# Spectrally regularised LVMs
[](https://doi.org/10.5281/zenodo.14717880)
*Spectrally-regularised-LVMs* is a Python-based [package](https://pypi.org/project/spectrally-regularised-lvms/) 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 try 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 capture non-duplicate information 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 framework for LVMs with spectral regularisation that caters to a variety of LVM objective functions.
# Documentation
Please visit the [documentation](http://spectrally-regularised-lvms.readthedocs.io/) page 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](https://pypi.org/project/pip/):
```console
$ pip install spectrally-regularised-lvms
```
A more detailed discussion regarding installation is given in the [documentation](http://spectrally-regularised-lvms.readthedocs.io/).
# Requirements
This package used Python ≥ 3.11 or later to run. For other python dependencies, please check the `pyproject.toml`
[file](https://github.com/RyanBalshaw/spectrally-regularised-LVMs/blob/main/pyproject.toml) included in this repository. The dependencies of this package are as follows:
| Package | Version |
|:-----------------------------------------------:|:-----------------:|
| [Python](https://www.python.org/) | ≥ 3.11 |
| [Numpy](https://numpy.org/) | ≥ 1.23.1, < 2.0 |
| [Matplotlib](https://matplotlib.org/) | ≥ 3.5.2 |
| [SciPy](https://scipy.org/) | ≥ 1.8.1 |
| [scikit-learn](https://scikit-learn.org/) | ≥ 1.1.2 |
| [tqdm](https://github.com/tqdm/tqdm) | ≥ 4.64.1 |
| [SymPy](https://www.sympy.org/en/index.html) | ≥ 1.1.1, < 1.13 |
| [Poetry](https://python-poetry.org/) | ≥ 2.0 |
# API usage
Please visit [the docs](http://spectrally-regularised-lvms.readthedocs.io/) for all supporting API documentation for this package.
# Contributing
This package uses [Poetry](https://python-poetry.org/) for dependency management and Python packaging and [git](https://git-scm.com/) for version control. To get started, first install git and Poetry. Then one may clone this repository via
```console
$ git clone git@github.com:RyanBalshaw/spectrally-regularised-LVMs.git
$ cd spectrally-regularised-LVMs
```
Then, install the necessary dependencies in a local environment via
```console
$ poetry install --with dev,docs
$ poetry shell
```
If you obtain an error with the `poetry shell` command, please make sure to see this [release note](https://python-poetry.org/blog/announcing-poetry-2.0.0/#poetry-export-and-poetry-shell-only-available-via-plugins) for Poetry version 2. You can check your poetry version with `poetry --version`.
This will install all necessary package dependencies and activate the virtual environment. You can then set up the [pre-commit](https://pre-commit.com/) hooks via
```console
$ pre-commit install -t pre-push
pre-commit installed at .git/hooks/pre-commit
```
# License
This project is licensed under MIT License - see the [LICENSE](https://github.com/RyanBalshaw/spectrally-regularised-LVMs/blob/main/LICENSE) file for details.
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