Metadata-Version: 2.1
Name: inference-logic
Version: 0.1.0
Summary: declarative programming on json-like objects in Python
Home-page: https://github.com/gecBurton/inference_logic
Author: George EC Burton
Author-email: g.e.c.burton@gmail.com
License: MIT license
Keywords: inference_logic
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5

====================
Inference Logic
====================


.. image:: https://img.shields.io/pypi/v/inference_logic.svg
        :target: https://pypi.python.org/pypi/inference_logic

.. image:: https://img.shields.io/travis/gecBurton/inference_logic.svg
        :target: https://travis-ci.com/gecBurton/inference_logic

.. image:: https://readthedocs.org/projects/json-inference-logic/badge/?version=latest
        :target: https://json-inference-logic.readthedocs.io/en/latest/?badge=latest
        :alt: Documentation Status


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     :target: https://pyup.io/repos/github/gecBurton/inference_logic/
     :alt: Updates



The goal of this project is to explore how to write a minimal
set of features that allows a programmer to code declaratively
in native Python.

The code is loosely based on Prolog, but rather than use the Prolog
Term/functor structure it uses JSON-like dicts and tuples.

The success of this project is measured by the number of the
`99 problems`_ solved to keep the code focussed on delivering
features and not bike-shedding

This code is experimental and incomplete. Do not use it in your
work or school! If you wish to use a serious, well tested
declarative tool in Python use the excellent pyDatalog_.

* Free software: MIT license
* Documentation: https://json-inference-logic.readthedocs.io.

tldr
----

.. code-block:: python

    from inference_logic import Variable, Rule, search

    X, Y, Z, C, P = Variable.factory("X", "Y", "Z", "C", "P")

    db = [
        dict(parent="Abe", child="Homer"),
        dict(parent="Homer", child="Lisa"),
        dict(parent="Homer", child="Bart"),
        dict(parent="Homer", child="Maggie"),
        Rule(dict(ancestor=X, descendant=Z), dict(parent=X, child=Z)),
        Rule(
            dict(ancestor=X, descendant=Z),
            dict(parent=X, child=Y),
            dict(ancestor=Y, descendant=Z),
        ),
    ]

    query = dict(ancestor=P, descendant=C)
    results = search(db, query)


    assert next(results) == {C: "Lisa", P: "Abe"}
    assert next(results) == {C: "Bart", P: "Abe"}
    assert next(results) == {C: "Maggie", P: "Abe"}
    assert next(results) == {C: "Homer", P: "Abe"}
    assert next(results) == {C: "Lisa", P: "Homer"}
    assert next(results) == {C: "Bart", P: "Homer"}
    assert next(results) == {C: "Maggie", P: "Homer"}



Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

This was originally inspired by py4fun_ some of this code here comes directly
from this project.

Thank you to kclaurelie_ for helping to solve the fundamental
problem that had been bugging me for months!

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
.. _`99 problems`: https://www.ic.unicamp.br/~meidanis/courses/mc336/2009s2/prolog/problemas/
.. _pyDatalog: https://pypi.org/project/pyDatalog/
.. _py4fun: https://www.openbookproject.net/py4fun/prolog/prolog1.html
.. _kclaurelie: https://github.com/kclaurelie
.. _LINQ: https://docs.microsoft.com/en-us/dotnet/csharp/programming-guide/concepts/linq/


=======
History
=======

0.1.0 (2020-10-05)
------------------

* First release on PyPI.


