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SimpleAI is an easy to use lib used in python. Many of the artificial intelligence algorithms are described in the book “Artificial Intelligence, a Modern Approach”, from Stuart Russel and Peter Norvig.

This implementation takes some of the ideas from the Norvig’s implementation (the aima-python lib), but it’s made with a more “pythonic” approach, and more emphasis on creating a stable, modern and maintainable version. We are testing the majority of the lib, it’s available via pip install, has a standard repository and lib architecture, well documented, respects the python pep8 guidelines, provides only working code (no placeholders for future things), etc. Even the internal code is written with readability in mind, not only the external API.

This new release adds a few statistical classification methods to SIMPLEAI with the intention of start replicating the machine learning aspects of aima-python.

There is a special focus in decision tree learning, where three different methods for decision tree learning are added, with one of them following strictly the aima pseudo-code, being particularly useful for teaching. The other classifiers added are Naive Bayes and K-Nearest Neighbors.

This release also includes lots of tests for the classifiers, documentation, and a few sample uses of the classifiers.


Welcome to simpleai’s documentation! — simpleai 0.7.11 documentation

Originally posted at http://www.machinalis.com/