Create matrix with entries unique to row and column R









up vote
2
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I need to find all possible 5x5 matrix of integers 1-5 unique to each row and column (imagine a Sudoku) in R.



Is there any efficient way of doing this without creating all 120C5 matrices and then finding the fitting ones?



Thank you!










share|improve this question























  • sapply(1:5, function(i) (1:5 - i) %% 5 + 1). (This was an easy one, but for future questions, please show some effort on your part. This isn't a free-code-service. If this is homework, I hope I get an "A".)
    – r2evans
    Nov 8 at 23:02











  • Hi sorry I need to find all possible combinations not just one - sorry for not making that clear.
    – Maximilian Niroomand
    Nov 8 at 23:05






  • 1




    It's an interesting problem, and one I may put on the back burner for a while. However, it's too much for quick Q/As on SO, so you may not get a response. Or you may, who knows. But this will be more effort than most questions (that I see) typically require. Good luck.
    – r2evans
    Nov 8 at 23:09






  • 1




    Such matrices are called latin squares. See stackoverflow.com/a/49203589/1320535 for how to generate random ones. Generating all of them is indeed not straightforward, see en.wikipedia.org/wiki/Latin_square#Algorithms.
    – Julius Vainora
    Nov 8 at 23:18






  • 1




    I agree with @r2evans. This is a computationally interesting question. I strongly recommend that you yourself give this a try first. You can find resources on the net, and even here on SO. Show a code attempt at solving the problem, and I can guarantee that you will get a much better response (and you will learn a lot more as well). At the moment this reads too much like "gimme teh codez".
    – Maurits Evers
    Nov 8 at 23:19















up vote
2
down vote

favorite












I need to find all possible 5x5 matrix of integers 1-5 unique to each row and column (imagine a Sudoku) in R.



Is there any efficient way of doing this without creating all 120C5 matrices and then finding the fitting ones?



Thank you!










share|improve this question























  • sapply(1:5, function(i) (1:5 - i) %% 5 + 1). (This was an easy one, but for future questions, please show some effort on your part. This isn't a free-code-service. If this is homework, I hope I get an "A".)
    – r2evans
    Nov 8 at 23:02











  • Hi sorry I need to find all possible combinations not just one - sorry for not making that clear.
    – Maximilian Niroomand
    Nov 8 at 23:05






  • 1




    It's an interesting problem, and one I may put on the back burner for a while. However, it's too much for quick Q/As on SO, so you may not get a response. Or you may, who knows. But this will be more effort than most questions (that I see) typically require. Good luck.
    – r2evans
    Nov 8 at 23:09






  • 1




    Such matrices are called latin squares. See stackoverflow.com/a/49203589/1320535 for how to generate random ones. Generating all of them is indeed not straightforward, see en.wikipedia.org/wiki/Latin_square#Algorithms.
    – Julius Vainora
    Nov 8 at 23:18






  • 1




    I agree with @r2evans. This is a computationally interesting question. I strongly recommend that you yourself give this a try first. You can find resources on the net, and even here on SO. Show a code attempt at solving the problem, and I can guarantee that you will get a much better response (and you will learn a lot more as well). At the moment this reads too much like "gimme teh codez".
    – Maurits Evers
    Nov 8 at 23:19













up vote
2
down vote

favorite









up vote
2
down vote

favorite











I need to find all possible 5x5 matrix of integers 1-5 unique to each row and column (imagine a Sudoku) in R.



Is there any efficient way of doing this without creating all 120C5 matrices and then finding the fitting ones?



Thank you!










share|improve this question















I need to find all possible 5x5 matrix of integers 1-5 unique to each row and column (imagine a Sudoku) in R.



Is there any efficient way of doing this without creating all 120C5 matrices and then finding the fitting ones?



