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Logical Reasoning for Deep Nets

Project description

========= Pymetheus

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PyMetheus: Deep Nets for Logical Reasoning

Features

  • Provides an out of the box tool to learn (fuzz) first order logic with the use of an underlying vector space

Features

  • Create a Logic Deep Network

.. code-block:: python

import pymetheus
import itertools
from pymetheus.pymetheus import LogicNet

ll = LogicNet()

..

  • Introduce Some Constants

.. code-block:: python

ll.constant("Rome")
ll.constant("Milan")
ll.constant("Italy")

..

  • Introduce Some Predicates and Knowledge

.. code-block:: python

ll.predicate("capital")
ll.predicate("country")

ll.knowledge("country(Milan,Italy)")
ll.knowledge("capital(Rome,Italy)")

ll.zeroing() # Initialize KB with all knowledge as false

..

  • Add quantified rule with data .. code-block:: python

    rule = "forall ?a,?b: capital(?a,?b) -> country(?a,?b)" ll.universal_rule(rule) var = ["Italy", "Rome", "Milan"] ll.variable("?a", var) ll.variable("?b", var) ..

  • Learn and Reason

.. code-block:: python

ll.learn(epochs=1000, batch_size=25)


ll.reason("capital(Rome,Italy)", True)

..

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

0.1.0 (2019-08-22)

  • First release on PyPI.

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