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How to efficiently unroll a matrix by value with numpy?



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7















I have a matrix M with values 0 through N within it. I'd like to unroll this matrix to create a new matrix A where each submatrix A[i, :, :] represents whether or not M == i.



The solution below uses a loop.



# Example Setup
import numpy as np

np.random.seed(0)
N = 5
M = np.random.randint(0, N, size=(5,5))

# Solution with Loop
A = np.zeros((N, M.shape[0], M.shape[1]))
for i in range(N):
A[i, :, :] = M == i


This yields:



M
array([[4, 0, 3, 3, 3],
[1, 3, 2, 4, 0],
[0, 4, 2, 1, 0],
[1, 1, 0, 1, 4],
[3, 0, 3, 0, 2]])

M.shape
# (5, 5)


A
array([[[0, 1, 0, 0, 0],
[0, 0, 0, 0, 1],
[1, 0, 0, 0, 1],
[0, 0, 1, 0, 0],
[0, 1, 0, 1, 0]],
...
[[1, 0, 0, 0, 0],
[0, 0, 0, 1, 0],
[0, 1, 0, 0, 0],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 0]]])

A.shape
# (5, 5, 5)


Is there a faster way, or a way to do it in a single numpy operation?










share|improve this question
























  • It would be better if you explain it in detail.

    – Marios Nikolaou
    Apr 5 at 22:16






  • 2





    @MariosNikolaou just copy/paste his code and print(M);print(A)...I edited it for you though

    – Reedinationer
    Apr 5 at 22:18












  • @Reedinationer i did it.

    – Marios Nikolaou
    Apr 5 at 22:21











  • I would not recommend pasting output for this code as the input is randomised without a seed.

    – coldspeed
    Apr 5 at 22:21






  • 1





    i == M compare int with array 5x5 ? and then save it in A?

    – Marios Nikolaou
    Apr 5 at 22:26

















7















I have a matrix M with values 0 through N within it. I'd like to unroll this matrix to create a new matrix A where each submatrix A[i, :, :] represents whether or not M == i.



The solution below uses a loop.



# Example Setup
import numpy as np

np.random.seed(0)
N = 5
M = np.random.randint(0, N, size=(5,5))

# Solution with Loop
A = np.zeros((N, M.shape[0], M.shape[1]))
for i in range(N):
A[i, :, :] = M == i


This yields:



M
array([[4, 0, 3, 3, 3],
[1, 3, 2, 4, 0],
[0, 4, 2, 1, 0],
[1, 1, 0, 1, 4],
[3, 0, 3, 0, 2]])

M.shape
# (5, 5)


A
array([[[0, 1, 0, 0, 0],
[0, 0, 0, 0, 1],
[1, 0, 0, 0, 1],
[0, 0, 1, 0, 0],
[0, 1, 0, 1, 0]],
...
[[1, 0, 0, 0, 0],
[0, 0, 0, 1, 0],
[0, 1, 0, 0, 0],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 0]]])

A.shape
# (5, 5, 5)


Is there a faster way, or a way to do it in a single numpy operation?










share|improve this question
























  • It would be better if you explain it in detail.

    – Marios Nikolaou
    Apr 5 at 22:16






  • 2





    @MariosNikolaou just copy/paste his code and print(M);print(A)...I edited it for you though

    – Reedinationer
    Apr 5 at 22:18












  • @Reedinationer i did it.

    – Marios Nikolaou
    Apr 5 at 22:21











  • I would not recommend pasting output for this code as the input is randomised without a seed.

    – coldspeed
    Apr 5 at 22:21






  • 1





    i == M compare int with array 5x5 ? and then save it in A?

    – Marios Nikolaou
    Apr 5 at 22:26













7












7








7








I have a matrix M with values 0 through N within it. I'd like to unroll this matrix to create a new matrix A where each submatrix A[i, :, :] represents whether or not M == i.



The solution below uses a loop.



