Metadata-Version: 2.1
Name: py_rete
Version: 0.0.7.dev44
Summary: A basic Python implementation of the RETE algorithm.
Home-page: https://pypi.python.org/pypi/py_rete/
Author: Christopher J. MacLellan
Author-email: maclellan.christopher@gmail.com
License: MIT
Project-URL: Source Code, https://github.com/cmaclell/py_rete
Description: # py_rete
        
        [![Build Status](https://travis-ci.com/cmaclell/py_rete.svg?branch=master)](https://travis-ci.com/cmaclell/py_rete) [![Coverage Status](https://coveralls.io/repos/github/cmaclell/py_rete/badge.svg?branch=master)](https://coveralls.io/github/cmaclell/py_rete?branch=master)
        
        ## Introduction
        
        The py_rete project aims to implement a Rete engine in native python. This
        system is built using one the description of the Rete algorithms provided by
        [Doorenbos (1995)][doorenbos]. It also makes heavy use of ideas from the
        [Experta project][experta] (although no code is used from this project as it
        utilizes an LGPL license).
        
        The purpose of this system is to support basic expert / production system AI
        capabilities in a way that is easy to integrate with other Python based AI/ML
        systems.
        
        ## Installation
        
        This package is installable via pip with the following command:
        `pip install -U py_rete`.
        
        It can also be installed directly from GitHub with the following command:
        `pip install -U git+https://github.com/cmaclell/py_rete@master`
        
        ## The Basics
        
        The two high-level structures to support reasoning with py_rete are **facts**
        and **productions**. 
        
        ### Facts
        
        Facts represent the basic units of knowledge that the productions match over.
        Here are a few examples of facts and how they work.
        
        1. *Facts* are a subclass of dict, so you can treat them similar to dictionaries.
        
        ```python
        >>> f = Fact(a=1, b=2)
        >>> f['a']
        1
        ```
        
        2. *Facts* extend dictionaries, so they also support positional values without
           keys. These values are assigned numerical indices based on their position.
        
        ```python
        >>> f = Fact('a', 'b', 'c')
        >>> f[0]
        'a'
        ```
        
        3. *Facts* can support mixed positional and named arguments, but positional
           must come before named and named arguments do not get positional references.
        
        ```python
        >>> f = Fact('a', 'b', c=3, d=4)
        >>> f[0]
        'a'
        >>> f['c']
        3
        ```
        
        5. *Facts* support nesting with other facts. 
        
        ```python
        >>> f = Fact(subfact=Fact())
        Fact(subfact=Fact())
        ```
        
        Note that there will be issues if facts contain other data structures that
        contain facts (they will not be properly added to the rete network or to
        productions).
        
        ### Productions
        
        Similar to Experta's rules, *Productions* are functions that are decorated with
        conditions that govern when they execute and bind the arguments necessary for
        their execution.
        
        *Productions* have two components:
        * Conditions, which are essentially facts that can contain pattern matching
          variables.
        * A Function, which is executed for each rule match, with the arguments to the
          function being passed the bindings from pattern matching variables.
        
        Here is an example of a simple *Productions* that binds with all *Facts* that
        have the color red and prints 'I found something red' for each one:
        
        ```python
        @Production(Fact(color='red'))
        def alert_something_red():
            print("I found something red")
        ```
        
        Productions also support logical operators to express more complex conditions.
        
        ```python
        @Production(AND(OR(Fact(color='red'),
                           Fact(color='blue')),
        	        NOT(Fact(color='green'))))
        def alert_something_complex():
            print("I found something red or blue without any green present")
        ```
        
        Bitwise logical operators can be used as shorthand to make composing complex conditions easier.
        ```python
        @Production((Fact(color='red') | Fact(color='blue')) & ~Fact(color='green'))
        def alert_something_complex2():
            print("I found something red or blue without any green present")
        ```
        
