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

from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
# 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():
 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()