├── .github └── ISSUE_TEMPLATE │ ├── bug_report.md │ └── feature_request.md ├── AVCDecisionTreeForest.m ├── Applications └── QuantileRegressionForLocalExtrema.m ├── AprioriAlgorithm.m ├── AprioriAlgorithmV10.1.m ├── AprioriAlgorithmV9.m ├── BiSectionalKMeans.m ├── ChernoffFaces.m ├── ClassifierEnsembles.m ├── CrossTabulate.m ├── Data └── Big-Data-in-Healthcare │ └── HHA-551-Questions-for-Anton.txt ├── DataReshape.m ├── DocumentTermMatrixConstruction.m ├── Documentation ├── A-monad-for-Latent-Semantic-Analysis-workflows.pdf ├── A-monad-for-Quantile-Regression-workflows.pdf ├── A-monad-for-classification-workflows.pdf ├── Adaptive-Numerical-Lebesgue-integration-by-set-measure-estimates.pdf ├── Antonov WTC-2011 - A fast and agile IIR.pdf ├── Basic theory and construction of naive Bayesian classifiers.pdf ├── Classification of genome data with n-gram models.pdf ├── Classification-of-geometric-data.pdf ├── Fast-and-agile-item-item-recommender.pdf ├── Finding local extrema in noisy data using Quantile Regression.pdf ├── Finding-all-structural-breaks-in-time-series.pdf ├── Functional parsers for an integration requests language grammar.pdf ├── Implementation-of-Object-Oriented-Programming-Design-Patterns-in-Mathematica.pdf ├── Importance-of-variables-investigation-guide.pdf ├── Independent-component-analysis-for-multidimensional-signals.pdf ├── Making-Chernoff-faces-for-data-visualization.pdf ├── MappingSMRtoSBR │ ├── Mapping-Sparse-Matrix-Recommender-to-Streams-Blending-Recommender.lyx │ └── Mapping-Sparse-Matrix-Recommender-to-Streams-Blending-Recommender.pdf ├── Misc │ ├── AI-misconceptions-reasons.pdf │ ├── Application-AI-and-ML.pdf │ ├── Making-competitions-classifiers-mind-map.pdf │ ├── Object-oriented-approaches-in-Mathematica-mind-map.pdf │ └── Time-series-framework-for-prediction-simulation-and-recommendations.pdf ├── Monad-code-generation-and-extension.pdf ├── Morphological analysis of algorithms vs data.pdf ├── Morphological analysis of algorithms vs problems.pdf ├── Mosaic plots for data visualization.pdf ├── MovieLens genre associations.pdf ├── Outlier detection in a list of numbers.pdf ├── Parametrized-event-records-data-transformations.pdf ├── Quantile regression robustness.pdf ├── Quantile regression through linear programming.pdf ├── ROCFunctions-example-usage.pdf ├── RSparseMatrix.pdf ├── SSparseMatrix.pdf ├── Simple-monadic-programming.pdf ├── Topic and thesaurus extraction from a document collection.pdf ├── Tries.pdf └── Waveform recognition with decision trees.pdf ├── EBNF ├── TimeSeriesConversationalEngineGrammar.ebnf └── TimeSpecificationsGrammar.ebnf ├── EclatAlgorithm.m ├── Examples └── SimpleTimeSeriesConversationalEngine.m ├── FunctionalParsers.m ├── IndependentComponentAnalysis.m ├── Java └── TriesWithFrequencies │ ├── README.md │ └── src │ ├── Experiments.java │ ├── Trie.java │ └── TrieFunctions.java ├── JavaTriesWithFrequencies.m ├── LICENSE ├── Lua ├── FunctionalParsers │ ├── FunctionalParsers.lua │ └── FunctionalParsersTests.lua ├── MathematicaFunctions.lua └── MathematicaFunctionsTests.lua ├── MarkdownDocuments ├── A-monad-for-Latent-Semantic-Analysis-workflows.md ├── A-monad-for-Quantile-Regression-workflows.md ├── A-monad-for-classification-workflows.md ├── AI-vision-via-WL.md ├── Adaptive-Numerical-Lebesgue-integration-by-set-measure-estimates.md ├── Applying-AI-and-ML-to-finance-and-technology.md ├── Call-graph-generation-for-context-functions.md ├── Classification-and-Association-Rules-for-Census-Income-Data.md ├── Comparison-of-dimension-reduction-algorithms-over-mandala-images-generation.md ├── Diagrams │ ├── A-monad-for-Latent-Semantic-Analysis-workflows │ │ ├── LSA-workflows.jpg │ │ ├── LSAMon-Data-Load-Hamlet-echo.png │ │ ├── LSAMon-Data-Load-StateOfUnionSpeeches-echo.png │ │ ├── LSAMon-Extracting-statistical-thesauri-echo.png │ │ ├── LSAMon-Extracting-topics-Hamlet-topics-table.png │ │ ├── LSAMon-Extracting-topics-StateOfUnionSpeeches-topics-table.png │ │ ├── LSAMon-Find-most-important-documents-table.png │ │ ├── LSAMon-Future-directions-parsed-LSA-commands-table.png │ │ ├── LSAMon-Introduction-pipeline-echos.png │ │ ├── LSAMon-Introduction-pipeline.png │ │ ├── LSAMon-LSI-weight-functions-combinations-application-table.png │ │ ├── LSAMon-LSI-weight-functions-table.png │ │ ├── LSAMon-Making-of-the-document-term-matrix-CrossTabulate.png │ │ ├── LSAMon-Making-of-the-document-term-matrix-echoes.png │ │ ├── LSAMon-Mapping-queries-and-documents-to-terms-query-matrix.png │ │ ├── LSAMon-Mapping-queries-and-documents-to-topics-context-sub-matrix.png │ │ ├── LSAMon-Mapping-queries-and-documents-to-topics-query-matrix.png │ │ ├── LSAMon-Monad-Design-formula-1.png │ │ ├── LSAMon-Monad-Design-formula-2.png │ │ ├── LSAMon-Overview-StMon-usage-descriptions-table.png │ │ ├── LSAMon-Overview-operations-context-interactions-table.png │ │ ├── LSAMon-Overview-operations-usage-descriptions-table.png │ │ ├── LSAMon-Overview-setters-droppers-takers-context-interactions-table.png │ │ ├── LSAMon-Tags-representation-heatmap.png │ │ ├── LSAMon-The-monad-head-echo.png │ │ ├── LSAMon-The-utilization-of-SSparseMatrix-lsaHamlet-queries-to-terms-matrix.png │ │ ├── LSAMon-The-utilization-of-SSparseMatrix-lsaSpeeces-docTermMat-plot.png │ │ ├── LSAMon-The-utilization-of-SSparseMatrix-lsaSpeeches-terms-similarities-matrix.png │ │ ├── LSAMon-The-utilization-of-SSparseMatrix-lsaSpeeches-topics-similarities-matrix.png │ │ ├── LSAMon-The-utilization-of-SSparseMatrix-random-matrix.png │ │ ├── LSAMon-Unit-tests-random-pipelines-sample-table.png │ │ └── LSAMon-pipeline.jpg │ ├── A-monad-for-Quantile-Regression-workflows │ │ ├── Default-basis-to-fit-output-1-and-2.png │ │ ├── Default-basis-to-fit-output-3.png │ │ ├── Dependent-variable-simulation-output-1.png │ │ ├── Errors-and-error-plots-output-1.png │ │ ├── Estimating-conditional-distributions-output-1.png │ │ ├── Finding-local-extrema-in-noisy-data-output-1.png │ │ ├── Finding-outliers-output-1.png │ │ ├── Finding-outliers-output-2.png │ │ ├── Future-plans-conversational-agent-output-1.png │ │ ├── Lifting-data-to-the-monad-output.png │ │ ├── Moving-average-moving-median-and-moving-map-output-1.png │ │ ├── Plotting-outliers-output-2.png │ │ ├── QRMon-StMon-functions-overview-table.png │ │ ├── QRMon-distData.png │ │ ├── QRMon-finData.png │ │ ├── QRMon-formula-1.png │ │ ├── QRMon-formula-2.png │ │ ├── QRMon-introduction-monad-pipeline-example-echo.png │ │ ├── QRMon-introduction-monad-pipeline-example-table.png │ │ ├── QRMon-monad-functions-overview-table.png │ │ ├── QRMon-monad-functions-shortcuts-table.png │ │ ├── QRMon-pipeline.jpg │ │ ├── QRMon-tsData.png │ │ ├── Quantile-regression-fit-and-Least-squares-fit-basis.png │ │ ├── Quantile-regression-fit-and-Least-squares-fit-output-1.png │ │ ├── Quantile-regression-fit-and-Least-squares-fit-output-2.png │ │ ├── Quantile-regression-with-B-splines-output-1.png │ │ ├── Quantile-regression-with-B-splines-output-2.png │ │ ├── Quantile-regression-with-B-splines-output-3.png │ │ ├── Quantile-regression-workflow-extended-small.jpg │ │ ├── Quantile-regression-workflow-extended.jpg │ │ ├── The-monad-head-output.png │ │ ├── Unit-tests-TestReportObject-open-icon.png │ │ ├── Unit-tests-output-1.png │ │ └── Unit-tests-random-pipelines-sample.png │ ├── A-monad-for-classification-workflows │ │ ├── ClCon-MNIST-example-full.png │ │ ├── ClCon-MNIST-example-output.png │ │ ├── ClCon-State-monad-formula.png │ │ ├── ClCon-StateMonad-functions-table.png │ │ ├── ClCon-best-thresholds-example-output.png │ │ ├── ClCon-classifier-testing-ConfusionMatrixPlot-echo.png │ │ ├── ClCon-classifier-testing-ROCListLinePlot-echo.png │ │ ├── ClCon-classifier-testing-ROCListLinePlot-survived-echo.png │ │ ├── ClCon-classifier-testing-ROCPlot-echo.png │ │ ├── ClCon-components-interaction.jpg │ │ ├── ClCon-conditional-continuation-example-output.png │ │ ├── ClCon-datasets-dimensions.png │ │ ├── ClCon-development-cycle.jpg │ │ ├── ClCon-dimension-reduction-example-echo.png │ │ ├── ClCon-direct-unit-tests-TestReport-icon.png │ │ ├── ClCon-dsMushroom-summary.png │ │ ├── ClCon-dsTitanic-summary.png │ │ ├── ClCon-dsWineQuality-summary.png │ │ ├── ClCon-ensemble-classifier-example-1.png │ │ ├── ClCon-ensemble-classifier-example-2.png │ │ ├── ClCon-generic-monad-formula.png │ │ ├── ClCon-lifting-data-example-1.png │ │ ├── ClCon-lifting-data-example-2.png │ │ ├── ClCon-monad-head-example.png │ │ ├── ClCon-pipeline-TraceMonad-Echo-output.png │ │ ├── ClCon-pipeline-TraceMonad-table.png │ │ ├── ClCon-pipeline.jpg │ │ ├── ClCon-quick-data-sample.png │ │ ├── ClCon-quick-data-summary-ds.png │ │ ├── ClCon-quick-data-summary-mlr.png │ │ ├── ClCon-random-pipelines-TestReport-icon.png │ │ ├── ClCon-random-pipelines-tests-sample-table.png │ │ ├── ClCon-simple-dsTitanic-pipeline.png │ │ ├── ClCon-table-of-operations-setters-takers.png │ │ ├── Classification-workflow-extended.jpg │ │ ├── Classification-workflow-horizontal-layout.jpg │ │ └── Making-competitions-classifiers-mind-map.png │ ├── AI-vision-via-WL │ │ ├── 032vcq74auyv9.png │ │ ├── 08ijuwuchj31q.png │ │ ├── 0cmyq0lep1q7f.png │ │ ├── 0f0fuo9nexxl8.png │ │ ├── 0iyello2xfyfo.png │ │ ├── 0nmz56wwuboz3.png │ │ ├── 0rt43fezbbp4b.png │ │ ├── 0scq7lbpp7xfs.png │ │ ├── 0wmip47gloav0.png │ │ ├── 13qqfe3pzqfn9.png │ │ ├── 175o8ba3cxgoh.png │ │ ├── 1lpfhko7c2g6e.png │ │ ├── 1n90yjgjx3mkf.png │ │ ├── 1qni2g4n8vywf.png │ │ ├── 1u02ytqvf7xi9.png │ │ ├── 1ukmn97ui4o98.png │ │ └── 1xg1w9gct6yca.png │ ├── Doomsday-clock-parsing-and-plotting │ │ ├── DoomsdayClock-LLM-table-with-Date-sample.png │ │ ├── DoomsdayClock-data-table-with-Date-sample.png │ │ ├── DoomsdayClock-time-series-tooltip.png │ │ ├── DoomsdayClock-time-series.png │ │ ├── DoomsdayClockGauge-definition.png │ │ ├── DoomsdayClockGauge-examples.png │ │ ├── DoomsdayClockGauge-first.png │ │ ├── GetDoomsdayClockButton.png │ │ ├── Gramar-random-sentences.png │ │ ├── Parsing-LLM-example-function.png │ │ ├── Parsing-verification-table-doomsdat.png │ │ ├── Parsing-verification-table-integer-names-1.png │ │ ├── Parsing-verification-table-integer-names-2.png │ │ ├── Parsing-verification-table-integer-names-3.png │ │ └── Parsing-verification-table-robust-parser.png │ ├── Finding-all-structural-breaks-in-time-series │ │ ├── Application-of-Chow-Test-SP500.png │ │ ├── Application-of-Chow-Test-chowStats.png │ │ ├── Application-of-Chow-Test-chowStats2.png │ │ ├── Computation-local-maxima.png │ │ ├── Computation-structural-breaks-plots.png │ │ ├── Data-used-SP500.png │ │ ├── Data-used-Wk2.png │ │ ├── Future-plans-time-series-states.png │ │ └── Introductions-example.png │ ├── OpenAIMode-demo │ │ ├── 06wybw0d8ntd6.png │ │ ├── 0wyc32bgh7gso.png │ │ ├── 11og8fps5xhgk.png │ │ └── 1iqsuwy5jkmit.png │ ├── Parametrized-event-records-data-transformations │ │ ├── ERTMon-DateListPlot-USA-Temperature.png │ │ ├── ERTMon-RecordsSummary-entityAttributes.png │ │ ├── ERTMon-RecordsSummary-eventRecords.png │ │ ├── ERTMon-compute-variable-statistic-1.png │ │ ├── ERTMon-compute-variable-statistic-2.png │ │ ├── ERTMon-contingency-matrices.png │ │ ├── ERTMon-contingency-matrix.png │ │ ├── ERTMon-echo-data-summary.png │ │ ├── ERTMon-entityAttributes-sample.png │ │ ├── ERTMon-eventRecords-sample.png │ │ ├── ERTMon-large-pipeline-example-output.png │ │ ├── ERTMon-large-pipeline-example.png │ │ ├── ERTMon-moving-average-application.png │ │ ├── ERTMon-normalization.png │ │ ├── ERTMon-records-groups-maxTime.png │ │ ├── ERTMon-records-groups-minTime.png │ │ ├── ERTMon-repeated-execution-workflow.jpg │ │ ├── ERTMon-repeated-execution-workflow.pdf │ │ ├── ERTMon-restriction-and-aggregation.png │ │ ├── ERTMon-small-pipeline-example.png │ │ ├── ERTMon-wCompSpec.png │ │ ├── ERTMon-workflows.jpg │ │ └── ERTMon-workflows.pdf │ ├── Primitive-root-generation-trails │ │ ├── 03q03q9hobjx5.png │ │ ├── 05fw4gbxvzil3.png │ │ ├── 0ajyn6ixlitgd.png │ │ ├── 0i5ilivzw0nl5.png │ │ ├── 0ir0c5f83rko2.png │ │ ├── 0ja9nttj7gvy9.png │ │ ├── 0vmbr8ahsrf68.png │ │ ├── 0w93mw9n87rvn.png │ │ ├── 19gvpmjp7dx8d.png │ │ ├── 1aa33rtlvkbnh.png │ │ ├── 1qeiu9fz57as7.png │ │ ├── 1s2uag61bl0wu.png │ │ ├── 1tavxw8a8s8c7.png │ │ ├── 1wjcl3g3a3wd5.png │ │ ├── Animation-snapshot-1.png │ │ └── Blog-post-featured-image.png │ └── Trie-based-classifiers-evaluation │ │ ├── 08tac97m1v31b.png │ │ ├── 0aczl0mnjpi4x.png │ │ ├── 0e2tg6vw1ssh1.png │ │ ├── 0jlvgunwaouqi.png │ │ ├── 0m4somgwuj73s.png │ │ ├── 0n9qun2v81o7r.png │ │ ├── 0yvrxcu8fjfuv.png │ │ ├── 157jxydgclea9.png │ │ ├── 192nfe8h97k9k.png │ │ ├── 1hdocpn5z00u2.png │ │ ├── 1jqv1g8o5a2uw.png │ │ └── 1xlmbkphhwhsf.png ├── Doomsday-clock-parsing-and-plotting.md ├── Fast-and-agile-item-item-recommender.md ├── Finding-all-structural-breaks-in-time-series.md ├── Implementation-of-Object_Oriented-Programming-Design-Patterns-in-Mathematica.md ├── Importance-of-variables-investigation-guide.md ├── Independent-component-analysis-for-multidimensional-signals.md ├── Making-Chernoff-faces-for-data-visualization.md ├── Monad-code-generation-and-extension.md ├── OpenAIMode-demo.md ├── Parametrized-event-records-data-transformations.md ├── ParetoPrincipleAdherenceExamples.md ├── Primitive-root-generation-trails.md ├── ROC-for-Classifier-Ensembles-Bootstrapping-Damaging-and-Interpolation.md ├── ROCFunctions-Example-Usage.md ├── RandomTabularDataset-proclaim.md ├── Rapid-specification-of-random-tabular-datasets-generation.md ├── The-Great-conversation-in-USA-presidential-speeches.md ├── Trie-based-classifiers-evaluation.md └── Tries-with-frequencies-in-Java.md ├── MathematicaForPredictionUtilities.m ├── Misc ├── AdaptiveNumericalLebesgueIntegration.m ├── AprioriAlgorithmViaTries.m ├── ArrayOfFunctionsRule.m ├── AssociationTriesWithFrequencies.m ├── CallGraph.m ├── ChowTestStatistic.m ├── ComputationalSpecCompletion.m ├── ComputationalWorkflowTypeClassifier.m ├── GitHubDataObjects.m ├── GitHubPlots.m ├── HeatmapPlot.m ├── HextileBins.m ├── HextileGraph.m ├── LLMVision.m ├── LSAMonForImageCollections.m ├── LeftAlignedPresentationReStyling.m ├── MDQuantileRegression.m ├── NeuralNetworkGraph.m ├── OpenAIRequest.m ├── ParallelCoordinatesPlot.m ├── RSparseMatrix.m ├── RandomTabularDataset.m ├── Soundex.m ├── SymbolicRegressionByQRMon.m ├── TileBins.m ├── TileGraph.m ├── TilingUtilizationFunctions.m ├── UMLDiagramGeneration.m └── WeatherEventRecords.m ├── MonadicProgramming ├── MaybeMonadCodeGenerator.m ├── MonadicAnomaliesFinder.m ├── MonadicAnomalyzer.m ├── MonadicContextualClassification.m ├── MonadicEventRecordsTransformations.m ├── MonadicGeometricNearestNeighbors.m ├── MonadicLatentSemanticAnalysis.m ├── MonadicLatentSemanticAnalysisV11.3.m ├── MonadicNeuralNetworks.m ├── MonadicPhraseCompletion-JavaTrie.m ├── MonadicPhraseCompletion.m ├── MonadicQuantileRegression.m ├── MonadicSparseMatrixRecommender.m ├── MonadicStructuralBreaksFinder.m ├── MonadicTextAnalyzer-JavaTrie.m ├── MonadicTextAnalyzer.m ├── MonadicTracing.m ├── Package-structure-and-development-workflow.md ├── README.md └── StateMonadCodeGenerator.m ├── MosaicPlot.m ├── MosaicPlotV9.m ├── NGramMarkovChains.m ├── NGramMarkovChainsV9.m ├── NaiveBayesianClassifier.m ├── NonNegativeMatrixFactorization.m ├── Notebooks └── Finding-all-structural-breaks-in-time-series.nb ├── OPL └── Quantile regression.mod ├── OutlierIdentifiers.m ├── ParetoPrincipleAdherence.m ├── QuantileRegression.m ├── R ├── DataConversionFunctions.R ├── DocumentTermWeightFunctions.R ├── FunctionalParsers │ └── FunctionalParsers.R ├── HubItemDynamicRanks.R ├── JavaTriesWithFrequencies.R ├── NonNegativeMatrixFactorization.R ├── OutlierIdentifiers.R ├── ParetoLawFunctions.R ├── RecommenderTestFunctions.R ├── SMRExtraFunctions.R ├── SMRInterfaceGeneral │ ├── server.R │ └── ui.R ├── SparseMatrixRecommender.R ├── TimeSeriesRecommender.R ├── TimeSeriesSMRInterfaceGeneral │ ├── server.R │ └── ui.R ├── TriesWithFrequencies.R └── VariableImportanceByClassifiers.R ├── README ├── README.md ├── ROCFunctions.m ├── SSparseMatrix.m ├── SparseMatrixRecommenderFramework.m ├── TriesWithFrequencies.m ├── TriesWithFrequenciesV9.m ├── UnitTests ├── BiSectionalKMeans-Unit-Tests.mt ├── ChernoffFaces-Unit-Tests.wlt ├── DataReshape-Unit-Tests.wlt ├── GeneratedStateMonadTests.m ├── HextileBins-Unit-Tests.mt ├── JavaTriesWithFrequencies-Unit-Tests.wlt ├── KMeans-Unit-Tests.mt ├── MonadicContextualClassification-Unit-Tests.wlt ├── MonadicContextualClassificationRandomPipelinesUnitTests.m ├── MonadicLatentSemanticAnalysis-Unit-Tests.wlt ├── MonadicLatentSemanticAnalysis-Unit-TestsV11.3.wlt ├── MonadicLatentSemanticAnalysisRandomPipelinesUnitTests.m ├── MonadicQuantileRegression-Unit-Tests.wlt ├── MonadicQuantileRegressionRandomPipelinesUnitTests.m ├── MonadicSparseMatrixRecommender-Unit-Tests.mt ├── MonadicSparseMatrixRecommenderRandomPipelinesUnitTests.m ├── ParallelCoordinatesPlot-Unit-Tests.wlt ├── QuantileRegression-Unit-Tests.mt ├── RandomTabularDataset-Unit-Tests.wlt ├── RecordsSummary-Unit-Tests.wlt ├── SSparseMatrix-tests.wlt ├── TileBins-Unit-Tests.mt └── TriesWithFrequencies-Unit-Tests.wlt └── VariableImportanceByClassifiers.m /.github/ISSUE_TEMPLATE/bug_report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Bug report 3 | about: Create a report to help us improve 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Describe the bug** 11 | A clear and concise description of what the bug is. 12 | 13 | **To Reproduce** 14 | Steps to reproduce the behavior: 15 | 1. 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Copyright (C) 2014 Anton Antonov 4 | 5 | This program is free software: you can redistribute it and/or modify 6 | it under the terms of the GNU General Public License as published by 7 | the Free Software Foundation, either version 3 of the License, or 8 | (at your option) any later version. 9 | 10 | This program is distributed in the hope that it will be useful, 11 | but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | GNU General Public License for more details. 14 | 15 | You should have received a copy of the GNU General Public License 16 | along with this program. If not, see . 17 | 18 | Written by Anton Antonov, 19 | antononcube @ gmail . com, 20 | Windermere, Florida, USA. 21 | *) 22 | 23 | (* Version 1.0 *) 24 | (* This grammar is intended to be parsed by the functions in the Mathematica package FunctionalParses.m at GitHub, 25 | see https://github.com/antononcube/MathematicaForPrediction/blob/master/FunctionalParsers.m . 26 | In order to parse this grammar specification either: 27 | - copy all of the grammar rule lines and paste them within a pair of string quotes, or 28 | - use Get in Mathematica. 29 | *) 30 | 31 | " 32 | = 'find' | 'compute' | 'calculate' | 'show' ; 33 | = ( ( 'temperature' | 'pressure' | 'wind' , 'speed' ) <& ( 'of' | 'for' ) ) , <@ TSWeatherSpec[#]& ; 34 | = [ 'the' ] &> , ( 'temperature' | 'pressure' | 'wind' , 'speed' ) <@ TSWeatherSpec[Reverse[#]]& ; 35 | = | ; 36 | = '_LetterString' | '_LetterString' , [ ',' ] , '_LetterString' , [ [ ',' ] , '_LetterString' ] <@ TSCitySpec[Flatten[{#}]]& ; 37 | = '_String' <@ TSCompanySpec ; 38 | = ( [ 'the' ] &> ( [ 'stock' ] &> 'price' | 'trade' &> 'volume' ) <& [ 'of' | 'for' ] ) , <@ TSFinancialData ; 39 | = [ 'the' ] &> , ( [ 'stock' ] &> 'price' | 'trade' &> 'volume' ) <@ TSFinancialData[Reverse[#]]& ; 40 | = | ; 41 | = [ 'the' ] &> 'last' , [ 'loaded' ] , ( 'data' | 'file' ) <@ TSPastData[Flatten[{#}]]& ; 42 | = | | ; 43 | = [ ( '1' | 'one' | 'a' ) ] , 'regression' , 'quantile' | 'quantile' , 'regression' <@ TSBSplineQRegression[1]& ; 44 | = [ 'Range[1,40]' ] <& ( 'regression' , 'quantiles' ) <@ TSBSplineQRegression[#]& ; 45 | = | ; 46 | = [ 'the' ] &> [ ( 'top' | 'bottom' | 'largest' | 'smallest' | 'all' ) ] , 'outliers' <@ TSOutliers[Flatten[#]]& ; 47 | = ( 'least' , 'squares' , [ 'fit' ] , [ 'with' | 'of' ] ) &> '_String' <@ TSLeastSquaresFit[#]& ; 48 | = | | ; 49 | = [ ] &> <@ TSOperateCommand[#]& ; 50 | = , ( 'for' | 'on' | 'in' | 'over' | 'of' ) &> <@ TSOperateOnDataCommand[#]& ; 51 | = ( 'load' , [ 'data' ] , 'file' ) &> ( '_String' ) <@ TSLoadFile[#]& ; 52 | = ( 'load' , [ 'the' ] , [ 'data' ] ) &> <@ TSLoadData[#]& ; 53 | = 'start' , 'over' | 'clear' <@ TSStartOver[Flatten[{#}]]& ; 54 | = 'clear' , ( 'plots' | 'plots' | 'graphics' ) <@ TSClearGraphics ; 55 | = 'what' , ( ( 'operations' , 'are' | [ 'are' ] , [ 'the' ] , 'operations' ) , [ 'implemented' | 'in' ] ) | [ 'what' ] , ( 'operation' | 'operations' ) , ( 'I' , 'can' | 'to' ) , ( 'use' | 'do' ) <@ TSWhatOperations[Flatten[{#}]]& ; 56 | = 'help' | [ 'all' ] , 'commands' <@ TSHelp[Flatten[{#}]]& ; 57 | = | ; 58 | = [ 'plot' | 'plots' ] , 'joined' | 'Joined' , '->' , 'True' | 'Joined->True' <@ TSPlotJoined ; 59 | = [ 'plot' | 'plots' ] , ( 'not' | 'non' ) , 'joined' | 'Joined' , '->' , 'False' | 'Joined->False' <@ TSPlotNotJoined ; 60 | = 'plot' , 'data' <@ TSPlotData ; 61 | = | | ; 62 | = | | | | | | | ; 63 | " -------------------------------------------------------------------------------- /EBNF/TimeSpecificationsGrammar.ebnf: -------------------------------------------------------------------------------- 1 | 2 | (* 3 | Time specifications grammar in EBNF 4 | Copyright (C) 2016 Anton Antonov 5 | 6 | This program is free software: you can redistribute it and/or modify 7 | it under the terms of the GNU General Public License as published by 8 | the Free Software Foundation, either version 3 of the License, or 9 | (at your option) any later version. 10 | 11 | This program is distributed in the hope that it will be useful, 12 | but WITHOUT ANY WARRANTY; without even the implied warranty of 13 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14 | GNU General Public License for more details. 15 | 16 | You should have received a copy of the GNU General Public License 17 | along with this program. If not, see . 18 | 19 | Written by Anton Antonov, 20 | antononcube @ gmail . com, 21 | Windermere, Florida, USA. 22 | *) 23 | 24 | (* Version 0.7 *) 25 | 26 | (* This grammar is intended to be parsed by the functions in the Mathematica package FunctionalParses.m at GitHub, 27 | see https://github.com/antononcube/MathematicaForPrediction/blob/master/FunctionalParsers.m . 28 | In order to parse this grammar specification either: 29 | - copy all of the grammar rule lines and paste them within a pair of string quotes, or 30 | - use Get in Mathematica. 31 | *) 32 | 33 | " 34 | = | | | ; 35 | = 'hour' | 'day' | 'week' | 'month' | 'year' | 'lifetime' ; 36 | = 'hours' | 'days' | 'weeks' | 'months' | 'years' | 'lifetimes' ; 37 | = , | ( 'a' | 'one' ) , ; 38 | = | | ; 39 | = [ 'the' ] &> ( 'next' | 'last' ) , ( | ( 'few' | ) , ) ; 40 | = 'between' , , 'and' , \ 41 | | 'from' , , 'to' , | [ 'in' | 'during' ] &> \ 42 | | 'between' , , 'and' , \ 43 | ; 44 | = | | | \ 45 | | | | | \ 46 | | <@ TimeSpec[If[ListQ[#],Flatten[#],#]]& ; 47 | = 'now' | 'right' , 'now' | 'just' , 'now' ; 48 | = 'today' | 'yesterday' | 'tomorrow' | 'the' , 'day' , 'before' , 'yesterday' ; 49 | = 'monday' | 'tuesday' | 'wednesday' | 'thursday' | 'friday' | 'saturday' | 'sunday' ; 50 | = 'mondays' | 'tuesdays' | 'wednesdays' | 'thursdays' | 'fridays' | 'saturdays' | 'sundays' ; 51 | = 'mon' | 'tue' | 'wed' | 'thu' | 'fri' | 'sat' | 'sun' ; 52 | = | | ; 53 | = 'week' , ; 54 | = 'Range[1,52]' ; 55 | = [ 'the' ] , 'week' , , 'of' , ; 56 | = 'january' | 'february' | 'march' | 'april' | \ 57 | 'may' | 'june' | 'july' | 'august' | 'september' | 'october' | \ 58 | 'november' | 'december' ; 59 | = 'jan' | 'feb' | 'mar' | 'apr' | 'may' | 'jun' | 'jul' | 'aug' | 'sep' | 'oct' | 'nov' | 'dec' ; 60 | = | ; 61 | = , [ 'of' ] , [ ] ; 62 | = 'year' , | ; 63 | = 'Range[1900,2100]' ; 64 | = 'ramadan' | 'christmas' | 'thanksgiving' | \ 65 | 'memorial' , 'day' | 'lincoln' , 'day' | 'new' , 'year' | 'mother' , 'day' ; 66 | = , ( 'before' | 'after' ) , ; 67 | = '_?NumberQ' ; 68 | = 'Range[0,23]' , [ ( 'am' | 'pm' ) ] ; 69 | = ( , | , ) , , [ , ( 'am' | 'pm' ) ] ; 70 | " -------------------------------------------------------------------------------- /Java/TriesWithFrequencies/README.md: -------------------------------------------------------------------------------- 1 | # Tries with frequencies in Java 2 | 3 | Anton Antonov 4 | [MathematicaForPrediction at GitHub](https://github.com/antononcube/MathematicaForPrediction) 5 | [MathematicaVsR at GitHub](https://github.com/antononcube/MathematicaVsR) 6 | January 2017 7 | 8 | ## Structure 9 | 10 | The file ["src/Trie.java"](https://github.com/antononcube/MathematicaForPrediction/blob/master/Java/TriesWithFrequencies/src/Trie.java) 11 | contains the definition of the class `Trie`. 