python实现逻辑logistic回归：预测病马的死亡率

from numpy import *  
#加载数据 
def loadDataSet():  
    dataMat = []; labelMat = []  
    fr = open('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  
 
#计算sigmoid函数 

def sigmoid(inX):  
 return 1.0/(1+exp(-inX))  
 
#梯度上升算法-计算回归系数 
def gradAscent(dataMatIn, classLabels):  
    dataMatrix = mat(dataMatIn)          #转换为numpy数据类型 
    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(wei):  
 import matplotlib.pyplot as plt  
    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, 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()  

def stocGradAscent0(dataMatrix, classLabels):  
    dataMatrix = array(dataMatrix)  
    m,n = shape(dataMatrix)  
    alpha = 0.1 
    weights = ones(n)  
 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算法 
def stocGradAscent1(dataMatrix, classLabels, numInter = 150):  
    dataMatrix = array(dataMatrix)  
    m,n = shape(dataMatrix)  
    weights = ones(n)  
 for j in range(numInter):  
        dataIndex = range(m)  
 for i in range(m):  
            alpha = 4 / (1.0+j+i) + 0.01 #alpha值每次迭代时都进行调整 
            randIndex = int(random.uniform(0, len(dataIndex)))            #随机选取更新 
            h = sigmoid(sum(dataMatrix[randIndex] * weights))  
            error = classLabels[randIndex] - h  
            weights = weights + alpha * error * dataMatrix[randIndex]  
 del[dataIndex[randIndex]]  
 return weights  

#案例-从疝气病症预测病马的死亡率 
def classifyVector(inX, weights):  
    prob = sigmoid(sum(inX*weights))  
 if prob > 0.5: return 1.0 
 else: return 0.0 
 
def colicTest():  
    frTrain = open('horseColicTraining.txt')  
    frTest = open('horseColicTest.txt')  
    trainingSet = []; trainingLabels = []  
 for line in frTrain.readlines():  
        currLine = line.strip().split('\t')  
        lineArr =[]  
 for i in range(21):  
            lineArr.append(float(currLine[i]))  
        trainingSet.append(lineArr)  
        trainingLabels.append(float(currLine[21]))  
    trainWeights = stocGradAscent1(trainingSet, trainingLabels, 500)  
    errorCount = 0; numTestVec = 0.0 
 for line in frTest.readlines():  
        numTestVec += 1.0 
        currLine = line.strip().split('\t')  
        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  
 
def multiTest():  
    numTests = 10;errorSum = 0.0 
 for k in range(numTests):  
        errorSum += colicTest()  
 print 'after %d iterations the average error rate is: %f' %(numTests, errorSum/float(numTests))