对于《机器学习实战》中逻辑斯谛回归算法公式理解：

weights = weights + alpha * dataMatrix.transpose() * error  附：logistic算法[python] view plain copydef sigmoid(inX):      return 1.0/(1+exp(-inX))        def gradAscent(dataMatIn, classLabels):      dataMatrix = mat(dataMatIn)      labelMat = mat(classLabels).transpose()      m, n = shape(dataMatrix)      alpha = 0.001      maxCycles = 500      weights = ones((n, 1))      for k in range(maxCycles):          h = sigmoid(dataMatrix*weights)          error = (labelMat - h)          weights = weights + alpha * dataMatrix.transpose() * error      return weights

原理推导如下：