fixed indentation issue in knnplots.py

Author: chihchunchen

knnplots.py

@@ -4,58 +4,49 @@
 import numpy as np
 
 def plotvector(XTrain, yTrain, XTest, yTest, weights, upperLim = 310):
-	results = []
-	for n in range(1, upperLim, 4):
-    	    clf = neighbors.KNeighborsClassifier(n_neighbors = n, weights = weights)
-    	    clf = clf.fit(XTrain, yTrain)
-    	    preds = clf.predict(XTest)
-    	    accuracy = clf.score(XTest, yTest)
-    	    results.append([n, accuracy])
- 
-	results = np.array(results)
-	return(results)
+    results = []
+    for n in range(1, upperLim, 4):
+        clf = neighbors.KNeighborsClassifier(n_neighbors = n, weights = weights)
+        clf = clf.fit(XTrain, yTrain)
+        preds = clf.predict(XTest)
+        accuracy = clf.score(XTest, yTest)
+        results.append([n, accuracy])
+    results = np.array(results)
+    return(results)
 
 def plotaccuracy(XTrain, yTrain, XTest, yTest, upperLim):
-	pltvector1 = plotvector(XTrain, yTrain, XTest, yTest, weights = "uniform")
-	pltvector2 = plotvector(XTrain, yTrain, XTest, yTest, weights = "distance")
-	line1 = plt.plot(pltvector1[:,0], pltvector1[:,1], label = "uniform")
-	line2 = plt.plot(pltvector2[:,0], pltvector2[:,1],  label = "distance")
-	plt.legend(loc=3)
-	plt.ylim(0.5, 1)
-	plt.title("Accuracy with Increasing K")
-	plt.show()
+    pltvector1 = plotvector(XTrain, yTrain, XTest, yTest, weights = "uniform")
+    pltvector2 = plotvector(XTrain, yTrain, XTest, yTest, weights = "distance")
+    line1 = plt.plot(pltvector1[:,0], pltvector1[:,1], label = "uniform")
+    line2 = plt.plot(pltvector2[:,0], pltvector2[:,1],  label = "distance")
+    plt.legend(loc=3)
+    plt.ylim(0.5, 1)
+    plt.title("Accuracy with Increasing K")
+    plt.show()
 
 
 
 def decisionplot(XTrain, yTrain, n_neighbors, weights):
-	h = .02  # step size in the mesh
-    
-	Xtrain = XTrain[:, :2] # we only take the first two features.
-    
-	# Create color maps
-	cmap_light = ListedColormap(["#FFAAAA", "#AAFFAA", "#AAAAFF"])
-	cmap_bold = ListedColormap(["#FF0000", "#00FF00", "#0000FF"])
-
-	clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
-	clf.fit(Xtrain, yTrain)
-
-	# Plot the decision boundary. For that, we will assign a color to each
-	# point in the mesh [x_min, m_max]x[y_min, y_max].
-	x_min, x_max = Xtrain[:, 0].min() - 1, Xtrain[:, 0].max() + 1
-	y_min, y_max = Xtrain[:, 1].min() - 1, Xtrain[:, 1].max() + 1
-	xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
-                     	np.arange(y_min, y_max, h))
-	Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
-
-	# Put the result into a color plot
-	Z = Z.reshape(xx.shape)
-	plt.figure()
-	plt.pcolormesh(xx, yy, Z, cmap = cmap_light)
-
-	# Plot also the training points
-	plt.scatter(Xtrain[:, 0], Xtrain[:, 1], c = yTrain, cmap = cmap_bold)
-	plt.xlim(xx.min(), xx.max())
-	plt.ylim(yy.min(), yy.max())
-	plt.title("2-Class classification (k = %i, weights = '%s')"
-          	% (n_neighbors, weights))
-	plt.show()
+    h = .02  # step size in the mesh
+    Xtrain = XTrain[:, :2] # we only take the first two features.
+    # Create color maps
+    cmap_light = ListedColormap(["#FFAAAA", "#AAFFAA", "#AAAAFF"])
+    cmap_bold = ListedColormap(["#FF0000", "#00FF00", "#0000FF"])
+    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
+    clf.fit(Xtrain, yTrain)
+    # Plot the decision boundary. For that, we will assign a color to each
+    # point in the mesh [x_min, m_max]x[y_min, y_max].
+    x_min, x_max = Xtrain[:, 0].min() - 1, Xtrain[:, 0].max() + 1
+    y_min, y_max = Xtrain[:, 1].min() - 1, Xtrain[:, 1].max() + 1
+    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),  np.arange(y_min, y_max, h))
+    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
+    # Put the result into a color plot
+    Z = Z.reshape(xx.shape)
+    plt.figure()
+    plt.pcolormesh(xx, yy, Z, cmap = cmap_light)
+    # Plot also the training points
+    plt.scatter(Xtrain[:, 0], Xtrain[:, 1], c = yTrain, cmap = cmap_bold)
+    plt.xlim(xx.min(), xx.max())
+    plt.ylim(yy.min(), yy.max())
+    plt.title("2-Class classification (k = %i, weights = '%s')" % (n_neighbors, weights))
+    plt.show()