How to efficiently unroll a matrix by value with numpy?





.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty{ height:90px;width:728px;box-sizing:border-box;
}







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
    yesterday






  • 2





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

    – Reedinationer
    yesterday













  • @Reedinationer i did it.

    – Marios Nikolaou
    yesterday











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

    – coldspeed
    yesterday






  • 1





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

    – Marios Nikolaou
    yesterday


















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
    yesterday






  • 2





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

    – Reedinationer
    yesterday













  • @Reedinationer i did it.

    – Marios Nikolaou
    yesterday











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

    – coldspeed
    yesterday






  • 1





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

    – Marios Nikolaou
    yesterday














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 yesterday









coldspeed

140k25156241




140k25156241










asked yesterday









seveibarseveibar

1,29711225




1,29711225













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

    – Marios Nikolaou
    yesterday






  • 2





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

    – Reedinationer
    yesterday













  • @Reedinationer i did it.

    – Marios Nikolaou
    yesterday











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

    – coldspeed
    yesterday






  • 1





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

    – Marios Nikolaou
    yesterday



















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

    – Marios Nikolaou
    yesterday






  • 2





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

    – Reedinationer
    yesterday













  • @Reedinationer i did it.

    – Marios Nikolaou
    yesterday











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

    – coldspeed
    yesterday






  • 1





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

    – Marios Nikolaou
    yesterday

















It would be better if you explain it in detail.

– Marios Nikolaou
yesterday





It would be better if you explain it in detail.

– Marios Nikolaou
yesterday




2




2





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

– Reedinationer
yesterday







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

– Reedinationer
yesterday















@Reedinationer i did it.

– Marios Nikolaou
yesterday





@Reedinationer i did it.

– Marios Nikolaou
yesterday













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

– coldspeed
yesterday





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

– coldspeed
yesterday




1




1





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

– Marios Nikolaou
yesterday





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

– Marios Nikolaou
yesterday












3 Answers
3






active

oldest

votes


















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
    yesterday



















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
    yesterday













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

    – coldspeed
    yesterday



















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
    yesterday






  • 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
    yesterday






  • 1





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

    – Paul Panzer
    yesterday












Your Answer






StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");

StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);

StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});

function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});


}
});














draft saved

draft discarded


















StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f55543949%2fhow-to-efficiently-unroll-a-matrix-by-value-with-numpy%23new-answer', 'question_page');
}
);

Post as a guest















Required, but never shown

























3 Answers
3






active

oldest

votes








3 Answers
3






active

oldest

votes









active

oldest

votes






active

oldest

votes









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
    yesterday
















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
    yesterday














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 yesterday

























answered yesterday









user3483203user3483203

31.8k82857




31.8k82857













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

    – seveibar
    yesterday



















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

    – seveibar
    yesterday

















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

– seveibar
yesterday





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

– seveibar
yesterday













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
    yesterday













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

    – coldspeed
    yesterday
















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
    yesterday













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

    – coldspeed
    yesterday














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 yesterday

























answered yesterday









coldspeedcoldspeed

140k25156241




140k25156241








  • 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
    yesterday













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

    – coldspeed
    yesterday














  • 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
    yesterday













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

    – coldspeed
    yesterday








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
yesterday







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
yesterday















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

– coldspeed
yesterday





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

– coldspeed
yesterday











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
    yesterday






  • 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
    yesterday






  • 1





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

    – Paul Panzer
    yesterday
















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
    yesterday






  • 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
    yesterday






  • 1





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

    – Paul Panzer
    yesterday














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 yesterday

























answered yesterday









Paul PanzerPaul Panzer

31.5k21845




31.5k21845








  • 1





    This is great, really interesting answer.

    – user3483203
    yesterday






  • 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
    yesterday






  • 1





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

    – Paul Panzer
    yesterday














  • 1





    This is great, really interesting answer.

    – user3483203
    yesterday






  • 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
    yesterday






  • 1





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

    – Paul Panzer
    yesterday








1




1





This is great, really interesting answer.

– user3483203
yesterday





This is great, really interesting answer.

– user3483203
yesterday




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
yesterday





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
yesterday




1




1





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

– Paul Panzer
yesterday





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

– Paul Panzer
yesterday


















draft saved

draft discarded




















































Thanks for contributing an answer to Stack Overflow!


  • Please be sure to answer the question. Provide details and share your research!

But avoid



  • Asking for help, clarification, or responding to other answers.

  • Making statements based on opinion; back them up with references or personal experience.


To learn more, see our tips on writing great answers.




draft saved


draft discarded














StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f55543949%2fhow-to-efficiently-unroll-a-matrix-by-value-with-numpy%23new-answer', 'question_page');
}
);

Post as a guest















Required, but never shown





















































Required, but never shown














Required, but never shown












Required, but never shown







Required, but never shown

































Required, but never shown














Required, but never shown












Required, but never shown







Required, but never shown







Popular posts from this blog

GameSpot

日野市

Tu-95轟炸機