矩阵的哪些行等于某个向量 [英] Which lines of a matrix are equal to a certain vector

问题描述

我有一段代码搜索矩阵 boxes 的哪些行等于给定的向量 x.这段代码使用了apply函数，不知道能不能进一步优化一下?

I have a piece of code searching which lines of a matrix boxes are equal to a given vector x. This codes uses the apply function, and i wonder if it can be optimized more ?

x = floor(runif(4)*10)/10
boxes = as.matrix(do.call(expand.grid, lapply(1:4, function(x) {
  seq(0, 1 - 1/10, length = 10)
})))

# can the following line be more optimised ? :
result <- which(sapply(1:nrow(boxes),function(i){all(boxes[i,] == x)}))

我自己没有设法摆脱 apply 功能，但也许你会有比我更好的想法:)

I did not manage to get rid of the apply function myself but maybe you'll have better ideas than me :)

推荐答案

一个选项是 which(colSums(t(boxes) == x) == ncol(boxes)).

向量是按列循环的，所以我们需要在用 == 与 x 进行比较之前转置 boxes.然后我们可以选择 which 列(转置行)具有 ncol(boxes) 的总和，即所有 TRUE 值.

Vectors are recycled column-wise, so we need to transpose boxes before comparing to x with ==. Then we can pick which column (transposed row) has a sum of ncol(boxes), i.e. all TRUE values.

这是这个(可能不具有代表性)示例的基准

Here's a benchmark for this (possibly not representative) example

Irnv <- function() which(sapply(1:nrow(boxes),function(i){all(boxes[i,] == x)}))
ICT <- function() which(colSums(t(boxes) == x) == ncol(boxes))
RS <- function() which(rowSums(mapply(function(i, j) boxes[, i] == j, seq_len(ncol(boxes)), x)) == length(x))
RS2 <- function(){ 
  boxes <- data.frame(boxes)
  which(rowSums(mapply(`==`, boxes, x)) == length(x))
}
akrun <- function() which(rowSums((boxes == x[col(boxes)])) == ncol(boxes))


microbenchmark(Irnv(), ICT(), RS(), RS2(), akrun())
# Unit: microseconds
#     expr       min         lq       mean     median         uq       max neval
#   Irnv() 19218.470 20122.2645 24182.2337 21882.8815 24949.1385 66387.719   100
#    ICT()   300.308   323.2830   466.0395   342.3595   430.1545  7878.978   100
#     RS()   566.564   586.2565   742.4252   617.2315   688.2060  8420.927   100
#    RS2()   698.257   772.3090  1017.0427   842.2570   988.9240  9015.799   100
#  akrun()   442.667   453.9490   579.9102   473.6415   534.5645  6870.156   100

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