学习Python:逻辑回归算法

1. 逻辑回归简单算法思想

2. 逻辑回归Python算法

@机器学习实战
#加载二维数据
import numpy as np
def loadDataSet():
dataMat =[];
labelMat=[]
fr = open('testSet.txt')
for line in fr.readlines():
lineArr = line.strip().split()                            #以空格分隔split，bing并去掉空格strip
dataMat.append([1.0,float(lineArr[0]),float(lineArr[1])]) # 1对应w0系数，第一列对应w1,第二列对应w2
labelMat.append(int(lineArr[2]))                           #第三列标签
return dataMat,labelMat
def sigmoid(inX):
return 1.0/(1+exp(-inX))

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

#分类器计算
def classifyVector(inX,weights):
prob = sigmoid(sum(inX*weights))
if prob>0.5:return 1.0
else : return 0.0

参考文献：