# Example Setup
import numpy as np

np.random.seed(0)
N = 5
M = np.random.randint(0, N, size=(5,5))

# Solution with Loop
A = np.zeros((N, M.shape[0], M.shape[1]))
for i in range(N):
A[i, :, :] = M == i


This yields:



M
array([[4, 0, 3, 3, 3],
[1, 3, 2, 4, 0],
[0, 4, 2, 1, 0],
[1, 1, 0, 1, 4],
[3, 0, 3, 0, 2]])

M.shape
# (5, 5)


A
array([[[0, 1, 0, 0, 0],
[0, 0, 0, 0, 1],
[1, 0, 0, 0, 1],
[0, 0, 1, 0, 0],
[0, 1, 0, 1, 0]],
...
[[1, 0, 0, 0, 0],
[0, 0, 0, 1, 0],
[0, 1, 0, 0, 0],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 0]]])

A.shape
# (5, 5, 5)


Is there a faster way, or a way to do it in a single numpy operation?










share|improve this question
















I have a matrix M with values 0 through N within it. I'd like to unroll this matrix to create a new matrix A where each submatrix A[i, :, :] represents whether or not M == i.



The solution below uses a loop.



# Example Setup
import numpy as np

np.random.seed(0)
N = 5
M = np.random.randint(0, N, size=(5,5))

# Solution with Loop
A = np.zeros((N, M.shape[0], M.shape[1]))
for i in range(N):
A[i, :, :] = M == i


This yields:



M
array([[4, 0, 3, 3, 3],
[1, 3, 2, 4, 0],
[0, 4, 2, 1, 0],
[1, 1, 0, 1, 4],
[3, 0, 3, 0, 2]])

M.shape
# (5, 5)


A
array([[[0, 1, 0, 0, 0],
[0, 0, 0, 0, 1],
[1, 0, 0, 0, 1],
[0, 0, 1, 0, 0],
[0, 1, 0, 1, 0]],
...
[[1, 0, 0, 0, 0],
[0, 0, 0, 1, 0],
[0, 1, 0, 0, 0],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 0]]])

A.shape
# (5, 5, 5)


Is there a faster way, or a way to do it in a single numpy operation?







python arrays numpy






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Apr 5 at 22:24









coldspeed

141k25157245




141k25157245










asked Apr 5 at 22:06









seveibarseveibar

1,31411225




1,31411225












  • It would be better if you explain it in detail.

    – Marios Nikolaou
    Apr 5 at 22:16






  • 2





    @MariosNikolaou just copy/paste his code and print(M);print(A)...I edited it for you though

    – Reedinationer
    Apr 5 at 22:18












  • @Reedinationer i did it.

    – Marios Nikolaou
    Apr 5 at 22:21











  • I would not recommend pasting output for this code as the input is randomised without a seed.

    – coldspeed
    Apr 5 at 22:21






  • 1





    i == M compare int with array 5x5 ? and then save it in A?

    – Marios Nikolaou
    Apr 5 at 22:26

















  • It would be better if you explain it in detail.

    – Marios Nikolaou
    Apr 5 at 22:16






  • 2





    @MariosNikolaou just copy/paste his code and print(M);print(A)...I edited it for you though

    – Reedinationer
    Apr 5 at 22:18












  • @Reedinationer i did it.

    – Marios Nikolaou
    Apr 5 at 22:21











  • I would not recommend pasting output for this code as the input is randomised without a seed.

    – coldspeed
    Apr 5 at 22:21






  • 1





    i == M compare int with array 5x5 ? and then save it in A?

    – Marios Nikolaou
    Apr 5 at 22:26
















It would be better if you explain it in detail.

– Marios Nikolaou
Apr 5 at 22:16





It would be better if you explain it in detail.

– Marios Nikolaou
Apr 5 at 22:16




2




2





@MariosNikolaou just copy/paste his code and print(M);print(A)...I edited it for you though

– Reedinationer
Apr 5 at 22:18






@MariosNikolaou just copy/paste his code and print(M);print(A)...I edited it for you though

– Reedinationer
Apr 5 at 22:18














@Reedinationer i did it.

– Marios Nikolaou
Apr 5 at 22:21





@Reedinationer i did it.

– Marios Nikolaou
Apr 5 at 22:21













I would not recommend pasting output for this code as the input is randomised without a seed.

