https://github.com/minaskar/sinflow

Sliced Iterative Normalizing Flow with Minimal Dependencies
https://github.com/minaskar/sinflow

density-estimation machine-learning normalizing-flow normalizing-flows numpy sampling scikit-learn scipy spline splines

Last synced: 22 days ago
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Sliced Iterative Normalizing Flow with Minimal Dependencies

• Host: GitHub
• URL: https://github.com/minaskar/sinflow
• Owner: minaskar
• License: gpl-3.0
• Created: 2024-10-05T13:34:15.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-10-13T19:34:45.000Z (23 days ago)
• Last Synced: 2024-10-13T19:45:27.546Z (23 days ago)
• Topics: density-estimation, machine-learning, normalizing-flow, normalizing-flows, numpy, sampling, scikit-learn, scipy, spline, splines
• Language: Python
• Homepage: https://sinflow.readthedocs.io
• Size: 146 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# sinflow

[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://github.com/minaskar/sinflow/blob/master/LICENSE)

[![Documentation Status](https://readthedocs.org/projects/sinflow/badge/?version=latest)](https://sinflow.readthedocs.io/en/latest/?badge=latest)

``sinflow`` is a Python implementation of the sliced iterative normalizing flow (SINF) algorithm

for density estimation and sampling. The package has minimal dependencies, requiring only

``numpy`` and ``scipy``. The code is designed to be easy to use and flexible, with a focus on

performance and scalability. The package is designed to be used in a similar way to ``scikit-learn``,

with a simple and consistent API.

## Documentation

Read the docs at [sinflow.readthedocs.io](https://sinflow.readthedocs.io) for more information, examples and tutorials.

## Installation

To install ``sinflow`` using ``pip`` run:

```bash

pip install sinflow

```

or, to install from source:

```bash

git clone https://github.com/minaskar/sinflow.git

cd pocomc

python setup.py install

```

## Basic example

For instance, if you wanted to draw samples from a 10-dimensional Rosenbrock distribution with a uniform prior, you would do something like:

```python

import sinflow as sf

import numpy as np

from sklearn.datasets import make_moons

# Generate some data

x, _ = make_moons(n_samples=5000, noise=0.15)

# Fit a normalizing flow model

flow = sf.Flow()

flow.fit(x)

# Sample from the model

samples = flow.sample(1000)

# Evaluate the log-likelihood of the samples

log_prob = flow.log_prob(samples)

# Evaluate the forward transformation

z, log_det_forward = flow.forward(x)

# Invert the transformation

x_reconstructed, log_det_inverse = flow.inverse(z)

```

## Attribution & Citation

Please cite the following paper if you found this code useful in your research:

```bash

@article{karamanis2024sinflow,

    title={},

    author={},

    journal={},

    year={2024}

}

```

## Licence

Copyright 2024-Now Minas Karamanis and contributors.

``sinflow`` is free software made available under the GPL-3.0 License. For details see the `LICENSE` file.