def loadDataSet():
dataMat = []; labelMat = []
fr = open('/Users/hakuri/Desktop/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))
返回回归系数，对应于每个特征值，for循环实现了递归梯度上升算法。 def gradAscent(dataMatIn, classLabels): dataMatrix = mat(dataMatIn) #convert to NumPy matrix labelMat = mat(classLabels).transpose() #convert to NumPy matrix m,n = shape(dataMatrix) alpha = 0.001 maxCycles = 500 weights = ones((n,1)) for k in range(maxCycles): #heavy on matrix operations h = sigmoid(dataMatrix*weights) #matrix mult error = (labelMat - h) #vector subtraction weights = weights + alpha * dataMatrix.transpose()* error #matrix mult return weights
结果，返回了特征值的回归系数。我们的数据集有两个特征值分别是x1，x2。我们又增设了了x0变量。得到的结果
[[ 4.12414349] [ 0.48007329] [-0.6168482 ]] 我们得出x1和x2的关系（设x0=1），0=4.12414349+0.48007329*x1-0.6168482*x2 画出x1与x2的关系图
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