Metadata-Version: 2.1
Name: pymetheus
Version: 0.3.0
Summary: Logical Reasoning for Deep Nets
Home-page: https://github.com/vinid/pymetheus
Author: Federico Bianchi
Author-email: federico.bianchi@unimib.it
License: GNU General Public License v3
Description: =========
        Pymetheus
        =========
        
        
        .. image:: https://img.shields.io/pypi/v/pymetheus.svg
                :target: https://pypi.python.org/pypi/pymetheus
        
        .. image:: https://img.shields.io/travis/vinid/pymetheus.svg
                :target: https://travis-ci.org/vinid/pymetheus
        
        .. image:: https://readthedocs.org/projects/pymetheus/badge/?version=latest
                :target: https://pymetheus.readthedocs.io/en/latest/?badge=latest
                :alt: Documentation Status
        
        
        
        
        PyMetheus: Deep Nets for Logical Reasoning
        
        
        * Free software: GNU General Public License v3
        * Documentation: https://pymetheus.readthedocs.io.
        
        
        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.
        
Keywords: pymetheus
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
