mola is a Python library for doing algebra with matrices. It covers the basic operations such as matrix addition and multiplication, transposes, and inverse. Additionally, it has some miscellaneous data analytical tools for regression, clustering etc. It is written without any external Python libraries.
I wrote mola as a hobby project to remind myself of the linear algebra I studied in uni and to practice my Python programming. Particularly, this is an exercise in publishing my first Python library.
Install with "pip install mola" or download the GitHub repository for a more recent version.
See main.py for examples and read the documentation.
- Python 3.x (written on Python 3.9, so that's sure to work)
- basic matrix operations implemented (addition, multiplication, transpose, inverse)
- matrices with labeled rows and columns
- user-friendly wrappers for function fitting (including linear least squares regression, Tikhonov regularization, Gauss-Newton iteration for nonlinear fitting)
- some basic matrix decompositions (QR decomposition and eigendecomposition)
- clustering with hard k-means and fuzzy c-means and density-based clustering (see examples/visualize_clustering.py for a demonstration)
Matrix is the main class of mola. In practice, the elements of the matrix are implemented with lists. Most of its functionality involves calling methods from this class.
LabeledMatrix inherits Matrix and allows labeling of rows and columns, as well as overriding certain setter and getter functions to use those labels.
- checks to see if matrix is positive/negative definite/semidefinite
- different matrix norms (only Frobenius and Euclidean norm implemented right now)
- more decompositions (SVD)
- user-friendly wrapper for logistic regression
- preprocessing functions for matrix data (center and scale, e.g., z-scores)
- example data analysis project to showcase existing features
MIT License
Copyright (c) 2023 Jere Lavikainen
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