Metadata-Version: 2.1
Name: studenttmixture
Version: 1.1
Summary: Mixture modeling algorithms using the Student's t-distribution
Home-page: https://github.com/jlparki/mix_T
Author: Jonathan Parkinson
Author-email: jlparkinson1@gmail.com
License: MIT
Platform: UNKNOWN
Description-Content-Type: text/markdown
License-File: LICENSE

# studenttmixture

Mixtures of multivariate Student's t distributions are widely used for clustering
data that may contain outliers, but scipy and scikit-learn do not at present
offer classes for fitting Student's t mixture models. This package provides classes
for:

1) Modeling / clustering a dataset using a finite mixture of multivariate Student's
t distributions fit via the EM algorithm. This is analogous to scikit-learn's 
GaussianMixture.
2) Modeling / clustering a dataset using a mixture of multivariate Student's 
t distributions fit via the variational mean-field approximation. This is analogous to
scikit-learn's BayesianGaussianMixture.

Unittests for the package are in the tests folder.

### Installation

    pip install studenttmixture

Note that starting in version 0.0.2.3, this package contains C extensions and is therefore
distributed as a source distribution which is automatically compiled on install. 

It is unusual but problems with source distribution pip packages that contain C extensions are occasionally
observed on Windows, e.g. an error similar to this:

    error: Microsoft Visual C++ 14.0 is required.

in the unlikely event you encounter this, I recommend the solution described under this 
[StackOverflow and links](https://stackoverflow.com/questions/44951456/pip-error-microsoft-visual-c-14-0-is-required).

### Usage

- [EMStudentMixture](https://github.com/jlparkI/mix_T/blob/main/Documentation/Finite_Mixture_Docs.md)<br>
- [VariationalStudentMixture](https://github.com/jlparkI/mix_T/blob/main/Documentation/Variational_Mixture_Docs.md)<br>
- [Tutorial: Modeling with mixtures](https://github.com/jlparkI/mix_T/blob/main/Documentation/Tutorial.md)<br>

### Background

- [Deriving the mean-field formula](https://jlparki.github.io/mean_field.pdf)<br>


