Logistic 回归

机器学习实战代码实现：

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
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 = ones ((n,1))
for k in range(maxCycles):
h = sigmoid(dataMatrix*weight)
error = (labelMat-h)
weights =weights+alpha*dataMatrix.transpose()*error
return weights

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]
ax.plot(x,y)
plt.xlabel('X1'); plt.ylabel('X2');
plt.show()

def stocGradAscent0(dataMatrix,classLabels):
m,n = shape(dataMatrix)
alpha = 0.01
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

def stocGradAscent1(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

马疝气病检测:

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(array(trainingSet),trainingLabels,500)
errorCount=0;numTestVec = 0.0
for line in frTestVec +=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))