Metadata-Version: 2.0
Name: aitools
Version: 0.1.3
Summary: Artificial Intelligence Tool Kit Mark Two.
Home-page: https://github.com/imohitawasthi/aitools
Author: Mohit Awasthi
Author-email: imohitawasthi@gmail.com
License: Apache Software License 2.0
Description-Content-Type: UNKNOWN
Keywords: aitools
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Dist: Click (>=6.0)
Requires-Dist: matplotlib
Requires-Dist: pandas

=======
AITools
=======


.. image:: https://img.shields.io/pypi/v/aitools.svg
        :target: https://pypi.python.org/pypi/aitools

.. image:: https://img.shields.io/travis/imohitawasthi/aitools.svg
        :target: https://travis-ci.org/imohitawasthi/aitools

.. image:: https://readthedocs.org/projects/aitools/badge/?version=latest
        :target: https://aitools.readthedocs.io/en/latest/?badge=latest
        :alt: Documentation Status


.. image:: https://pyup.io/repos/github/imohitawasthi/aitools/shield.svg
     :target: https://pyup.io/repos/github/imohitawasthi/aitools/
     :alt: Updates



Artificial Intelligence Tool Kit Mark Two.
------------------------------------------


* Free software: Apache Software License 2.0
* Documentation: https://aitools.readthedocs.io.


Features
--------

* N-GRAM

    * Predict the next word of the sentence that further can be use for predicting the entire sentence.
    * Generation of text like a paragraph/article on Health or environment or any other topic (given relevant data).
    * Generation of the next GOT books!!!.

    * Next Phase - Use of bayesian statistics and more nice probability models for getting the best results.

Look For: Demo file n_gram.py

* K-MEANS

    * Creates clusters and classify new nodes into those clusters.
    * Efficient and easy to implement.

    * Next Phase - Multiple algorithms implementation.

Look For: Demo file k_means.py and k_means_color_cluster.py

* Logistic Regression

    * From probability of event happening or not to the next Data point, it is a very versatile algorithm.
    * Simple Implementation and Training routines.
    * Uses Gradient Descent and Sigmoid Function.

    * Next Phase - Improvements in Gradient Descent is still in progress.

Look For: Demo file logistic_regression.py

* Naive Bayes

    * Algorithm based on Bayesian statistics, which is capable of creating classifications like an intent classifier or sentiment analysis engine(A true complex one not the Pathetic Twitter thing).
    * Created using little bit of probability and bayesian statistics with a hint of programming and with a lot of love.

    * Next Phase - Flexibility and more control on different algorithm.

Look For: Demo file naive_bayes.py

* Decision Tree

    * Algorithm based on Gini logic and info gain for prediction.
    * Trees can be very use full but may lead to aver fitting easily Be Cautious.

    * Next Phase - Flexibility and more control on different algorithm.

Look For: Demo file decision_tree.py

* Pre Processing

    * Utils for Pre Processing Data.

    * Next Phase - More Cool Functions.

Look For: Demo file pre_processing.py

* Others

    * In build Mathematics Util.
        * Vector Math, Probability, and some Statistic
        * Util will be upgraded as per the need.
    * Gradient Descent.
        * Entire Gradient Descent algorithm combined into one.
        * Usability Docs will be Provided Soon.

* Next Updates

    * Improving all algorithms and giving proper documentation on usage.
    * Providing more user control on algorithm selection and output handling.
    * Detailed Demo files.
    * Sudo codes and algorithm explanations.
    * Will be available to lower version( > 2.7).



Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage


=======
History
=======

0.1.0 (2019-04-14)
------------------

* GitHub Setup.
* Travis CI Build Added.
* Documentation skeleton added on ReadTheDocs.
* First release on PyPI.

0.1.0 (2019-04-21)
------------------

* N-Gram algorithm version one completed.
* First level of documentation.
* N-Gram demo files added.

0.1.0 (2019-04-25)
------------------

* Gradient Descent Created.
* Word Stemmer Added(Still needs improvements and not been used through out the project).

0.1.0 (2019-04-28)
------------------

* Logistic Regression Added.
* Demo file added.

0.1.2 (2019-05-01)
------------------

* Naive Bayes Created.


0.1.2 (2019-05-01)
------------------

* Pre Processing Added.


0.1.2 (2019-05-01)
------------------

* First Level of Documentation Created.


0.1.2 (2019-05-27)
------------------

* Lost Track of most of the things.
* Finally We have following
    * K Means
    * Logistic Regression
    * N Gram
    * Naive Bayes
    * Pre Processing

    * SVM in progress.


0.1.3 (2019-06-24)
------------------

* Decision Tree Added(Gini and Impurity Method).
* Previous algorithm improvements are in progress.


