[英]find neighbouring elements of a matrix in R
Edit: huge thanks to the users below for great contributions and to Gregor for benchmarking.编辑:非常感谢以下用户的巨大贡献和 Gregor 的基准测试。
Say I have a matrix filled with integer values like this...假设我有一个填充了这样的整数值的矩阵......
mat <- matrix(1:100, 10, 10)
I can create a list of x, y coordinates of each element like this...我可以像这样创建每个元素的 x、y 坐标列表......
addresses <- expand.grid(x = 1:10, y = 1:10)
Now for each of these coordinates (ie for each element in mat) i would like to find the neighboring elements (including diagonals this should make 8 neighbors).现在对于这些坐标中的每一个(即对于 mat 中的每个元素),我想找到相邻元素(包括对角线,这应该是 8 个相邻元素)。
I'm sure there is an easy way, can anyone help?我确定有一个简单的方法,有人可以帮忙吗?
What I have tried so far is to loop through and for each element record the neighboring elements as follows;到目前为止,我所尝试的是循环并为每个元素记录相邻元素,如下所示;
neighbours <- list()
for(i in 1:dim(addresses)[1]){
x <- addresses$x[i]
y <- addresses$y[i]
neighbours[[i]] <- c(mat[y-1, x ],
mat[y-1, x+1],
mat[y , x+1],
mat[y+1, x+1],
mat[y+1, x ],
mat[y+1, x-1],
mat[y , x-1],
mat[y-1, x-1])
}
This runs into problems when it hits the edge of the matrix, particularly when the index is greater than the edge of the matrix.当它碰到矩阵的边缘时会遇到问题,特别是当索引大于矩阵的边缘时。
Here's a nice example. 这是一个很好的例子。 I did 4x4 so we can see it easily, but it's all adjustable by
n
. 我做了4x4所以我们可以很容易地看到它,但它都可以通过
n
调整。 It's also fully vectorized so should have good speed. 它也完全矢量化,所以应该具有良好的速度。
n = 4
mat = matrix(1:n^2, nrow = n)
mat.pad = rbind(NA, cbind(NA, mat, NA), NA)
With the padded matrix, the neighbors are just n by n submatrices, shifting around. 使用填充矩阵,邻居只有n个子矩阵,转移。 Using compass directions as labels:
使用罗盘方向作为标签:
ind = 2:(n + 1) # row/column indices of the "middle"
neigh = rbind(N = as.vector(mat.pad[ind - 1, ind ]),
NE = as.vector(mat.pad[ind - 1, ind + 1]),
E = as.vector(mat.pad[ind , ind + 1]),
SE = as.vector(mat.pad[ind + 1, ind + 1]),
S = as.vector(mat.pad[ind + 1, ind ]),
SW = as.vector(mat.pad[ind + 1, ind - 1]),
W = as.vector(mat.pad[ind , ind - 1]),
NW = as.vector(mat.pad[ind - 1, ind - 1]))
mat
# [,1] [,2] [,3] [,4]
# [1,] 1 5 9 13
# [2,] 2 6 10 14
# [3,] 3 7 11 15
# [4,] 4 8 12 16
neigh[, 1:6]
# [,1] [,2] [,3] [,4] [,5] [,6]
# N NA 1 2 3 NA 5
# NE NA 5 6 7 NA 9
# E 5 6 7 8 9 10
# SE 6 7 8 NA 10 11
# S 2 3 4 NA 6 7
# SW NA NA NA NA 2 3
# W NA NA NA NA 1 2
# NW NA NA NA NA NA 1
So you can see for the first element mat[1,1]
, starting at North and going clockwise the neighbors are the first column of neigh
. 所以,你可以看到第一个元素
mat[1,1]
开始在北部和去顺时针邻居们的第一列neigh
。 The next element is mat[2,1]
, and so on down the columns of mat
. 下一个元素是
mat[2,1]
,依此类推mat
的列。 (You can also compare to @mrip's answer and see that our columns have the same elements, just in a different order.) (您也可以与@ mrip的答案进行比较,看看我们的列具有相同的元素,只是顺序不同。)
Small matrix 小矩阵
mat = matrix(1:16, nrow = 4)
mbm(gregor(mat), mrip(mat), marat(mat), u20650(mat), times = 100)