Thank you!







r matrix






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 8 at 23:07

























asked Nov 8 at 22:58









Maximilian Niroomand

184




184











  • sapply(1:5, function(i) (1:5 - i) %% 5 + 1). (This was an easy one, but for future questions, please show some effort on your part. This isn't a free-code-service. If this is homework, I hope I get an "A".)
    – r2evans
    Nov 8 at 23:02











  • Hi sorry I need to find all possible combinations not just one - sorry for not making that clear.
    – Maximilian Niroomand
    Nov 8 at 23:05






  • 1




    It's an interesting problem, and one I may put on the back burner for a while. However, it's too much for quick Q/As on SO, so you may not get a response. Or you may, who knows. But this will be more effort than most questions (that I see) typically require. Good luck.
    – r2evans
    Nov 8 at 23:09






  • 1




    Such matrices are called latin squares. See stackoverflow.com/a/49203589/1320535 for how to generate random ones. Generating all of them is indeed not straightforward, see en.wikipedia.org/wiki/Latin_square#Algorithms.
    – Julius Vainora
    Nov 8 at 23:18






  • 1




    I agree with @r2evans. This is a computationally interesting question. I strongly recommend that you yourself give this a try first. You can find resources on the net, and even here on SO. Show a code attempt at solving the problem, and I can guarantee that you will get a much better response (and you will learn a lot more as well). At the moment this reads too much like "gimme teh codez".
    – Maurits Evers
    Nov 8 at 23:19

















  • sapply(1:5, function(i) (1:5 - i) %% 5 + 1). (This was an easy one, but for future questions, please show some effort on your part. This isn't a free-code-service. If this is homework, I hope I get an "A".)
    – r2evans
    Nov 8 at 23:02











  • Hi sorry I need to find all possible combinations not just one - sorry for not making that clear.
    – Maximilian Niroomand
    Nov 8 at 23:05






  • 1




    It's an interesting problem, and one I may put on the back burner for a while. However, it's too much for quick Q/As on SO, so you may not get a response. Or you may, who knows. But this will be more effort than most questions (that I see) typically require. Good luck.
    – r2evans
    Nov 8 at 23:09






  • 1




    Such matrices are called latin squares. See stackoverflow.com/a/49203589/1320535 for how to generate random ones. Generating all of them is indeed not straightforward, see en.wikipedia.org/wiki/Latin_square#Algorithms.
    – Julius Vainora
    Nov 8 at 23:18






  • 1




    I agree with @r2evans. This is a computationally interesting question. I strongly recommend that you yourself give this a try first. You can find resources on the net, and even here on SO. Show a code attempt at solving the problem, and I can guarantee that you will get a much better response (and you will learn a lot more as well). At the moment this reads too much like "gimme teh codez".
    – Maurits Evers
    Nov 8 at 23:19
















sapply(1:5, function(i) (1:5 - i) %% 5 + 1). (This was an easy one, but for future questions, please show some effort on your part. This isn't a free-code-service. If this is homework, I hope I get an "A".)
– r2evans
Nov 8 at 23:02





sapply(1:5, function(i) (1:5 - i) %% 5 + 1). (This was an easy one, but for future questions, please show some effort on your part. This isn't a free-code-service. If this is homework, I hope I get an "A".)
– r2evans
Nov 8 at 23:02













Hi sorry I need to find all possible combinations not just one - sorry for not making that clear.
– Maximilian Niroomand
Nov 8 at 23:05




Hi sorry I need to find all possible combinations not just one - sorry for not making that clear.
– Maximilian Niroomand
Nov 8 at 23:05




1




1




It's an interesting problem, and one I may put on the back burner for a while. However, it's too much for quick Q/As on SO, so you may not get a response. Or you may, who knows. But this will be more effort than most questions (that I see) typically require. Good luck.
– r2evans
Nov 8 at 23:09




It's an interesting problem, and one I may put on the back burner for a while. However, it's too much for quick Q/As on SO, so you may not get a response. Or you may, who knows. But this will be more effort than most questions (that I see) typically require. Good luck.
– r2evans
Nov 8 at 23:09




