experta

========= |Experta|

|pypi| |version| |tests| |docs| |coverage|

Experta is a Python library for building expert systems strongly inspired by CLIPS_.

.. code-block:: python

from random import choice from experta import *

class Light(Fact): """Info about the traffic light.""" pass

class RobotCrossStreet(KnowledgeEngine): @Rule(Light(color='green')) def green_light(self): print("Walk")

   @Rule(Light(color='red'))
   def red_light(self):
       print("Don't walk")

   @Rule(AS.light << Light(color=L('yellow') | L('blinking-yellow')))
   def cautious(self, light):
       print("Be cautious because light is", light["color"])

.. code-block:: python

engine = RobotCrossStreet() engine.reset() engine.declare(Light(color=choice(['green', 'yellow', 'blinking-yellow', 'red']))) engine.run() Be cautious because light is blinking-yellow

Migrating from Pyknow

Experta is a Pyknow fork. Just replace any pyknow references in your code/examples to experta and everything should work the same.

Examples

You can find some more examples on GitHub_.

.. _CLIPS: http://clipsrules.sourceforge.net .. _GitHub: https://github.com/nilp0inter/experta/tree/develop/docs .. |Experta| image:: https://raw.githubusercontent.com/nilp0inter/experta/develop/docs/static/expertalogo_small.png .. |pypi| image:: https://img.shields.io/pypi/v/experta.svg :target: https://pypi.python.org/pypi/experta

.. |version| image:: https://img.shields.io/pypi/pyversions/experta.svg :target: https://pypi.python.org/pypi/experta

.. |tests| image:: https://travis-ci.org/nilp0inter/experta.svg?branch=master :target: https://travis-ci.org/nilp0inter/experta

.. |docs| image:: https://readthedocs.org/projects/experta/badge/?version=stable :target: https://experta.readthedocs.io/en/stable/ :alt: Documentation Status

.. |coverage| image:: https://codecov.io/gh/nilp0inter/experta/branch/develop/graph/badge.svg :target: https://codecov.io/gh/nilp0inter/experta :alt: codecov.io

TODO

CHANGELOG

1.9.4

* Resolved #10. Corner case in DepthStrategy in which the first
  activation of the agenda didn't get removed.


1.9.3

• Resolved #11 that was introduced with #7, that caused the same behavior under different conditions.

1.9.2

* Resolved #7. In some situations last activation was removed right
  after entering the agenda.


1.9.1

• Resolved #3. Rules should not be called with already retracted facts.

1.9.0

* Drop Python 3.4 support.
* Use setup.cfg to save all package metadata.


1.8.0-1.8.2

• Rebranded to Experta.

1.7.0

* Implemented the template system.
* Replaced warnings by watchers messages.
* Fixed freeze() with frozen objects.
* Fixed unfreeze() with unfrozen objects.
* Parametrized DefFacts via reset() kwargs.


1.6.0

• Improved overall performance.

1.5.0

* Added Python version 3.7 to tox.ini.
* Monkey and bananas example.
* Fixed bug, numeric index args gets repeated in a weird way introduced in `1.4.0`.
* Pass only the defined args in absence of kwargs.


1.4.0

• Order integer facts keys after making a copy.
• as_dict method for Fact.
• freeze and unfreeze method documentation.
• unfreeze method in pyknow.utils.
• Zebra example from Clips.

1.3.0

* `pyknow.operator` module.
* Nested matching.
* Added Talk 'Sistemas Expertos en Python con PyKnow - PyConES 2017' to docs
  folder.


1.2.0

• Freeze fact values as the default behavior to address Issue #9.
• Added pyknow.utils.anyof to mitigate Issue #7.
• Raise RuntimeError if a fact value is modified after declare().
• Added MATCH and AS objects.

1.1.1

* Removing the borg optimization for P field constraints.
* Use the hash of the check in the sorting of the nodes to always
  generate the same alpha part of the network.


1.1.0

• Allow any kind of callable in Predicate Field Constraints (P()).

1.0.1

* DNF of OR clause inside AND or Rule was implemented wrong.


1.0.0

• RETE matching algorithm.
• Better Rule decorator system.
• Facts are dictionaries.
• Documentation.

<1.0.0

* Unestable API.
* Wrong matching algorithm.
* Bad performance
* PLEASE DON'T USE THIS.