PyData Sparse is maturing, but there's still some way to go. Note that it does have a format (CSD) that generalizes CSR/CSC, seeAdd CSDpydata/sparse#258. The roadmaphttps://scipy.github.io/devdocs/roadmap.html#implement-sparse-arrays-in-addition-to-sparse-matricesstates that at some ...
GPUGNN/ ├── graph_generation.py # Generates synthetic and real-world test graphs ├── claude_pycuda_sparse.py # Base sparse matrix multiplication implementation ├── claude_pycuda_sparse_csr_csc.py # CSR-CSC optimized implementation ├── claude_pycuda_sparse_tiled.py # Tiled implement...
In the single label multiclass case, the rows of the returned matrix sum to 1.Parameters --- X : array-like, shape = [n_samples, n_features]Returns --- T : (sparse) array-like, shape = [n_samples, n_classes] Returns the probability...
In the single label multiclass case, the rows of the returned matrix sum to 1. Parameters --- X : array-like, shape = [n_samples, n_features] Returns --- T : (sparse) array-like, shape = [n_samples, n_classes] Returns the probability of the sample for each class in the model...
sparse import dok_matrix from scipy.sparse import lil_matriximport numpy as npfrom .validation import check_array, _assert_all_finitedef _unique_multiclass(y): if hasattr(y, "__array__"): return np.unique(np.asarray(y)) else: return set(y)def _unique_indicator(y):...