        In addition to matching simple facts, pattern matching variables can be used to
        match values from Facts. Matching ensures that variable bindings are consistent
        across conditions. Additionally, variables are passed to arguments in the function
        with the same name during matching. For example, the following production finds
        a Fact with a lastname attribute.  For each Fact it finds, it prints "I found a
        fact with a lastname attribute: `<lastname>`".  Note, the `V('lastname')`
        corresponds to a variable named lastname that can bind with values from Facts
        during matching.  Additionally the variable (`V('lastname')`) and the function
        argument `lastname` match have the same name, which enables the matcher to the
        variable bindings into the function.
        ```python
        @Production(Fact(lastname=V('lastname')))
        def found_relatives(lastname):
            print("I found a fact with a lastname: {}".format(lastname))
        ```
        
        It is also possible to employ functional tests (lambdas or functions) using
        `Filter` conditions. Like the function that is being decorated, Filter
        conditions pass variable bindings to their equivelently named function
        arguments. It is important to note that positive facts that bind with these
        variables need to be listed in the production before the tests that use them.
        ```python
        @Production(Fact(value=V('a')) &
                    Fact(value=V('b')) &
                    Filter(lambda a, b: a > b) &
                    Fact(value=V('c')) &
                    Filter(lambda b, c: b > c))
        def three_values(a, b, c):
            print("{} is greater than {} is greater than {}".format(a, b, c))
        ```
        
        It is also possible to bind *facts* to variables as well, using the bitshift
        operator.
        ```python
        @Production(V('name_fact') << Fact(name=V('name')))
        def found_name(name_fact):
            print("I found a name fact {}".format(name_fact))
        ```
        
        ### ReteNetwork
        
        To engage in reasoning *facts* and *productions* are loaded into a
        **ReteNetwork**, which facilitates the matching and application of productions
        to facts.
        
        Here is how you create a network:
        
        ```python
        net = ReteNetwork()
        ```
        
        Once a network has been created, then facts can be added to it.
        ```python
        f1 = Fact(light_color="red")
        net.add_fact(f1)
        ```
        
        Note, facts added to the network cannot contain any variables or they will
        trigger an exception when added. Additionally, once a fact has been added to
        network it is assigned a unique internal identifier.
        
        This makes it possible to update the fact.
        ```python
        f1['light_color'] = "green"
        net.update_fact(f1)
        ```
        
        It also make it possible to remove the fact.
        ```python
        net.remove_fact(f1)
        ```
        
        When updating a fact, note that it is not updated in the network until
        the `update_fact` method is called on it. An update essentially equates to
        removing and re-adding the fact.
        
        Productions can also be added to the network. Productions also can make use of
        the `net` variable, which is automatically bound to the Rete network the
        production has been added to. This makes it possible for productions to update
        the contents of the network when they are fired. For example, the following functions
        have an argument called `net` that is bound to the rete network even though there is
        no variable by that name in the production conditions.
        ```python
        >>> f1 = Fact(light_color="red")
        >>> 
        >>> @Production(V('fact') << Fact(light_color="red"))
        >>> def make_green(net, fact):
        >>>	print('making green')
        >>>     fact['light_color'] = 'green'
        >>>     net.update_fact(fact)
        >>> 
        >>> @Production(V('fact') << Fact(light_color="green"))
        >>> def make_red(net, fact):
        >>>	print('making red')
        >>>     fact['light_color'] = 'red'
        >>>     net.update_fact(fact)
        >>> 
        >>> light_net = ReteNetwork()
        >>> light_net.add_fact(f1)
        >>> light_net.add_production(make_green)
        >>> light_net.add_production(make_red)
        ```
        
        Once the above fact and productions have been added the network can be run.
        ```python
        >>> light_net.run(5)
        making green
        making red
        making green
        making red
        making green
        ```
        
        The number passed to run denotes how many rules the network should fire
        before terminating.
        
        In addition to this high-level function for running the network, there
        are also some lower-level capabilities that can be used to more closely control
        the rule execution.
        
        For example, you can get all the production matches from the matches property.
        ```python
        matches = list(light_net.matches)
        ```
        
        You can also get just the new matches.
        ```python
        new = list(light_net.new_matches)
        ```
        
        You can fire one of the matches.
        ```python
        >>> matches[0].fire()
        making red
        ```
        
        [experta]: https://github.com/nilp0inter/experta
        [doorenbos]: http://reports-archive.adm.cs.cmu.edu/anon/1995/CMU-CS-95-113.pdf
        
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: PyPy
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