如何防止 NAN 问题？

Question

我收到Mean of empty slice 运行时警告。 当我打印出我的变量是什么（numpy 数组）时，有几个 其中包含nan 值。运行时警告正在查看行 58 作为问题。我可以进行哪些更改才能使其正常工作？

有时程序会毫无问题地运行。大多数时候确实如此 不是。

这是一个从头开始的 K-Means 算法，它是聚类的 虹膜数据集。它首先提示用户输入金额 他们想要的质心（集群）。然后它随机生成说 给定范围内的簇数来自加载中的数字 在文本文件中。

我在 else 语句中有 break 值来防止无限 循环。

是不是因为当我减去 文件中数据点的质心？

我在运行时遇到错误：

How Many Centrouds? 3
Dimensionality of Data:  (150, 4)
Starting Centroiuds:
 [[ 1.4  7.9  0.2  3.4]
 [ 7.8  0.2  4.3  1.4]
 [ 5.7  6.9  3.   6.6]]
t0 :
 [[[-3.7  4.4 -1.2  3.2]
  [ 2.7 -3.3  2.9  1.2]
  [ 0.6  3.4  1.6  6.4]]

 [[-3.5  4.9 -1.2  3.2]
  [ 2.9 -2.8  2.9  1.2]
  [ 0.8  3.9  1.6  6.4]]

 [[-3.3  4.7 -1.1  3.2]
  [ 3.1 -3.   3.   1.2]
  [ 1.   3.7  1.7  6.4]]

 ..., 
 [[-5.1  4.9 -5.   1.4]
  [ 1.3 -2.8 -0.9 -0.6]
  [-0.8  3.9 -2.2  4.6]]

 [[-4.8  4.5 -5.2  1.1]
  [ 1.6 -3.2 -1.1 -0.9]
  [-0.5  3.5 -2.4  4.3]]

 [[-4.5  4.9 -4.9  1.6]
  [ 1.9 -2.8 -0.8 -0.4]
  [-0.2  3.9 -2.1  4.8]]]

Warning (from warnings module):
  File "C:\Python27\lib\site-packages\numpy\core\_methods.py", line 59
    warnings.warn("Mean of empty slice.", RuntimeWarning)
RuntimeWarning: Mean of empty slice.

Warning (from warnings module):
  File "C:\Python27\lib\site-packages\numpy\core\_methods.py", line 68
    ret, rcount, out=ret, casting='unsafe', subok=False)
RuntimeWarning: invalid value encountered in true_divide
---------------
Starting Centroids:

[[ 1.4  7.9  0.2  3.4]
 [ 7.8  0.2  4.3  1.4]
 [ 5.7  6.9  3.   6.6]]


Starting NewMeans:

[[        nan         nan         nan         nan]
 [ 5.84333333  3.054       3.75866667  1.19866667]
 [        nan         nan         nan         nan]]
Starting Centroids Now:

[[        nan         nan         nan         nan]
 [ 5.84333333  3.054       3.75866667  1.19866667]
 [        nan         nan         nan         nan]]


NewMeans now:
[[        nan         nan         nan         nan]
 [ 5.84333333  3.054       3.75866667  1.19866667]
 [        nan         nan         nan         nan]]

Python 代码：

import numpy as np
from pprint import pprint
import random
import sys
import warnings

arglist = sys.argv 

#UNCOMMENT BELOW IN FINAL PROGRAM
'''
NoOfCentroids = int(arglist[2])
dataPointsFromFile = np.array(np.loadtxt(sys.argv[1], delimiter = ','))
'''

dataPointsFromFile = np.array(np.loadtxt('iris.txt', delimiter = ','))

NoOfCentroids = input('How Many Centrouds? ')

dataRange = ([])

#UNCOMMENT BELOW IN FINAL PROGRAM
'''
with open(arglist[1]) as f:
    print 'Points in data set: ',sum(1 for _ in f)
'''
dataRange.append(round(np.amin(dataPointsFromFile),1))
dataRange.append(round(np.amax(dataPointsFromFile),1))
dataRange = np.asarray(dataRange)

dataPoints = np.array(dataPointsFromFile)
print 'Dimensionality of Data: ', dataPoints.shape

randomCentroids = []
data = ([])
templist = []
i = 0

while i<NoOfCentroids:
    for j in range(len(dataPointsFromFile[1,:])):
        cat = round(random.uniform(np.amin(dataPointsFromFile),np.amax(dataPointsFromFile)),1)
        templist.append(cat)
    randomCentroids.append(templist)
    templist = []
    i = i+1

centroids = np.asarray(randomCentroids)

def kMeans(array1, array2):
    ConvergenceCounter = 1
    keepGoing = True
    StartingCentroids = np.copy(centroids)
    print 'Starting Centroiuds:\n {}'.format(StartingCentroids)
    while keepGoing:      
        #--------------Find The new means---------#
        t0 = StartingCentroids[None, :, :] - dataPoints[:, None, :]
        print 't0 :\n {}'.format(t0)
        t1 = np.linalg.norm(t0, axis=-1)
        t2 = np.argmin(t1, axis=-1)
        #------Push the new means to a new array for comparison---------#
        CentroidMeans = []
        for x in range(len(StartingCentroids)):
            CentroidMeans.append(np.mean(dataPoints[t2 == [x]], axis=0))
        #--------Convert to a numpy array--------#
        NewMeans = np.asarray(CentroidMeans)
        #------Compare the New Means with the Starting Means------#
        if np.array_equal(NewMeans,StartingCentroids):
            print ('Convergence has been reached after {} moves'.format(ConvergenceCounter))
            print ('Starting Centroids:\n{}'.format(centroids))
            print ('Final Means:\n{}'.format(NewMeans))
            print ('Final Cluster assignments: {}'.format(t2))
            for x in xrange(len(StartingCentroids)):
                print ('Cluster {}:\n'.format(x)), dataPoints[t2 == [x]]
            for x in xrange(len(StartingCentroids)):
                print ('Size of Cluster {}:'.format(x)), len(dataPoints[t2 == [x]])
            keepGoing = False
        else:
            print 15*'-'
            ConvergenceCounter  = ConvergenceCounter +1
            print 'Starting Centroids:\n'
            print StartingCentroids
            print '\n'
            print 'Starting NewMeans:\n'
            print NewMeans
            StartingCentroids =np.copy(NewMeans)
            print 'Starting Centroids Now:\n'
            print StartingCentroids
            print '\n'
            print 'NewMeans now:'
            print NewMeans
            break


kMeans(centroids, dataPoints)

Answer 1

我假设警告出现在

np.mean(dataPoints[t2 == [x]], axis=0)

如果 t2 == [x] 全部为 False（t2 和 x 之间不匹配，则 dataPoints[...] 将是一个空数组，从而导致 mean 警告。

我认为您需要更加小心地进行该测试。如果掩码数组为空，甚至可以跳过mean。

== 带有浮点值的测试是不可预测的。您需要使用 np.isclose 或 np.allclose 之类的东西来测试具有容差的等价性。

第二个警告来自mean calc 的后期，大概是在尝试除以 0 时，元素的数量。

完整的mean 代码可以在numpy.core._methods.py 中找到。

总之，不要试图取空数组的mean。