1




1




Such matrices are called latin squares. See stackoverflow.com/a/49203589/1320535 for how to generate random ones. Generating all of them is indeed not straightforward, see en.wikipedia.org/wiki/Latin_square#Algorithms.
– Julius Vainora
Nov 8 at 23:18




Such matrices are called latin squares. See stackoverflow.com/a/49203589/1320535 for how to generate random ones. Generating all of them is indeed not straightforward, see en.wikipedia.org/wiki/Latin_square#Algorithms.
– Julius Vainora
Nov 8 at 23:18




1




1




I agree with @r2evans. This is a computationally interesting question. I strongly recommend that you yourself give this a try first. You can find resources on the net, and even here on SO. Show a code attempt at solving the problem, and I can guarantee that you will get a much better response (and you will learn a lot more as well). At the moment this reads too much like "gimme teh codez".
– Maurits Evers
Nov 8 at 23:19





I agree with @r2evans. This is a computationally interesting question. I strongly recommend that you yourself give this a try first. You can find resources on the net, and even here on SO. Show a code attempt at solving the problem, and I can guarantee that you will get a much better response (and you will learn a lot more as well). At the moment this reads too much like "gimme teh codez".
– Maurits Evers
Nov 8 at 23:19













1 Answer
1






active

oldest

votes

















up vote
3
down vote



accepted










As I said in my comment above, matrices of this type are called Latin squares.



For a start, we already know that there are 56 so-called reduced Latin squares and 161280 all Latin squares of size 5. The reduced Latin squares are such that both the first column and the first row are just 1, 2, 3, 4, 5 (in this order). Given those reduced Latin squares, one may easily (as long as the size is not greater than 5) generate all Latin squares: permute all the rows except the first one and permute all the columns. Hence, as expected, 161280=5!*4!*56.



By restricting the first row and column one could generate 4!*3!*2!=288 matrices and check which 56 are Latin squares. However, I'm going to skip that and take their list from here.



First we read and rearrange the data



reduced <- read.table("http://users.cecs.anu.edu.au/~bdm/data/reduced5.txt", head = FALSE, colClasses = "character")
reduced <- lapply(1:nrow(reduced), function(r) matrix(as.numeric(unlist(strsplit(unlist(reduced[r, ]), ""))) + 1, 5))
length(reduced)
# [1] 56


Now let's generate all 5! and 4! permutations of 1, 2, 3, 4, 5 and 1, 2, 3, 4, respectively.



library(combinat)
perms5 <- permn(1:5)
perms4 <- permn(1:4)


Lastly, we go over all the reduced Latin squares and permute them in all possible ways



allLS <- sapply(reduced, function(m) 
LS <- vector("list", gamma(6) * gamma(5))
for(i in 1:gamma(5))
for(j in 1:gamma(6))
LS[[(i - 1) * gamma(6) + j]] <- m[perms4[[i]] + 1, perms5[[j]]]
LS
)


Takes just a couple of seconds and we have the result



length(allLS)
# [1] 161280


It is easy to verify that they all are different



table(table(sapply(allLS, paste0, collapse = "")))
# 1
# 161280


and you could also check if they all are Latin squares.






share|improve this answer






















  • Very impressive, Julius! Now I can turn off my back-burner. (I really like list()[rep(...)] instead of replicate(n,NULL,simplify=F), too ... much faster.)
    – r2evans
    Nov 9 at 0:29






  • 1




    @r2evans, thanks! And now your comment reminded me of something even nicer and faster: vector("list", n). Around another 5 times faster than list()[rep(1, n)].
    – Julius Vainora
    Nov 9 at 0:33











  • I had already benchmarked the first two (2 OOM faster), this third is another OOM faster still (where n <- gamma(6)*gamma(5)). I don't know why I keep forgetting the stable vector function ...
    – r2evans
    Nov 9 at 0:37











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1 Answer
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1 Answer
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oldest

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oldest

votes








up vote
3
down vote



accepted










As I said in my comment above, matrices of this type are called Latin squares.



