西瓜书习题3.3

1.梯度上升法解答

# -*- coding: utf-8 -*-
'''西瓜书第三章课后题3.3'''
#梯度上升法
from numpy import *
import operator
from os import listdir


def loaddata():
    dataMat=[];labelMat=[]
    #fr=open(r'E:\机器学习\西瓜书\data33.txt')
    fr=open(r'E:\机器学习\机器学习实战\machinelearninginaction\Ch05\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]))
    return dataMat,labelMat

def sigmoid(inX):
    return 1.0/(1+exp(-inX))

#梯度上升算法
def gradAscent(dataMat,classLabels):
    dataMatrix=mat(dataMat)
    labelMat=mat(classLabels).transpose()
    m,n=shape(dataMatrix)
    alpha=0.001
    maxCycles=500
    weights=ones((n,1))
    for k in range(maxCycles):
        h=sigmoid(dataMatrix*weights)
        error=(labelMat-h)
        weights=weights+alpha*dataMatrix.transpose()*error
    return weights

#画出决策边界
def plotBestFit(weights):
    import matplotlib.pyplot as plt 
    dataMat,labelMat=loaddata()
    dataArr=array(dataMat)
    n=shape(dataArr)[0]
    xcord1=[];ycord1=[]
    xcord2=[];ycord2=[]
    for i in range(n):
        if int(labelMat[i])==1:
            xcord1.append(dataArr[i,1]);ycord1.append(dataArr[i,2])
        else:
            xcord2.append(dataArr[i,1]);ycord2.append(dataArr[i,2])
    fig=plt.figure()
    ax=fig.add_subplot(111)
    ax.scatter(xcord1,ycord1,s=30,c='red',marker='s')
    ax.scatter(xcord2,ycord2,s=30,c='green')
    x=arange(0.0,1.0,0.1)
    y=(-weights[0]-weights[1]*x)/weights[2]
    ax.plot(x,y)
    plt.xlabel('x1');plt.ylabel('x2')
    plt.show()

dataArr,labelMat=loaddata()
print(gradAscent(dataArr,labelMat))
weights=gradAscent(dataArr,labelMat)
print(plotBestFit(weights.getA()))