Metadata-Version: 2.2
Name: akde
Version: 0.1.0
Summary: Multivariate Adaptive Kernel Density Estimation via Gaussian Mixture Models
Home-page: https://github.com/trungnth/akde
Author: Trung Nguyen
Author-email: trungnth@dnri.vn
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
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﻿# AKDE: Adaptive Kernel Density Estimation

AKDE provides a fast, adaptive kernel density estimator based on the Gaussian Mixture Model for multidimensional data. The [original MATLAB implementation][matlab] by Zdravko Botev does not appear to reference the algorithm described in the [corresponding paper][paper]. This Python re-implementation includes automatic grid construction for arbitrary dimensions and provides a detailed explanation of the method.

# Installation

You can install it via pip:  pip install akde

# Usage
Providing any data (economy, biology, geoscience, astronomy, physics...) in n by d numpy array (n rows, d columns)
```
from akde import akde
pdf, meshgrids, bandwidth = akde(data)

```

# KDE Visualization
Using contour plot or imshow for 2D data, isosurface or volume plot for 3D data


[matlab]: https://www.mathworks.com/matlabcentral/fileexchange/58312-kernel-density-estimator-for-high-dimensions
[paper]: https://dx.doi.org/10.1214/10-AOS799

