Mathematica implementations of machine learning algorithms used for prediction and personalization.
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Applications It is better to have NearestWithOutliers->False by default. Sep 29, 2015
Documentation First version of Fast-and-agile IIR. Feb 28, 2017
EBNF Time specifications grammar rules in EBNF. Oct 13, 2016
Examples Package file with a simple time series conversational engine implemen… Nov 29, 2014
Java/TriesWithFrequencies Fix shell command. Apr 20, 2017
Lua Corrected references. Mar 16, 2016
MarkdownDocuments Image links to imgur uploaded images. Mar 6, 2017
Misc Implemented graph function specification for UMLClassGraph. Feb 6, 2017
OPL OPL code Jan 10, 2014
R Removed redundant print. Apr 18, 2017
UnitTests Added tests for JavaTrieNodeProbabilities behavior with other functions. Jan 29, 2017
AVCDecisionTreeForest.m No linear combinations of numerical variables are used if the number … Jan 5, 2016
AprioriAlgorithm.m Added a new function, ItemRules, that makes it easier to obtain asso… Jan 6, 2016
AprioriAlgorithmV9.m Snapshot of Apriori algorithm implementation with version 9. Oct 30, 2014
ChernoffFaces.m Spelling of "forehead". Oct 2, 2016
ClassifierEnsembles.m Better explanations. Oct 13, 2016
DocumentTermMatrixConstruction.m Further code improvement. Sep 19, 2016
FunctionalParsers.m Better descriptions. Apr 6, 2016
IndependentComponentAnalysis.m Simplified some code. May 23, 2016
JavaTriesWithFrequencies.m Extended the signature of JavaTrieRandomChoice to resemble RandomChoice. Feb 14, 2017
LICENSE GPL 3. Jan 11, 2017
MathematicaForPredictionUtilities.m Added upvalue defintion of MatrixForm for Associations that "XTABMatr… Jan 4, 2017
MosaicPlot.m Better explanations. Feb 16, 2017
NGramMarkovChains.m More robust implementation of the generation function -- handling the… Sep 8, 2014
NGramMarkovChainsV9.m Moved the implementation of for Version 9 into a file with 'V9' suffix. Sep 8, 2014
NaiveBayesianClassifier.m Added implementations of NBCClassificationSuccessCounts and NBCClassi… Oct 20, 2013
NonNegativeMatrixFactorization.m Added the function NearestWords. Minor changes of explanations and us… Mar 3, 2016
OutlierIdentifiers.m Added the function ColorPlotOutliers. May 5, 2016
QuantileRegression.m Better explanations. Apr 30, 2016
README 3nd README commit Jul 4, 2013
README.md Added hyperlinks. Jan 11, 2017
ROCFunctions.m Changed AUC to AUROC. Oct 11, 2016
SparseMatrixRecommenderFramework.m Explanations update. Jun 10, 2016
TriesWithFrequencies.m Added TrieMemberQ and changed TriePosition, TrieCompleteMatch, and Tr… Jan 7, 2017
VariableImportanceByClassifiers.m Better usage message. Jan 3, 2016

README.md

Mission statement

This open source project is for Mathematica implementations of statistical and machine learning algorithms that can be used for data analysis, 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").

Here are some fairly unique to the Mathematica landscape algorithms:

The implemented algorithms are (usually) well documented. There are also fair amount of documents with related applications.

Some of the algorithms have counterparts implementations in R or other languages.

Associated blog (at WordPress)

There is a blog associated with this project: http://mathematicaforprediction.wordpress.com .

Anton Antonov
04.07.2013, Florida, USA
11.01.2017, Florida, USA (updated)