![]() ![]() Also for some simple situations it may be sufficient to just separately track an index permutation, but this is not convenient in my case. Something like this might be possible with numpy's "advanced indexing" but my understanding is that such a solution would not be in-place. Maybe I am just failing at searching the internet. Right now I am manually swapping rows and columns, but I would have expected numpy to have a nice function f(M, v) where M has n rows and columns, and v has n entries, so that f(M, v) updates M according to the index permutation v. Mathematically this corresponds to pre-multiplying the matrix by the permutation matrix P and post-multiplying it by P^-1 = P^T, but this is not a computationally reasonable solution. You can use the permutation function as required by you in the program.I want to modify a dense square transition matrix in-place by changing the order of several of its rows and columns, using python's numpy library. We have also discussed the difference between the numpy permutation() and numpy shuffle() with the example explained in detail. We have discussed all the ways through which we can use the permutation concept and also discussed the example in detail. In this tutorial, we have learned about the concept of numpy random permutation.
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