12 | 13 | The file ["src/TrieFunctions.java"](https://github.com/antononcube/MathematicaForPrediction/blob/master/Java/TriesWithFrequencies/src/TrieFunctions.java) 14 | has implementations of a variety of functions that can used over tries. 15 | 16 | The file ["src/Experiments.java"](https://github.com/antononcube/MathematicaForPrediction/blob/master/Java/TriesWithFrequencies/src/Experiments.java) 17 | is only used to do sanity check tests over the implementations. 18 | 19 | We call a trie "word" a list of strings. 20 | 21 | ## Use through a Mathematica package 22 | 23 | The Mathematica package 24 | [JavaTriesWithFrequencies.m](https://github.com/antononcube/MathematicaForPrediction/blob/master/JavaTriesWithFrequencies.m) 25 | provides functions for utilizing the implemented Java Trie functionalities. 26 | 27 | The test file 28 | [JavaTriesWithFrequencies-Unit-Tests.wlt](https://github.com/antononcube/MathematicaForPrediction/blob/master/UnitTests/JavaTriesWithFrequencies-Unit-Tests.wlt) 29 | provides unit tests for 30 | [JavaTriesWithFrequencies.m](https://github.com/antononcube/MathematicaForPrediction/blob/master/JavaTriesWithFrequencies.m). 31 | 32 | In order to use the package the corresponding .jar file must be made -- see the next section. 33 | 34 | ## How to use in Mathematica directly 35 | 36 | In order to use the defined Java functions in Mathematica the following steps have to be taken. 37 | 38 | ### Making a jar file 39 | 40 | In the local directory "src" execute the following commands: 41 | 42 | src> mkdir build 43 | src> javac -d ./build *.java; cd build; jar cvf ../../TriesWithFrequencies.jar *; cd ../ 44 | 45 | (Skip the first line if you have the directory "src/build" already.) 46 | 47 | ### Mathematica JLink set-up 48 | 49 | $JavaTriesWithFrequenciesPath = "<>/MathematicaForPrediction/Java/TriesWithFrequencies"; 50 | 51 | Needs["JLink`"]; 52 | AddToClassPath[$JavaTriesWithFrequenciesPath]; 53 | ReinstallJava[JVMArguments->"-Xmx2g"] 54 | 55 | LoadJavaClass["java.util.Collections"]; 56 | LoadJavaClass["java.util.Arrays"]; 57 | 58 | LoadJavaClass["Trie"]; 59 | LoadJavaClass["TrieFunctions"]; 60 | 61 | ### Basic trie creation and retrieval 62 | 63 | Get dictionary words starting with "b": 64 | 65 | dWords = DictionaryLookup["b*"]; 66 | Length[dWords] 67 | (* 4724 *) 68 | 69 | Create a trie with the words: 70 | 71 | Block[{}, 72 | (* Make a list of words. *) 73 | jWords = MakeJavaObject[dWords]; 74 | jWords = Arrays`asList[jWords]; 75 | 76 | (* Make a string object (that represents a spliting regexp pattern). *) 77 | jSp = MakeJavaObject[""]; 78 | 79 | (* Create the trie specifying the words to be split into characters. *) 80 | jTr = TrieFunctions`createBySplit[jWords, jSp]; 81 | 82 | (* Optionally convert the node frequencies into probabilties. *) 83 | (*jTr=TrieFunctions`nodeProbabilities[jTr]*) 84 | ]; 85 | 86 | Get the sub-trie that corresponds to "bark": 87 | 88 | jSubTr = TrieFunctions`retrieve[jTr, Arrays`asList[MakeJavaObject[Characters["bark"]]]] 89 | (* JLink`Objects`vm4`JavaObject17330643155288065 *) 90 | 91 | 92 | Get JSON form of the sub-trie: 93 | 94 | ImportString[jSubTr@toJSON[], "JSON"] 95 | 96 | (* {"value" -> 10., "key" -> "k", 97 | "children" -> {{"value" -> 1., "key" -> "s", 98 | "children" -> {}}, {"value" -> 7., "key" -> "e", 99 | "children" -> {{"value" -> 2., "key" -> "r", 100 | "children" -> {{"value" -> 1., "key" -> "s", 101 | "children" -> {}}}}, {"value" -> 1., "key" -> "d", 102 | "children" -> {}}, {"value" -> 4., "key" -> "e", 103 | "children" -> {{"value" -> 4., "key" -> "p", 104 | "children" -> {{"value" -> 1., "key" -> "s", 105 | "children" -> {}}, {"value" -> 2., "key" -> "e", 106 | "children" -> {{"value" -> 2., "key" -> "r", 107 | "children" -> {{"value" -> 1., "key" -> "s", 108 | "children" -> {}}}}}}}}}}}}, {"value" -> 1., 109 | "key" -> "i", 110 | "children" -> {{"value" -> 1., "key" -> "n", 111 | "children" -> {{"value" -> 1., "key" -> "g", 112 | "children" -> {}}}}}}}} *) 113 | 114 | If we load the package [TriesWithFrequencies.m](https://github.com/antononcube/MathematicaForPrediction/blob/master/TriesWithFrequencies.m) : 115 | 116 | Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/TriesWithFrequencies.m"] 117 | 118 | we can visualize the obtained sub-trie (Java object) using the function `ToTrieFromJSON` and `TrieForm`: 119 | 120 | TrieForm@ToTrieFromJSON@ImportString[jSubTr@toJSON[], "JSON"] 121 | 122 | !["SubTrie-of-dictionary-trie-by-bark"](http://i.imgur.com/sRlL357.png) 123 | 124 | 125 | ## How to use in R 126 | 127 | TBD... 128 | 129 | ## References 130 | 131 | -------------------------------------------------------------------------------- /Java/TriesWithFrequencies/src/Trie.java: -------------------------------------------------------------------------------- 1 | //# Tries with frequencies Java implementation 2 | //# Copyright (C) 2016 Anton Antonov 3 | //# 4 | //# This program is free software: you can redistribute it and/or modify 5 | //# it under the terms of the GNU General Public License as published by 6 | //# the Free Software Foundation, either version 3 of the License, or 7 | //# (at your option) any later version. 8 | //# 9 | //# This program is distributed in the hope that it will be useful, 10 | //# but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | //# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | //# GNU General Public License for more details. 13 | //# 14 | //# You should have received a copy of the GNU General Public License 15 | //# along with this program. If not, see . 16 | //# 17 | //# Written by Anton Antonov, 18 | //# antononcube @ gmail. com , 19 | //# Windermere, Florida, USA. 20 | //# 21 | //# Version 1.0 22 | //# The Java code in this file corresponds to the Mathematica package 23 | //# "Tries with frequencies" also written by Anton Antonov: 24 | //# https://github.com/antononcube/MathematicaForPrediction/blob/master/TriesWithFrequencies.m . 25 | //# There is also an R package with implementing that functionality: 26 | //# https://github.com/antononcube/MathematicaForPrediction/blob/master/R/TriesWithFrequencies.R . 27 | //# Both packages are part of the MathematicaForPrediction project at GitHub. 28 | //# 29 | //# For detailed explanations see the blog post: 30 | //# "Tries with frequencies for data mining", 31 | //# https://mathematicaforprediction.wordpress.com/2013/12/06/tries-with-frequencies-for-data-mining/ . 