Metadata-Version: 2.4
Name: geokde-t2
Version: 0.1.0
Summary: Geospatial Kernel Density Estimation (KDE) on Toroidal Manifolds (Standalone Module)
Author-email: Liviu Ioan Cadar <lee@axiom-corp.com>
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: axiom-t2>=0.1.2

# geokde-t2

**Geospatial Kernel Density Estimation (KDE) on Toroidal Manifolds (Standalone Module)**

This is a standalone module of the `axiom-t2` geometry-aware machine learning toolkit for toroidal manifolds. It provides standalone packaging for applications requiring a lightweight dependency footprint.

## Installation

```bash
pip install geokde-t2
```

## Quick Start

```python
from geokde import GeoKDE, flat_kde
import numpy as np

# Fit GeoKDE on geospatial data in radians
X = np.random.uniform(-np.pi, np.pi, (100, 2))
kde = GeoKDE()
kde.fit(X)
scores = kde.score_samples(X[:5])
print("Log-likelihoods:", scores)
```

## Reference

For the underlying mathematics and details on toroidal manifolds, please refer to the main toolkit paper: `axiom_t2_toolkit_paper.pdf`.
