LogisticRegression_OneVsAll_scikit-learn.py

#-*- coding: utf-8 -*-
from scipy import io as spio
import numpy as np
from sklearn import svm
from sklearn.linear_model import LogisticRegression



def logisticRegression_oneVsAll():
    data = loadmat_data("data_digits.mat") 
    X = data['X']   # 获取X数据，每一行对应一个数字20x20px
    y = data['y']   # 这里读取mat文件y的shape=(5000, 1)
    y = np.ravel(y) # 调用sklearn需要转化成一维的(5000,)
    
    model = LogisticRegression()
    model.fit(X, y) # 拟合
    
    predict = model.predict(X) #预测
    
    print u"预测准确度为：%f%%"%np.mean(np.float64(predict == y)*100)

# 加载mat文件
def loadmat_data(fileName):
    return spio.loadmat(fileName)

if __name__ == "__main__":
    logisticRegression_oneVsAll()