32 | 33 | package TriesWithFrequencies; 34 | 35 | import java.util.HashMap; 36 | import java.util.List; 37 | import java.util.Map; 38 | 39 | public class Trie { 40 | 41 | protected String key; 42 | protected Double value; 43 | protected Map children; 44 | 45 | public String getKey() { 46 | return key; 47 | } 48 | public void setKey(String key) { 49 | this.key = key; 50 | } 51 | public Double getValue() { 52 | return value; 53 | } 54 | public void setValue(Double value) { 55 | this.value = value; 56 | } 57 | public Map getChildren() { 58 | return children; 59 | } 60 | public void setChildren( Map children ) { 61 | this.children = children; 62 | } 63 | 64 | public Trie( ) { } 65 | 66 | public Trie( String key, Double value ) { 67 | this.setKey( key ); 68 | this.setValue( value ); 69 | } 70 | 71 | public Trie( String key, Double value, Map children ) { 72 | this.setKey( key ); 73 | this.setValue( value ); 74 | this.setChildren(children); 75 | } 76 | 77 | protected String toStringRec( int n ) { 78 | String offset = ""; 79 | String childStr = ""; 80 | int k=0; 81 | for ( int i = 0; i < n; i++ ){ 82 | offset = offset + " "; 83 | } 84 | if ( this.getChildren() != null && !this.getChildren().isEmpty() ) { 85 | for ( Trie elem : this.getChildren().values() ) { 86 | if ( k == 0 ) { 87 | childStr = "\n" + offset + elem.toStringRec( n+1 ); 88 | } else { 89 | childStr = childStr + ",\n" + offset + elem.toStringRec( n+1 ); 90 | } 91 | k++; 92 | } 93 | } else { 94 | childStr = ""; 95 | } 96 | return "[ key=" + this.getKey() + ", value=" + this.getValue() + ", children=" + childStr + "]"; 97 | } 98 | 99 | public String toString( ) { 100 | return this.toStringRec( 1 ); 101 | } 102 | 103 | 104 | protected String toJSONRec( int maxLevel, int n ) { 105 | String childStr = ""; 106 | int k = 0; 107 | if ( this.getChildren() != null && ( maxLevel < 0 || n <= maxLevel ) ) { 108 | for ( Trie elem : this.getChildren().values() ) { 109 | if ( k == 0 ) { 110 | childStr = elem.toJSONRec( maxLevel,n+1 ); 111 | } else { 112 | childStr = childStr + ", " + elem.toJSONRec( maxLevel,n+1 ); 113 | } 114 | k++; 115 | } 116 | childStr = "[" + childStr + "]"; 117 | } else { 118 | childStr = "[]"; 119 | } 120 | return "{ \"key\":" + "\"" + this.getKey() + "\"" + ", \"value\":" + this.getValue() + ", \"children\":" + childStr + "}"; 121 | } 122 | 123 | public String toJSON( int maxLevel ) { 124 | return this.toJSONRec( maxLevel, 1 ); 125 | } 126 | 127 | public String toJSON( ) { 128 | return this.toJSONRec( -1, 1 ); 129 | } 130 | 131 | //! @description Deep copy of a trie. 132 | public Trie clone() { 133 | 134 | Trie res = new Trie(); 135 | 136 | res.setKey( this.getKey() ); 137 | res.setValue( this.getValue() ); 138 | 139 | if ( !(this.getChildren() == null || this.getChildren().isEmpty() ) ) { 140 | 141 | Map resChildren = new HashMap<>(); 142 | 143 | for ( Trie elem : this.getChildren().values() ) { 144 | resChildren.put( elem.getKey(), elem.clone() ); 145 | } 146 | 147 | res.setChildren( resChildren ); 148 | } 149 | 150 | return res; 151 | } 152 | 153 | //! @description Deep comparison of a trie. 154 | public Boolean equals( Trie tr ) { 155 | 156 | if ( !this.getKey().equals( tr.getKey() ) || !this.getValue().equals( tr.getValue() ) ) { 157 | return false; 158 | } 159 | 160 | Boolean b = !(this.getChildren() == null || this.getChildren().isEmpty() ); 161 | Boolean bTr = !(tr.getChildren() == null || tr.getChildren().isEmpty() ); 162 | 163 | if ( b && bTr ) { 164 | 165 | if ( this.getChildren().size() != tr.getChildren().size() ) { 166 | return false; 167 | } 168 | 169 | for ( Trie elem : this.getChildren().values() ) { 170 | 171 | if ( !tr.getChildren().containsKey( elem.getKey() ) ) { 172 | return false; 173 | } 174 | 175 | if ( ! elem.equals( tr.getChildren().get( elem.getKey() ) ) ) { 176 | return false; 177 | } 178 | } 179 | 180 | return true; 181 | 182 | } else if ( b != bTr ) { 183 | 184 | return false; 185 | 186 | } 187 | 188 | return true; 189 | } 190 | } 191 | 192 | -------------------------------------------------------------------------------- /Lua/FunctionalParsers/FunctionalParsersTests.lua: -------------------------------------------------------------------------------- 1 | --[[ 2 | Tests for Functional Parsers in Lua 3 | Copyright (C) 2013-2016 Anton Antonov 4 | 5 | This program is free software: you can redistribute it and/or modify 6 | it under the terms of the GNU General Public License as published by 7 | the Free Software Foundation, either version 3 of the License, or 8 | (at your option) any later version. 9 | 10 | This program is distributed in the hope that it will be useful, 11 | but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | GNU General Public License for more details. 14 | 15 | You should have received a copy of the GNU General Public License 16 | along with this program. If not, see . 17 | 18 | Written by Anton Antonov, 19 | antononcube @ gmail . com, 20 | Windermere, Florida, USA. 21 | --]] 22 | 23 | --[[ 24 | 25 | Lua is free software distributed under the terms of the MIT license reproduced here: http://www.lua.org/license.html. . 26 | Lua may be used for any purpose, including commercial purposes, at absolutely no cost. 27 | No paperwork, no royalties, no GNU-like "copyleft" restrictions, either. 28 | Just download it and use it (http://www.lua.org) . 29 | 30 | See the full Lua licence at : http://www.lua.org/license.html . 31 | 32 | --]] 33 | 34 | require("FunctionalParsers") 35 | 36 | 37 | 38 | function dPredQ(d) 39 | if mf.MemberQ({"0","1","2","3","4","5","6","7","8","9"}, d) then 40 | return true 41 | else 42 | return false 43 | end 44 | end 45 | 46 | pDigit = parse.satisfy( dPredQ ) 47 | -- pNumber = fp.ParsePredicate( dPredQ ) 48 | 49 | res = pDigit( { "2", "t" } ) 50 | print( table.unpack( res[1][1] ) ) 51 | print( res[1][2] ) 52 | 53 | print("-----------------------") 54 | print("parse.alt") 55 | pAorB = parse.alt( parse.symbol("a"), parse.symbol("b") ) 56 | 57 | res = pAorB( { "a", "b" } ) 58 | print( "#res = ", #res, res ) 59 | mf.Print(res) 60 | print() 61 | 62 | 63 | res = pAorB( { "k", "b" } ) 64 | print( "#res = ", #res, res ) 65 | mf.Print(res) 66 | print() 67 | 68 | res = parse.symbol("a")({"a","b"}) 69 | mf.Print("parse.symbol(\"a\")({\"a\",\"b\"})", res) 70 | 71 | 72 | res1 = mf.ParseComposeWithResults( parse.symbol("b"), res ) 73 | mf.Print("res1= ", res1) 74 | 75 | print("-----------------------") 76 | print("parse.seq") 77 | pAB = parse.seq( pAorB, pAorB ) 78 | res = mf.Map( pAB, { {"a","b"}, {"b","b"}, {"b","a"}} ) 79 | mf.Print( "#res = ", #res, " ", res ) 80 | 81 | 82 | print("-----------------------") 83 | print("parse.seqr") 84 | pBar = parse.seqr( parse.alt( parse.symbol("a"), parse.symbol("the") ), parse.symbol("bar") ) 85 | res = mf.Map( pBar, { {"a","bar"}, {"the","bar"} } ) 86 | mf.Print( "#res = ", #res, " ", res ) 87 | 88 | 89 | print("-----------------------") 90 | print("parse.parenthesized") 91 | pPAB = parse.parenthesized( pAorB ) 92 | res = mf.Map( pPAB, { {"(","b",")"}, {"(","a",")"}, {"(","b",")"} } ) 93 | mf.Print( "#res = ", #res, " ", res ) 94 | 95 | 96 | print("-----------------------") 97 | print("parse.seql") 98 | pPAB = parse.parenthesized( parse.seql( parse.symbol("a"), parse.option1( parse.symbol("b") ) ) ) 99 | res = mf.Map( pPAB, { {"(","a","b",")"}, {"(","a",")"} } ) 100 | mf.Print( "#res = ", #res, " ", res ) 101 | 102 | 103 | print("-----------------------") 104 | print("parse.many1") 105 | res = parse.many1( pAorB )( { "a", "b", "a", "a" } ) 106 | mf.Print( "#res = ", #res, " ", res ) 107 | 108 | print("-----------------------") 109 | print("parse.many") 110 | res = parse.many( pAorB )( { "a", "b", "a", "a" } ) 111 | mf.Print( "#res = ", #res, " ", res ) 112 | 113 | print("-----------------------") 114 | print("parse.listof") 115 | pLAorB = parse.listof( pAorB, parse.symbol("*") ) 116 | res = pLAorB( { "a", "*", "b", "*", "a", "*", "a" } ) 117 | mf.Print( "#res = ", #res, " ", res ) 118 | 119 | print("-----------------------") 120 | print("parse.chainleft") 121 | pCLAorB = parse.chainleft( pAorB, parse.symbol("*"), function (s1, s2) return s1..s2 end ) 122 | res = pCLAorB( { "a", "*", "b", "*", "a", "*", "a" } ) 123 | mf.Print( "#res = ", #res, " ", res ) 124 | 125 | 126 | print("-----------------------") 127 | print("parse.chainright") 128 | pCRAorB = parse.chainright( pAorB, parse.symbol("*"), function (s1, s2) return s1..s2 end ) 129 | res = pCLAorB( { "a", "*", "b", "*", "a", "*", "a" } ) 130 | mf.Print( "#res = ", #res, " ", res ) 131 | 132 | 133 | print("-----------------------") 134 | print("parsing a number") 135 | --pNumber = parse.listof( pDigit, parse.epsilon ) 136 | pNumber = parse.apply( function (parsed) 137 | return mf.Fold( function (acc, s) return 10*acc + s-"0" end, 0, parsed) 138 | end, 139 | parse.many1( pDigit ) ) 140 | res = pNumber( { "1", "6", "7", "0"} ) 141 | mf.Print( "#res = ", #res, " ", res ) 142 | print( type(res[1][2])) -------------------------------------------------------------------------------- /MarkdownDocuments/Call-graph-generation-for-context-functions.md: -------------------------------------------------------------------------------- 1 | # Call graph generation for context functions 2 | 3 | Anton Antonov 4 | [MathematicaForPrediction at WordPress](https://mathematicaforprediction.wordpress.com) 5 | [MathematicaForPrediction at GitHub](https://github.com/antononcube/MathematicaForPrediction) 6 | [MathematicaVsR at GitHub](https://github.com/antononcube/MathematicaVsR) 7 | January 2019 8 | 9 | ## In brief 10 | 11 | This document describes the package 12 | [CallGraph.m](https://github.com/antononcube/MathematicaForPrediction/blob/master/Misc/CallGraph.m) 13 | for making call graphs between the functions that belong to specified contexts. 