Trying to make a graph from a sparse matrix: not enough values to unpack (expected 2, got 0)

So I’m trying to make a graph with squares that are colored according to probability densities stored in the 7×7 matrix ‘nprob’. nprob = prob/sum print(nprob.todense()) x,y = np.meshgrid(np.arange(0,…

Initialize high dimensional sparse matrix

I want to initialize 300,000 x 300,0000 sparse matrix using sklearn, but it requires memory as if it was not sparse: >>> from scipy import sparse >>> sparse.rand(300000,300000,.1) …

How to convert a PyTorch sparse_coo_tensor into a PyTorch dense tensor?

I create a sparse_coo tensor in PyTorch: import torch # create indices i = torch.tensor([[0, 1, 1], [2, 0, 2]]) # create values v = torch.tensor([3, 4, 5], dtype=torch.float32) # …

Python matrix multiplication: sparse multiply dense

Given the code snippet: B = A @ M – T where A is a CSR scipy sparse matrix, M and T are two numpy arrays. Question: During the matrix operations, does numpy treat A as a dense matrix, or M and T as …

Why is ‘scipy.sparse.linalg.spilu’ less efficient than ‘scipy.linalg.lu’ for sparse matrix?

I posted this question on https://scicomp.stackexchange.com, but received no attention. As long as I get answer in one of them, I will inform in the other. I have a matrix B which is sparse and try to utilize a function scipy.sparse.linalg.spilu specialized for sparse matrix to factorize B. Could you please explain why this function is significantly less efficient than the function scipy.linalg.lu for general matrix? Thank you so much! The computation time is Answer Your matrix B is not sparse at all. More than half of the elements in B are non-zeros. Of course spilu would be less efficient

Python-Scipy sparse Matrices – what is A[i, j] doing?

According to my previous question here (Python – Multiply sparse matrix row with non-sparse vector by index) direct indexing of sparse matrices is not possible (at least not if you don’t want to work with the three arrays by which the sparse.csr matrix is defined, data, indices, indptr). But I just found out, that given a csr-sparse matrix A, this operation works fine and produces correct results: A[i, j]. What I also noticed: It is horribly slow, even slower than working with dense matrices. I couldn’t find any information about this indexing method so I am wondering: What exactly is