LogisticRegression_scikit-learn.py

from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.cross_validation import train_test_split
import numpy as np

def logisticRegression():
    data = loadtxtAndcsv_data("data1.txt", ",", np.float64) 
    X = data[:,0:-1]
    y = data[:,-1]
    
    # 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)
    
    # 归一化
    scaler = StandardScaler()
    scaler.fit(x_train)
    x_train = scaler.fit_transform(x_train)
    x_test = scaler.fit_transform(x_test)
    
    #逻辑回归
    model = LogisticRegression()
    model.fit(x_train,y_train)
    
    # 预测
    predict = model.predict(x_test)
    right = sum(predict == y_test)
    
    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 ('测试集准确率：%f%%'%(right*100.0/predict.shape[0]))          #计算在测试集上的准确度

# 加载txt和csv文件
def loadtxtAndcsv_data(fileName,split,dataType):
    return np.loadtxt(fileName,delimiter=split,dtype=dataType)

# 加载npy文件
def loadnpy_data(fileName):
    return np.load(fileName)



if __name__ == "__main__":
    logisticRegression()