spectrally-regularised-LVMs 0.1.2

An implementation of linear LVMs with a spectral regulariser.

Spectrally regularised LVMs

Current version: 0.1.1

Spectrally-regularised-LVMs is a Python-based package which facilitates the estimation of the linear latent variable model (LVM) parameters with a unique spectral regularisation term in single channel time-series applications.

Purpose

LVMs are a statistical methodology which tries to capture the underlying structure in some observed data. This package caters to single channel time-series applications and provides a methodology to estimate the LVM parameters. The model parameters are encouraged to be diverse via a spectral regularisation term which penalises source duplication of the spectral information captured by the latent sources.

The purpose of this package is to provide a complete methodology that caters to a variety of LVM objective functions.

Documentation

Please visit the docs for all supporting documentation for this package.

Installation

The package is designed to be used through the Python API, and can be installed using pip:

$ pip install spectrally-regularised-LVMs

Requirements

This package used Python ≥ 3.10 or later to run. For other python dependencies, please check the pyproject.toml file included in this repository. The dependencies of this package are as follows:

Package	Version
Python	≥ 3.10
Numpy	≥ 1.23.1
Matplotlib	≥ 3.5.2
SciPy	≥ 1.8.1
scikit-learn	≥ 1.1.2
tqdm	≥ 4.64.1
SymPy	≥ 1.1.1

API usage

Please visit the docs for all supporting API documentation for this package.

Contributing

This package uses Poetry for dependency management and Python packaging and git for version control. To get started, first install git and Poetry and then clone this repository via

$ git clone [e-mail]:RyanBalshaw/spectrally-regularised-LVMs.git
$ cd spectrally-regularised-LVMs

Then, install the necessary dependencies in a local environment via

$ poetry install --with dev,docs
$ poetry shell

This will install all necessary package dependencies and activate the virtual environment. You can then set up the pre-commit hooks via

$ pre-commit install
pre-commit installed at .git/hooks/pre-commit

License

This project is licensed under MIT License - see the LICENSE file for details.