改变的地方numpy的功能输出数组 [英] Altering numpy function output array in place

问题描述

我试图写一个阵列上执行数学运算并返回结果的函数。一个简单的例子可以是：

I'm trying to write a function that performs a mathematical operation on an array and returns the result. A simplified example could be:

def original_func(A):
    return A[1:] + A[:-1]

有关速度和避免分配每个函数调用一个新的输出数组，我想有输出数组作为参数，并改变它在的地方：

For speed-up and to avoid allocating a new output array for each function call, I would like to have the output array as an argument, and alter it in place:

def inplace_func(A, out):
    out[:] = A[1:] + A[:-1]

然而，以下面的方式调用这两个函数时，

However, when calling these two functions in the following manner,

A = numpy.random.rand(1000,1000)
out = numpy.empty((999,1000))

C = original_func(A)

inplace_func(A, out)

原有的功能似乎是<青霉>快两倍作为就地功能。这又如何解释呢？应该不是就地功能会更快，因为它没有分配内存？

the original function seems to be twice as fast as the in-place function. How can this be explained? Shouldn't the in-place function be quicker since it doesn't have to allocate memory?

推荐答案

我的认为的答案是以下内容：

I think that the answer is the following:

在这两种情况下，你计算 A [1：] + A [： - 1]。，并在这两种情况下，你实际上创建中间矩阵

In both cases, you compute A[1:] + A[:-1], and in both cases, you actually create an intermediate matrix.

在第二种情况下，会发生什么，不过，是你的明确的整个大新分配的阵列复制到一个保留内存。复制这样的阵列需要大约相同的时间与原始的操作，所以你实际上两倍的时间。

What happens in the second case, though, is that you explicitly copy the whole big newly allocated array into a reserved memory. Copying such an array takes about the same time as the original operation, so you in fact double the time.

要总结向上，在第一种情况下，你做的：

To sum-up, in the first case, you do:

compute A[1:] + A[:-1] (~10ms)

在第二种情况下，你做

compute A[1:] + A[:-1] (~10ms)
copy the result into out (~10ms)

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