归一化特征值

def autoNorm(dataSet):
    minVals = dataSet.min(0)
    maxVals = dataSet.max(0)
    ranges = maxVals - minVals
    normDataSet = zeros(shape(dataSet))
    m = dataSet.shape[0]
    normDataSet = dataSet - tile(minVals, (m, 1))
    normDataSet = normDataSet / tile(ranges, (m, 1))  # element wise divide
    return normDataSet, ranges, minVals

dataset.min(0)是提取一个矩阵的每列的最小值，然后组成一个行向量，例如：

[[1,2,5],

[4,8,8],

[0,6,4]] 结果为[0,2,4]

tile(minVals, (m, 1))是把一个矩阵搞成m行倍，列为1倍，例如：:tile(minVals, (4, 1))

结果为：  [[0,2,4],

                [0,2,4],

                [0,2,4],

                [0,2,4]]