Metadata-Version: 2.1
Name: pyrichlet
Version: 0.0.9
Summary: A package for doing density estimation and clustering using Gaussian mixtures with BNP weighting models
Home-page: https://github.com/cabo40/pyrichlet
Author: Fidel Selva
Author-email: cfso100@gmail.com
License: Apache License, Version 2.0
Project-URL: Bug Tracker, https://github.com/cabo40/pyrichlet/issues
Project-URL: Documentation, https://pyrichlet.readthedocs.io
Project-URL: Source Code, https://github.com/cabo40/pyrichlet
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: tqdm
Requires-Dist: matplotlib
Requires-Dist: importlib_resources; python_version < "3.9"

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# Project description

Pyrichlet is a package for doing density estimation and clustering using 
Gaussian mixtures with BNP weighting models

# Installation

With pip:

```
pip install pyrichlet
```

For a specific version:

```
pip install pyrichlet==0.0.9
```


# Usage

This is a quick guide. For a more detailed usage see
https://pyrichlet.readthedocs.io/en/main/index.html.


The mixture models that this package implements are

- `DirichletDistributionMixture`
- `DirichletProcessMixture`
- `PitmanYorMixture`
- `GeometricProcessMixture`
- `BetaInBetaMixture`
- `BetaInDirichletMixture`
- `BetaBernoulliMixture`
- `BetaBinomialMixture`

They can be fitted for an array or dataframe using a Gibbs sampler or
variational Bayes methods,

```python
from pyrichlet import mixture_models

mm = mixture_models.DirichletProcessMixture()
y = [1, 2, 3, 4]
mm.fit_gibbs(y, init_groups=2)

mm.fit_variational(y, n_groups=2)
```

and use the fitted class to do density estimation

```python
x = 2.5
f_x = mm.gibbs_eap_density(x)
f_x = mm.var_eap_density(x)
```

or clustering

```python
mm.var_map_cluster()
mm.gibbs_map_cluster()
mm.gibbs_eap_spectral_consensus_cluster()
```
