机器学习实战-逻辑回归

#encoding:utf-8
from numpy import *
import math
#数据下载与处理~打开文本，逐行读取，前两行对应值x1,x2，第三行对应类别标签。并且将x0都设为1.0
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])])#将x0设为1.0
labelMat.append(int(lineArr[2]))
return dataMat,labelMat
#sigmoid函数-阶跃函数-将值代入此函数，得到0~1之间的数值
def sigmoid(inX):
return 1.0/(1+math.exp(-inX))
#梯度上升算法~
#输入：dataMatIn~2维数组~每列分别表示不同的特征（x0,x1,x2）~每行表示每个训练样本
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
m,n = shape(dataMatrix)
alpha = 0.001#向目标移动的步长
maxCycles = 500#迭代次数
weights = ones((n,1))
for k in range(maxCycles):              #heavy on matrix operations
h = sigmoid(dataMatrix*weights)     #matrix mult
error = (labelMat - h)              #计算真实类别与预测类别的差值，接下来按照差值方向来调整回归系数
weights = weights + alpha * dataMatrix.transpose()* error #回归系数计算
return weights#返回训练好的回归系数
#画出决策边界
def plotBestFit(weights):
import matplotlib.pyplot as plt
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]#最佳拟合直线
ax.plot(x, y)
plt.xlabel('X1'); plt.ylabel('X2');
plt.show()
#随机梯度上升~~在梯度上升上略加修改
def stocGradAscent0(dataMatrix, classLabels):
m,n = shape(dataMatrix)
alpha = 0.01
weights = ones(n)   #初始化为1
for i in range(m):
h = sigmoid(sum(dataMatrix[i]*weights))
error = classLabels[i] - h#h和error都是向量
weights = weights + alpha * error * dataMatrix[i]
return weights
#改进的随机梯度上升
def stocGradAscent1(dataMatrix, classLabels, numIter=150):#默认迭代次数50次
m,n = shape(dataMatrix)
weights = ones(n)   #initialize to all ones
for j in range(numIter):
dataIndex = range(m)
for i in range(m):
alpha = 4/(1.0+j+i)+0.0001    #每次迭代都调整alpha值
randIndex = int(random.uniform(0,len(dataIndex)))#随机选取样本更新回归系数
h = sigmoid(sum(dataMatrix[randIndex]*weights))
error = classLabels[randIndex] - h
weights = weights + alpha * error * dataMatrix[randIndex]
del(dataIndex[randIndex])
return weights

#病马预测
#逻辑回归分类函数
#输入：特征向量、回归系数    返回：1,0
def classifyVector(inX, weights):
prob = sigmoid(sum(inX*weights))
if prob > 0.5: return 1.0
else: return 0.0
#打开测试集、训练集，进行格式化预处理
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(array(trainingSet), trainingLabels, 1000)
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]):#分类结果与测试集比较
errorCount += 1
errorRate = (float(errorCount)/numTestVec)
print "the error rate of this test is: %f" % errorRate
return errorRate
#调用colictTest() 10次~并求结果平均值
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))