– coldspeed
Apr 5 at 22:21





I would not recommend pasting output for this code as the input is randomised without a seed.

– coldspeed
Apr 5 at 22:21




1




1





i == M compare int with array 5x5 ? and then save it in A?

– Marios Nikolaou
Apr 5 at 22:26





i == M compare int with array 5x5 ? and then save it in A?

– Marios Nikolaou
Apr 5 at 22:26












3 Answers
3






active

oldest

votes


















6














Broadcasted comparison is your friend:



B = (M[None, :] == np.arange(N)[:, None, None]).view(np.int8)

np.array_equal(A, B)
# True


The idea is to expand the dimensions in such a way that the comparison can be broadcasted in the manner desired.




As pointed out by @Alex Riley in the comments, you can use np.equal.outer to avoid having to do the indexing stuff yourself,



B = np.equal.outer(np.arange(N), M).view(np.int8)

np.array_equal(A, B)
# True





share|improve this answer




















  • 1





    Good answer - just to point out there's a superfluous newaxis in your indexing for M (resulting in a 4D array). You could use M[None, :] instead to get the 3D array. An alternative to avoid fiddly indexing is to use np.equal.outer(np.arange(N), M).view(np.int8).

    – Alex Riley
    Apr 5 at 22:34












  • @AlexRiley Thanks for that! And the outer solution is quite neat.

    – coldspeed
    Apr 5 at 22:45


















6














You can make use of some broadcasting here:



P = np.arange(N)
Y = np.broadcast_to(P[:, None], M.shape)
T = np.equal(M, Y[:, None]).astype(int)



Alternative using indices:



X, Y = np.indices(M.shape)
Z = np.equal(M, X[:, None]).astype(int)





share|improve this answer

























  • This answer was really helpful towards my understanding of broadcasting, thank you!

    – seveibar
    Apr 6 at 0:58


















3














You can index into the identity matrix like so



 A = np.identity(N, int)[:, M]


or so



 A = np.identity(N, int)[M.T].T


Or use the new (v1.15.0) put_along_axis



A = np.zeros((N,5,5), int)
np.put_along_axis(A, M[None], 1, 0)


Note if N is much larger than 5 then creating an NxN identity matrix may be considered wasteful. We can mitigate this using stride tricks:



def read_only_identity(N, dtype=float):
z = np.zeros(2*N-1, dtype)
s, = z.strides
z[N-1] = 1
return np.lib.stride_tricks.as_strided(z[N-1:], (N, N), (-s, s))





share|improve this answer




















  • 1





    This is great, really interesting answer.

    – user3483203
    Apr 5 at 23:47






  • 1





    Hi Paul, this answer is really elegant, but the identity answers seem specific to the case where the N=M.shape[0]=M.shape[1]. Is the solution similarly elegant for N=/=M.shape[0]=/=M.shape[1]? Thanks for the answer, learning a lot!

    – seveibar
    Apr 6 at 0:55






  • 1





    @seveibar I've updated the answer. It is really just a matter of replacing the correct 5s with Ns.

    – Paul Panzer
    Apr 6 at 1:13











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3 Answers
3






active

oldest

votes








3 Answers
3






active

oldest

votes









active

oldest

votes






active

oldest

votes









6














Broadcasted comparison is your friend:



B = (M[None, :] == np.arange(N)[:, None, None]).view(np.int8)

np.array_equal(A, B)
# True


The idea is to expand the dimensions in such a way that the comparison can be broadcasted in the manner desired.




As pointed out by @Alex Riley in the comments, you can use np.equal.outer to avoid having to do the indexing stuff yourself,



B = np.equal.outer(np.arange(N), M).view(np.int8)

np.array_equal(A, B)
# True





share|improve this answer




















  • 1





    Good answer - just to point out there's a superfluous newaxis in your indexing for M (resulting in a 4D array). You could use M[None, :] instead to get the 3D array. An alternative to avoid fiddly indexing is to use np.equal.outer(np.arange(N), M).view(np.int8).

    – Alex Riley
    Apr 5 at 22:34












  • @AlexRiley Thanks for that! And the outer solution is quite neat.

