📝 some updates

Author: lawlite19

LogisticRegression/LogisticRegression_scikit-learn.py

@@ -2,7 +2,8 @@
 
 from sklearn.linear_model import LogisticRegression
 from sklearn.preprocessing import StandardScaler
-from sklearn.cross_validation import train_test_split
+# from sklearn.cross_validation import train_test_split  # 0.18版本之后废弃
+from sklearn.model_selection import train_test_split
 import numpy as np
 
 def logisticRegression():

SVM/SVM_scikit-learn.py

@@ -1,71 +1,69 @@
+#-*- coding: utf-8 -*-
 import numpy as np
 from scipy import io as spio
 from matplotlib import pyplot as plt
 from sklearn import svm
 
+
 def SVM():
-    '''data1——线性分类'''
+    '''data1——线性分类'''
     data1 = spio.loadmat('data1.mat')
     X = data1['X']
     y = data1['y']
     y = np.ravel(y)
-    plot_data(X,y)
-    
-    model = svm.SVC(C=1.0,kernel='linear').fit(X,y) # 指定核函数为线性核函数
-    plot_decisionBoundary(X, y, model)  # 画决策边界
-    '''data2——非线性分类'''
+    plot_data(X, y)
+
+    model = svm.SVC(C=1.0, kernel='linear').fit(X, y)  # 指定核函数为线性核函数
+    plot_decisionBoundary(X, y, model)  # 画决策边界
+    '''data2——非线性分类'''
     data2 = spio.loadmat('data2.mat')
     X = data2['X']
     y = data2['y']
     y = np.ravel(y)
-    plt = plot_data(X,y)
+    plt = plot_data(X, y)
     plt.show()
-    
-    model = svm.SVC(gamma=100).fit(X,y)     # gamma为核函数的系数，值越大拟合的越好
-    plot_decisionBoundary(X, y, model,class_='notLinear')   # 画决策边界
-    
-    
-    
-# 作图
-def plot_data(X,y):
-    plt.figure(figsize=(10,8))
-    pos = np.where(y==1)    # 找到y=1的位置
-    neg = np.where(y==0)    # 找到y=0的位置
-    p1, = plt.plot(np.ravel(X[pos,0]),np.ravel(X[pos,1]),'ro',markersize=8)
-    p2, = plt.plot(np.ravel(X[neg,0]),np.ravel(X[neg,1]),'g^',markersize=8)
+
+    model = svm.SVC(gamma=100).fit(X, y)  # gamma为核函数的系数，值越大拟合的越好
+    plot_decisionBoundary(X, y, model, class_='notLinear')  # 画决策边界
+
+
+# 作图
+def plot_data(X, y):
+    plt.figure(figsize=(10, 8))
+    pos = np.where(y == 1)  # 找到y=1的位置
+    neg = np.where(y == 0)  # 找到y=0的位置
+    p1, = plt.plot(np.ravel(X[pos, 0]), np.ravel(X[pos, 1]), 'ro', markersize=8)
+    p2, = plt.plot(np.ravel(X[neg, 0]), np.ravel(X[neg, 1]), 'g^', markersize=8)
     plt.xlabel("X1")
     plt.ylabel("X2")
-    plt.legend([p1,p2],["y==1","y==0"])
+    plt.legend([p1, p2], ["y==1", "y==0"])
     return plt
-    
-# 画决策边界
-def plot_decisionBoundary(X,y,model,class_='linear'):
+
+
+# 画决策边界
+def plot_decisionBoundary(X, y, model, class_='linear'):
     plt = plot_data(X, y)
-    
-    # 线性边界        
-    if class_=='linear':
+
+    # 线性边界        
+    if class_ == 'linear':
         w = model.coef_
         b = model.intercept_
-        xp = np.linspace(np.min(X[:,0]),np.max(X[:,0]),100)
-        yp = -(w[0,0]*xp+b)/w[0,1]
-        plt.plot(xp,yp,'b-',linewidth=2.0)
+        xp = np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), 100)
+        yp = -(w[0, 0] * xp + b) / w[0, 1]
+        plt.plot(xp, yp, 'b-', linewidth=2.0)
         plt.show()
-    else:  # 非线性边界
-        x_1 = np.transpose(np.linspace(np.min(X[:,0]),np.max(X[:,0]),100).reshape(1,-1))
-        x_2 = np.transpose(np.linspace(np.min(X[:,1]),np.max(X[:,1]),100).reshape(1,-1))
-        X1,X2 = np.meshgrid(x_1,x_2)
+    else:  # 非线性边界
+        x_1 = np.transpose(np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), 100).reshape(1, -1))
+        x_2 = np.transpose(np.linspace(np.min(X[:, 1]), np.max(X[:, 1]), 100).reshape(1, -1))
+        X1, X2 = np.meshgrid(x_1, x_2)
         vals = np.zeros(X1.shape)
         for i in range(X1.shape[1]):
-            this_X = np.hstack((X1[:,i].reshape(-1,1),X2[:,i].reshape(-1,1)))
-            vals[:,i] = model.predict(this_X)
-        
-        plt.contour(X1,X2,vals,[0,1],color='blue')
+            this_X = np.hstack((X1[:, i].reshape(-1, 1), X2[:, i].reshape(-1, 1)))
+            vals[:, i] = model.predict(this_X)
+
+        plt.contour(X1, X2, vals, [0, 1], color='blue')
         plt.show()
-    
 
 
 if __name__ == "__main__":
-    SVM()
-    
-    
-    
+    SVM()