Question

我收到Mean of empty slice运行时警告。当我打印出我的变量（numpy数组）时，有几个其中包含nan个值。运行时警告正在查看行 58作为问题。我可以改变什么才能使它发挥作用？

有时程序会运行没有问题。大部分时间都是如此不。

这是一种从头开始的K-Means算法聚类虹膜数据集。它首先提示用户的数量他们想要的质心（簇）。然后它随机生成说给定范围内的集群数量与已加载的集合中的数量相同在文本文件中。

我在else语句中有break值以防止无限循环。

是因为当我减去数字时，我的数字会低于零来自文件中数据点的质心？

我跑的时候出错：

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

我认为警告出现在

中

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

如果t2 == [x]全部为假（t2与x之间不匹配），则dataPoints[...]将为空数组，从而产生mean警告。

我认为你需要对这个测试更加小心。如果蒙版数组为空，甚至可以跳过mean。

具有浮动值的

==测试是不可预测的。您需要使用类似np.isclose或np.allclose的内容来测试容差的等效性。

第二个警告来自mean计算的后期，大概是在尝试除以0时，元素的数量。

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

总而言之，请勿尝试获取空数组的mean。

如何防止NAN问题？

1 个答案: