R 用偏移量i将矩阵拆分为大小为n的块（矢量化方法）

我想将大小为

kxl

的矩阵拆分为大小为

nxn

的块，考虑一组

o

（就像Mathematica的函数那样）

例如，给定一个矩阵

a

类似

A <- matrix(seq(1:16), nrow = 4, ncol = 4)

     [,1] [,2] [,3] [,4]
[1,]    1    5    9   13
[2,]    2    6   10   14
[3,]    3    7   11   15
[4,]    4    8   12   16

如果offset等于2或3，那么本例的输出应该只是我从中得到的子矩阵

A[1:3, 1:3]

---

如何将其矢量化？

可能有一种更优雅的方法。下面是通过编写一个模拟mathematica

Partition

函数的

myPartition

函数来实现的。首先使用

Map

沿行轴和列轴构建可能的索引，其中使用

seq

考虑

offset

，然后使用

cross2

from

purrr

构建子集索引的所有可能组合的列表。最后使用

lappy

对矩阵进行子集，并返回子集矩阵列表

偏移量

1

、

2

和

3

的测试结果如下所示，其表现似乎与预期相符：

library(purrr)
ind <- function(k, n, o) Map(`:`, seq(1, k-n+1, by = o), seq(n, k, by = o))

# this is a little helper function that generates subset index according to dimension of the 
# matrix, the first sequence construct the starting point of the subset index with an interval 
# of o which is the offset while the second sequence construct the ending point of the subset index
# use Map to construct vector from start to end which in OP's case will be 1:3 and 2:4. 

myPartition <- function(mat, n, o) {
    lapply(cross2(ind(nrow(mat),n,o), ind(ncol(mat),n,o)), function(i) mat[i[[1]], i[[2]]])
}

# This is basically an lapply. we use cross2 to construct combinations of all subset index
# which will be 1:3 and 1:3, 1:3 and 2:4, 2:4 and 1:3 and 2:4 and 2:4 in OP's case. Use lapply
# to loop through the index and subset.

# Testing case for offset = 1
myPartition(A, 3, 1)

# [[1]]
#      [,1] [,2] [,3]
# [1,]    1    5    9
# [2,]    2    6   10
# [3,]    3    7   11

# [[2]]
#      [,1] [,2] [,3]
# [1,]    2    6   10
# [2,]    3    7   11
# [3,]    4    8   12

# [[3]]
#      [,1] [,2] [,3]
# [1,]    5    9   13
# [2,]    6   10   14
# [3,]    7   11   15

# [[4]]
#      [,1] [,2] [,3]
# [1,]    6   10   14
# [2,]    7   11   15
# [3,]    8   12   16

# Testing case for offset = 2
myPartition(A, 3, 2)
# [[1]]
#      [,1] [,2] [,3]
# [1,]    1    5    9
# [2,]    2    6   10
# [3,]    3    7   11

# Testing case for offset = 3
myPartition(A, 3, 3)
# [[1]]
#      [,1] [,2] [,3]
# [1,]    1    5    9
# [2,]    2    6   10
# [3,]    3    7   11

库（purrr）
ind如何使用
base
R，其思想是在考虑
偏移量的同时，生成大小为
n*n
的所有可能窗口（即
风
）。然后在矩阵
A
中打印
winds
元素的所有可能排列（即
perms
）。它适用于大小为
k*l
的任何
A

A <- matrix(seq(1:16), nrow = 4, ncol = 4)
c <- ncol(A); r <- nrow(A)
offset <- 1; size <- 3
sq <- seq(1, max(r,c), offset)
winds <- t(sapply(sq, function(x) c(x,(x+size-1))))
winds <- winds[winds[,2]<=max(r, c),] # check the range
if (is.vector(winds)) dim(winds) <- c(1,2) # vector to matrix
perms <- expand.grid(list(1:nrow(winds), 1:nrow(winds)))
out=apply(perms, 1, function(x) {
   a11 <- winds[x[1],1];a12 <- winds[x[1],2];a21 <- winds[x[2],1];a22 <- winds[x[2],2]
   if (ifelse(r<c, a12<=r, a22<=c)) { # check the range
       cat("A[", a11, ":", a12, ", ", a21, ":", a22, "]", sep="", "\n")
       print(A[a11:a12, a21:a22])
   }
})

# A[1:3, 1:3]
     # [,1] [,2] [,3]
# [1,]    1    5    9
# [2,]    2    6   10
# [3,]    3    7   11
# A[2:4, 1:3]
     # [,1] [,2] [,3]
# [1,]    2    6   10
# [2,]    3    7   11
# [3,]    4    8   12
# A[1:3, 2:4]
     # [,1] [,2] [,3]
# [1,]    5    9   13
# [2,]    6   10   14
# [3,]    7   11   15
# A[2:4, 2:4]
     # [,1] [,2] [,3]
# [1,]    6   10   14
# [2,]    7   11   15
# [3,]    8   12   16

对于
offset=2
和
size=2
：

# A[1:2, 1:2]
     # [,1] [,2]
# [1,]    1    5
# [2,]    2    6
# A[3:4, 1:2]
     # [,1] [,2]
# [1,]    3    7
# [2,]    4    8
# A[1:2, 3:4]
     # [,1] [,2]
# [1,]    9   13
# [2,]   10   14
# A[3:4, 3:4]
     # [,1] [,2]
# [1,]   11   15
# [2,]   12   16

您可以使用负索引，尽管这可能是特定于大小写的：
apply（combn（c（-1，-1，-4，-4），2），2，function（x）{list（A[x[1]，x[2]]）
如果
offset==2
，我们应该有什么？@Psidom，一个两行两列的偏移量。在上述示例的4 x 4矩阵中，偏移量2和偏移量3都给出了子矩阵
A[1:3，1:3]
，这可能是类似的-特别是如果删除了对阵列部分的简化
# A[1:3, 1:3]
     # [,1] [,2] [,3]
# [1,]    1    5    9
# [2,]    2    6   10
# [3,]    3    7   11

# A[1:2, 1:2]
     # [,1] [,2]
# [1,]    1    5
# [2,]    2    6
# A[3:4, 1:2]
     # [,1] [,2]
# [1,]    3    7
# [2,]    4    8
# A[1:2, 3:4]
     # [,1] [,2]
# [1,]    9   13
# [2,]   10   14
# A[3:4, 3:4]
     # [,1] [,2]
# [1,]   11   15
# [2,]   12   16