| Name | mcfa JSON |
| Version |
0.1.5
JSON |
| download |
| home_page | https://github.com/maxmahlke/mcfa.git |
| Summary | Mixtures of Common Factor Analyzers with missing data |
| upload_time | 2024-01-16 13:39:24 |
| maintainer | |
| docs_url | None |
| author | Max Mahlke |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
[](https://arxiv.org/abs/2203.11229) [](https://github.com/psf/black)
<p align="center">
<img width="260" src="https://raw.githubusercontent.com/maxmahlke/mcfa/main/gfx/logo_mcfa.png">
</p>
This `python` package implements the Mixtures of Common Factor Analyzers model
introduced by [Baek+ 2010](https://ieeexplore.ieee.org/document/5184847). It
uses [tensorflow](https://www.tensorflow.org/) to implement a stochastic
gradient descent, which allows for model training without prior imputation of
missing data. The interface resembles the [sklearn](https://scikit-learn.org/stable/) model API.
# Documentation
Refer to the `docs/documentation.ipynb` for the documentation and
`docs/4d_gaussian.ipynb` for an example application.
# Install
Install from PyPi using `pip`:
$ pip install mcfa
The minimum required `python` version is 3.8.
# Alternatives
- [EMMIXmfa](https://github.com/suren-rathnayake/EMMIXmfa) in `R`
- [Casey+ 2019](https://github.com/andycasey/mcfa) in `python`
Compared to this implementation, Casey+ 2019 use an EM-algorithm instead of a
stochastic gradient descent. This requires the imputation of the missing values
**before** the model training. On the other hand, there are more initialization
routines the lower space loadings and factors available in the Casey+ 2019 implementation.
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