![]() ![]() To permute both rows and columns I think you either have to run it twice, or pull some ugly shenanigans with numpy.unravel_index that hurts my head to think about. PVec0, pVec1 = calcMyPermutationVectors()Īrr.take(pVec0, axis=0, out=arr, mode="clip")Īrr.take(pVec1, axis=1, out=arr, mode="clip") So in total you call should look something like the following: #Inplace Rearrange On a final note, take seems to be a array method, so instead of np.take(i, rr, axis=0) If you don't set the mode it will make a copy to restore the array state on a Python exception (see here). To do it in-place, all you need to do is specify the "out" parameter to be the same as the input array AND you have to set the mode="clip" or mode="wrap". Here is an example of doing a random permutation of an identity matrix's rows: import numpy as npĪrray(, You can do this in-place with numpy's take() function, but it requires a bit of hoop jumping. Warning: The below example works properly, but using the full set of parameters suggested at the post end exposes a bug, or at least an "undocumented feature" in the numpy.take() function. I currently copy matrices this large unnecessarily, but I would hope this could be easily avoided for permutation. Actually I would like to use matrices as large as possible, but I don't want to deal with the inconvenience of not being able to hold them in RAM, and I do O(N^3) LAPACK operations on the matrix which would also limit the practical matrix size. The matrix is small enough to fit into desktop RAM but big enough that I do not want to copy it thoughtlessly. Or they mean counting or enumerating all possible permutations. Sometimes when people talk about permutations, they only mean the sampling of random permutations, for example as part of a procedure to obtain p-values in statistics. ![]() Also for some simple situations it may be sufficient to just separately track an index permutation, but this is not convenient in my case. (n-1)->0 p np.random.permutation(n) P np.eye(n)p random. random.sample(population, k): This function returns a list of k unique. Something like this might be possible with numpy's "advanced indexing" but my understanding is that such a solution would not be in-place. TSP example using numpy functions (for efficiency) (C) Fair Isaac Corp. shuffle(sequence): This function shuffles the elements of the sequence in place. Maybe I am just failing at searching the internet. So you could use function to generate the index array and use it to shuffle multiple arrays. 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. you want to generate some permutation of sequence then use random permutation() function. 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. The NumPy random is a module help to generate random numbers. I’m going to assign to as he’s been working on PRNG updates and may have ideas about how to improve performance.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. On GPU, JAX looks better: 10 loops, best of 5: 57.6 ms per loop %timeit perm_jit(key, x_jax).block_until_ready() # routine is already JIT-compiled, so another JIT doesn't help much: %timeit random.permutation(key, x_jax).block_until_ready() Revisiting this: has been deprecated in favor of, but the performance issue remains in the current main branch of JAX (run on a Colab CPU): import numpy as np ![]()
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