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#_*_ coding:utf-8 _*_

#writer : lgy

#datetime : 2016-04-26

from numpy import *

import matplotlib.pyplot as plt

from sklearn.linear_model import LogisticRegression as LogReg

def loadDataSet(filename):

dataMat = [];labelMat = []

fr = open(filename)

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 storgradAscent(dataMatIn,classLabels,numIter):

dataMatrix = mat(dataMatIn)

labelMatrix = mat(classLabels).transpose()

m,n = shape(dataMatrix)

alpha = 0.01

weights = ones((n))

print weights

for j in range(numIter):

# dataindex = range(m)

for i in range(m):

# alpha mei ci die dai shi xu yao tiao zheng

# alpha = 4 / (1.0 + j + i) + 0.1

# randIndex = int(random.uniform(0,len(dataindex)))

# print dataMatrix[randIndex]

h = sigmoid(sum(dataMatrix[i]*weights))

error = classLabels[i] - h

weights = weights + alpha * error * dataMatrix[i]

print weights

# del(dataindex[randIndex])

# weights = list(set(weights))

return weights

def gradAscent(dataMatIn,classLabels):

# zhuan huan wei Numpy ju zhen shu ju lei xing

dataMatrix = mat(dataMatIn)

labelMatrix = mat(classLabels).transpose()

m,n = shape(dataMatrix)

alpha = 0.001

maxCycles = 500

weights = ones((n,1))

for k in range(maxCycles):

#ju zhen xiang cheng

h = sigmoid(dataMatrix*weights)

error = (labelMatrix - h)

weights = weights + alpha * dataMatrix.transpose() * error

# print k,weights

# classifier = LogReg() # 使用类，参数全是默认的

# classifier.fit(dataMatrix, labelMatrix) # 训练数据来学习，不需要返回值

# return classifier

return weights

def predictData():

dataMatIn,classLabels = loadDataSet('testSet.txt')

dataMatrix = mat(dataMatIn)

labelMatrix = mat(classLabels).transpose()

classifier = LogReg() # 使用类，参数全是默认的

classifier.fit(dataMatrix, labelMatrix) # 训练数据来学习，不需要返回值

tdataMatIn,tclassLabels = loadDataSet('dataSet.txt')

tdataMatrix = mat(tdataMatIn)

tlabelMatrix = mat(tclassLabels).transpose()

count = 0

rCount = 0

prCount = 0

for i in range(0,len(dataMatIn)):

res = classifier.predict(dataMatIn[i])

if res == 1:

count += 1

if tclassLabels[i] == 1:

prCount += 1

rCount += 1

else:

if tclassLabels[i] == 1:

rCount += 1

return count,rCount,prCount

def getPreSet(tlabelMatrix,lab):

count = 0

PreSet = []

for i in range(0,len(lab)):

# print int(lab[i]),int(labelMatrix[i])

if int(lab[i]) == int(tlabelMatrix[i]):

count += 1

PreSet.append(tlabelMatrix[i])

return PreSet,count

def plotBestFit(wei):

weights = wei.getA()

print weights

dataMat,labelMat = loadDataSet('testSet.txt')

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,0])

ycord1.append(dataArr[i,1])

else :

xcord2.append(dataArr[i,0])

ycord2.append(dataArr[i,1])

fig = plt.figure()

ax = fig.add_subplot(111)

ax.scatter(xcord1,ycord1,s=10,c='red',marker='s')

ax.scatter(xcord2,ycord2,s=10,c='green')

x = arange(0.0,20.0,1)

# print x

y = (- weights[0] - weights[1]*x)/weights[2]

ax.plot(x,y)

plt.xlabel('X1');plt.ylabel('X2')

plt.show()

def main():

P,R,PandR = predictData()

print P,R,PandR

Pre = float(PandR)/P

Ref = float(PandR)/R

F1 = Pre * Ref * 2 /(Pre + Ref)

print "F1 : "+ str(F1)

dataMatIn,classLabels = loadDataSet('testSet.txt')

# weights = gradAscent(dataMatIn,classLabels)

weights = storgradAscent(dataMatIn,classLabels,150)

plotBestFit(weights)

if __name__ == '__main__':

main()