sinflow 0.1.3

Sliced Iterative Normalizing Flow

sinflow

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 for more information, examples and tutorials.

Installation

To install sinflow using pip run:

pip install sinflow

or, to install from source:

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:

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:

@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.