Mission statement
This open source project is for Mathematica (Wolfram Language) implementations of statistical and Machine Learning
algorithms that can be used for data analysis, forecast, prediction, and recommendation systems.
License matters
All code files and executable documents are with the license GPL 3.0. For details see
http://www.gnu.org/licenses/ .
All documents are with the license Creative Commons Attribution 4.0 International (CC BY 4.0).
For details see https://creativecommons.org/licenses/by/4.0/ .
Organization
The algorithms implementations are given in Mathematica package files ("*.m").
Explanations or presentations about the algorithms are given in Mathematica notebook files ("*.nb"), in
PDF files,
or in
Markdown files.
Here are some fairly unique to the Mathematica / WL landscape algorithms:
- mosaic plots;
- outlier identifiers;
- associative rules finding;
- Prefix trees (Tries);
- Quantile Regression;
- Chernoff faces;
- Non-Negative Matrix Factorization (NNMF);
- Independent Component Analysis (ICA);
- Receiver Operating Characteristic (ROC);
- classifier ensembles;
- a framework for Linear vector space representations of document collections;
- an Item-item recommender framework based on Sparse linear algebra;
- a generator of Naive Bayesian Classifiers (NBC's);
- Functional parsers.
The implemented algorithms are (usually) well documented. There is a fair amount of documents with related applications.
There are also
monadic programming implementations
closely related to the "main directory" packages.
Some of the algorithms have counterpart implementations in R or other languages.
(The code in the R directory in this repository though is not updated, it is just kept for references.
See the corresponding, actively worked on, dedicated repository
R-packages.)
Associated blog (at WordPress)
There is a blog associated with this project, see
MathematicaForPrediction at WordPress.
Anton Antonov
04.07.2013, Florida, USA
11.01.2017, Florida, USA (updated)
09.17.2019, Florida, USA (updated)