14 | 15 | The main function is `CallGraph` that gives a graph with vertices that are functions names and edges that show 16 | which functions call which other functions. With the default option values the graph vertices are labeled and 17 | have tooltips with function usage messages. 18 | 19 | ## General design 20 | 21 | The call graphs produced by the main package function `CallGraph` are assumed to be used for studying or refactoring 22 | of large code bases written with Mathematica / Wolfram Language. 23 | 24 | The argument of `CallGraph` is a context string or a list of context strings. 25 | 26 | With the default values of its options `CallGraph` produces a graph with labeled nodes and the labels have tooltips 27 | that show the usage messages of the functions from the specified contexts. 28 | It is assumed that this would be the most useful call graph type for studying the codes of different sets of packages. 29 | 30 | We can make simple, non-label, non-tooltip call graph using `CallGraph[ ... , "UsageTooltips" -> False ]`. 31 | 32 | The simple call graph can be modified with the functions: 33 | 34 | CallGraphAddUsageMessages, CallGraphAddPrintDefinitionsButtons, CallGraphBiColorCircularEmbedding 35 | 36 | Each of those functions is decorating the simple graph in a particular way. 37 | 38 | ## Package load 39 | 40 | This loads the package [CallGraph.m](https://github.com/antononcube/MathematicaForPrediction/blob/master/Misc/CallGraph.m) : 41 | 42 | Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/Misc/CallGraph.m"] 43 | 44 | The following packages are used in the examples below. 45 | 46 | Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/MonadicProgramming/MonadicQuantileRegression.m"] 47 | 48 | Get["https://raw.githubusercontent.com/szhorvat/IGraphM/master/IGInstaller.m"]; 49 | Needs["IGraphM`"] 50 | 51 | ## Usage examples 52 | 53 | ### Generate a call graph with usage tooltips 54 | 55 | CallGraph["IGraphM`", GraphLayout -> "SpringElectricalEmbedding", ImageSize -> Large] 56 | 57 | [![IGraphM-call-graph-with-usage-tooltips](https://i.imgur.com/ShOVJMVl.png)](https://i.imgur.com/ShOVJMV.png) 58 | 59 | ### Generate a call graph by excluding symbols 60 | 61 | gr = CallGraph["IGraphM`", Exclusions -> Map[ToExpression, Names["IG*Q"]], ImageSize -> 900] 62 | 63 | 64 | [![IGraphM-call-graph-with-exclusions](https://i.imgur.com/n2y0KrKl.png)](https://i.imgur.com/n2y0KrK.png) 65 | 66 | ### Generate call graph with buttons to print definitions 67 | 68 | gr0 = CallGraph["IGraphM`", "UsageTooltips" -> False]; 69 | gr1 = CallGraphAddPrintDefinitionsButtons[gr0, GraphLayout -> "StarEmbedding", ImageSize -> 900] 70 | 71 | [![IGraphM-call-graph-with-definition-buttons](https://i.imgur.com/wbRcNoEl.png)](https://i.imgur.com/wbRcNoE.png) 72 | 73 | ### Generate circular embedding graph color 74 | 75 | cols = RandomSample[ ColorData["Rainbow"] /@ Rescale[Range[VertexCount[gr1]]]]; 76 | 77 | CallGraphBiColorCircularEmbedding[ gr1, "VertexColors" -> cols, ImageSize -> 900 ] 78 | 79 | [![IGraphM-call-graph-bicolor-Bezie](https://i.imgur.com/BMgO1rEl.png)](https://i.imgur.com/BMgO1rE.png) 80 | 81 | (The core functions used for the implementation of `CallGraphBiColorCircularEmbedding` were taken from kglr's Mathematica Stack Exchange answer: https://mathematica.stackexchange.com/a/188390/34008 . Those functions were modified to take additional arguments.) 82 | 83 | ## Options 84 | 85 | The package functions "CallGraph*" take all of the options of the function Graph. 86 | Below are described the additional options of CallGraph. 87 | 88 | - "PrivateContexts" 89 | Should the functions of the private contexts be included in the call graph. 90 | 91 | - "SelfReferencing" 92 | Should the self referencing edges be excluded or not. 93 | 94 | - "AtomicSymbols" 95 | Should atomic symbols be included in the call graph. 96 | 97 | - Exclusions 98 | Symbols to be excluded from the call graph. 99 | 100 | - "UsageTooltips" 101 | Should vertex labels with the usage tooltips be added. 102 | 103 | - "UsageTooltipsStyle" 104 | The style of the usage tooltips. 105 | 106 | ## Possible issues 107 | 108 | - With large context (e.g. "System`") the call graph generation might take long time. (See the TODOs below.) 109 | 110 | - With "PrivateContexts"->False the call graph will be empty if the public functions do not depend on each other. 111 | 112 | - For certain packages the scanning of the down values would produce (multiple) error messages or warnings. 113 | 114 | ## Future plans 115 | 116 | The following is my TODO list for this project. 117 | 118 | 1. Special handling for the "System`" context. 119 | 120 | 2. Use the symbols up-values to make the call graph. 121 | 122 | 3. Consider/implement call graph making with specified patterns and list of symbols. 123 | 124 | - Instead of just using contexts and exclusions. (The current approach/implementation.) 125 | 126 | 4. 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https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/c825e21097b90f9329ce9fc8b3b0f317fdd6d5e4/MarkdownDocuments/Diagrams/Trie-based-classifiers-evaluation/1xlmbkphhwhsf.png -------------------------------------------------------------------------------- /MarkdownDocuments/OpenAIMode-demo.md: -------------------------------------------------------------------------------- 1 | # OpenAIMode demo 2 | 3 | **...for Mathematica notebooks** 4 | 5 | Anton Antonov 6 | [MathematicaForPrediction at WordPress](https://mathematicaforprediction.wordpress.com) 7 | [MathematicaForPrediction at GitHub](https://github.com/antononcube/MathematicaForPrediction) 8 | April 2023 9 | 10 | ***[Video recording](https://youtu.be/htUIOqcS9uA)*** 11 | 12 | ----- 13 | 14 | ## Setup 15 | 16 | - It is assumed that the paclet [OpenAILink](https://resources.wolframcloud.com/PacletRepository/resources/ChristopherWolfram/OpenAILink/) is installed 17 | 18 | - ... and the required setup steps are completed. 19 | 20 | - Install the paclet [OpenAIMode](https://resources.wolframcloud.com/PacletRepository/resources/AntonAntonov/OpenAIMode/) 21 | 22 | ```mathematica 23 | PacletInstall["AntonAntonov/OpenAIMode"] 24 | ``` 25 | 26 | ![11og8fps5xhgk](./Diagrams/OpenAIMode-demo/11og8fps5xhgk.png) 27 | 28 | ```mathematica 29 | Needs["AntonAntonov`OpenAIMode`"] 30 | ``` 31 | 32 | --- 33 | 34 | ## Demo 35 | 36 | Let us show how the notebook style works: 37 | 38 | - Needs 39 | 40 | - OpenAIMode 41 | 42 | - Text completion cell (shortcut: "Shift-|") 43 | 44 | - Tweak invocation parameters with SetOptions 45 | 46 | - Image generation cell (shortcuts: "Tab") 47 | 48 | ### Screenshots 49 | 50 | ![06wybw0d8ntd6](./Diagrams/OpenAIMode-demo/06wybw0d8ntd6.png) 51 | 52 | ![1iqsuwy5jkmit](./Diagrams/OpenAIMode-demo/1iqsuwy5jkmit.png) 53 | 54 | --- 55 | 56 | ## How does it work? 57 | 58 | Consider the following flowchart: 59 | 60 | ```mathematica 61 | ResourceFunction["MermaidJS"]["flowchart TDOpenAI{{OpenAI}}OpenAILink[[\"OpenAILink\"]]TCC[\"Text completion cell\"]IGC[\"Image generation cell\"] OC[\"Output cell\"]UI[/\"User input\"/]UI --> TCCUI --> IGCTCC -.