1 # Author Qian Chenglong
  2 
  3 from numpy import *
  4 from numpy.ma import arange
  5 
  6 
  7 def loadDataSet():
  8     dataMat = []
  9     labelMat = []
 10     fr = open(\'testSet.txt\')
 11     for line in fr.readlines():
 12         lineArr = line.strip().split()
 13         dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
 14         labelMat.append(int(lineArr[2]))
 15     return dataMat, labelMat
 16 
 17 #sigmoid归一化函数
 18 #输入：z=w1x1+w2x2+w3x3......
 19 #s输出：归一化结果
 20 def sigmoid(inX):
 21     return 1.0 / (1 + exp(-inX))
 22 
 23 
 24 \'\'\'
 25 logistic回归梯度上升优化算法
 26 param dataMatIn: 处理后的数据集
 27 param classLabels: 分类标签
 28 return: 权重值
 29  \'\'\'
 30 def gradAscent(dataMatIn, classLabels):
 31     dataMatrix = mat(dataMatIn)  # convert to NumPy matrix（矩阵）
 32     labelMat = mat(classLabels).transpose()  # convert to NumPy matrix
 33     m, n = shape(dataMatrix)          #m行  n列
 34     alpha = 0.001                       #步长
 35     maxCycles = 500
 36     weights = ones((n, 1))              #系数，权重
 37     for k in range(maxCycles):  # heavy on matrix operations
 38         h = sigmoid(dataMatrix * weights)  # matrix mult
 39         error = (labelMat - h)  # vector subtraction
 40         weights = weights + alpha * dataMatrix.transpose() * error  # transpose()矩阵转置
 41     return weights
 42 
 43 \'\'\'
 44 画出数据集和logisitic回归最佳拟合直线的函数
 45 param weights:
 46 return:
 47 最后的分割方程是y=(-w0-w1*x)/w2
 48 \'\'\'
 49 def plotBestFit(weights):
 50     import matplotlib.pyplot as plt
 51     dataMat, labelMat = loadDataSet()
 52     dataArr = array(dataMat)
 53     n = shape(dataArr)[0]
 54     xcord1 = []
 55     ycord1 = []
 56     xcord2 = []
 57     ycord2 = []
 58     for i in range(n):
 59         if int(labelMat[i]) == 1:
 60             xcord1.append(dataArr[i, 1]);
 61             ycord1.append(dataArr[i, 2])
 62         else:
 63             xcord2.append(dataArr[i, 1]);
 64             ycord2.append(dataArr[i, 2])
 65     fig = plt.figure()
 66     ax = fig.add_subplot(111)
 67     ax.scatter(xcord1, ycord1, s=30, c=\'red\', marker=\'s\')
 68     ax.scatter(xcord2, ycord2, s=30, c=\'green\')
 69     x = arange(-3.0, 3.0, 0.1)
 70     y = (-weights[0] - weights[1] * x) / weights[2]
 71     ax.plot(x, y)
 72     plt.xlabel(\'X1\')
 73     plt.ylabel(\'X2\')
 74     plt.show()
 75 
 76 \'\'\'随机梯度上升
 77 param dataMatIn: 处理后的数据集
 78 param classLabels: 分类标签
 79 return: 权重值\'\'\'
 80 def stocGradAscent0(dataMatrix, classLabels):
 81     m, n = shape(dataMatrix)
 82     alpha = 0.01
 83     weights = ones(n)  # initialize to all ones
 84     for i in range(m):
 85         h = sigmoid(sum(dataMatrix[i] * weights))
 86         error = classLabels[i] - h
 87         weights = weights + alpha * error * dataMatrix[i]
 88     return weights
 89 
 90 \'\'\'改进的随机梯度上升
 91 param dataMatIn: 处理后的数据集
 92 param classLabels: 分类标签
 93 return: 权重值\'\'\'
 94 def stocGradAscent1(dataMatrix, classLabels, numIter=150):
 95     m, n = shape(dataMatrix)
 96     weights = ones(n)  # initialize to all ones
 97     for j in range(numIter):
 98         dataIndex = range(m)
 99         for i in range(m):
100             alpha = 4 / (1.0 + j + i) + 0.0001  # apha decreases with iteration, does not
101             randIndex = int(random.uniform(0, len(dataIndex)))  # go to 0 because of the constant
102             h = sigmoid(sum(dataMatrix[randIndex] * weights))
103             error = classLabels[randIndex] - h
104             weights = weights + alpha * error * dataMatrix[randIndex]
105             del (dataIndex[randIndex])
106     return weights