Permute all unique enumerations of a vector in R
I'm trying to find a function that will permute all the unique permutations of a vector, while not counting juxtapositions within subsets of the same element type. For example:
dat <- c(1,0,3,4,1,0,0,3,0,4)
has
factorial(10)
> 3628800
possible permutations, but only 10!/(2!*2!*4!*2!)
factorial(10)/(factorial(2)*factorial(2)*factorial(2)*factorial(4))
> 18900
unique permutations when ignoring juxtapositions within subsets of the same element type.
I can get this by using unique() and the permn() function from the package combinat
unique( permn(dat) )
but this is computationally very expensive, since it involves enumerating n!, which can be an order of magnitude more permutati开发者_运维知识库ons than I need. Is there a way to do this without first computing n!?
EDIT: Here's a faster answer; again based on the ideas of Louisa Grey and Bryce Wagner, but with faster R code thanks to better use of matrix indexing. It's quite a bit faster than my original:
> ddd <- c(1,0,3,4,1,0,0,3,0,4)
> system.time(up1 <- uniqueperm(d))
user system elapsed
0.183 0.000 0.186
> system.time(up2 <- uniqueperm2(d))
user system elapsed
0.037 0.000 0.038
And the code:
uniqueperm2 <- function(d) {
dat <- factor(d)
N <- length(dat)
n <- tabulate(dat)
ng <- length(n)
if(ng==1) return(d)
a <- N-c(0,cumsum(n))[-(ng+1)]
foo <- lapply(1:ng, function(i) matrix(combn(a[i],n[i]),nrow=n[i]))
out <- matrix(NA, nrow=N, ncol=prod(sapply(foo, ncol)))
xxx <- c(0,cumsum(sapply(foo, nrow)))
xxx <- cbind(xxx[-length(xxx)]+1, xxx[-1])
miss <- matrix(1:N,ncol=1)
for(i in seq_len(length(foo)-1)) {
l1 <- foo[[i]]
nn <- ncol(miss)
miss <- matrix(rep(miss, ncol(l1)), nrow=nrow(miss))
k <- (rep(0:(ncol(miss)-1), each=nrow(l1)))*nrow(miss) +
l1[,rep(1:ncol(l1), each=nn)]
out[xxx[i,1]:xxx[i,2],] <- matrix(miss[k], ncol=ncol(miss))
miss <- matrix(miss[-k], ncol=ncol(miss))
}
k <- length(foo)
out[xxx[k,1]:xxx[k,2],] <- miss
out <- out[rank(as.numeric(dat), ties="first"),]
foo <- cbind(as.vector(out), as.vector(col(out)))
out[foo] <- d
t(out)
}
It doesn't return the same order, but after sorting, the results are identical.
up1a <- up1[do.call(order, as.data.frame(up1)),]
up2a <- up2[do.call(order, as.data.frame(up2)),]
identical(up1a, up2a)
For my first attempt, see the edit history.
The following function (which implements the classic formula for repeated permutations just like you did manually in your question) seems quite fast to me:
upermn <- function(x) {
n <- length(x)
duplicates <- as.numeric(table(x))
factorial(n) / prod(factorial(duplicates))
}
It does compute n! but not like permn function which generates all permutations first.
See it in action:
> dat <- c(1,0,3,4,1,0,0,3,0,4)
> upermn(dat)
[1] 18900
> system.time(uperm(dat))
user system elapsed
0.000 0.000 0.001
UPDATE: I have just realized that the question was about generating all unique permutations not just specifying the number of them - sorry for that!
You could improve the unique(perm(...)) part with specifying unique permutations for one less element and later adding the uniqe elements in front of them. Well, my explanation may fail, so let the source speak:
uperm <- function(x) {
u <- unique(x) # unique values of the vector
result <- x # let's start the result matrix with the vector
for (i in 1:length(u)) {
v <- x[-which(x==u[i])[1]] # leave the first occurance of duplicated values
result <- rbind(result, cbind(u[i], do.call(rbind, unique(permn(v)))))
}
return(result)
}
This way you could gain some speed. I was lazy to run the code on the vector you provided (took so much time), here is a small comparison on a smaller vector:
> dat <- c(1,0,3,4,1,0,0)
> system.time(unique(permn(dat)))
user system elapsed
0.264 0.000 0.268
> system.time(uperm(dat))
user system elapsed
0.147 0.000 0.150
I think you could gain a lot more by rewriting this function to be recursive!
