semvec/pypi-packages huggingface dataset:
pysarpu 0.1.0
PU learning under SAR assumption with unknown propensity. Implementation of the general SAR-EM algorithm.
License: MIT license
Requires Python: >=3.6
Keywords: pysarpu

=======
pysarpu
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.. image:: https://img.shields.io/pypi/v/pysarpu.svg
        :target: https://pypi.python.org/pypi/pysarpu

.. image:: https://img.shields.io/travis/ocoudray/pysarpu.svg
        :target: https://travis-ci.com/ocoudray/pysarpu

.. image:: https://readthedocs.org/projects/pysarpu/badge/?version=latest
        :target: https://pysarpu.readthedocs.io/en/latest/?version=latest
        :alt: Documentation Status




PU learning under SAR assumption with unknown propensity. Implementation of the general SAR-EM algorithm.


* Free software: MIT license
* Documentation: https://pysarpu.readthedocs.io.


Features
--------

* TODO

Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage


=======
History
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0.1.0 (2022-10-07)
------------------

* First release on PyPI.
backfire 0.0.0
Normalizing flows in PyTorch
License: MIT license
Requires Python: >=3.8
Keywords: normalizing flows,probability,density,generative,deep learning,torch

# Backfire - Normalizing flows in PyTorch

`backfire` is a Python package that implements normalizing flows in PyTorch. The philosophy of `backfire` is to rely as much as possible on distributions and transformations already existing in PyTorch. Unfortunately, the `Distribution` and `Transformation` classes of `torch` are not sub-classes of `nn.Module`, which means they don't implement a forward method, you cannot send their internal tensors to GPU with `.to('cuda')` or even get their parameters with `.parameters()`.

To solve this problem, `backfire` defines two abstract classes: `DistributionModule` and `TransformModule`. The former is any `nn.Module` whose forward pass returns a `Distribution`. Similarly, the latter is any `nn.Module` whose forward pass returns a `Transform`. Then, a normalizing flow is simply a `nn.Module` that is constructed from a list of `TransformModule` and a base `DistributionModule` and its parameters are handled just like any neural network. This design allows for flow implementations that are easy to understand and extend.

## Installation

The `backfire` package is available on [PyPI](https://pypi.org/project/backfire), which means it is installable via `pip`.

```
pip install backfire
```

Alternatively, if you need the latest features, you can install it from the repository.

```
pip install git+https://github.com/francois-rozet/backfire
```

## Contributing

If you have a question, an issue or would like to contribute, please read our [contributing guidelines](CONTRIBUTING.md).

## Documentation

The documentation is made with [Sphinx](https://www.sphinx-doc.org) and [Furo](https://github.com/pradyunsg/furo) and is hosted at [francois-rozet.github.io/backfire](https://francois-rozet.github.io/backfire).