Metadata-Version: 1.1
Name: functionalfilet
Version: 0.5.1
Summary: Evolutionnary Neural Network Model with PyTorch
Home-page: UNKNOWN
Author: FabienFrfr (Fabien Furfaro)
Author-email: <fabien.furfaro@gmail.com>
License: UNKNOWN
Description: .. -*- mode: rst -*-
        
        
        |GitHub|_ |PyPi|_ |DOI|_
        
        
        .. |GitHub| image:: https://img.shields.io/github/v/release/fabienfrfr/functionalfilet
        .. _GitHub: https://github.com/fabienfrfr/functionalfilet
        
        .. |PyPi| image:: https://img.shields.io/pypi/v/functionalfilet
        .. _PyPi: https://pypi.org/project/functionalfilet
        
        
        .. |DOI| image:: https://img.shields.io/badge/arXiv-ANNFE-%3CCOLOR%3E.svg
        .. _DOI: https://arxiv.org/abs/2205.10118
        
        
        .. |PythonMinVersion| replace:: 3.5
        .. |PyTorchMinVersion| replace:: 1.0.0
        
        
        .. image:: https://raw.githubusercontent.com/fabienfrfr/functionalfilet/main/branding/logo.png
          :target: https://pypi.org/project/functionalfilet/
        
        
        **Functional Filet** is a Python module for machine learning build on top of Pytorch.
        
        This project explores a new method of constructing linear neural networks using evolutionary algorithms and graphs with gradient descent for adjustment of network weights.
        
        More details in the Arxiv preprint `An Artificial Neural Network Functionalized by Evolution <https://arxiv.org/abs/2205.10118>`__ (model based).
        
        Installation
        ------------
        
        Dependencies
        ~~~~~~~~~~~~
        
        Functional-Filet requires:
        
        - Python (>= |PythonMinVersion|)
        - NumPy
        - Pandas
        - PyTorch (>= |PyTorchMinVersion|)
        - Torchvision
        - Matplotlib
        - Networkx
        
        Optionally, you need:
        
        - Scikit-learn
        - Seaborn
        - Gym
        
        =======
        
        Functional-Filet is stable only from version 0.5.1, any previous version corresponds to the development phase.
        
        However, there are several possible optimizations, in particular on the restructuring of *Torch* tensors in Python which could be done in C++. For this, it is possible that there will be several code modifications in the future.
        
        
        The documentation includes more detailed `installation and examples instructions <https://github.com/fabienfrfr/functionalfilet/blob/main/doc/notebook.ipynb>`_.
        
        
        User installation
        ~~~~~~~~~~~~~~~~~
        
        If you already have a working installation of numpy and pytorch,
        the easiest way to install scikit-learn is using ``pip``::
        
            python3 -m pip install functionalfilet
        
        
        Development
        -----------
        
        We welcome new contributors of all experience levels.
        
        Important links
        ~~~~~~~~~~~~~~~
        
        - Official source code repo: https://github.com/fabienfrfr/functionalfilet
        - Download releases: https://pypi.org/project/functionalfilet
        
        Source code
        ~~~~~~~~~~~
        
        You can check the latest sources with the command::
        
            git clone https://github.com/fabienfrfr/functionalfilet.git
        
        
        Utilization
        -----------
        
        Once installed, if we consider two variables *feature X* and *label y* already executed upstream of the code, here is a simple example of use in the case of a classification problem::
        
        	# package
        	from functionalfilet import model as ff 
        	# init model
        	model = ff.FunctionalFilet()
        	# train
        	model.fit(X,y)
        	# test
        	y_pred = model.predict(X, index=seeder_idx)
        
        
        Existing code
        ~~~~~~~~~~~~~
        
        There is in the *example* directory of the git, several code to play with the learning parameters in simple cases. A brief summary is described at the top of each file::
        
        	python3 -m IPython
        	# universal approximation theorem
        	run example/uat_regression.py
        	# classification with overlapping and unbalance
        	run example/blob_classification.py
        	# reinforcment leaning with time dependancy
        	run example/gym_RL-CartPole-v0.py
        
        Citation
        ~~~~~~~~
        If you take inspiration from my machine learning algorithm for a scientific publication, we would appreciate citations::
        
        	@article{furfaro2022artificial,
        	title={An Artificial Neural Network Functionalized by Evolution},
        	author={Furfaro, Fabien and Bar-Hen, Avner and Berthelot, Geoffroy},
        	journal={arXiv preprint arXiv:2205.10118},
        	year={2022}
        	}
        
        **Attribution required : Fabien Furfaro (CC 4.0 BY NC ND SA)**
Keywords: python,pytorch,graph,machine learning,evolution
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
