在R中,如何将矩阵不同边距上的累加总和的计算概括为多维数组?
例如,给定矩阵
a2 <- array(1:6, dim = c(2,3))
[,1] [,2] [,3] [1,] 1 3 5 [2,] 2 4 6
可以使用apply
计算不同边距的累积总和:
apply(a2, 2, cumsum)
[,1] [,2] [,3] [1,] 1 3 5 [2,] 3 7 11
t(apply(a2, 1, cumsum))
[,1] [,2] [,3] [1,] 1 4 9 [2,] 2 6 12
请注意,在后一种情况下,需要进行一些重塑。现在的问题是:
您将如何计算多维数组的累积总和?
例如,对于像这样的三维数组:
a3 <- array(1:24, dim = c(2,3,4))
我对行,列和第三维的累加总和感兴趣,保留了原始数组的结构。具体来说,该行的累计总和应为:
, , 1 [,1] [,2] [,3] [1,] 1 4 9 [2,] 2 6 12 , , 2 [,1] [,2] [,3] [1,] 7 16 27 [2,] 8 18 30 , , 3 [,1] [,2] [,3] [1,] 13 28 45 [2,] 14 30 48 , , 4 [,1] [,2] [,3] [1,] 19 40 63 [2,] 20 42 66
n维数组的答案是什么?
答案 0 :(得分:3)
一种方法是使用旧的for
循环
res <- a3
for (k in 1:dim(a3)[3]) res[, , k] <- t(apply(a3[, , k], 1, cumsum))
res
#, , 1
#
# [,1] [,2] [,3]
#[1,] 1 4 9
#[2,] 2 6 12
#
#, , 2
#
# [,1] [,2] [,3]
#[1,] 7 16 27
#[2,] 8 18 30
#
#, , 3
#
# [,1] [,2] [,3]
#[1,] 13 28 45
#[2,] 14 30 48
#
#, , 4
#
# [,1] [,2] [,3]
#[1,] 19 40 63
#[2,] 20 42 66
答案 1 :(得分:1)
这几乎可以满足您的要求,但是结果是转置的
apply(a3, c(1, 3), cumsum)
#, , 1
# [,1] [,2]
#[1,] 1 2
#[2,] 4 6
#[3,] 9 12
#, , 2
# [,1] [,2]
#[1,] 7 8
#[2,] 16 18
#[3,] 27 30
#, , 3
# [,1] [,2]
#[1,] 13 14
#[2,] 28 30
#[3,] 45 48
#, , 4
# [,1] [,2]
#[1,] 19 20
#[2,] 40 42
#[3,] 63 66
我不知道如何在相同的apply
调用中转置结果(应该有一种方法)。我尝试过
t(apply(a3, c(1, 3), cumsum))
apply(a3, c(1, 3), function(x) t(cumsum(x)))
但这不起作用。但是,现在,如果我们再次使用apply
并转置,我们可以恢复原始结构。
apply(apply(a3, c(1, 3), cumsum), c(1, 3), t)
答案 2 :(得分:1)
使用apply
,然后使用aperm
。唯一棘手的部分是正确设置边距:
aperm(apply(a3, -2, cumsum), c(2, 1, 3))
其中的每一个也可以工作:
aperm(apply(a3, c(1, 3), cumsum), c(2, 1, 3))
aperm(apply(a3, c(3, 1), cumsum), c(3, 1, 2))
apply(apply(a3, -2, cumsum), -2, c)
apply(apply(a3, c(1, 3), cumsum), c(1, 3), c)
library(plyr)
aa <- aperm(aaply(a3, c(1, 3), cumsum), c(1, 3, 2))
dimnames(aa) <- NULL
答案 3 :(得分:0)
从@G推断。格洛腾迪克的答案,此函数使用aperm
来计算n维数组任何边距上的累积和:
array_cumsum <- function(a, margin) {
n <- length(dim(a))
permorder <- append(x = 2:n, 1, margin - 1)
aperm(apply(a, -margin, cumsum), permorder)
}
例如,使用一个简单的由1组成的数组,以便轻松查看累积和,该函数可用于计算第二维上的边距:
a <- array(1, dim = c(2,3,4))
array_cumsum(a3, 2)
# , , 1
#
# [,1] [,2] [,3]
# [1,] 1 2 3
# [2,] 1 2 3
#
# , , 2
#
# [,1] [,2] [,3]
# [1,] 1 2 3
# [2,] 1 2 3
#
# , , 3
#
# [,1] [,2] [,3]
# [1,] 1 2 3
# [2,] 1 2 3
#
# , , 4
#
# [,1] [,2] [,3]
# [1,] 1 2 3
# [2,] 1 2 3
以及第3维:
array_cumsum(a3, 3)
# , , 1
#
# [,1] [,2] [,3]
# [1,] 1 1 1
# [2,] 1 1 1
#
# , , 2
#
# [,1] [,2] [,3]
# [1,] 2 2 2
# [2,] 2 2 2
#
# , , 3
#
# [,1] [,2] [,3]
# [1,] 3 3 3
# [2,] 3 3 3
#
# , , 4
#
# [,1] [,2] [,3]
# [1,] 4 4 4
# [2,] 4 4 4