UPDATE (again): I have tried to make up a recursive function with my limited knowledge:
uperm <- function(x) {
u <- sort(unique(x))
l <- length(u)
if (l == length(x)) {
return(do.call(rbind,permn(x)))
}
if (l == 1) return(x)
result <- matrix(NA, upermn(x), length(x))
index <- 1
for (i in 1:l) {
v <- x[-which(x==u[i])[1]]
newindex <- upermn(v)
if (table(x)[i] == 1) {
result[index:(index+newindex-1),] <- cbind(u[i], do.call(rbind, unique(permn(v))))
} else {
result[index:(index+newindex-1),] <- cbind(u[i], uperm(v))
}
index <- index+newindex
}
return(result)
}
Which has a great gain:
> system.time(unique(permn(c(1,0,3,4,1,0,0,3,0))))
user system elapsed
22.808 0.103 23.241
> system.time(uperm(c(1,0,3,4,1,0,0,3,0)))
user system elapsed
4.613 0.003 4.645
Please report back if this would work for you!
One option that hasn't been mentioned here is the allPerm function from the multicool package. It can be used pretty easily to get all the unique permutations:
library(multicool)
perms <- allPerm(initMC(dat))
dim(perms)
# [1] 18900 10
head(perms)
# [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
# [1,] 4 4 3 3 1 1 0 0 0 0
# [2,] 0 4 4 3 3 1 1 0 0 0
# [3,] 4 0 4 3 3 1 1 0 0 0
# [4,] 4 4 0 3 3 1 1 0 0 0
# [5,] 3 4 4 0 3 1 1 0 0 0
# [6,] 4 3 4 0 3 1 1 0 0 0
In benchmarking I found it to be faster on dat than the solutions from the OP and daroczig but slower than the solution from Aaron.
I don't actually know R, but here's how I'd approach the problem:
Find how many of each element type, i.e.
4 X 0
2 X 1
2 X 3
2 X 4
Sort by frequency (which the above already is).
Start with the most frequent value, which takes up 4 of the 10 spots. Determine the unique combinations of 4 values within the 10 available spots. (0,1,2,3),(0,1,2,4),(0,1,2,5),(0,1,2,6) ... (0,1,2,9),(0,1,3,4),(0,1,3,5) ... (6,7,8,9)
Go to the second most frequent value, it takes up 2 of 6 available spots, and determine it's unique combinations of 2 of 6. (0,1),(0,2),(0,3),(0,4),(0,5),(1,2),(1,3) ... (4,6),(5,6)
Then 2 of 4: (0,1),(0,2),(0,3),(1,2),(1,3),(2,3)
And the remaining values, 2 of 2: (0,1)
Then you need to combine them into each possible combination. Here's some pseudocode (I'm convinced there's a more efficient algorithm for this, but this shouldn't be too bad):
lookup = (0,1,3,4)
For each of the above sets of combinations, example: input = ((0,2,4,6),(0,2),(2,3),(0,1))
newPermutation = (-1,-1,-1,-1,-1,-1,-1,-1,-1,-1)
for i = 0 to 3
index = 0
for j = 0 to 9
if newPermutation(j) = -1
if index = input(i)(j)
newPermutation(j) = lookup(i)
break
else
index = index + 1
Another option is the iterpc package, I believe it is the fastest of the existing method. More importantly, the result is in dictionary order (which may be somehow preferable).
dat <- c(1, 0, 3, 4, 1, 0, 0, 3, 0, 4)
library(iterpc)
getall(iterpc(table(dat), order=TRUE))
The benchmark indicates that iterpc is significant faster than all other methods described here
library(multicool)
library(microbenchmark)
microbenchmark(uniqueperm2(dat),
allPerm(initMC(dat)),
getall(iterpc(table(dat), order=TRUE))
)
Unit: milliseconds
expr min lq mean median
uniqueperm2(dat) 23.011864 25.33241 40.141907 27.143952
allPerm(initMC(dat)) 1713.549069 1771.83972 1814.434743 1810.331342
getall(iterpc(table(dat), order = TRUE)) 4.332674 5.18348 7.656063 5.989448
uq max neval
64.147399 74.66312 100
1855.869670 1937.48088 100
6.705741 49.98038 100
As this question is old and continues to attract many views, this post is solely meant to inform R users of the current state of the language with regards to performing the popular task outlined by the OP. As @RandyLai alludes to, there are packages developed with this task in mind. They are: arrangements and RcppAlgos*.
