diff --git a/SVM/SVM_scikit-learn.py b/SVM/SVM_scikit-learn.py
index 3cdbb6a..ab33fe8 100644
--- a/SVM/SVM_scikit-learn.py
+++ b/SVM/SVM_scikit-learn.py
@@ -4,16 +4,16 @@
 from sklearn import svm
 
 def SVM():
-    '''data1�������Է���'''
+    '''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) # ָ���˺���Ϊ���Ժ˺���
-    plot_decisionBoundary(X, y, model)  # �����߽߱�
-    '''data2���������Է���'''
+    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']
@@ -21,16 +21,16 @@ def SVM():
     plt = plot_data(X,y)
     plt.show()
     
-    model = svm.SVC(gamma=100).fit(X,y)     # gammaΪ�˺�����ϵ����ֵԽ����ϵ�Խ��
-    plot_decisionBoundary(X, y, model,class_='notLinear')   # �����߽߱�
+    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
     
     
     
-# ��ͼ
+# 作图
 def plot_data(X,y):
     plt.figure(figsize=(10,8))
-    pos = np.where(y==1)    # �ҵ�y=1��λ��
-    neg = np.where(y==0)    # �ҵ�y=0��λ��
+    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")
@@ -38,11 +38,11 @@ def plot_data(X,y):
     plt.legend([p1,p2],["y==1","y==0"])
     return plt
     
-# �����߽߱�
+# 画决策边界
 def plot_decisionBoundary(X,y,model,class_='linear'):
     plt = plot_data(X, y)
     
-    # ���Ա߽�        
+    # 线性边界        
     if class_=='linear':
         w = model.coef_
         b = model.intercept_
@@ -50,7 +50,7 @@ def plot_decisionBoundary(X,y,model,class_='linear'):
         yp = -(w[0,0]*xp+b)/w[0,1]
         plt.plot(xp,yp,'b-',linewidth=2.0)
         plt.show()
-    else:  # �����Ա߽�
+    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)