# Unit: microseconds
# expr min lq mean median uq max neval cld
# gregor(mat) 25.054 30.0345 34.04585 31.9960 34.7130 61.879 100 a
# mrip(mat) 420.167 443.7120 482.44136 466.1995 483.4045 1820.121 100 c
# marat(mat) 746.462 784.0410 812.10347 808.1880 832.4870 911.570 100 d
# u20650(mat) 186.843 206.4620 220.07242 217.3285 230.7605 269.850 100 b
On a larger matrix I had to take out user20650's function because it tried to allocate a 232.8 Gb vector, and I also took out Marat's answer after waiting for about 10 minutes. 在一个更大的矩阵上,我不得不取出user20650的功能,因为它试图分配一个232.8 Gb的矢量,我等了大约10分钟后也拿出了Marat的答案。
mat = matrix(1:500^2, nrow = 500)
mbm(gregor(mat), mrip(mat), times = 100)
# Unit: milliseconds
# expr min lq mean median uq max neval cld
# gregor(mat) 19.583951 21.127883 30.674130 21.656866 22.433661 127.2279 100 b
# mrip(mat) 2.213725 2.368421 8.957648 2.758102 2.958677 104.9983 100 a
So it looks like in any case where time matters, @mrip's solutions is by far the fastest. 所以在任何情况下,时间都很重要,@ mrip的解决方案是迄今为止最快的。
Functions used: 使用的功能:
gregor = function(mat) {
n = nrow(mat)
mat.pad = rbind(NA, cbind(NA, mat, NA), NA)
ind = 2:(n + 1) # row/column indices of the "middle"
neigh = rbind(N = as.vector(mat.pad[ind - 1, ind ]),
NE = as.vector(mat.pad[ind - 1, ind + 1]),
E = as.vector(mat.pad[ind , ind + 1]),
SE = as.vector(mat.pad[ind + 1, ind + 1]),
S = as.vector(mat.pad[ind + 1, ind ]),
SW = as.vector(mat.pad[ind + 1, ind - 1]),
W = as.vector(mat.pad[ind , ind - 1]),
NW = as.vector(mat.pad[ind - 1, ind - 1]))
return(neigh)
}
mrip = function(mat) {
m2<-cbind(NA,rbind(NA,mat,NA),NA)
addresses <- expand.grid(x = 1:4, y = 1:4)
ret <- c()
for(i in 1:-1)
for(j in 1:-1)
if(i!=0 || j !=0)
ret <- rbind(ret,m2[addresses$x+i+1+nrow(m2)*(addresses$y+j)])
return(ret)
}
get.neighbors <- function(rw, z, mat) {
# Convert to absolute addresses
z2 <- t(z + unlist(rw))
# Choose those with indices within mat
b.good <- rowSums(z2 > 0)==2 & z2[,1] <= nrow(mat) & z2[,2] <= ncol(mat)
mat[z2[b.good,]]
}
marat = function(mat) {
n.row = n.col = nrow(mat)
addresses <- expand.grid(x = 1:n.row, y = 1:n.col)
# Relative addresses
z <- rbind(c(-1,0,1,-1,1,-1,0,1), c(-1,-1,-1,0,0,1,1,1))
apply(addresses, 1,
get.neighbors, z = z, mat = mat) # Returns a list with neighbors
}
u20650 = function(mat) {
w <- which(mat==mat, arr.ind=TRUE)
d <- as.matrix(dist(w, "maximum", diag=TRUE, upper=TRUE))
# extract neighbouring values for each element
# extract where max distance is one
a <- apply(d, 1, function(i) mat[i == 1] )
names(a) <- mat
return(a)
}
This will get you a matrix with columns corresponding to neighbors of each entry in the matrix: 这将为您提供一个矩阵,其中的列对应于矩阵中每个条目的邻居:
mat <- matrix(1:16, 4, 4)
m2<-cbind(NA,rbind(NA,mat,NA),NA)
addresses <- expand.grid(x = 1:4, y = 1:4)
ret<-c()
for(i in 1:-1)
for(j in 1:-1)
if(i!=0 || j !=0)
ret<-rbind(ret,m2[addresses$x+i+1+nrow(m2)*(addresses$y+j)])
> ret
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
[1,] 6 7 8 NA 10 11 12 NA 14 15 16 NA NA NA
[2,] 2 3 4 NA 6 7 8 NA 10 11 12 NA 14 15
[3,] NA NA NA NA 2 3 4 NA 6 7 8 NA 10 11
