k-近邻算法（kNN）准备数据：归一化数值

 #准备数据：归一化数值
 def autoNorm(dataSet):      #autoNorm()函数可以自动将数字特征值转换为0到1的区间
     minVals = dataSet.min(0)
     maxVals = dataSet.max(0)    #ddataSet.max(0)中的参数0使得函数可以从列中选取最小值
     ranges = maxVals - minVals
     normDataSet = zeros(shape(dataSet))
     m = dataSet.shape[0]
     #newValue = (oldValue-min)/(max-min)，该公式可以将任意取值范围的特征值转换为0到1区间内的值
     #tile()函数将变量内容复制成输入矩阵同样大小的矩阵（具体特征值相除）
     #在numpy库中，矩阵除法需要使用函数linalg.solve(matA,matB)
     normDataSet = dataSet - tile(minVals, (m,1))
     normDataSet = normDataSet/tile(ranges, (m,1))
     return normDataSet, ranges, minVals

运行结果：

 >>>normMat, ranges, minVals = kNN.autoNorm(datingDataMat)
 >>>normMat
 array([[1., 1., 1.],
        [0., 0., 0.],
        [0., 0., 0.],
        ...,
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]])
 >>>ranges
 array([4.092000e+04, 8.326976e+00, 9.539520e-01])
 >>>minVals
 array([0., 0., 0.])

出现的错误：

 >>>normMat, ranges, minVals = kNN.autoNorm(datingDataMat)
 Traceback (most recent call last):
   File "<input>", line 1, in <module>
 NameError: name 'kNN' is not defined

 >>>normMat, ranges, minVals = kNN.autoNorm(datingDataMat)
 Traceback (most recent call last):
   File "<input>", line 1, in <module>
 AttributeError: module 'kNN' has no attribute 'autoNorm'

解决办法：

　　个人解决办法：重启PyCharm，运行kNN.py，重新完整的输入运行命令，问题就解决了

 >>>from numpy import *
 >>>random.rand(4,4)
 >>>randMat = mat(random.rand(4,4))
 >>>randMat.I
 >>>invRandMat = randMat.I
 >>>myEye = randMat*invRandMat
 >>>myEye - eye(4)
 >>>group,labels = kNN.createDataSet()
 >>>group
 >>>labels
 >>>kNN.classify0([0,0], group, labels, 3)
 >>>datingDataMat,datingLabels = kNN.file2matrix('datingTestSet.txt')
 >>>datingDataMat
 >>>datingLabels[0:16]
 >>>import matplotlib
 >>>import matplotlib.pyplot as plt
 >>>fig = plt.figure()
 >>>ax = fig.add_subplot(111)
 >>>ax.scatter(datingDataMat[:,1], datingDataMat[:,2])
 >>>plt.show()
 >>>normMat, ranges, minVals = kNN.autoNorm(datingDataMat)
 >>>normMat
 array([[1., 1., 1.],
        [0., 0., 0.],
        [0., 0., 0.],
        ...,
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]])
 >>>ranges
 array([4.092000e+04, 8.326976e+00, 9.539520e-01])
 >>>minVals
 array([0., 0., 0.])