机器学习经典算法-logistic回归代码详解
一、算法简要 我们希望有这么一种函数:接受输入然后预测出类别,这样用于分类。这里,用到了数学中的sigmoid函数,sigmoid函数的具体表达式和函数图象如下: 可以较为清楚的看到,当输入的x小于0时,函数值<0.5,将分类预测为0;当输入的x大于0时,函数值>0.5,将分类预测为1。 1.1 预测函数的表示 1.2参数的求解 二、代码实现 函数sigmoid计算相应的函数值;gradAscent实现的batch-梯度上升,意思就是在每次迭代中所有数据集都考虑到了;而stoGradAscent0中,则是将数据集中的示例都比那里了一遍,复杂度大大降低;stoGradAscent1则是对随机梯度上升的改进,具体变化是alpha每次变化的频率是变化的,而且每次更新参数用到的示例都是随机选取的。 from numpy import * import matplotlib.pyplot as plt def loadDataSet(): dataMat = [] labelMat = [] fr = open('testSet.txt') for line in fr.readlines(): lineArr = line.strip('n').split('t') dataMat.append([1.0,float(lineArr[0]),float(lineArr[1])]) labelMat.append(int(lineArr[2])) fr.close() return dataMat,labelMat def sigmoid(inX): return 1.0/(1+exp(-inX)) def gradAscent(dataMatIn,classLabels): dataMatrix = mat(dataMatIn) labelMat = mat(classLabels).transpose() m,n=shape(dataMatrix) alpha = 0.001 maxCycles = 500 weights = ones((n,1)) errors=[] for k in range(maxCycles): h = sigmoid(dataMatrix*weights) error = labelMat - h errors.append(sum(error)) weights = weights + alpha*dataMatrix.transpose()*error return weights,errors def stoGradAscent0(dataMatIn,classLabels): m,n=shape(dataMatIn) alpha = 0.01 weights = ones(n) for i in range(m): h = sigmoid(sum(dataMatIn[i]*weights)) error = classLabels[i] - h weights = weights + alpha*error*dataMatIn[i] return weights def stoGradAscent1(dataMatrix,classLabels,numIter = 150): m,n=shape(dataMatrix) weights = ones(n) for j in range(numIter): dataIndex=range(m) for i in range(m): alpha= 4/(1.0+j+i)+0.01 randIndex = int(random.uniform(0,len(dataIndex))) h = sigmoid(sum(dataMatrix[randIndex]*weights)) error = classLabels[randIndex]-h weights=weights+alpha*error*dataMatrix[randIndex] del(dataIndex[randIndex]) return weights def plotError(errs): k = len(errs) x = range(1,k+1) plt.plot(x,errs,'g--') plt.show() def plotBestFit(wei): weights = wei.getA() dataMat,labelMat = loadDataSet() 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,c='green') x = arange(-3.0,3.0,0.1) y=(-weights[0]-weights[1]*x)/weights[2] ax.plot(x,y) plt.xlabel('x1') plt.ylabel('x2') plt.show() def classifyVector(inX,weights): prob = sigmoid(sum(inX*weights)) if prob>0.5: return 1.0 else: return 0 def colicTest(ftr,fte,numIter): frTrain = open(ftr) frTest = open(fte) trainingSet=[] trainingLabels=[] for line in frTrain.readlines(): currLine = line.strip('n').split('t') lineArr=[] for i in range(21): lineArr.append(float(currLine[i])) trainingSet.append(lineArr) trainingLabels.append(float(currLine[21])) frTrain.close() trainWeights = stoGradAscent1(array(trainingSet),trainingLabels,numIter) errorCount = 0 numTestVec = 0.0 for line in frTest.readlines(): numTestVec += 1.0 currLine = line.strip('n').split('t') lineArr=[] for i in range(21): lineArr.append(float(currLine[i])) if int(classifyVector(array(lineArr),trainWeights))!=int(currLine[21]): errorCount += 1 frTest.close() errorRate = (float(errorCount))/numTestVec return errorRate def multiTest(ftr,numT,numIter): errors=[] for k in range(numT): error = colicTest(ftr,numIter) errors.append(error) print "There "+str(len(errors))+" test with "+str(numIter)+" interations in all!" for i in range(numT): print "The "+str(i+1)+"th"+" testError is:"+str(errors[i]) print "Average testError: ",float(sum(errors))/len(errors) ''''' data,labels = loadDataSet() weights0 = stoGradAscent0(array(data),labels) weights,errors = gradAscent(data,labels) weights1= stoGradAscent1(array(data),labels,500) print weights plotBestFit(weights) print weights0 weights00 = [] for w in weights0: weights00.append([w]) plotBestFit(mat(weights00)) print weights1 weights11=[] for w in weights1: weights11.append([w]) plotBestFit(mat(weights11)) ''' multiTest(r"horseColicTraining.txt",r"horseColicTest.txt",10,500) 总结 以上就是本文关于机器学习经典算法-logistic回归代码详解的全部内容,希望对大家有所帮助。感兴趣的朋友可以继续参阅本站: python中实现k-means聚类算法详解 Python编程实现粒子群算法(PSO)详解 Python编程实现蚁群算法详解 如有不足之处,欢迎留言指出。感谢朋友们对本站的支持! (编辑:李大同) 【声明】本站内容均来自网络,其相关言论仅代表作者个人观点,不代表本站立场。若无意侵犯到您的权利,请及时与联系站长删除相关内容! |