[4,] 5 6 7 8 9 10 11 12 13 14 15 16 NA NA
[5,] NA NA NA NA 1 2 3 4 5 6 7 8 9 10
[6,] NA 5 6 7 NA 9 10 11 NA 13 14 15 NA NA
[7,] NA 1 2 3 NA 5 6 7 NA 9 10 11 NA 13
[8,] NA NA NA NA NA 1 2 3 NA 5 6 7 NA 9
[,15] [,16]
[1,] NA NA
[2,] 16 NA
[3,] 12 NA
[4,] NA NA
[5,] 11 12
[6,] NA NA
[7,] 14 15
[8,] 10 11
Here is another approach: 这是另一种方法:
n.col <- 5
n.row <- 10
mat <- matrix(seq(n.col * n.row), n.row, n.col)
addresses <- expand.grid(x = 1:n.row, y = 1:n.col)
# Relative addresses
z <- rbind(c(-1,0,1,-1,1,-1,0,1),c(-1,-1,-1,0,0,1,1,1))
get.neighbors <- function(rw) {
# Convert to absolute addresses
z2 <- t(z + unlist(rw))
# Choose those with indices within mat
b.good <- rowSums(z2 > 0)==2 & z2[,1] <= nrow(mat) & z2[,2] <=ncol(mat)
mat[z2[b.good,]]
}
apply(addresses,1, get.neighbors) # Returns a list with neighbors
Perhaps you may be able to use a distance function here using the row and column indices of the matrix elements. 也许您可以使用矩阵元素的行和列索引来使用距离函数。
# data
(mat <- matrix(16:31, 4, 4))
[,1] [,2] [,3] [,4]
[1,] 16 20 24 28
[2,] 17 21 25 29
[3,] 18 22 26 30
[4,] 19 23 27 31
# find distances between row and column indexes
# interested in values where the distance is one
w <- which(mat==mat, arr.ind=TRUE)
d <- as.matrix(dist(w, "maximum", diag=TRUE, upper=TRUE))
# extract neighbouring values for each element
# extract where max distance is one
a <- apply(d, 1, function(i) mat[i == 1] )
names(a) <- mat
a
$`16`
[1] "17" "20" "21"
$`17`
[1] "16" "18" "20" "21" "22"
$`18`
[1] "17" "19" "21" "22" "23
... ....
... ....
Needs tidied, but maybe gives an idea 需要整理,但可能会提出一个想法
There was also an much faster solution posted here: List of n first Neighbors from a 3d Array R这里也发布了一个更快的解决方案: List of n first Neighbors from a 3d Array R
for large matrices this solution is much faster, see my benchmark here:对于大型矩阵,此解决方案要快得多,请在此处查看我的基准测试:
For small matrices I want to show another very interesting solution, which is fast for small matrices, but slow for bigger ones:对于小矩阵,我想展示另一个非常有趣的解决方案,它对小矩阵来说很快,但对大矩阵来说很慢:
Using complex numbers and the abs
function.使用复数和
abs
函数。
get_neighbor <- function(matrix, x=1,y=1){
z <- complex(real = rep(1:nrow(matrix), ncol(matrix)),
imaginary = rep(1:ncol(matrix), each = nrow(matrix)))
lookup <- lapply(seq_along(z), function(x){
## calculate distance
dist <- which(abs(z - z[x]) < 2)
## remove those with dist == 0 -> it´s the number itself
dist[which(dist != x)]
})
index <- (y-1)*(nrow(matrix))+x
matrix[lookup[[index]]]
}
We can try the following base R code我们可以尝试以下基本 R 代码
f <- Vectorize(function(mat, x, y) {
X <- pmin(pmax(x + c(-1:1), 1), nrow(mat))
Y <- pmin(pmax(y + c(-1:1), 1), ncol(mat))
mat[as.matrix(subset(
unique(expand.grid(X = X, Y = Y)),
!(X == x & Y == y)
))]
},vectorize.args = c("x","y"))
neighbors <- with(addresses, f(mat,x,y))
and we will see我们会看到
> head(neighbors)
[[1]]
[1] 2 11 12
[[2]]
[1] 1 3 11 12 13
[[3]]
[1] 2 4 12 13 14
[[4]]
[1] 3 5 13 14 15
[[5]]
[1] 4 6 14 15 16
[[6]]
[1] 5 7 15 16 17
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