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.
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.
Please visit the documentation page for all supporting documentation for this package.
The package is designed to be used through the Python API, and can be installed using pip:
$ pip install spectrally-regularised-LVMsA more detailed discussion regarding installation is given in the documentation.
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 |
| Poetry | ≥ 1.4 |
Please visit the docs for all supporting API documentation for this package.
This package uses Poetry for dependency management and Python packaging and git for version control. To get started, first install git and Poetry. Then one may clone this repository via
$ git clone git@github.com:RyanBalshaw/spectrally-regularised-LVMs.git
$ cd spectrally-regularised-LVMsThen, install the necessary dependencies in a local environment via
$ poetry install --with dev,docs
$ poetry shellThis 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-commitThis project is licensed under MIT License - see the LICENSE file for details.