bsxfun是否真的在元素上应用了? [英] Is bsxfun really applied element-wise?

问题描述

假设我具有以下功能:

function x = printAndKeepX(x, y)
    x
    y
end

，我像这样调用bsxfun:

bsxfun(@printAndKeepX, 1:4, 1);

实际上是逐个元素，我希望printAndKeepX被调用4次，而(x, y)的参数分别是(1, 1)，(2, 1)，(3, 1)和(4, 1) .但是输出显示，仅在(x, y)为([1 2 3 4], 1)的情况下调用了它一次:

Were bsxfun really element-by-element, I would expect printAndKeepX to be called 4 times, with the arguments (x, y) being (1, 1), (2, 1), (3, 1) and (4, 1), respectively. But the output shows that it is called just once with (x, y) being ([1 2 3 4], 1):

x =
     1     2     3     4

y =
     1

为什么?我怎么知道什么被认为是元素"?

Why? How can I know what's considered an "element"?

修改:
文档建议，有时被调用函数可以接收两个标量，有时一个可以接收一个标量.向量/矩阵和标量.我能确定哪些会发生吗?

The documentation suggests that sometimes the called function can receive two scalars and sometimes a vector/matrix and a scalar. Can I know for sure which of these is going to happen?

我对bsxfun的常规版本和GPU版本都感兴趣.

I'm interested in both the regular and GPU versions of bsxfun.

推荐答案

文档还指出:

fun还必须支持标量扩展，例如，如果A或B是标量，则C是将标量应用于其他输入数组中的每个元素的结果.

fun must also support scalar expansion, such that if A or B is a scalar, C is the result of applying the scalar to every element in the other input array.

在您的情况下B实际上是标量，因此您的函数仅在A上应用一次.

In your case B is in fact a scalar, so your function is applied on A only once.

当输入数组是矩阵时，也是如此.例如，考虑使用大小为 m ×A = { a _ij }调用bsxfun的情况> n 和大小为 m ×1的向量B = { b _ij }. B将沿第二维复制 n 次，该函数的调用方式如下:

The same applies when the input arrays are matrices. For example, consider a case where bsxfun is invoked with a matrix A={ a_ij } of size m×n and a vector B={ b_ij } of size m×1. B would be replicated n times along the second dimension, and the function would be called as follows:

function([a11, ..., a1n], b1)
function([a21, ..., a2n], b2)
...
function([am1, ..., amn], bm)

function([a11, ..., a1n], b1)
function([a21, ..., a2n], b2)
...
function([am1, ..., amn], bm)

这将导致对向量标量输入的 n 函数调用，而不是对成对的标量的 mn 函数调用.如果将该函数向量化，则可能会显着提高性能.

This results in n function calls for vector-scalar inputs rather then mn function calls for pairs of scalars. If the function is vectorized, it may be reflected in a possibly significant performance gain.

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