-> OpenAIInputExecuteToText -.-> OpenAILinkIGC -.-> OpenAIInputExecuteToImage -.-> OpenAILinkOpenAILink <-.-> OpenAIOpenAILink -.-> OCTCC --> OCIGC --> OCsubgraph NotebookTCC OpenAIInputExecuteToTextIGC OpenAIInputExecuteToImageOCend ", "PDF", ImageSize -> 1000] 62 | ``` 63 | 64 | ![0wyc32bgh7gso](./Diagrams/OpenAIMode-demo/0wyc32bgh7gso.png) 65 | 66 | --- 67 | 68 | ## Concluding remarks 69 | 70 | ### Similar work 71 | 72 | - DSLMode and [RakuMode](https://community.wolfram.com/groups/-/m/t/2434981) 73 | 74 | - [DarkMode](https://resources.wolframcloud.com/FunctionRepository/resources/DarkMode/) and [DraculaTheme](https://resources.wolframcloud.com/FunctionRepository/resources/DraculaTheme/) 75 | 76 | ### Future plans (...maybe) 77 | 78 | More documentation 79 | 80 | Should this notebook style functions be part of [OpenAILink](https://resources.wolframcloud.com/PacletRepository/resources/ChristopherWolfram/OpenAILink/)? 81 | 82 | Based on feedback: 83 | 84 | - Better default options 85 | 86 | - Additional OpenAI cells -------------------------------------------------------------------------------- /MarkdownDocuments/RandomTabularDataset-proclaim.md: -------------------------------------------------------------------------------- 1 | 2 | *(Comment to https://community.wolfram.com/groups/-/m/t/2109909)* 3 | 4 | ## Implementations 5 | 6 | I find this to be a great discussion topic! 7 | 8 | Of course, it is best to have a 9 | [Wolfram Function Repository (WFR)](https://resources.wolframcloud.com/FunctionRepository/) function that generates random datasets. 10 | I implemented such function and submitted it to WFR -- see 11 | [`RandomTabularDataset`](https://www.wolframcloud.com/obj/antononcube/DeployedResources/Function/RandomTabularDataset). 12 | 13 | Another, closely related WFR function is 14 | [`ExampleDataset`](https://resources.wolframcloud.com/FunctionRepository/resources/ExampleDataset). 15 | 16 | **Remark:** Note that I prefer the name `RandomTabularDataset` instead of `RandomDataset`. In Mathematica / WL 17 | datasets can be (deeply) hierarchical objects. Tabular datasets are simpler than the general WL datasets, 18 | but tabular data is very common, easier to explain and to reason with. 19 | 20 | ## Motivations 21 | 22 | My motivations are very similar to those of OP: rapid prototyping (of proof of concepts), 23 | thorough testing of algorithms, making unit tests. 24 | 25 | More specifically I want to: 26 | 27 | - Be able to quickly produce example datasets for my 28 | [Data Wrangling classes](https://community.wolfram.com/groups/-/m/t/2112820) 29 | 30 | - Have a large corpus of datasets to test the 31 | [Data Transformations Workflows Conversational Agent](https://github.com/antononcube/Raku-DSL-English-DataQueryWorkflows) 32 | I develop 33 | 34 | - Have a large corpus of datasets to illustrate data quality verification algorithms or frameworks, like this 35 | [Data Quality Monitoring Module](https://github.com/antononcube/HowToBeADataScientistImpostor-book/blob/master/Part-5-Software-engineering-skills/Data-Quality-Monitoring-Module.md) 36 | 37 | ## Demonstration 38 | 39 | The resource function 40 | [`ExampleDataset`](https://resources.wolframcloud.com/FunctionRepository/resources/ExampleDataset) 41 | makes datasets from 42 | [`ExampleData`](https://reference.wolfram.com/language/ref/ExampleData.html). 43 | Here is an example dataset: 44 | 45 | 46 | ```mathematica 47 | dsAW = ResourceFunction["ExampleDataset"][{"Statistics", "AnimalWeights"}] 48 | ``` 49 | 50 | Here is a similar random dataset: 51 | 52 | ```mathematica 53 | SeedRandom[23]; 54 | dsCW = ResourceFunction["https://www.wolframcloud.com/obj/antononcube/DeployedResources/Function/RandomTabularDataset"][ 55 | {60, {"Creature", "BodyWeight", "BrainWeight"}}, 56 | "Generators" -> <| 57 | 1 -> (Table[StringJoin[RandomChoice[CharacterRange["a", "z"], 5]], #] &), 58 | 2 -> FindDistribution[Normal@dsAW[All, "BodyWeight"]], 59 | 3 -> FindDistribution[Normal@dsAW[All, "BrainWeight"]]|>]; 60 | IQB = Interval[Quartiles[N@Normal[dsAW[All, #BrainWeight/#BodyWeight &]]][[{1, 3}]]]; 61 | dsCW[Select[IntervalMemberQ[IQB, #BrainWeight/ #BodyWeight] &]] 62 | ``` 63 | 64 | **Remark:** Instead of quartile boundaries filtering we can filter with 65 | `AnomalyDetection[Normal[dsAW[All, #BrainWeight/#BodyWeight &]]]`, 66 | but the latter is prone to produce results that are "too far off." 67 | 68 | ## Neat example 69 | 70 | A random dataset with values produced by resource functions that generate random objects: 71 | 72 | ```mathematica 73 | SeedRandom[3]; 74 | ResourceFunction[ 75 | "https://www.wolframcloud.com/obj/antononcube/DeployedResources/\ 76 | Function/RandomTabularDataset"][{5, {"Mondrian", "Mandala", "Haiku", "Scribble", "Maze", "Fortune"}}, 77 | "Generators" -> 78 | <| 79 | 1 -> (ResourceFunction["RandomMondrian"][] &), 80 | 2 -> (ResourceFunction["RandomMandala"][] &), 81 | 3 -> (ResourceFunction["RandomEnglishHaiku"][] &), 82 | 4 -> (ResourceFunction["RandomScribble"][] &), 83 | 5 -> (ResourceFunction["RandomMaze"][12] &), 84 | 6 -> (ResourceFunction["RandomFortune"][] &)|>, 85 | "PointwiseGeneration" -> True] 86 | ``` 87 | -------------------------------------------------------------------------------- /Misc/AprioriAlgorithmViaTries.m: -------------------------------------------------------------------------------- 1 | (* 2 | Implementation of the Apriori algorithm via Tries in Mathematica 3 | Copyright (C) 2022 Anton Antonov 4 | 5 | This program is free software: you can redistribute it and/or modify 6 | it under the terms of the GNU General Public License as published by 7 | the Free Software Foundation, either version 3 of the License, or 8 | (at your option) any later version. 9 | 10 | This program is distributed in the hope that it will be useful, 11 | but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | GNU General Public License for more details. 14 | 15 | You should have received a copy of the GNU General Public License 16 | along with this program. If not, see . 17 | 18 | Written by Anton Antonov, 19 | ʇǝu˙oǝʇsod@ǝqnɔuouoʇuɐ, 20 | Windermere, Florida, USA. 21 | *) 22 | 23 | (* 24 | Mathematica is (C) Copyright 1988-2022 Wolfram Research, Inc. 25 | 26 | Protected by copyright law and international treaties. 27 | 28 | Unauthorized reproduction or distribution subject to severe civil 29 | and criminal penalties. 30 | 31 | Mathematica is a registered trademark of Wolfram Research, Inc. 32 | *) 33 | 34 | 35 | If[Length[DownValues[TriesWithFrequencies`TriesCreate]] == 0, 36 | Echo["TriesWithFrequencies.m", "Importing from GitHub:"]; 37 | Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/TriesWithFrequencies.m"] 38 | ]; 39 | 40 | (***********************************************************) 41 | (* Package definition *) 42 | (***********************************************************) 43 | 44 | BeginPackage["AprioriAlgorithmViaTries`"]; 45 | 46 | Apriori::usage = "Apriori[baskets : {_List ..}, minSupport_?NumericQ, minItemsNumber_Integer, maxItemsNumberArg : (_Integer | Infinity)]"; 47 | 48 | Begin["`Private`"]; 49 | 50 | Needs["TriesWithFrequencies`"]; 51 | 52 | Clear[ScanBasket]; 53 | ScanBasket[basket_List, k_Integer, aFreqSets_?AssociationQ] := 54 | Block[{candidates}, 55 | candidates = Subsets[basket, {k, k}]; 56 | Select[candidates, Lookup[aFreqSets, Key@Most[#], False] && Lookup[aFreqSets, Key@{Last[#]}, False] &] 57 | ] /; k > 1; 58 | 59 | Clear[Apriori]; 60 | Options[Apriori] = {Counts -> False}; 61 | Apriori[ 62 | lsBasketsArg : {_List ..