Here is a method that creates a sparse scipy matrix based on data and indices of person and thing. person_u and thing_u are lists representing the unique entries for your rows and columns of pivot you want to create. Note: this assumes that your count column already has the value you want in it. from … Read more
The sparse library now has hstack and vstack for respectively concatenating matrices horizontally and vertically.
toarray returns an ndarray; todense returns a matrix. If you want a matrix, use todense; otherwise, use toarray.
Sorry if I’m not answering this completely enough, but hopefully I can provide some insight. CSC (Compressed Sparse Column) and CSR (Compressed Sparse Row) are more compact and efficient, but difficult to construct “from scratch”. Coo (Coordinate) and DOK (Dictionary of Keys) are easier to construct, and can then be converted to CSC or CSR … Read more
The scipy.sparse.*_matrix has several useful methods, for example, if a is e.g. scipy.sparse.csr_matrix: a.toarray() or a.A – Return a dense ndarray representation of this matrix. (numpy.array, recommended) a.todense() or a.M – Return a dense matrix representation of this matrix. (numpy.matrix)
Sorry, don’t have the reputation to comment, but I think you should take a look at the answer here: Keras, sparse matrix issue. I have tried it and it works correctly, just one note though, at least in my case, the shuffling led to really bad results, so I used this slightly modified non-shuffled alternative: … Read more
I replied over at scipy.org as well, but I thought I should add an answer here, in case others find this page when searching. You can turn the vector into a sparse diagonal matrix and then use matrix multiplication (with *) to do the same thing as broadcasting, but efficiently. >>> d = ssp.lil_matrix((3,3)) >>> … Read more
You can use the scipy.sparse.hstack to concatenate sparse matrices with the same number of rows (horizontal concatenation): from scipy.sparse import hstack hstack((X, X2)) Similarly, you can use scipy.sparse.vstack to concatenate sparse matrices with the same number of columns (vertical concatenation). Using numpy.hstack or numpy.vstack will create an array with two sparse matrix objects.
This problem has a very efficient (linear time) solution, though it requires a bit of discussion… Zeroth: clarifying the problem / LCP Per clarifications in the comments, @FooBar says the original problem is elementwise min; we need to find a z (or v) such that either the left argument is zero and the right argument … Read more