For a start, we already know that there are 56 so-called reduced Latin squares and 161280 all Latin squares of size 5. The reduced Latin squares are such that both the first column and the first row are just 1, 2, 3, 4, 5 (in this order). Given those reduced Latin squares, one may easily (as long as the size is not greater than 5) generate all Latin squares: permute all the rows except the first one and permute all the columns. Hence, as expected, 161280=5!*4!*56.



By restricting the first row and column one could generate 4!*3!*2!=288 matrices and check which 56 are Latin squares. However, I'm going to skip that and take their list from here.



First we read and rearrange the data



reduced <- read.table("http://users.cecs.anu.edu.au/~bdm/data/reduced5.txt", head = FALSE, colClasses = "character")
reduced <- lapply(1:nrow(reduced), function(r) matrix(as.numeric(unlist(strsplit(unlist(reduced[r, ]), ""))) + 1, 5))
length(reduced)
# [1] 56


Now let's generate all 5! and 4! permutations of 1, 2, 3, 4, 5 and 1, 2, 3, 4, respectively.



library(combinat)
perms5 <- permn(1:5)
perms4 <- permn(1:4)


Lastly, we go over all the reduced Latin squares and permute them in all possible ways



allLS <- sapply(reduced, function(m) 
LS <- vector("list", gamma(6) * gamma(5))
for(i in 1:gamma(5))
for(j in 1:gamma(6))
LS[[(i - 1) * gamma(6) + j]] <- m[perms4[[i]] + 1, perms5[[j]]]
LS
)


Takes just a couple of seconds and we have the result



length(allLS)
# [1] 161280


It is easy to verify that they all are different



table(table(sapply(allLS, paste0, collapse = "")))
# 1
# 161280


and you could also check if they all are Latin squares.






share|improve this answer






















  • Very impressive, Julius! Now I can turn off my back-burner. (I really like list()[rep(...)] instead of replicate(n,NULL,simplify=F), too ... much faster.)
    – r2evans
    Nov 9 at 0:29






  • 1




    @r2evans, thanks! And now your comment reminded me of something even nicer and faster: vector("list", n). Around another 5 times faster than list()[rep(1, n)].
    – Julius Vainora
    Nov 9 at 0:33











  • I had already benchmarked the first two (2 OOM faster), this third is another OOM faster still (where n <- gamma(6)*gamma(5)). I don't know why I keep forgetting the stable vector function ...
    – r2evans
    Nov 9 at 0:37















up vote
3
down vote



accepted










As I said in my comment above, matrices of this type are called Latin squares.



For a start, we already know that there are 56 so-called reduced Latin squares and 161280 all Latin squares of size 5. The reduced Latin squares are such that both the first column and the first row are just 1, 2, 3, 4, 5 (in this order). Given those reduced Latin squares, one may easily (as long as the size is not greater than 5) generate all Latin squares: permute all the rows except the first one and permute all the columns. Hence, as expected, 161280=5!*4!*56.



By restricting the first row and column one could generate 4!*3!*2!=288 matrices and check which 56 are Latin squares. However, I'm going to skip that and take their list from here.



First we read and rearrange the data



reduced <- read.table("http://users.cecs.anu.edu.au/~bdm/data/reduced5.txt", head = FALSE, colClasses = "character")
reduced <- lapply(1:nrow(reduced), function(r) matrix(as.numeric(unlist(strsplit(unlist(reduced[r, ]), ""))) + 1, 5))
length(reduced)
# [1] 56


Now let's generate all 5! and 4! permutations of 1, 2, 3, 4, 5 and 1, 2, 3, 4, respectively.



library(combinat)
perms5 <- permn(1:5)
perms4 <- permn(1:4)


