Metadata-Version: 1.1
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
Description: =======
        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.
        
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
