james-bowman/sparse
Sparse matrix formats for linear algebra supporting scientific and machine learning applications
This tool helps scientists and machine learning practitioners efficiently work with very large datasets where most data points are zero, such as in text analysis or sensor readings. It takes in structured numerical data (matrices) with many zeros and outputs processed matrices or vectors, saving memory and speeding up calculations. This is for professionals building analytical models or running simulations who need optimized computations.
167 stars.
Use this if you are working with large numerical datasets, like those found in machine learning feature sets or scientific simulations, where most of the values are zero and you need to perform calculations efficiently.
Not ideal if your numerical datasets are small, or if your matrices contain very few zero values, as the overhead of sparse formats may not provide a benefit.
Stars
167
Forks
23
Language
Go
License
MIT
Category
Last pushed
Feb 16, 2026
Commits (30d)
0
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