{"repo":"IBM/pylspack","free":true,"listed":false,"github":"https://github.com/IBM/pylspack","clone":"git clone https://github.com/IBM/pylspack.git","description":"A lightweight, multithreaded Python package for sketching, column selection, leverage scores and related computations.","language":"Python","stars":20,"topics":["matrix","linear-algebra","matrix-sketching","count-sketch","jlt","regression","random-projections","gram-matrix","leverage-score"],"license":"MIT","category":"chat-messaging","readme_excerpt":"pylspack A lightweight, multithreaded Py thon pack age for L everage S cores computations of tall-and-thin, dense or sparse matrices of arbitrary rank. Includes algorithms for: - statistical leverage scores - column subset selection - sketching (CountSketch and Gaussian embeddings) - squared row-norms computation - basic linear algebra tools like rank-k update, diagonal scaling and more. This package aims to provide quick access to multithreaded algorithms with minimal memory overhead in Python, which are compatible with standard numpy.ndarray and scipy.sparse.csr matrix data structures. The individual kernels can be used as a building block for various problems like low rank approximations , least squares regression , preconditioning , feature selection , clustering , to name a few. It is left to the user to unlock the package's full potential. The basic matrix algorithms of this package are developed in C++, using OpenMP for multithreading and SIMD instructions. As already noted, the implementation is designed for existing data structures of SciPy and Numpy and therefore the entire codebase is only tested and should only be used via the python wrappers. The C++ API can be used as a standalone package, but it has not been tested. Citation If you use this software in academic work, please consider citing the corresponding publications: - https://doi.org/10.1137/20m1314471 - https://doi.org/10.1145/3555370 Usage A simple usage example to compute the leverage scores of a sparse","default_branch":null,"files":null,"tree":[],"storefront":"/r/IBM","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/IBM/pylspack/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}