标签:error max nump rom nbsp 过程 average this rate
logistic回归实现预测病马死亡率
python3已经实现 代码还在更新中 写完全部注释以后在贴上来
# -*- coding: utf-8 -*- from numpy import * #加载数据集 def loadDataSet(): dataMat = []; labelMat = [] fr = open(‘testSet.txt‘) for line in fr.readlines():#逐行读取 lineArr = line.strip().split()#按空格分割字符并剔除空格 dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])#前两列是数据 labelMat.append(int(lineArr[2]))#第三列是标签 return dataMat,labelMat #定义sigmoid函数 def sigmoid(inX): return 1.0/(1+exp(-inX)) #梯度上升法 def gradAscent(dataMatIn, classLabels): dataMatrix = mat(dataMatIn) #转换为NumPy矩阵类型 labelMat = mat(classLabels).transpose() #转换为NumPy矩阵类型,并求转置 m,n = shape(dataMatrix) alpha = 0.001#步长 maxCycles = 500#迭代次数 weights = ones((n,1))#初始回归系数 for k in range(maxCycles): #循环500次 h = sigmoid(dataMatrix*weights) #向量相乘 h也是向量 error = (labelMat - h) #向量相减 weights = weights + alpha * dataMatrix.transpose()* error #调整回归系数 return weights #画出决策边界 def plotBestFit(weights): import matplotlib.pyplot as plt #用的时候再导入 dataMat,labelMat=loadDataSet() dataArr = array(dataMat) #转化为数组 n = shape(dataArr)[0] #计算行数 xcord1 = []; ycord1 = [] #两类点的坐标 xcord2 = []; ycord2 = [] for i in range(n): if int(labelMat[i])== 1: #分类过程 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(-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() #随机梯度上升法 def stocGradAscent0(dataMatrix, classLabels): m,n = shape(dataMatrix) alpha = 0.01 #步长 weights = ones(n) #出初始回归系数 for i in range(m): h = sigmoid(sum(dataMatrix[i]*weights)) error = (classLabels[i] - h) weights = weights + alpha * error * dataMatrix[i] return weights #改进的随机梯度上升法 def stocGradAscent1(dataMatrix, classLabels, numIter=150): m,n = shape(dataMatrix) weights = ones(n) #初始回归系数 for j in range(numIter): dataIndex = list(range(m))#此处注意Python2和python3的区别 for i in range(m): alpha = 4/(1.0+j+i)+0.0001 #动态调整步长 randIndex = int(random.uniform(0,len(dataIndex)))#随机选取样本 h = sigmoid(sum(dataMatrix[randIndex]*weights)) error = classLabels[randIndex] - h weights = weights + alpha * error * dataMatrix[randIndex] del(dataIndex[randIndex]) #删掉该样本值进行下一次迭代 return weights #***********************完成具体的分析任务**************************# #输出分类结果 def classifyVector(inX, weights): prob = sigmoid(sum(inX*weights)) if prob > 0.5: return 1.0 else: return 0.0 #训练和测试过程 def colicTest(): frTrain = open(‘horseColicTraining.txt‘) frTest = open(‘horseColicTest.txt‘) trainingSet = [] trainingLabels = [] for line in frTrain.readlines():#训练过程 currLine = line.strip().split(‘\t‘)#按行读取并分割按行分割 lineArr =[] for i in range(21): lineArr.append(float(currLine[i])) trainingSet.append(lineArr) trainingLabels.append(float(currLine[21])) trainWeights = stocGradAscent1(array(trainingSet), trainingLabels, 1000) errorCount = 0; numTestVec = 0.0 for line in frTest.readlines():#测试过程 numTestVec += 1.0#测试的次数 currLine = line.strip().split(‘\t‘)#按行读取并分割按行分割 lineArr =[] for i in range(21): lineArr.append(float(currLine[i])) if int(classifyVector(array(lineArr), trainWeights))!= int(currLine[21]): errorCount += 1 errorRate = (float(errorCount)/numTestVec) print ("the error rate of this test is: %f" % errorRate) return errorRate #多次测试的函数 def multiTest(): numTests=10; errorSum=0; for k in range(numTests): errorSum+=colicTest() print (‘after %d iterations the average error rate is: %f ‘ %(numTests,errorSum/float(numTests)))
结果如下:
the error rate of this test is: 0.313433 the error rate of this test is: 0.358209 the error rate of this test is: 0.358209 the error rate of this test is: 0.402985 the error rate of this test is: 0.253731 the error rate of this test is: 0.417910 the error rate of this test is: 0.283582 the error rate of this test is: 0.283582 the error rate of this test is: 0.328358 the error rate of this test is: 0.417910 after 10 iterations the average error rate is: 0.341791
考虑到数据缺失问题,这个结果并不差
注意几个方法和概念
分类函数,梯度上升法,随机梯度上升法,改进的随机梯度上升法
标签:error max nump rom nbsp 过程 average this rate
原文地址:https://www.cnblogs.com/Aaron12/p/8987235.html