Logistic回归编程实战

Logistic回归编程实战

上一节学习了Logistic回归，这一节就针对该算法进行编程实战，所使用的是python2.7。

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以下是本项目的全部代码

# -*- coding:utf-8 -*-from numpy import *import matplotlib.pyplot as pltclass Logister(object):    def __init__(self,path):        self.path=path    #将导入的文件转换为矩阵形式    def file2matrix(self,delimiter):        recordlist = []        fp = open(self.path,"rb")   # 读取文件内容        content = fp.read()        fp.close()        rowlist = content.splitlines()  # 按行转换为一维表        # 逐行遍历      # 结果按分隔符分割为行向量        recordlist=[map(eval, row.split(delimiter)) for row in rowlist if row.strip()]        return mat(recordlist)  # 返回转换后的矩阵形式    # 绘制分类点    def drawScatterbyLabel(self,plt,Input):        m,n=shape(Input)        target = Input[:,-1]        for i in xrange(m):            if target[i]==0:                plt.scatter(Input[i,0],Input[i,1],c='blue',marker='o')            else:                plt.scatter(Input[i,0],Input[i,1],c='red',marker='s')    #Logistic函数    def logistic(self,wTx):        return 1.0/(1.0+exp(-wTx))    def buildMat(self,dataSet):        m,n=shape(dataSet)        dataMat = zeros((m,n))        dataMat[:,0] = 1        dataMat[:,1:] = dataSet[:,:-1]        return  dataMat    # 分类函数    def classifier(self,testData, weights):        prob = self.logistic(sum(testData*weights)) # 求取概率--判别算法        if prob > 0.5:            return 1.0  # prob>0.5 返回为1        else:            return 0.0  # prob<=0.5 返回为0if __name__ == "__main__":    logis = Logister("testSet.txt")    input_data = logis.file2matrix("\t")    target = input_data[:,-1]    [m,n] = shape(input_data)    #print input_data    #print m,n    logis.drawScatterbyLabel(plt,input_data)    dataMat = logis.buildMat(input_data)    #print dataMat    #main()    alpha = 0.001  #梯度下降的参数    steps = 500  #迭代次数    weights = ones((n,1))  #初始化权重向量    for k in xrange(steps):        gradient = dataMat*mat(weights)        output = logis.logistic(gradient)        errors = target-output        weights = weights + alpha*dataMat.T*errors  #梯度下降    #print weights    #应用模型到测试数据中    testdata = mat([-0.147324,2.874846])  #测试数据    m,n = shape(testdata)    testmat = zeros((m,n+1))    testmat[:,0] = 1    testmat[:,1:] = testdata    print logis.classifier(testmat,weights)  #weights为前面训练得出的    #绘制决策边界    X = linspace(-5,5,100)    Y = -(double(weights[0])+X*(double(weights[1])))/double(weights[2])    plt.plot(X,Y)    plt.show()

运行结果:

1.0

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代码中设计的数据集文件”testSet.txt”,可在http://download.csdn.net/detail/louishao/9747154下载查看。