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FALL - Framework for Adaptive Life-Long Learning
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FALL is designed to enable both practitioners and researchers to develop and implement machine learning systems for streaming data, in the presence of changing conditions.
FALL is a framework for building adaptive learning systems capable of incrementally learning from streaming data, and adapting to changing conditions.
FALL is modular, based around a standard architecture for adaptive learning. This enables existing methods to be easily recreated, as well as novel methods to be easily implemented without re-implementing standard components.

.. image:: ../adaptation.gif
  :width: 800
  :alt: Adaptive Learning

Installation
============

``pip install fall-ml==0.0.1``

Documentation
=============

Please refer to the package `homepage <https://benhalstead.dev/FALL/>`_ for more information and documentation.

Examples
========

Please refer to the binder `link <https://mybinder.org/v2/gh/BenHals/FALL/HEAD?labpath=examples%2F>`_ for interactive examples. 

.. toctree::
   :maxdepth: 2
   :caption: Contents:

   fall.adaptive_learning.rst
   fall.classifiers.rst
   fall.concept_representations.rst
   fall.data.rst
   fall.repository.rst
   fall.states.rst
   fall.rst
   modules.rst