    – coldspeed
    Apr 5 at 22:45















6














Broadcasted comparison is your friend:



B = (M[None, :] == np.arange(N)[:, None, None]).view(np.int8)

np.array_equal(A, B)
# True


The idea is to expand the dimensions in such a way that the comparison can be broadcasted in the manner desired.




As pointed out by @Alex Riley in the comments, you can use np.equal.outer to avoid having to do the indexing stuff yourself,



B = np.equal.outer(np.arange(N), M).view(np.int8)

np.array_equal(A, B)
# True





share|improve this answer




















  • 1





    Good answer - just to point out there's a superfluous newaxis in your indexing for M (resulting in a 4D array). You could use M[None, :] instead to get the 3D array. An alternative to avoid fiddly indexing is to use np.equal.outer(np.arange(N), M).view(np.int8).

    – Alex Riley
    Apr 5 at 22:34












  • @AlexRiley Thanks for that! And the outer solution is quite neat.

    – coldspeed
    Apr 5 at 22:45













6












6








6







Broadcasted comparison is your friend:



B = (M[None, :] == np.arange(N)[:, None, None]).view(np.int8)

np.array_equal(A, B)
# True


The idea is to expand the dimensions in such a way that the comparison can be broadcasted in the manner desired.




As pointed out by @Alex Riley in the comments, you can use np.equal.outer to avoid having to do the indexing stuff yourself,



B = np.equal.outer(np.arange(N), M).view(np.int8)

np.array_equal(A, B)
# True





share|improve this answer















Broadcasted comparison is your friend:



B = (M[None, :] == np.arange(N)[:, None, None]).view(np.int8)

np.array_equal(A, B)
# True


The idea is to expand the dimensions in such a way that the comparison can be broadcasted in the manner desired.




As pointed out by @Alex Riley in the comments, you can use np.equal.outer to avoid having to do the indexing stuff yourself,



B = np.equal.outer(np.arange(N), M).view(np.int8)

np.array_equal(A, B)
# True






share|improve this answer














share|improve this answer



share|improve this answer








edited Apr 5 at 22:45

























answered Apr 5 at 22:19









coldspeedcoldspeed

141k25157245




141k25157245







  • 1





    Good answer - just to point out there's a superfluous newaxis in your indexing for M (resulting in a 4D array). You could use M[None, :] instead to get the 3D array. An alternative to avoid fiddly indexing is to use np.equal.outer(np.arange(N), M).view(np.int8).

    – Alex Riley
    Apr 5 at 22:34












  • @AlexRiley Thanks for that! And the outer solution is quite neat.

    – coldspeed
    Apr 5 at 22:45












  • 1





    Good answer - just to point out there's a superfluous newaxis in your indexing for M (resulting in a 4D array). You could use M[None, :] instead to get the 3D array. An alternative to avoid fiddly indexing is to use np.equal.outer(np.arange(N), M).view(np.int8).

    – Alex Riley
    Apr 5 at 22:34












  • @AlexRiley Thanks for that! And the outer solution is quite neat.

    – coldspeed
    Apr 5 at 22:45







1




1





Good answer - just to point out there's a superfluous newaxis in your indexing for M (resulting in a 4D array). You could use M[None, :] instead to get the 3D array. An alternative to avoid fiddly indexing is to use np.equal.outer(np.arange(N), M).view(np.int8).

– Alex Riley
Apr 5 at 22:34






Good answer - just to point out there's a superfluous newaxis in your indexing for M (resulting in a 4D array). You could use M[None, :] instead to get the 3D array. An alternative to avoid fiddly indexing is to use np.equal.outer(np.arange(N), M).view(np.int8).

– Alex Riley
Apr 5 at 22:34














@AlexRiley Thanks for that! And the outer solution is quite neat.

– coldspeed
Apr 5 at 22:45





@AlexRiley Thanks for that! And the outer solution is quite neat.

