尽管更改了另一个数组，但一个numpy数组却意外更改

问题描述

我在大型代码中发现了一个错误，并将问题简化为以下情况.

I found a bug in my large code, and I simplified the issue to the case below.

尽管在每个步骤中我都只更改w2，但是当在每个步骤中我打印出w1时，它也会被更改，因为在第一个循环的结尾我将它们分配为相等.我读过这篇文章，但是写了万一我写的w1 = w2[:]可以解决问题，但不能解决

Although in each step I only change w2, but when at each step I print out w1, it is also changed, because end of the first loop I assign them to be equal.I read for this but there was written in case I make w1 = w2[:] it will solve the issue but it does not

import numpy as np
import math

w1=np.array([[1,2,3],[4,5,6],[7,8,9]])
w2=np.zeros_like(w1)
print 'w1=',w1
for n in range(0,3):
    for i in range(0,3):
        for j in range(0,3):
            print 'n=',n,'i=',i,'j=',j,'w1=',w1
            w2[i,j]=w1[i,j]*2

    w1=w2[:]


#Simple tests
# w=w2[:]
# w1=w[:]

# p=[1,2,3]
# q=p[:];
# q[1]=0;
# print p

推荐答案

问题是，当您将值从w2分配回w1时，实际上并没有将值从w1传递给w2，但是实际上您是将两个变量指向相同的对象.

The issue is that when you're assigning values back to w1 from w2 you aren't actually passing the values from w1 to w2, but rather you are actually pointing the two variables at the same object.

您遇到的问题

w1 = np.array([1,2,3])
w2 = w1

w2[0] = 3

print(w2)   # [3 2 3]
print(w1)   # [3 2 3]

np.may_share_memory(w2, w1)  # True

解决方案

相反，您将需要复制值.使用numpy数组有两种常见的方法.

Instead you will want to copy over the values. There are two common ways of doing this with numpy arrays.

w1 = numpy.copy(w2)
w1[:] = w2[:]

示范

w1 = np.array([1,2,3])
w2 = np.zeros_like(w1)

w2[:] = w1[:]

w2[0] = 3

print(w2)   # [3 2 3]
print(w1)   # [1 2 3]

np.may_share_memory(w2, w1)   # False

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