1 #coding=utf-8
  2 from numpy import *
  3 
  4 def loadDataSet():
  5     dataMat = []
  6     labelMat = []
  7     fr = open(‘testSet.txt‘)
  8     for line in fr.readlines():
  9         lineArr = line.strip().split()
 10         dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
 11         labelMat.append(int(lineArr[2]))
 12     return dataMat, labelMat
 13     
 14 def sigmoid(inX):
 15     return 1.0/(1+exp(-inX))
 16     
 17 def gradAscend(dataMatIn, classLabels):
 18     dataMatrix = mat(dataMatIn)
 19     labelMat = mat(classLabels).transpose()
 20     m,n = shape(dataMatrix)
 21     alpha = 0.001
 22     maxCycle = 500
 23     weight = ones((n,1))
 24     for k in range(maxCycle):
 25         h = sigmoid(dataMatrix*weight)
 26         error = labelMat - h
 27         weight += alpha * dataMatrix.transpose() * error
 28         #plotBestFit(weight)
 29     return weight
 30 
 31 def gradAscendWithDraw(dataMatIn, classLabels):
 32     import matplotlib.pyplot as plt
 33     fig = plt.figure()
 34     ax = fig.add_subplot(311,ylabel=‘x0‘)
 35     bx = fig.add_subplot(312,ylabel=‘x1‘)
 36     cx = fig.add_subplot(313,ylabel=‘x2‘)
 37     dataMatrix = mat(dataMatIn)
 38     labelMat = mat(classLabels).transpose()
 39     m,n = shape(dataMatrix)
 40     alpha = 0.001
 41     maxCycle = 500
 42     weight = ones((n,1))
 43     wei1 = []
 44     wei2 = []
 45     wei3 = []
 46     for k in range(maxCycle):
 47         h = sigmoid(dataMatrix*weight)
 48         error = labelMat - h
 49         weight += alpha * dataMatrix.transpose() * error
 50         wei1.extend(weight[0])
 51         wei2.extend(weight[1])
 52         wei3.extend(weight[2])
 53     ax.plot(range(maxCycle), wei1)
 54     bx.plot(range(maxCycle), wei2)
 55     cx.plot(range(maxCycle), wei3)
 56     plt.xlabel(‘iter_num‘)
 57     plt.show()
 58     return weight
 59     
 60 def stocGradAscent0(dataMatrix, classLabels):
 61     m,n = shape(dataMatrix)
 62     
 63     alpha = 0.001
 64     weight = ones(n)
 65     for i in range(m):
 66         h = sigmoid(sum(dataMatrix[i]*weight))
 67         error = classLabels[i] - h
 68         weight = weight + alpha * error * dataMatrix[i]
 69     return weight
 70     
 71 def stocGradAscentWithDraw0(dataMatrix, classLabels):
 72     import matplotlib.pyplot as plt
 73     fig = plt.figure()
 74     ax = fig.add_subplot(311,ylabel=‘x0‘)
 75     bx = fig.add_subplot(312,ylabel=‘x1‘)
 76     cx = fig.add_subplot(313,ylabel=‘x2‘)
 77     m,n = shape(dataMatrix)
 78     
 79     alpha = 0.001
 80     weight = ones(n)
 81     wei1 = array([])
 82     wei2 = array([])
 83     wei3 = array([])
 84     numIter = 200
 85     for j in range(numIter):
 86         for i in range(m):
 87             h = sigmoid(sum(dataMatrix[i]*weight))
 88             error = classLabels[i] - h
 89             weight = weight + alpha * error * dataMatrix[i]
 90             wei1 =append(wei1, weight[0])
 91             wei2 =append(wei2, weight[1])
 92             wei3 =append(wei3, weight[2])
 93     ax.plot(array(range(m*numIter)), wei1)
 94     bx.plot(array(range(m*numIter)), wei2)
 95     cx.plot(array(range(m*numIter)), wei3)
 96     plt.xlabel(‘iter_num‘)
 97     plt.show()
 98     return weight
 99     
100 def stocGradAscent1(dataMatrix, classLabels, numIter=150):
101     m,n = shape(dataMatrix)
102     
103     #alpha = 0.001
104     weight = ones(n)
105     for j in range(numIter):
106         dataIndex = range(m)
107         for i in range(m):
108             alpha = 4/ (1.0+j+i) +0.01
109             randIndex = int(random.uniform(0,len(dataIndex)))
110             h = sigmoid(sum(dataMatrix[randIndex]*weight))
111             error = classLabels[randIndex] - h
112             weight = weight + alpha * error * dataMatrix[randIndex]
113             del(dataIndex[randIndex])
114     return weight
115     
116 def stocGradAscentWithDraw1(dataMatrix, classLabels, numIter=150):
117     import matplotlib.pyplot as plt
118     fig = plt.figure()
119     ax = fig.add_subplot(311,ylabel=‘x0‘)
120     bx = fig.add_subplot(312,ylabel=‘x1‘)
121     cx = fig.add_subplot(313,ylabel=‘x2‘)
122     m,n = shape(dataMatrix)
123     
124     #alpha = 0.001
125     weight = ones(n)
126     wei1 = array([])
127     wei2 = array([])
128     wei3 = array([])
129     for j in range(numIter):
130         dataIndex = range(m)
131         for i in range(m):
132             alpha = 4/ (1.0+j+i) +0.01
133             randIndex = int(random.uniform(0,len(dataIndex)))
134             h = sigmoid(sum(dataMatrix[randIndex]*weight))
135             error = classLabels[randIndex] - h
136             weight = weight + alpha * error * dataMatrix[randIndex]
137             del(dataIndex[randIndex])
138             wei1 =append(wei1, weight[0])
139             wei2 =append(wei2, weight[1])
140             wei3 =append(wei3, weight[2])
141     ax.plot(array(range(len(wei1))), wei1)
142     bx.plot(array(range(len(wei2))), wei2)
143     cx.plot(array(range(len(wei2))), wei3)
144     plt.xlabel(‘iter_num‘)
145     plt.show()
146     return weight
147     
148 def plotBestFit(wei):
149     import matplotlib.pyplot as plt
150     weight = wei
151     dataMat,labelMat = loadDataSet()
152     dataArr = array(dataMat)
153     n = shape(dataArr)[0]
154     xcord1 = []
155     ycord1 = []
156     xcord2 = []
157     ycord2 = []
158     for i in range(n):
159         if int(labelMat[i]) == 1:
160             xcord1.append(dataArr[i,1])
161             ycord1.append(dataArr[i,2])
162         else:
163             xcord2.append(dataArr[i,1])
164             ycord2.append(dataArr[i,2])
165     fig = plt.figure()
166     ax = fig.add_subplot(111)
167     ax.scatter(xcord1, ycord1, s=30, c=‘red‘, marker=‘s‘)
168     ax.scatter(xcord2, ycord2, s=30, c=‘green‘)
169     x = arange(-3.0, 3.0, 0.1)
170     y = (-weight[0] - weight[1]*x)/weight[2]
171     ax.plot(x,y)
172     plt.xlabel(‘X1‘)
173     plt.ylabel(‘X2‘)
174     plt.show()
175     
176 def main():
177     dataArr,labelMat = loadDataSet()
178     #w = gradAscendWithDraw(dataArr,labelMat)
179     w = stocGradAscentWithDraw0(array(dataArr),labelMat)
180     plotBestFit(w)
181     
182 if __name__ == ‘__main__‘:
183     main()