机器学习实战第二章代码清单2-3注释

归一化特征值
def autoNorm(dataSet):
minVals=dataSet.min(0)
#print minVals
maxVals=dataSet.max(0)
#print maxVals
ranges=maxVals-minVals
#print ranges
normDataSet=zeros(shape(dataSet))#先创建一个（0）返回矩阵，维度与DatSet一样
#print normDataSet
m=dataSet.shape[0]#获取dataSet的行数
normDataSet=dataSet-tile(minVals,(m,1))
normDataSet=normDataSet/tile(ranges,(m,1))
return normDataSet,ranges,minVals命令行程序:
>>> from numpy import *
>>> import k
>>> datingDatMat,datingLabels=file2matrix('datingTestSet2.txt')
>>> normMat,ranges,minVals=autoNorm(datingDataSet)结果：>>> normMat
array([[ 0.44832535, 0.39805139, 0.56233353],
[ 0.15873259, 0.34195467, 0.98724416],
[ 0.28542943, 0.06892523, 0.47449629],
...,
[ 0.29115949, 0.50910294, 0.51079493],
[ 0.52711097, 0.43665451, 0.4290048 ],
[ 0.47940793, 0.3768091 , 0.78571804]])
>>> ranges
array([ 9.12730000e+04, 2.09193490e+01, 1.69436100e+00])
>>> minVals
array([ 0. , 0. , 0.001156])