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。
以下是本项目的全部代码
# -*- coding:utf-8 -*- from numpy import * import matplotlib.pyplot as plt class 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0 if __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。
