Logistic迴歸

#coding=utf-8
import numpy as np
import pandas as pd

def loadDataSet():
    dataMat = []
    labelMat = []
    fr = open("E:\\testSet.txt")
    for line in fr.readlines():
        lineArr = line.strip().split()
        dataMat.append([1.0,float(lineArr[0]),float(lineArr[1])])
        labelMat.append(int(lineArr[2]))
    print(dataMat)
    print(labelMat)
    return dataMat,labelMat
    
def sigmoid(inX):
    return 1.0 / (1 + np.exp(-inX))

def gradAscent(dataMatIn, classLabels):
    dataMatrix = np.mat(dataMatIn)
    labelMat = np.mat(classLabels).transpose()
    print(dataMatrix)
    print(labelMat)
    m,n = np.shape(dataMatrix)
    alpha = 0.001
    maxCycles = 500
    weights = np.ones((n,1))
    for k in range(maxCycles):
        h = sigmoid(dataMatrix * weights)
        error = (labelMat-h)
        weights = weights + alpha * dataMatrix.transpose() * error
    return weights

if __name__ == "__main__":
    #data1,label1 = init_data()
    data1,label1 = loadDataSet()
    w = gradAscent(data1,label1)
    print('w:')
    print(w)