寻找多维数组代码的加速 [英] Looking for speedups for multidimensional array code

问题描述

我有 2 个多维数组 - 一个 4D 数组和一个 3D 数组 - 以及一些代码来查找沿维度的 4D 数组的最大值，并基于此从 3D 数组中进行选择.目前它很慢，我想加快速度.

I have 2 multidimensional arrays - a 4D array and a 3D array - and some code to to find the maximum of the 4D array along a dimension, and make an index for selecting from the 3D array based on this. At the moment it's quite slow and I'd like to speed things up.

正则表达式:

library(microbenchmark)

# Make some arrays to test with
array4d <- array( runif(5*500*50*5 ,-1,0),
                  dim = c(5, 500, 50, 5) )
array3d <- array( runif(5*500*5, 0, 1),
                        dim = c(5, 500, 5))

# The code of interest
microbenchmark( {
    max_idx <- apply(array4d, c(1,2,3), which.max )
    selections <- list()
    for( i in 1:dim(array4d)[3] ){
        selections[[i]] <- apply(array3d, c(1,2), which.max) == max_idx[ , , i]
    }
})

感谢任何提示！

(一个附带问题是我正在考虑将 which.max 替换为 nnet::which.is.max 以随机打破关系)

(A side issue is I'm considering replacing which.max by nnet::which.is.max to have random breaking of ties)

感谢@GKi，一个更快的解决方案，但我仍然希望有一些加速:

A faster solution thanks to @GKi, but I'm still hoping for some speedups:

max_idx <- apply(array4d, c(1,2,3), which.max)
max_idx2 <- apply(array3d, c(1,2), which.max)
selections <- lapply(seq_len(dim(array4d)[3]), function(i) max_idx2 == max_idx[ , , i])

推荐答案

你可以把 apply(array3d, c(1,2), which.max) 放在循环之外.

You can put apply(array3d, c(1,2), which.max) outside the loop.

microbenchmark( {
  max_idx <- apply(array4d, c(1,2,3), which.max)
  max_idx2 <- apply(array3d, c(1,2), which.max)
  selections <- lapply(seq_len(dim(array4d)[3]), function(i) max_idx2 == max_idx[ , , i])
},
{
  max_idx <- apply(array4d, c(1,2,3), which.max )
  selections <- list()
  for( i in 1:dim(array4d)[3] ){
    selections[[i]] <- apply(array3d, c(1,2), which.max) == max_idx[ , , i]
  }
})
#      min       lq     mean   median       uq      max neval cld
# 204.1650 228.0010 260.3101 256.0132 271.6664 433.8932   100  a 
# 396.5284 448.4167 495.3885 487.7741 530.9028 693.5601   100   b

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