=========
|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.
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"description": "=========\n|Experta| \n=========\n\n|pypi| |version| |tests| |docs| |coverage|\n\nExperta is a Python library for building expert systems strongly inspired\nby CLIPS_.\n\n.. code-block:: python\n\n from random import choice\n from experta import *\n\n\n class Light(Fact):\n \"\"\"Info about the traffic light.\"\"\"\n pass\n\n\n class RobotCrossStreet(KnowledgeEngine):\n @Rule(Light(color='green'))\n def green_light(self):\n print(\"Walk\")\n\n @Rule(Light(color='red'))\n def red_light(self):\n print(\"Don't walk\")\n\n @Rule(AS.light << Light(color=L('yellow') | L('blinking-yellow')))\n def cautious(self, light):\n print(\"Be cautious because light is\", light[\"color\"])\n\n\n.. code-block:: python\n\n >>> engine = RobotCrossStreet()\n >>> engine.reset()\n >>> engine.declare(Light(color=choice(['green', 'yellow', 'blinking-yellow', 'red'])))\n >>> engine.run()\n Be cautious because light is blinking-yellow\n\n\nMigrating from Pyknow\n---------------------\n\nExperta is a Pyknow fork. Just replace any `pyknow` references in your\ncode/examples to `experta` and everything should work the same.\n\n\nExamples\n--------\n\nYou can find some more examples on GitHub_.\n\n.. _CLIPS: http://clipsrules.sourceforge.net\n.. _GitHub: https://github.com/nilp0inter/experta/tree/develop/docs\n.. |Experta| image:: https://raw.githubusercontent.com/nilp0inter/experta/develop/docs/static/expertalogo_small.png\n.. |pypi| image:: https://img.shields.io/pypi/v/experta.svg\n :target: https://pypi.python.org/pypi/experta\n\n.. |version| image:: https://img.shields.io/pypi/pyversions/experta.svg\n :target: https://pypi.python.org/pypi/experta\n\n.. |tests| image:: https://travis-ci.org/nilp0inter/experta.svg?branch=master\n :target: https://travis-ci.org/nilp0inter/experta\n\n.. |docs| image:: https://readthedocs.org/projects/experta/badge/?version=stable\n :target: https://experta.readthedocs.io/en/stable/\n :alt: Documentation Status\n\n.. |coverage| image:: https://codecov.io/gh/nilp0inter/experta/branch/develop/graph/badge.svg\n :target: https://codecov.io/gh/nilp0inter/experta\n :alt: codecov.io\n\n\nTODO\n----\n\n\nCHANGELOG\n---------\n\n1.9.4\n~~~~~\n\n* Resolved #10. Corner case in DepthStrategy in which the first\n activation of the agenda didn't get removed.\n\n\n1.9.3\n~~~~~\n\n* Resolved #11 that was introduced with #7, that caused the same\n behavior under different conditions.\n\n\n1.9.2\n~~~~~\n\n* Resolved #7. In some situations last activation was removed right\n after entering the agenda.\n\n\n1.9.1\n~~~~~\n\n* Resolved #3. Rules should not be called with already retracted facts.\n\n\n1.9.0\n~~~~~\n\n* Drop Python 3.4 support.\n* Use setup.cfg to save all package metadata.\n\n\n1.8.0-1.8.2\n~~~~~~~~~~~\n\n* Rebranded to Experta.\n\n\n1.7.0\n~~~~~\n\n* Implemented the template system.\n* Replaced warnings by watchers messages.\n* Fixed freeze() with frozen objects.\n* Fixed unfreeze() with unfrozen objects.\n* Parametrized DefFacts via reset() kwargs.\n\n\n1.6.0\n~~~~~\n\n* Improved overall performance.\n\n\n1.5.0\n~~~~~\n\n* Added Python version 3.7 to tox.ini.\n* Monkey and bananas example.\n* Fixed bug, numeric index args gets repeated in a weird way introduced in `1.4.0`.\n* Pass only the defined args in absence of kwargs.\n\n\n1.4.0\n~~~~~\n\n* Order integer facts keys after making a copy.\n* as_dict method for Fact.\n* freeze and unfreeze method documentation.\n* unfreeze method in pyknow.utils.\n* Zebra example from Clips.\n\n\n1.3.0\n~~~~~\n\n* `pyknow.operator` module.\n* Nested matching.\n* Added Talk 'Sistemas Expertos en Python con PyKnow - PyConES 2017' to docs\n folder.\n\n\n1.2.0\n~~~~~\n\n* Freeze fact values as the default behavior to address Issue #9.\n* Added `pyknow.utils.anyof` to mitigate Issue #7.\n* Raise RuntimeError if a fact value is modified after declare().\n* Added MATCH and AS objects.\n\n\n1.1.1\n~~~~~\n\n* Removing the borg optimization for P field constraints.\n* Use the hash of the check in the sorting of the nodes to always\n generate the same alpha part of the network.\n\n\n1.1.0\n~~~~~\n\n* Allow any kind of callable in Predicate Field Constraints (P()).\n\n\n1.0.1\n~~~~~\n\n* DNF of OR clause inside AND or Rule was implemented wrong.\n\n\n1.0.0\n~~~~~\n\n* RETE matching algorithm.\n* Better Rule decorator system.\n* Facts are dictionaries.\n* Documentation.\n\n\n<1.0.0\n~~~~~~\n\n* Unestable API.\n* Wrong matching algorithm.\n* Bad performance\n* PLEASE DON'T USE THIS.\n",
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