from numpy import *
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 sigmod(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))
for k in range(maxCycles):
h=sigmod(dataMatrix*weights)
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 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=sigmod(sum(dataMatrix[randIndex]*weights))
error=classLabels[randIndex]-h
weights=weights+alpha*error*dataMatrix[randIndex]
del(dataIndex[randIndex])
return weights
alpha在每次迭代的时候会调整,虽然alpha会随着迭代次数不断减小,但永远不会减小到0