Lastly, we go over all the reduced Latin squares and permute them in all possible ways



allLS <- sapply(reduced, function(m) 
LS <- vector("list", gamma(6) * gamma(5))
for(i in 1:gamma(5))
for(j in 1:gamma(6))
LS[[(i - 1) * gamma(6) + j]] <- m[perms4[[i]] + 1, perms5[[j]]]
LS
)


Takes just a couple of seconds and we have the result



length(allLS)
# [1] 161280


It is easy to verify that they all are different



table(table(sapply(allLS, paste0, collapse = "")))
# 1
# 161280


and you could also check if they all are Latin squares.






share|improve this answer






















  • Very impressive, Julius! Now I can turn off my back-burner. (I really like list()[rep(...)] instead of replicate(n,NULL,simplify=F), too ... much faster.)
    – r2evans
    Nov 9 at 0:29






  • 1




    @r2evans, thanks! And now your comment reminded me of something even nicer and faster: vector("list", n). Around another 5 times faster than list()[rep(1, n)].
    – Julius Vainora
    Nov 9 at 0:33











  • I had already benchmarked the first two (2 OOM faster), this third is another OOM faster still (where n <- gamma(6)*gamma(5)). I don't know why I keep forgetting the stable vector function ...
    – r2evans
    Nov 9 at 0:37













up vote
3
down vote



accepted







up vote
3
down vote



accepted






As I said in my comment above, matrices of this type are called Latin squares.



For a start, we already know that there are 56 so-called reduced Latin squares and 161280 all Latin squares of size 5. The reduced Latin squares are such that both the first column and the first row are just 1, 2, 3, 4, 5 (in this order). Given those reduced Latin squares, one may easily (as long as the size is not greater than 5) generate all Latin squares: permute all the rows except the first one and permute all the columns. Hence, as expected, 161280=5!*4!*56.



By restricting the first row and column one could generate 4!*3!*2!=288 matrices and check which 56 are Latin squares. However, I'm going to skip that and take their list from here.



First we read and rearrange the data



reduced <- read.table("http://users.cecs.anu.edu.au/~bdm/data/reduced5.txt", head = FALSE, colClasses = "character")
reduced <- lapply(1:nrow(reduced), function(r) matrix(as.numeric(unlist(strsplit(unlist(reduced[r, ]), ""))) + 1, 5))
length(reduced)
# [1] 56


Now let's generate all 5! and 4! permutations of 1, 2, 3, 4, 5 and 1, 2, 3, 4, respectively.



library(combinat)
perms5 <- permn(1:5)
perms4 <- permn(1:4)


Lastly, we go over all the reduced Latin squares and permute them in all possible ways



allLS <- sapply(reduced, function(m) 
LS <- vector("list", gamma(6) * gamma(5))
for(i in 1:gamma(5))
for(j in 1:gamma(6))
LS[[(i - 1) * gamma(6) + j]] <- m[perms4[[i]] + 1, perms5[[j]]]
LS
)


Takes just a couple of seconds and we have the result



length(allLS)
# [1] 161280


It is easy to verify that they all are different



table(table(sapply(allLS, paste0, collapse = "")))
# 1
# 161280


and you could also check if they all are Latin squares.






share|improve this answer














As I said in my comment above, matrices of this type are called Latin squares.



For a start, we already know that there are 56 so-called reduced Latin squares and 161280 all Latin squares of size 5. The reduced Latin squares are such that both the first column and the first row are just 1, 2, 3, 4, 5 (in this order). Given those reduced Latin squares, one may easily (as long as the size is not greater than 5) generate all Latin squares: permute all the rows except the first one and permute all the columns. Hence, as expected, 161280=5!*4!*56.



By restricting the first row and column one could generate 4!*3!*2!=288 matrices and check which 56 are Latin squares. However, I'm going to skip that and take their list from here.



