Python 我能对这些错误做些什么？我可以重新启动程序再试一次吗？

我有一个程序，从命令行获取csv的数据文件，以及用于聚类的整数值（这是一个从头开始的K-Means程序）。我经常遇到这些错误：

Traceback (most recent call last):
  File "NewK.py", line 73, in <module>
    kMeans(centroids,dataPoints)
  File "NewK.py", line 53, in kMeans
    CentroidMeans.append(np.mean(dataPoints[t2 == [x]], axis=0))
  File "C:\Python27\lib\site-packages\numpy\core\fromnumeric.py", line 2942, in
mean
    out=out, **kwargs)
  File "C:\Python27\lib\site-packages\numpy\core\_methods.py", line 68, in _mean

    ret, rcount, out=ret, casting='unsafe', subok=False)
KeyboardInterrupt

这种情况并非每次都发生，但确实经常发生。处理这个问题的最佳方法是什么？是的，这是家庭作业：）

Python代码：

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

arglist = sys.argv 

NoOfCentroids = int(arglist[2])

dataPointsFromFile = np.array(np.loadtxt(sys.argv[1], delimiter = ','))

dataRange = ([])

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)
    print '\n'
    randomCentroids.append(templist)
    templist = []
    i = i+1

centroids = np.asarray(randomCentroids)

def kMeans(array1, array2):
    ConvergenceCounter = 1
    keepGoing = True
    StartingCentroids = np.copy(centroids)
    while keepGoing:      
        #--------------Find The new means---------#
        t0 = StartingCentroids[None, :, :] - dataPoints[:, None, :]
        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 xrange(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:
            ConvergenceCounter  = ConvergenceCounter +1
            StartingCentroids =np.copy(NewMeans)

kMeans(centroids, dataPoints)

将numpy导入为np
从pprint导入pprint
随机输入
导入系统
进口警告
arglist=sys.argv
NoOfCentroids=int（arglist[2]）
dataPointsFromFile=np.array（np.loadtxt（sys.argv[1]，分隔符='，'））
数据范围=（[]）
将open（arglist[1]）作为f：
打印“数据集中的点：”，求和（f中的1）
append（round（np.amin（dataPointsFromFile），1））
追加（round（np.amax（dataPointsFromFile），1））
dataRange=np.asarray（dataRange）
dataPoints=np.array（dataPointsFromFile）
打印“数据的维度：”，dataPoints.shape
随机质心=[]
数据=（[]）
圣殿骑士=[]
i=0
后者是一个警告，而不是一个错误。前者是一个
键盘中断
，你在按ctrl-c吗？是的，我正在使用
键盘中断
，因为如果我有时不按，程序就会暂停。你知道有什么可以帮忙的吗？后者是一个警告，而不是一个错误。前者是一个
键盘中断
，你在按ctrl-c吗？是的，我正在使用
键盘中断
，因为如果我有时不按，程序就会暂停。你知道有什么可以帮忙的吗？
import numpy as np
from pprint import pprint
import random
import sys
import warnings

arglist = sys.argv 

NoOfCentroids = int(arglist[2])

dataPointsFromFile = np.array(np.loadtxt(sys.argv[1], delimiter = ','))

dataRange = ([])

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)
    print '\n'
    randomCentroids.append(templist)
    templist = []
    i = i+1

centroids = np.asarray(randomCentroids)

def kMeans(array1, array2):
    ConvergenceCounter = 1
    keepGoing = True
    StartingCentroids = np.copy(centroids)
    while keepGoing:      
        #--------------Find The new means---------#
        t0 = StartingCentroids[None, :, :] - dataPoints[:, None, :]
        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 xrange(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:
            ConvergenceCounter  = ConvergenceCounter +1
            StartingCentroids =np.copy(NewMeans)

kMeans(centroids, dataPoints)