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GeezerVison is showing github.com — GitHub - antononcube/MathematicaForPrediction: Mathematica implementations of machine learning algorithms used for prediction and personalization.

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.github/ISSUE_TEMPLATE
.github/ISSUE_TEMPLATE
 
 
Applications
Applications
 
 
Data/Big-Data-in-Healthcare
Data/Big-Data-in-Healthcare
 
 
Documentation
Documentation
 
 
EBNF
EBNF
 
 
Examples
Examples
 
 
Java/TriesWithFrequencies
Java/TriesWithFrequencies
 
 
Lua
Lua
 
 
MarkdownDocuments
MarkdownDocuments
 
 
Misc
Misc
 
 
MonadicProgramming
MonadicProgramming
 
 
Notebooks
Notebooks
 
 
OPL
OPL
 
 
R
R
 
 
UnitTests
UnitTests
 
 
AVCDecisionTreeForest.m
AVCDecisionTreeForest.m
 
 
AprioriAlgorithm.m
AprioriAlgorithm.m
 
 
AprioriAlgorithmV10.1.m
AprioriAlgorithmV10.1.m
 
 
AprioriAlgorithmV9.m
AprioriAlgorithmV9.m
 
 
BiSectionalKMeans.m
BiSectionalKMeans.m
 
 
ChernoffFaces.m
ChernoffFaces.m
 
 
ClassifierEnsembles.m
ClassifierEnsembles.m
 
 
CrossTabulate.m
CrossTabulate.m
 
 
DataReshape.m
DataReshape.m
 
 
DocumentTermMatrixConstruction.m
DocumentTermMatrixConstruction.m
 
 
EclatAlgorithm.m
EclatAlgorithm.m
 
 
FunctionalParsers.m
FunctionalParsers.m
 
 
IndependentComponentAnalysis.m
IndependentComponentAnalysis.m
 
 
JavaTriesWithFrequencies.m
JavaTriesWithFrequencies.m
 
 
LICENSE
LICENSE
 
 
MathematicaForPredictionUtilities.m
MathematicaForPredictionUtilities.m
 
 
MosaicPlot.m
MosaicPlot.m
 
 
MosaicPlotV9.m
MosaicPlotV9.m
 
 
NGramMarkovChains.m
NGramMarkovChains.m
 
 
NGramMarkovChainsV9.m
NGramMarkovChainsV9.m
 
 
NaiveBayesianClassifier.m
NaiveBayesianClassifier.m
 
 
NonNegativeMatrixFactorization.m
NonNegativeMatrixFactorization.m
 
 
OutlierIdentifiers.m
OutlierIdentifiers.m
 
 
ParetoPrincipleAdherence.m
ParetoPrincipleAdherence.m
 
 
QuantileRegression.m
QuantileRegression.m
 
 
README
README
 
 
README.md
README.md
 
 
ROCFunctions.m
ROCFunctions.m
 
 
SSparseMatrix.m
SSparseMatrix.m
 
 
SparseMatrixRecommenderFramework.m
SparseMatrixRecommenderFramework.m
 
 
TriesWithFrequencies.m
TriesWithFrequencies.m
 
 
TriesWithFrequenciesV9.m
TriesWithFrequenciesV9.m
 
 
VariableImportanceByClassifiers.m
VariableImportanceByClassifiers.m
 
 

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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
  • Framework for Linear vector space representations of document collections
  • Item-item recommender framework based on Sparse linear algebra
  • Generator of Naive Bayesian Classifiers (NBC's)
  • Functional parsers
  • Software monad generator

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 packages listed above have:

  • Counterpart implementations in Python, R, or other languages
  • Related Wolfram Function Repository functions

(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.


Support & appreciation

"Buy Me A Coffee"

😅 ☕️ 😀


Anton Antonov
04.07.2013, Florida, USA
11.01.2017, Florida, USA (updated)
09.17.2019, Florida, USA (updated)
29.10.2022, Florida, USA (updated)

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Mathematica implementations of machine learning algorithms used for prediction and personalization.

mathematicaforprediction.wordpress.com

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