First we read and rearrange the data



reduced <- read.table("http://users.cecs.anu.edu.au/~bdm/data/reduced5.txt", head = FALSE, colClasses = "character")
reduced <- lapply(1:nrow(reduced), function(r) matrix(as.numeric(unlist(strsplit(unlist(reduced[r, ]), ""))) + 1, 5))
length(reduced)
# [1] 56


Now let's generate all 5! and 4! permutations of 1, 2, 3, 4, 5 and 1, 2, 3, 4, respectively.



library(combinat)
perms5 <- permn(1:5)
perms4 <- permn(1:4)


Lastly, we go over all the reduced Latin squares and permute them in all possible ways



allLS <- sapply(reduced, function(m) 
LS <- vector("list", gamma(6) * gamma(5))
for(i in 1:gamma(5))
for(j in 1:gamma(6))
LS[[(i - 1) * gamma(6) + j]] <- m[perms4[[i]] + 1, perms5[[j]]]
LS
)


Takes just a couple of seconds and we have the result



length(allLS)
# [1] 161280


It is easy to verify that they all are different



table(table(sapply(allLS, paste0, collapse = "")))
# 1
# 161280


and you could also check if they all are Latin squares.







share|improve this answer














share|improve this answer



share|improve this answer








edited Nov 9 at 0:32

























answered Nov 9 at 0:00









Julius Vainora

27.6k75878




27.6k75878











  • Very impressive, Julius! Now I can turn off my back-burner. (I really like list()[rep(...)] instead of replicate(n,NULL,simplify=F), too ... much faster.)
    – r2evans
    Nov 9 at 0:29






  • 1




    @r2evans, thanks! And now your comment reminded me of something even nicer and faster: vector("list", n). Around another 5 times faster than list()[rep(1, n)].
    – Julius Vainora
    Nov 9 at 0:33











  • I had already benchmarked the first two (2 OOM faster), this third is another OOM faster still (where n <- gamma(6)*gamma(5)). I don't know why I keep forgetting the stable vector function ...
    – r2evans
    Nov 9 at 0:37

















  • Very impressive, Julius! Now I can turn off my back-burner. (I really like list()[rep(...)] instead of replicate(n,NULL,simplify=F), too ... much faster.)
    – r2evans
    Nov 9 at 0:29






  • 1




    @r2evans, thanks! And now your comment reminded me of something even nicer and faster: vector("list", n). Around another 5 times faster than list()[rep(1, n)].
    – Julius Vainora
    Nov 9 at 0:33











  • I had already benchmarked the first two (2 OOM faster), this third is another OOM faster still (where n <- gamma(6)*gamma(5)). I don't know why I keep forgetting the stable vector function ...
    – r2evans
    Nov 9 at 0:37
















Very impressive, Julius! Now I can turn off my back-burner. (I really like list()[rep(...)] instead of replicate(n,NULL,simplify=F), too ... much faster.)
– r2evans
Nov 9 at 0:29




Very impressive, Julius! Now I can turn off my back-burner. (I really like list()[rep(...)] instead of replicate(n,NULL,simplify=F), too ... much faster.)
– r2evans
Nov 9 at 0:29




1




1




@r2evans, thanks! And now your comment reminded me of something even nicer and faster: vector("list", n). Around another 5 times faster than list()[rep(1, n)].
– Julius Vainora
Nov 9 at 0:33





@r2evans, thanks! And now your comment reminded me of something even nicer and faster: vector("list", n). Around another 5 times faster than list()[rep(1, n)].
– Julius Vainora
Nov 9 at 0:33













I had already benchmarked the first two (2 OOM faster), this third is another OOM faster still (where n <- gamma(6)*gamma(5)). I don't know why I keep forgetting the stable vector function ...
– r2evans
Nov 9 at 0:37





I had already benchmarked the first two (2 OOM faster), this third is another OOM faster still (where n <- gamma(6)*gamma(5)). I don't know why I keep forgetting the stable vector function ...
– r2evans
Nov 9 at 0:37


















 

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