– coldspeed
Apr 5 at 22:45













6














You can make use of some broadcasting here:



P = np.arange(N)
Y = np.broadcast_to(P[:, None], M.shape)
T = np.equal(M, Y[:, None]).astype(int)



Alternative using indices:



X, Y = np.indices(M.shape)
Z = np.equal(M, X[:, None]).astype(int)





share|improve this answer

























  • This answer was really helpful towards my understanding of broadcasting, thank you!

    – seveibar
    Apr 6 at 0:58















6














You can make use of some broadcasting here:



P = np.arange(N)
Y = np.broadcast_to(P[:, None], M.shape)
T = np.equal(M, Y[:, None]).astype(int)



Alternative using indices:



X, Y = np.indices(M.shape)
Z = np.equal(M, X[:, None]).astype(int)





share|improve this answer

























  • This answer was really helpful towards my understanding of broadcasting, thank you!

    – seveibar
    Apr 6 at 0:58













6












6








6







You can make use of some broadcasting here:



P = np.arange(N)
Y = np.broadcast_to(P[:, None], M.shape)
T = np.equal(M, Y[:, None]).astype(int)



Alternative using indices:



X, Y = np.indices(M.shape)
Z = np.equal(M, X[:, None]).astype(int)





share|improve this answer















You can make use of some broadcasting here:



P = np.arange(N)
Y = np.broadcast_to(P[:, None], M.shape)
T = np.equal(M, Y[:, None]).astype(int)



Alternative using indices:



X, Y = np.indices(M.shape)
Z = np.equal(M, X[:, None]).astype(int)






share|improve this answer














share|improve this answer



share|improve this answer








edited Apr 5 at 22:39

























answered Apr 5 at 22:19









user3483203user3483203

32.1k82857




32.1k82857












  • This answer was really helpful towards my understanding of broadcasting, thank you!

    – seveibar
    Apr 6 at 0:58

















  • This answer was really helpful towards my understanding of broadcasting, thank you!

    – seveibar
    Apr 6 at 0:58
















This answer was really helpful towards my understanding of broadcasting, thank you!

– seveibar
Apr 6 at 0:58





This answer was really helpful towards my understanding of broadcasting, thank you!

– seveibar
Apr 6 at 0:58











3














You can index into the identity matrix like so



 A = np.identity(N, int)[:, M]


or so



 A = np.identity(N, int)[M.T].T


Or use the new (v1.15.0) put_along_axis



A = np.zeros((N,5,5), int)
np.put_along_axis(A, M[None], 1, 0)


Note if N is much larger than 5 then creating an NxN identity matrix may be considered wasteful. We can mitigate this using stride tricks:



def read_only_identity(N, dtype=float):
z = np.zeros(2*N-1, dtype)
s, = z.strides
z[N-1] = 1
return np.lib.stride_tricks.as_strided(z[N-1:], (N, N), (-s, s))





share|improve this answer




















  • 1





    This is great, really interesting answer.

    – user3483203
    Apr 5 at 23:47






  • 1





    Hi Paul, this answer is really elegant, but the identity answers seem specific to the case where the N=M.shape[0]=M.shape[1]. Is the solution similarly elegant for N=/=M.shape[0]=/=M.shape[1]? Thanks for the answer, learning a lot!

    – seveibar
    Apr 6 at 0:55






  • 1





    @seveibar I've updated the answer. It is really just a matter of replacing the correct 5s with Ns.

    – Paul Panzer
    Apr 6 at 1:13















3














You can index into the identity matrix like so



 A = np.identity(N, int)[:, M]


or so



 A = np.identity(N, int)[M.T].T


Or use the new (v1.15.0) put_along_axis



A = np.zeros((N,5,5), int)
np.put_along_axis(A, M[None], 1, 0)


Note if N is much larger than 5 then creating an NxN identity matrix may be considered wasteful. We can mitigate this using stride tricks:



def read_only_identity(N, dtype=float):
z = np.zeros(2*N-1, dtype)
s, = z.strides
z[N-1] = 1
return np.lib.stride_tricks.as_strided(z[N-1:], (N, N), (-s, s))





share|improve this answer




















  • 1





    This is great, really interesting answer.

