pyndl 0.3.4

Naive discriminative learning implements learning and classification models based on the Rescorla-Wagner equations.

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 have suggestings concerning the pyndl package, contact us under konstantin (dot) sering (ät) uni-tuebingen.de.

Note

This package is not intended to be used under python2.

Getting Started

These instructions will get you a copy of the pyndl package on your local machine. If you only want to use pyndl as a python package use pip3 in order to install it into your python3 environment. If you want to inspect and change the code download and install it via git clone and python3 setup.py. For details see below.

Prerequisites

You need python 3.5 or newer and git installed on your machine. We recommend to install Minicoda (https://conda.io/miniconda.html) before installing pyndl or to create a virtualenv within your personal folder.

Development

If you want to develop pyndl you should additionally install:

pip3 install --user tox pylint pytest pycodestyle sphinx

Installing

If you only want to use the package run you can install pyndl from pypi with:

pip3 install --user pyndl

If you want to inspect and change the source code as well as running tests and having local documentation, clone the repository and install the package in ‘development’ mode by running

git clone https://github.com/quantling/pyndl.git
cd pyndl
python3 setup.py develop --user

Documentation and Examples

Documentation and examples can be found under http://pyndl.readthedocs.io/en/latest/ or in the doc/ folder after cloning the repository.

If you have installed sphinx you should be able to build the documentation with:

cd doc/
make html

The entry point for the html documentation is doc/build/html/index.html.

Running the tests

If you have cloned pyndl from github, you can start the tests by executing the following within the outer pyndl folder:

py.test
py.test doc/source/examples.rst

For full tests you can run:

tox -e test

For manually checking coding guidelines run:

pycodestyle pyndl tests
pylint --ignore-patterns='.*\.so' --rcfile=setup.cfg -j 2 pyndl tests

For more details on which tests run in the continuous testing environment look at the file tox.ini.

Deployment

In order to create a source dist package run:

python3 setup.py sdist

Contributing

Please read CONTRIBUTING.rst for details on our code of conduct and the process for submitting pull requests to us.

Versioning

At the moment we are still in alpha and therefore no stable API is guaranteed between minor version increments, but soon we will change to use SemVer for versioning. For the versions available, see the tags on this repository.

Authors

See also the list of contributors who participated in this project.

License

This project is licensed under the MIT License - see the LICENSE.txt file for details

Acknowledgments

This package is build as a python replacement for the R ndl2 package. Some ideas on how to build the API and how to efficiently run the Rescorla Wagner iterative learning on large text corpora are inspired by the way the ndl2 package solves this problems. The ndl2 package will be published to github in August 2017 and a reference will be placed here.