{"repo":"james-bowman/sparse","free":true,"listed":false,"github":"https://github.com/james-bowman/sparse","clone":"git clone https://github.com/james-bowman/sparse.git","description":"Sparse matrix formats for linear algebra supporting scientific and machine learning applications","language":"Go","stars":170,"topics":["go","golang","matrix","scientific-computing","matrices","csr","coo","csc","dictionary-of-keys","sparse-matrix"],"license":"MIT","category":"chat-messaging","readme_excerpt":"Sparse matrix formats Implementations of selected sparse matrix formats for linear algebra supporting scientific and machine learning applications. Compatible with the APIs in the Gonum package and interoperable with Gonum dense matrix types. Overview Machine learning applications typically model entities as vectors of numerical features so that they may be compared and analysed quantitively. Typically the majority of the elements in these vectors are zeros. In the case of text mining applications, each document within a corpus is represented as a vector and its features represent the vocabulary of unique words. A corpus of several thousand documents might utilise a vocabulary of hundreds of thousands (or perhaps even millions) of unique words but each document will typically only contain a couple of hundred unique words. This means the number of non-zero values in the matrix might only be around 1%. Sparse matrix formats capitalise on this premise by only storing the non-zero values thereby reducing both storage/memory requirements and processing effort for manipulating the data. Features Implementations of Sparse BLAS standard routines. Compatible with Gonum's APIs and interoperable with Gonum's dense matrix types. Implemented Formats: Sparse Matrix Formats: DOK (Dictionary Of Keys)) format COO (COOrdinate)) format (sometimes referred to as 'triplet') CSR (Compressed Sparse Row)) format CSC (Compressed Sparse Column)) format DIA (DIAgonal) format sparse vectors Other Format","default_branch":null,"files":null,"tree":[],"storefront":"/r/james-bowman","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/james-bowman/sparse/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."}