    – user3483203
    Apr 5 at 23:47






  • 1





    Hi Paul, this answer is really elegant, but the identity answers seem specific to the case where the N=M.shape[0]=M.shape[1]. Is the solution similarly elegant for N=/=M.shape[0]=/=M.shape[1]? Thanks for the answer, learning a lot!

    – seveibar
    Apr 6 at 0:55






  • 1





    @seveibar I've updated the answer. It is really just a matter of replacing the correct 5s with Ns.

    – Paul Panzer
    Apr 6 at 1:13













3












3








3







You can index into the identity matrix like so



 A = np.identity(N, int)[:, M]


or so



 A = np.identity(N, int)[M.T].T


Or use the new (v1.15.0) put_along_axis



A = np.zeros((N,5,5), int)
np.put_along_axis(A, M[None], 1, 0)


Note if N is much larger than 5 then creating an NxN identity matrix may be considered wasteful. We can mitigate this using stride tricks:



def read_only_identity(N, dtype=float):
z = np.zeros(2*N-1, dtype)
s, = z.strides
z[N-1] = 1
return np.lib.stride_tricks.as_strided(z[N-1:], (N, N), (-s, s))





share|improve this answer















You can index into the identity matrix like so



 A = np.identity(N, int)[:, M]


or so



 A = np.identity(N, int)[M.T].T


Or use the new (v1.15.0) put_along_axis



A = np.zeros((N,5,5), int)
np.put_along_axis(A, M[None], 1, 0)


Note if N is much larger than 5 then creating an NxN identity matrix may be considered wasteful. We can mitigate this using stride tricks:



def read_only_identity(N, dtype=float):
z = np.zeros(2*N-1, dtype)
s, = z.strides
z[N-1] = 1
return np.lib.stride_tricks.as_strided(z[N-1:], (N, N), (-s, s))






share|improve this answer














share|improve this answer



share|improve this answer








edited Apr 6 at 1:08

























answered Apr 5 at 23:45









Paul PanzerPaul Panzer

31.8k21845




31.8k21845







  • 1





    This is great, really interesting answer.

    – user3483203
    Apr 5 at 23:47






  • 1





    Hi Paul, this answer is really elegant, but the identity answers seem specific to the case where the N=M.shape[0]=M.shape[1]. Is the solution similarly elegant for N=/=M.shape[0]=/=M.shape[1]? Thanks for the answer, learning a lot!

    – seveibar
    Apr 6 at 0:55






  • 1





    @seveibar I've updated the answer. It is really just a matter of replacing the correct 5s with Ns.

    – Paul Panzer
    Apr 6 at 1:13












  • 1





    This is great, really interesting answer.

    – user3483203
    Apr 5 at 23:47






  • 1





    Hi Paul, this answer is really elegant, but the identity answers seem specific to the case where the N=M.shape[0]=M.shape[1]. Is the solution similarly elegant for N=/=M.shape[0]=/=M.shape[1]? Thanks for the answer, learning a lot!

    – seveibar
    Apr 6 at 0:55






  • 1





    @seveibar I've updated the answer. It is really just a matter of replacing the correct 5s with Ns.

    – Paul Panzer
    Apr 6 at 1:13







1




1





This is great, really interesting answer.

– user3483203
Apr 5 at 23:47





This is great, really interesting answer.

– user3483203
Apr 5 at 23:47




1




1





Hi Paul, this answer is really elegant, but the identity answers seem specific to the case where the N=M.shape[0]=M.shape[1]. Is the solution similarly elegant for N=/=M.shape[0]=/=M.shape[1]? Thanks for the answer, learning a lot!

– seveibar
Apr 6 at 0:55





Hi Paul, this answer is really elegant, but the identity answers seem specific to the case where the N=M.shape[0]=M.shape[1]. Is the solution similarly elegant for N=/=M.shape[0]=/=M.shape[1]? Thanks for the answer, learning a lot!

– seveibar
Apr 6 at 0:55




1




1





@seveibar I've updated the answer. It is really just a matter of replacing the correct 5s with Ns.

– Paul Panzer
Apr 6 at 1:13





@seveibar I've updated the answer. It is really just a matter of replacing the correct 5s with Ns.

– Paul Panzer
Apr 6 at 1:13

















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