Metadata-Version: 1.1
Name: pyndl
Version: 0.2.0
Summary: Naive discriminative learning implements learning and classification models based on the Rescorla-Wagner equations.
Home-page: https://github.com/quantling/pyndl
Author: David-Elias Künstle, Lennard Schneider, Konstantin Sering, Marc Weitz
Author-email: konstantin.sering@uni-tuebingen.de
License: MIT
Description: ============
        Readme pyndl
        ============
        
        .. image:: https://travis-ci.com/dekuenstle/pyndl.svg?token=nAo9MxVBWCT7YyFRxEP4&branch=master
            :target: https://travis-ci.com/dekuenstle/pyndl
        
        This python3 package is a collection of useful script in order to run tasks on
        huge amounts of text file corpora. Especially, it allows to efficiently apply
        the Rescorla-Wagner learning rule to these corpora.
        
        .. warning::
        
            This package is still in alpha and there might be some API changes in the
            near future. If you want to suggest us some contact us under konstantin
            (dot) sering (ät) uni-tuebingen.de.
        
        .. note::
        
            This package is not intended to be used under python2.
        
        
        Installation
        ============
        Install with::
        
            python setup.py install [--user]
        
        or development install with::
        
            python setup.py develop
        
        in order to build a source package run::
        
            python setup.py sdist
        
        
        Usage
        =====
        This package is intended to be used as a python package in (small) python
        script or via the ipython REPL. For example code and explanations look into
        ``doc/source/examples.rst``.
        
        
        Development
        ===========
        
        
        Documentation
        -------------
        The documentation and the doc-strings within the source code should follow the
        numpy doc-string conventions (which are used by pandas as well).
        
        https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt
        
        http://pandas.pydata.org/pandas-docs/stable/contributing.html#contributing-to-the-documentation
        
        
        Install
        -------
        You can install this package with pip from a folder, git repository, or sdist
        bundle::
        
            pip install ~/pyndl/|git+ssh://git@github.com/<user>/pyndl.git|pyndl-<version>.tar.gz
        
        
        Testing
        =======
        Check if the package does what it is supposed to do by running the test with
        ``py.test``::
        
            py.test-3 --cov-report html --cov=pyndl
        
        The more general way of testing - including unit tests, documentation checks
        and style checking - can be done via tox. Tox does testing in a virtual
        environment and installs all dependencies. ::
        
            (pip install tox)
            tox
        
        
        Important terminology
        =====================
        Some terminology used in these modules and scripts which descripe specific
        behaviour:
        
        cue :
            A cue is something that gives a hint on something else. The something else
            is called outcome. Examples for cues in a text corpus are trigraphs or
            preceeding words for the word or meaning of the word.
        
        outcome :
            A something that happens or is the result of an event. Examples are words,
            the meaning of the word, or lexomes.
        
        event :
            An event connects cues with outcomes. In any event one or more unordered
            cues are present and one or more outcomes are present.
        
        weights :
            The weights represent the learned experience / association between all cues
            and outcomes of interest. Usually, some meta data is stored alongside the
            learned weights.
        
        cue file :
            A cue file contains a list of all cues that are interesting for a specific
            question. It is a utf-8 encoded tab delimitered text file with a header in
            the first line. It has two columns. The first column contains the cue and
            the second column contains the frequency of the cue. There is one cue per
            line. The ordering does not matter.
        
        outcome file :
            An outcome file contains a list of all outcomes that are interesting for a
            specific question. It is a utf-8 encoded tab delimitered text file with a
            header in the first line. It has two columns. The first column contains the
            outcome and the second column contains the frequency of the outcome. There
            is one outcome per line. The ordering does not matter.
        
        symbol file :
            A symbol file contains a list of all symbols that are allowed to occur in
            an outcome or a cue. It is a utf-8 encoded tab delimitered text file with a
            header in the first line. It has two columns. The first column contains the
            symbol and the second column contains the frequency of the symbol. There is
            one symbol per line. The ordering does not matter.
        
        event file :
            An event file contains a list of all events that should be learned. The
            learning will start at the first event and continue to the last event in
            order of the lines. The event file is a utf-8 encoded tab delimitered text
            file with a header in the first line. It has three columns. The first
            column contains an underscore delimitered list of all cues. The second
            column contains an underscore delimitered list of all outcomes. The third
            column contains the frequency of the event. The ordering of the cues and
            outcomes does not matter. There is one event per line. The ordering of the
            lines in the file *does* matter.
        
        corpus file :
            A corpus file is a utf-8 encoded text file that contains huge amounts of
            text. A ``---end.of.document---`` or ``---END.OF.DOCUMENT---`` string marks
            where an old document finished and a new document starts.
        
        weights file :
            A weights file contains the learned weights between cues and outcomes. The
            netCDF format is used to store these information along side with meta data,
            which contains the learning parameters, the time needed to calculate the
            weights, the version of the software used and other information.
        
        
        
Platform: Linux
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
