# -*- coding: utf-8 -*-

LogisticRegression/LogisticRegression_scikit-learn.py

@@ -1,3 +1,5 @@
+# -*- coding: utf-8 -*-
+
 from sklearn.linear_model import LogisticRegression
 from sklearn.preprocessing import StandardScaler
 from sklearn.cross_validation import train_test_split
@@ -8,32 +10,32 @@ def logisticRegression():
     X = data[:,0:-1]
     y = data[:,-1]
 
-    # ����Ϊѵ�����Ͳ��Լ�
+    # 划分为训练集和测试集
     x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.2)
 
-    # ��һ��
+    # 归一化
     scaler = StandardScaler()
     scaler.fit(x_train)
     x_train = scaler.fit_transform(x_train)
     x_test = scaler.fit_transform(x_test)
 
-    #�߼��ع�
+    # 逻辑回归
     model = LogisticRegression()
     model.fit(x_train,y_train)
 
-    # Ԥ��
+    # 预测
     predict = model.predict(x_test)
     right = sum(predict == y_test)
 
-    predict = np.hstack((predict.reshape(-1,1),y_test.reshape(-1,1)))   # ��Ԥ��ֵ����ʵֵ����һ�飬�ù۲�
+    predict = np.hstack((predict.reshape(-1,1),y_test.reshape(-1,1)))   # 将预测值和真实值放在一块，好观察
     print(predict)
-    print('���Լ�׼ȷ�ʣ�%f%%'%(right*100.0/predict.shape[0]))          #�����ڲ��Լ��ϵ�׼ȷ��
+    print('测试集准确率：%f%%'%(right*100.0/predict.shape[0]))          # 计算在测试集上的准确度
 
-# ����txt��csv�ļ�
+# 加载txt和csv文件
 def loadtxtAndcsv_data(fileName,split,dataType):
     return np.loadtxt(fileName,delimiter=split,dtype=dataType)
 
-# ����npy�ļ�
+# 加载npy文件
 def loadnpy_data(fileName):
     return np.load(fileName)