logistic算法解析

<span style="font-family: Arial, Helvetica, sans-serif; background-color: rgb(255, 255, 255);">对于《机器学习实战》中逻辑斯谛回归算法，其中有一行不好理解：</span>

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

原理推导如下：

附：logistic算法

def sigmoid(inX):return 1.0/(1+exp(-inX))def gradAscent(dataMatIn, classLabels):dataMatrix = mat(dataMatIn)labelMat = mat(classLabels).transpose()m, n = shape(dataMatrix)alpha = 0.001maxCycles = 500weights = ones((n, 1))for k in range(maxCycles):h = sigmoid(dataMatrix*weights)error = (labelMat - h)weights = weights + alpha * dataMatrix.transpose() * errorreturn weights