-vs-=使用numpy的运算符 [英] - vs -= operators with numpy

问题描述

我的python代码中有一些与-和-=相关的奇怪行为.我正在使用numpy编写QR分解，并在双循环中包含以下代码行:

I'm having some strange behavior in my python code related to - and -=. I'm writing a QR decomposition using numpy, and have the following line of code in a double loop:

v = v - r[i,j] * q[:,i]

其中q和r都是numpy.array，而v是另一个numpy.array的切片，作为v = x[:,j].

where q and r are both numpy.array, and v is a slice of another numpy.array taken as v = x[:,j].

上面的代码并非在所有情况下都能正常工作.但是，如果我进行以下更改:

The above code doesn't work as expected in all cases. However, if I make the following change:

v -= r[i,j] * q[:,i]

然后一切都完美无瑕.

我的印象是这两行应该是相同的.为了测试-=和_ = _ -的工作方式是否不同，我创建了以下代码段

I was under the impression that those two lines should be identical. To test whether -= and _ = _ - were working differently, I created the following snippet

import numpy

x = numpy.array(range(0,6))
y = numpy.array(range(0,6))

u = x[3:5]
v = y[3:5]

print u,v

u = u - [1,1]
v -= [1,1]

print u,v

再次按预期工作，在两个打印语句中均生成[2 3] [2 3].

which again works as expected, producing [2 3] [2 3] at both print statements.

所以我很困惑为什么这两行表现不同.我唯一想到的可能是我有时要处理极小的数字(数量级为10 ^ -8或更小)，并且存在一些-=更好的精度问题?随着x的元素变小，第一行的性能越来越差.

So I'm entirely confused why those two lines perform differently. The only possible thing I can think of is that I am dealing with extremely small numbers sometimes (on the order of 10^-8 or smaller) and there is some precision issue that -= is better at? The first line performs increasingly worse as the elements of x get smaller.

对于是否有与此类似的问题还有其他帖子，我深表歉意，我无法搜索-和-=，而且我不知道除任务分配/操作员之外是否还有其他正确的用语.

I apologize if there's any other posts about this similar issue, I can't search for - and -= and I don't know if there's any correct terms for these besides assignment/operators.

感谢您的帮助！

推荐答案

当v是切片时，v -= X和v = v - X会产生非常不同的结果.考虑

When v is a slice, then v -= X and v = v - X produce very different results. Consider

>>> x = np.arange(6)
>>> v = x[1:4]
>>> v -= 1
>>> v
array([0, 1, 2])
>>> x
array([0, 0, 1, 2, 4, 5])

其中v -= 1更新切片，并因此更新其查看的数组.

where v -= 1 updates the slice, and therefore the array that it views, in-place, vs.

>>> x = np.arange(6)
>>> v = x[1:4]
>>> v = v - 1
>>> v
array([0, 1, 2])
>>> x
array([0, 1, 2, 3, 4, 5])

其中，v = v - 1重置变量v，同时保持x不变.要在没有-=的情况下获得前一个结果，您必须

where v = v - 1 resets the variable v while leaving x untouched. To obtain the former result without -=, you'd have to do

v[:] = v - 1

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