Variational Bayes algorithm for Normal Inverse Gaussian Mixture Models
Installation
The package can be build using poetry and installed using pip:
pip install vbnigmmExamples
If you want to apply vbnigmm to your data, you can run the following code:
from vbnigmm import NormalInverseGaussMixture as Model
# x is numpy.ndarray of 2D
model = Model()
model.fit(x)
label = model.predict(x)Citation
If you use vbnigmm in a scientific paper, please consider citing the following paper:
Takashi Takekawa, Clustering of non-Gaussian data by variational Bayes for normal inverse Gaussian mixture models. arXiv preprint arXiv:2009.06002 (2020).
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Release files for vbnigmm 2.4.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| vbnigmm-2.4.1.tar.gz | 23.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| vbnigmm-2.4.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 54.6 kB
Release files / vbnigmm-2.4.1.tar.gz
| Download URL | vbnigmm-2.4.1.tar.gz |
|---|---|
| Size | 23.5 kB |
| Tags | Source |
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Release files / vbnigmm-2.4.1-py3-none-any.whl
| Download URL | vbnigmm-2.4.1-py3-none-any.whl |
|---|---|
| Size | 31.1 kB |
| Tags | Python 3 |
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