问题描述
我一直在查看此答案中的基准，并希望将它们与diag(用于不同的答案).不幸的是，diag似乎需要一段时间:
I was looking at the benchmarks in this answer, and wanted to compare them with diag (used in a different answer). Unfortunately, it seems that diag takes ages:
nc <- 1e4
set.seed(1)
m <- matrix(sample(letters,nc^2,replace=TRUE), ncol = nc)
microbenchmark(
diag = diag(m),
cond = m[row(m)==col(m)],
vec = m[(1:nc-1L)*nc+1:nc],
mat = m[cbind(1:nc,1:nc)],
times=10)
评论:我用identical进行了测试.我从"此作业问题的答案之一中得出了"cond".结果与整数矩阵1:26而不是letters相似.
Comments: I tested these with identical. I took "cond" from one of the answers to this homework question. Results are similar with a matrix of integers, 1:26 instead of letters.
结果:
Unit: microseconds
expr min lq mean median uq max neval
diag 604343.469 629819.260 710371.3320 706842.3890 793144.019 837115.504 10
cond 3862039.512 3985784.025 4175724.0390 4186317.5260 4312493.742 4617117.706 10
vec 317.088 329.017 432.9099 350.1005 629.460 651.376 10
mat 272.147 292.953 441.7045 345.9400 637.506 706.860 10
这只是一个矩阵子集运算，所以我不知道为什么会有这么多的开销.在函数内部，我看到了一些检查，然后是c(m)[v]，其中v是"vec"基准测试中使用的相同向量.给这两个计时...
It is just a matrix-subsetting operation, so I don't know why there's so much overhead. Looking inside the function, I see a few checks and then c(m)[v], where v is the same vector used in the "vec" benchmark. Timing these two...
v <- (1:nc-1L)*nc+1:nc
microbenchmark(diaglike=c(m)[v],vec=m[v])
# Unit: microseconds
# expr min lq mean median uq max neval
# diaglike 579224.436 664853.7450 720372.8105 712649.706 767281.5070 931976.707 100
# vec 334.843 339.8365 568.7808 646.799 663.5825 1445.067 100
...似乎我找到了罪魁祸首.因此，我的问题的新变化是:为什么在diag中似乎没有必要而且非常耗时的c?
...it seems I have found my culprit. So, the new variation on my question is: Why is there a seemingly unnecessary and very time-consuming c in diag?
推荐答案
摘要
从 R版本3.2.1 (World-著名的宇航员)diag()已收到更新.讨论移至 r-devel ，其中指出c()会剥离非名称属性，这可能就是将其放置在其中的原因.尽管有些人担心删除c()会在类似矩阵的对象上引起未知的问题，但是Peter Dalgaard发现，"diag()内的c()起作用的唯一情况是M[i,j] != M[(i-1)*m+j] AND c(M)会以列优先顺序将M字符串化，以使M[i,j] == c(M)[(i-1)*m+j]."
As of R version 3.2.1 (World-Famous Astronaut) diag() has received an update. The discussion moved to r-devel where it was noted that c() strips non-name attributes and may have been why it was placed there. While some people worried that removing c() would cause unknown issues on matrix-like objects, Peter Dalgaard found that, "The only case where the c() inside diag() has an effect is where M[i,j] != M[(i-1)*m+j] AND c(M) will stringize M in column-major order, so that M[i,j] == c(M)[(i-1)*m+j]."
卢克·蒂尔尼(Luke Tierney)测试了@Frank对c()的删除，发现它对CRAN或BIOC没有任何影响，因此在第27行.这导致diag()中相对较大的加速.下面是一个速度测试，显示了R 3.2.1版本的diag()的改进.
Luke Tierney tested @Frank 's removal of c(), finding it did not effect anything on CRAN or BIOC and so was implemented to replace c(x)[...] with x[...] on line 27. This leads to relatively large speedups in diag(). Below is a speed test showing the improvement with R 3.2.1's version of diag().
library(microbenchmark)
nc <- 1e4
set.seed(1)
m <- matrix(sample(letters,nc^2,replace=TRUE), ncol = nc)
microbenchmark(diagOld(m),diag(m))
Unit: microseconds
expr min lq mean median uq max neval
diagOld(m) 451189.242 526622.2775 545116.5668 531905.5635 540008.704 682223.733 100
diag(m) 222.563 646.8675 644.7444 714.4575 740.701 1015.459 100
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