5uXPlWtm/dSFqOcfaunRxuaaGzbZYN28sQDQn8gVlKyiSmhkcZG+7nc/+0Gy2t4+iRXmYmPHz+C9dSUZWPZNIILii88vJh2o55GZicwx9KE0rFcblL2PPG21x3cxOr1hZQmO/g/XeOYVLNrMkqZlfFSkpIUOyd4Edf+yQAF27dRWNtDUc9i3zw2z+T/NX7rKgo5fBQB5qkY/H4IDttZayrXEFlcSFiUmMiFqJoVSO15fnsfXMP5567i69//Ut84rMXEU36aD/czcJilLQSYtsZjWw9ZwcVK4opLl/B8eEhylasxGAwUFtbjsVhJCUI5GZkIQgatgwnrR3tfOvhR2jtOw76FHmFeTz86A/IyDXRVFnCY7/9dyoryghHIswGZ/EGlmpzeybmsZsckJCZHhxkcnAQVUljs+kpzC9g+/YddI8Pc+x4Nwdb2znn/F30TY5yYrgTWVY4+6xzqamrxJlpRRNNuIwS99z9JYKT0yipOH987BcMT3aiqAksZZk01a3g4qs/x8RAELOqx2W2YdXM+IaCtHT2cWywnXKjnpqGXEyKQDIc5mh7G955PwN9g8hJlZ7jx9m4YdXS6jQt81HcGw6nnWgsQVZmNpomYM+wY9QZkFPg90WQVRHBKKCk0kiiiYzMDBxOB2abkWRMRpZlAqEIsrKUqCIIIuFQDEGQSKWTGPQCqAqKrJFXUEhSBn9ylluuuYZH//0RLGqIUDxIIh7FIEF+QR46QUQQBObnfeTk5GAymDBIBqYnp1FViEZjRKNRbE4r8XgMi2VpZb2mcTXxWBgtpcNkMKLKKpKoJ5mQUTVIpmREaWnFDqDTgaqqGI1LSTWxWAxFU07WGvlbnHKxVlIKo5NeBkZGCccW8c9OsWXDGXzlph+SobNw5XV5ZORGef7ZvWxsaGTT1i2c6BnG71tATsWZmhylrCKXlY0raO8awpadQcuRY1j0VnZtq6f9+DSeiVEGh8Z55rmXGOvoIWw2YxKdrKpbzcU7L2DLzi0sKrMEhST5pWs4cnyM3rFR3mtpxVlkZ3EygMvtwDsyS2vXAE1rNtHfc4KFBS8V5UXorEudpxvWrqCktgrJlkl7ey/+xSiLVoHmuiYqawuIhgOoShjFqCIm4sz6PKxZv56S8kLaOvu59ebbSGQYaaqu4pzzz2Lbzu0EEnp6OoNUVdcwOtRPVqaLCy64gBxTJsMnhviws5VF4yT1q6s5cSjC+nU7GOyScWUYULQYZzTU0D8zgcOWx+iEj7LsIpJKEllMEgmGKa3MYHJqiB1nXMKLLx3g/IuXCgb5Az2QilBckMXE1CQZdju2LBHRLODKy2V6bpHiogIiQQ919W4uvGArpaVZxBMebvz0drqPt1JT3kDzpjJ6PLOsbGxi+9ZNTM54WfCHsThEEvOz3H3nxZRU5tJ+fIbfP/FLtq6v5cxdazA5khRWuUgIabbvaKB+ZT7ODCtjk3OU1WwimljEbkmyfmsdrswMylxlGAsE3jh0nFdff5WANs++X/8RgPeOH+HA8ABJzwK76tfzVO8IteYK8iUXSl05r4z38s/P/AJZVHjiqd9w7gU7+cTFl1Kcl0Xjyjp2NNdz1WW7efxnjxFZCNHctIoVtWVs27oGvWrkgw8Ok2NWqclwUu/ORqfE6O9pY/fO9WxqrKKufhVr1zTw1rsfUl2xjlhC5K2jxzj/Yxdx4UWXkk4kqSmpJJyGNWvWoDPasKAHWSLTmcfqxs3kZxUBMDvjZcYzgd8fwGa2YRRNtB3p5J57/xujY9Ps2fs647N+SqrKGPD5ae/tw2q04LBkYDZl8Ic/PcPBDz5ky+pNlJZk419I8Pi/PYHNasImCtx573287+njwl3nUGFQ+OYFF3HN5Tcg64MkggpaNMY111xKzO+jvLaYqDHM+Wdv4MFP3kR9TSFOm53F+QVmxybJyiolEkvjys7iRMcAsiKDKKBp/yM70GQy4Q8sYDXaUZMKCwtBCouKMBoNCIKAEQklrRKLxVGUNAgp0hEZRU0i6XVLER4SGEwGEok4KnxUZ1uP2WJDMIikk1H8c1EScYEzLqrijz9/jq/f830ee/xhsqxOdKIVJQ2e0Sl0mp5EOI5BMDEz7cUo6fDPzyGoAq5MO6oGiqrizM4mGo4gsJSXcKy1A7POhJxKkE6mEdCTSi6VYDWYRExWHaJRQtD9JWtRIi2nSaZTCJLIivpaqmrKEaW/7a+GfwCxDoViWK1WZuZmaWxsZKLXzoljY9SvyqamMp9EJIpnPERDfROvvXwARU1R21CEpgnMzswxMTpDPJLCMzXP5k1rMZskCovy6R8YYGrGQ21tBX0DQ+QW5DMxNU5VURHhcIyFSJz2zg4e+fEv8AyNsK2qAF0oxtysnz/9/nkGevsoLMxCVSQOfHiE/v5e1qyrxZBhYPO2daysdnPdtTuxZyVZt2oNACZJo7ezi3nvHNdecQW7zjiTw4cP03WiAxGIRcPs3LaG4gIjOW4LmXlVvPdBNxecs5sdZ29kZKCXnNxsvF4PgcA8k5OTWO1SxcPdAAAIl0lEQVQODGYzCwsLGI1G2nq7OHD0KN19vaRUjcw8M0l1jsVwiE/ens+JznaisQhT0ws47FkMDfrIcRcz6fGzel0NM1PjVNdWkJnpIDcvh8WFEAY7HD3ezsrmXDIL4wA4zU5i8zEq8ioR4xK+KQ+FBVk4HDo83m4uu3wH2dk68vLNhCJeItF5Rsf7MJhSyPIsVVUFjI0Pc7T9KDqzjT8++wYTk/NUVFdhtWQy2DvJ4uIsgflZ5oPzjI8EmBpP0NPTSZoRNp9RxJbtpTSvL0ckgsuupyjXjQETk8MzmAzZ9PWOMj09TabLQSy0iNtoYOfWUjZtbuLaKz+Ode2SzzozmCI7LvDgD++hO+pl7YYGKuvqsRnsSBGNgDdEhSOXmy65ip/f+xAOXxSdLGAy6liY9+GfD/L9H/6YGz95PYG5OQ7sP0R5TRUvvfYqazauweHOxJOIUNrUwGwizMZdW/jcpz/Fqy++SHjOh9tiZ3pgjPqNa7jos1dRVFeB0+bgiV8/yfcefIR1VU1ccsWVZNgNbF7fzMSYh0RK4qVX9zI+PssLf34V31wIgKqyXM7cup5kJEJXVxcjY+Mk0zLfvOdb+ObncNodfOrjVxMYnWJmYorFwAKhUIh4IorBaMRoNnH27nN57PFfMrMQJJKCydkgXYNDPP7nVxkdGOOs9WuYj3j5xKVbCUkyT+17n7LqevJKiqhd28jI7ARbd20ns9LN5R87D8+xfo60dbJu3Uo2rF/NbbfehNls4MiRQ5zoGmJkeAyDTkQSl7Iw/1IDPx6PU1RQiNGoR1ZSJBIxVBUmvR5kTSUZSxGOxrBYrEiiwMYNzQgaJBUVvd6IyWRCVRWMJumjjMKlGGVBEEik0kQiERx2PSazfqmOiJiiKKOa6Sk/hgy45tq7WLW6DkGUlyIzFAkllcao1xP7qNRDJB4mJycbQScQCYVxZthxZbmIx5MoGhQULEUcyXKSVCp1Mmb6L+4MRVE/ui6jadrJ9HRZVvjoNiBo0NfXh85owGwz/qdaecqjQZZZZplllvnfc8pX1ssss8wyy/zvWRbrZZZZZpnTgGWxXmaZZZY5DVgW62WWWWaZ04BlsV5mmWWWOQ1YFutllllmmdOA/2UPxv9fqKrKfffdR39/PwaDgQceeOC0aa7b0dHBD37wA5588knGx8f5xje+gSAIVFdXc++99yKKIj/72c94//330el03H333TQ2Np7qYf8V6XSau+++m+npaVKpFLfeeitVVVWnpS2KovCtb32L0dFRJEnioYceQtO009KWvzA/P8/ll1/Or3/9a3Q63Wlry6WXXnqyF2tRURFXX3013/3ud5EkiW3btvGFL3zhtNGCxx57jHfffZd0Os21117Lhg0b/v7zop0C3nzzTe3OO+/UNE3T2tvbtVtuueVUDOP/mF/+8pfahRdeqF155ZWapmnazTffrLW0tGiapmn33HOP9tZbb2ldXV3aDTfcoKmqqk1PT2uXX375qRzy3+S5557THnjgAU3TNC0QCGhnnHHGaWvL3r17tW984xuapmlaS0uLdsstt5y2tmiapqVSKe22227Tzj33XG1oaOi0tSWRSGiXXHLJX527+OKLtfHxcU1VVe2mm27Surq6TgstaGlp0W6++WZNURQtEoloP/3pT0/JvJwSN0hbWxvbt28HYPXq1XR1dZ2KYfwfU1JSwqOPPnryuLu7mw0bNgCwY8cODh48SFtbG9u2bUMQBAoKClAU5a86wv8jcN555/GlL33p5LEkSaetLWeffTbf+c53APB4PGRnZ5+2tgA8/PDDXHPNNeTkLFU/PF1t6evrIx6P85nPfIYbb7yR1tZWUqkUJSUlCILAtm3bOHTo0GmhBfv376empobPf/7z3HLLLZx55pmnZF5OiVhHIhFsNtvJY0laShf9R2f37t3odP/Dc6RpGoKwlEJrtVoJh8P/k21/Of+PhNVqxWazEYlE+OIXv8iXv/zl09YWAJ1Ox5133sl3vvMddu/efdra8uc//xmXy3VSvOD0fcZMJhOf/exn+dWvfsX999/PXXfdhdlsPnn9P7PlH1ELFhYW6Orq4ic/+Qn3338/d9xxxymZl1Pis7bZbESj0ZPHqqr+lQieLvzH2rPRaBSHw/E/2RaNRk/67f6RmJmZ4fOf/zzXXXcdF110EY888sjJa6ebLbC0Ir3jjju46qqrSCaTJ8+fTrY8//zzCILAoUOH6O3t5c477/yrldnpZEt5eTmlpaUIgkB5eTl2u51gMHjy+l9sSSQS//Ba4HQ6qaiowGAwUFFRgdFoxOv1nrz+95qXU7KyXrt2Lfv2LZXiPH78ODU1NadiGP/P1NfXc/jwYQD27dtHc3Mza9euZf/+/aiqisfjQVVVXC7XKR7pX+P3+/nMZz7D17/+dT7+8Y8Dp68tL774Io899hgAZrMZQRBoaGg4LW15+umneeqpp3jyySepq6vj4YcfZseOHaelLc899xzf+973AJidnSUej2OxWJiYmEDTNPbv33/Sln90LVi3bh0ffvghmqadtGXz5s1/93k5JYWc/rIDPDAwgKZpPPjgg1RWVv69h/F/xdTUFF/96lf505/+xOjoKPfccw/pdJqKigoeeOABJEni0UcfZd++faiqyl133UVzc/OpHvZf8cADD7Bnzx4qKipOnvvmN7/JAw88cNrZEovFuOuuu/D7l/oEfu5zn6OysvK0nJf/yA033MB9992HKIqnpS2pVIq77roLj8eDIAjccccdiKLIgw8+iKIobNu2ja985SunjRZ8//vf5/Dhw2iaxle+8hWKior+7vOyXHVvmWWWWeY0YDkpZpllllnmNGBZrJdZZpllTgOWxXqZZZZZ5jRgWayXWWaZZU4DlsV6mWWWWeY0YFmsl1lmmWVOA5bFepllllnmNGBZrJdZZpllTgP+OyRT+1Y0haOTAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(china)\n", + "plt.savefig('china.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T02:39:41.458958Z", + "start_time": "2018-06-13T02:39:41.254967Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(china_recolored)\n", + "plt.savefig('china_recolored.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-13T02:40:35.658870Z", + "start_time": "2018-06-13T02:40:35.650858Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(159419, 59392, 2.6841830549568964)" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "os.path.getsize('china.png'), os.path.getsize('china_recolored.png'), os.path.getsize('china.png') / os.path.getsize('china_recolored.png'), " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": false, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}