Efficiency
They are very efficient and quite easy to use for generating permutations of a multiset.
dat <- c(1, 0, 3, 4, 1, 0, 0, 3, 0, 4)
dim(RcppAlgos::permuteGeneral(sort(unique(dat)), freqs = table(dat)))
[1] 18900 10
microbenchmark(algos = RcppAlgos::permuteGeneral(sort(unique(dat)), freqs = table(dat)),
arngmnt = arrangements::permutations(sort(unique(dat)), freq = table(dat)),
curaccptd = uniqueperm2(dat), unit = "relative")
Unit: relative
expr min lq mean median uq max neval
algos 1.000000 1.000000 1.0000000 1.000000 1.000000 1.0000000 100
arngmnt 1.501262 1.093072 0.8783185 1.089927 1.133112 0.3238829 100
curaccptd 19.847457 12.573657 10.2272080 11.705090 11.872955 3.9007364 100
With RcppAlgos we can utilize parallel processing for even better efficiency on larger examples.
hugeDat <- rep(dat, 2)[-(1:5)]
RcppAlgos::permuteCount(sort(unique(hugeDat)), freqs = table(hugeDat))
[1] 3603600
microbenchmark(algospar = RcppAlgos::permuteGeneral(sort(unique(hugeDat)),
freqs = table(hugeDat), nThreads = 4),
arngmnt = arrangements::permutations(sort(unique(hugeDat)), freq = table(hugeDat)),
curaccptd = uniqueperm2(hugeDat), unit = "relative", times = 10)
Unit: relative
expr min lq mean median uq max neval
algospar 1.00000 1.000000 1.000000 1.000000 1.00000 1.00000 10
arngmnt 3.23193 3.109092 2.427836 2.598058 2.15965 1.79889 10
curaccptd 49.46989 45.910901 34.533521 39.399481 28.87192 22.95247 10
Lexicographical Order
A nice benefit of these packages is that the output is in lexicographical order:
head(RcppAlgos::permuteGeneral(sort(unique(dat)), freqs = table(dat)))
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 0 0 0 0 1 1 3 3 4 4
[2,] 0 0 0 0 1 1 3 4 3 4
[3,] 0 0 0 0 1 1 3 4 4 3
[4,] 0 0 0 0 1 1 4 3 3 4
[5,] 0 0 0 0 1 1 4 3 4 3
[6,] 0 0 0 0 1 1 4 4 3 3
tail(RcppAlgos::permuteGeneral(sort(unique(dat)), freqs = table(dat)))
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[18895,] 4 4 3 3 0 1 1 0 0 0
[18896,] 4 4 3 3 1 0 0 0 0 1
[18897,] 4 4 3 3 1 0 0 0 1 0
[18898,] 4 4 3 3 1 0 0 1 0 0
[18899,] 4 4 3 3 1 0 1 0 0 0
[18900,] 4 4 3 3 1 1 0 0 0 0
identical(RcppAlgos::permuteGeneral(sort(unique(dat)), freqs = table(dat)),
arrangements::permutations(sort(unique(dat)), freq = table(dat)))
[1] TRUE
Iterators
Additionally, both packages offer iterators that allow for memory efficient generation of permutation, one by one:
algosIter <- RcppAlgos::permuteIter(sort(unique(dat)), freqs = table(dat))
algosIter$nextIter()
[1] 0 0 0 0 1 1 3 3 4 4
algosIter$nextNIter(5)
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 0 0 0 0 1 1 3 4 3 4
[2,] 0 0 0 0 1 1 3 4 4 3
[3,] 0 0 0 0 1 1 4 3 3 4
[4,] 0 0 0 0 1 1 4 3 4 3
[5,] 0 0 0 0 1 1 4 4 3 3
## last permutation
algosIter$back()
[1] 4 4 3 3 1 1 0 0 0 0
## use reverse iterator methods
algosIter$prevNIter(5)
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 4 4 3 3 1 0 1 0 0 0
[2,] 4 4 3 3 1 0 0 1 0 0
[3,] 4 4 3 3 1 0 0 0 1 0
[4,] 4 4 3 3 1 0 0 0 0 1
[5,] 4 4 3 3 0 1 1 0 0 0
* I am the author of RcppAlgos
Another option is by using the Rcpp package. The difference is that it returns a list.
//[[Rcpp::export]]
std::vector<std::vector< int > > UniqueP(std::vector<int> v){
std::vector< std::vector<int> > out;
std::sort (v.begin(),v.end());
do {
out.push_back(v);
} while ( std::next_permutation(v.begin(),v.end()));
return out;
}
Unit: milliseconds
expr min lq mean median uq max neval cld
uniqueperm2(dat) 10.753426 13.5283 15.61438 13.751179 16.16061 34.03334 100 b
UniqueP(dat) 9.090222 9.6371 10.30185 9.838324 10.20819 24.50451 100 a
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