}, 63 | minSupportArg_?NumericQ, 64 | minItemsNumber_Integer : 1, 65 | maxItemsNumberArg : (_Integer | Infinity) : Infinity, 66 | opts :OptionsPattern[]] := 67 | Block[{lsBaskets = lsBasketsArg, minSupport = minSupportArg, maxItemsNumber, countsQ, 68 | aFreqSets, lsFreqSets, 69 | trBase, trSets, trSets2, aAllTries, k = 1, contQ = True, 70 | res}, 71 | 72 | (*Max number of items processing*) 73 | minSupport = If[ 0 <= minSupport <= 1, minSupport * Length[lsBaskets], minSupport]; 74 | 75 | (*Max number of items processing*) 76 | maxItemsNumber = Min[maxItemsNumberArg, Max[Length /@ lsBaskets]]; 77 | 78 | (*To use counts or not*) 79 | countsQ = TrueQ @ OptionValue[Apriori, Counts]; 80 | 81 | (*Make sure all baskets unique items*) 82 | lsBaskets = Union /@ lsBaskets; 83 | 84 | (*Make single items baskets trie*) 85 | trBase = TrieCreate[List /@ Flatten[lsBaskets]]; 86 | 87 | (*Remove the items that are not frequent enough*) 88 | trBase = TrieThresholdRemove[trBase, minSupport, "Postfix" -> None]; 89 | 90 | (*Verify early stop*) 91 | If[TrieDepth[trBase] == 1, 92 | Echo[Row[{"All items have support smaller than", Spacer[3], minSupport}]]; 93 | Return[{}] 94 | ]; 95 | 96 | (*Initial set of frequent sets*) 97 | aFreqSets = 98 | AssociationThread[List /@ Select[Tally[Flatten[lsBaskets]], #[[2]] >= minSupport &][[All, 1]], True]; 99 | 100 | (*First gathered trie*) 101 | aAllTries = <|k -> trBase|>; 102 | 103 | (*Main loop*) 104 | While[contQ && k < maxItemsNumber, 105 | 106 | k++; 107 | 108 | (*Scan the baskets and make trie with viable candidates*) 109 | trSets = TrieCreate[Join @@ Map[ScanBasket[#, k, aFreqSets] &, lsBaskets]]; 110 | 111 | (*Remove baskets that are not frequent enough*) 112 | trSets2 = TrieThresholdRemove[trSets, minSupport, "Postfix" -> None]; 113 | 114 | (*Get frequent sets from the trie*) 115 | lsNew = Select[Rest /@ TrieGetWords[trSets2], Length[#] == k &]; 116 | 117 | (*Update frequent sets*) 118 | If[Length[lsNew] == 0, 119 | contQ = False, 120 | (*ELSE*) 121 | aFreqSets = Join[aFreqSets, AssociationThread[lsNew, True]]; 122 | (*Add to gathered tries*) 123 | AppendTo[aAllTries, k -> trSets2] 124 | ]; 125 | 126 | ]; 127 | 128 | lsFreqSets = Select[Keys[aFreqSets], Length[#] >= minItemsNumber &]; 129 | res = Association@Map[# -> N[TrieRetrieve[aAllTries[Length[#]], #][$TrieValue]] &, lsFreqSets]; 130 | If[countsQ, res, res / Length[lsBaskets]] 131 | ]; 132 | 133 | End[]; 134 | 135 | EndPackage[]; -------------------------------------------------------------------------------- /Misc/ArrayOfFunctionsRule.m: -------------------------------------------------------------------------------- 1 | (* 2 | Array of functions numerical integration rule Mathematica Package 3 | Copyright (C) 2016 Anton Antonov 4 | 5 | This program is free software: you can redistribute it and/or modify 6 | it under the terms of the GNU General Public License as published by 7 | the Free Software Foundation, either version 3 of the License, or 8 | (at your option) any later version. 9 | 10 | This program is distributed in the hope that it will be useful, 11 | but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | GNU General Public License for more details. 14 | 15 | You should have received a copy of the GNU General Public License 16 | along with this program. If not, see . 17 | 18 | Written by Anton Antonov, 19 | antononcube @ gmail . com, 20 | Windermere, Florida, USA. 21 | *) 22 | 23 | (* :Title: ArrayOfFunctionsRule *) 24 | (* :Context: ArrayOfFunctionsRule` *) 25 | (* :Author: Anton Antonov *) 26 | (* :Date: 2016-09-10 *) 27 | 28 | (* :Package Version: 0.1 *) 29 | (* :Mathematica Version: *) 30 | (* :Copyright: (c) 2016 antonov *) 31 | (* :Keywords: *) 32 | (* :Discussion: *) 33 | (* 34 | 35 | When given an array of integrands NIntegrate is run separately over each array element. That is not 36 | necessary though -- the core NIntegrate integration strategies can work with any integrands as long as 37 | the error estimates are real numbers. 38 | 39 | The motivation for implementing ArrayOfFunctionsRule is to provide a significant speed-up for integrands 40 | that are arrays of functions. That is achived by evaluating all functions with the same integration rule 41 | abscissas and weights. 42 | 43 | ------- 44 | Example 45 | ------- 46 | 47 | Create a matrix of functions: 48 | 49 | funcsExpr = {Sqrt[x], Sin[x], 1/(1 + x), x^3}; 50 | funcsExpr = Table[i*funcsExpr, {i, 3}]; 51 | funcs = Map[Function[{fx}, Function[Evaluate[fx /. x -> #]]], funcsExpr, {2}] 52 | 53 | 54 | Numerically integrate with ArrayOfFunctionsRule: 55 | 56 | res = 57 | NIntegrate[1, {x, 0, 2}, 58 | Method -> {"GlobalAdaptive", "SingularityHandler" -> None, 59 | Method -> {ArrayOfFunctionsRule, "Functions" -> funcs}}] 60 | 61 | (* {{1.88562, 1.41615, 1.09861, 4.}, {3.77124, 2.83229, 2.19722, 8.}, {5.65685, 4.24844, 3.29584, 12.}} *) 62 | 63 | 64 | Compare with the standard NIntegrate 65 | 66 | res0 = NIntegrate[funcsExpr, {x, 0, 2}] 67 | 68 | (* {{1.88562, 1.41615, 1.09861, 4.}, {3.77124, 2.83229, 2.19722, 8.}, {5.65685, 4.24844, 3.29584, 12.}} *) 69 | 70 | Norm[res - res0, 2] 71 | 72 | (* 7.17894*10^-7 *) 73 | 74 | Note that the rule has to be used with a strategy specification that has the option 75 | 76 | "SingularityHandler" -> None 77 | 78 | and that the rule does not perform correctly over ranges with infinity. 79 | 80 | TODO: 81 | 82 | 1. Re-design signatures. 83 | 2. Implement multi-dimensional integration. 84 | 3. Better error or wrong specifications handling. 85 | 86 | This file was created using Mathematica Plugin for IntelliJ IDEA. 87 | 88 | Anton Antonov 89 | 2016-09-10 90 | 91 | *) 92 | 93 | BeginPackage["ArrayOfFunctionsRule`"] 94 | 95 | ArrayOfFunctionsRule::usage = "An NIntegrate numerical integration rule for integrands that are arrays of functions." 96 | 97 | Begin["`Private`"] 98 | 99 | Clear[ArrayOfFunctionsRule]; 100 | Options[ArrayOfFunctionsRule] = {"Method" -> "GaussKronrodRule", 101 | "Functions" -> {}, 102 | "ErrorsNormFunction" -> (Norm[#, Infinity] &)}; 103 | 104 | ArrayOfFunctionsRuleProperties = Part[Options[ArrayOfFunctionsRule], All, 1]; 105 | 106 | ArrayOfFunctionsRule::narr = "Array of functions is expected as a value of the option \"Functions\"."; 107 | 108 | ArrayOfFunctionsRule /: 109 | NIntegrate`InitializeIntegrationRule[ArrayOfFunctionsRule, nfs_, ranges_, ruleOpts_, allOpts_] := 110 | 111 | Module[{t, methodSpec, funcsArr, errNormFunc, pos, absc, weights, 112 | errweights, x, vars, funcsExpr, nf}, 113 | 114 | t = NIntegrate`GetMethodOptionValues[ArrayOfFunctionsRule, 115 | ArrayOfFunctionsRuleProperties, ruleOpts]; 116 | If[t === $Failed, Return[$Failed]]; 117 | {methodSpec, funcsArr, errNormFunc} = t; 118 | 119 | If[ !ArrayQ[funcsArr], 120 | Message[ArrayOfFunctionsRule::narr]; 121 | Return[$Failed]; 122 | ]; 123 | 124 | t = NIntegrate`MOptionValue[methodSpec, nfs, ranges, allOpts]; 125 | 126 | If[t === $Failed, Return[$Failed]]; 127 | 128 | If[ ArrayQ[funcsArr, _, Head[#] === Function &], 129 | vars = {x}; 130 | funcsExpr = Map[#[x] &, funcsArr, {Length@Dimensions[funcsArr]}], 131 | (* ELSE *) 132 | vars = nfs[[1]]["ArgumentNames"]; 133 | funcsExpr = funcsArr; 134 | ]; 135 | nf = Experimental`CreateNumericalFunction[{#, {}} & /@ vars, funcsExpr, Dimensions[funcsExpr], _Real & /@ vars]; 136 | 137 | ArrayOfFunctionsRule[t, funcsArr, errNormFunc, nf] 138 | ]; 139 | 140 | 141 | ArrayOfFunctionsRule[methodRule_, funcsVec_, errNormFunc_, nf_]["ApproximateIntegral"[region_]] := 142 | Block[{a, b, currentRule, integrals, errors, absc, weights, errweights, res}, 143 | 144 | {absc, weights, errweights} = methodRule[[1]]; 145 | 146 | {a, b} = region["Boundaries"][[1]]; 147 | currentRule = region["GetRule"[]]; 148 | region["SetRule"[methodRule]]; 149 | region["SetIntegrand"[nf]]; 150 | res = region["ApplyRule"]; 151 | region["SetRule"[currentRule]]; 152 | {integrals, errors} = res[[1 ;; 2]]; 153 | (*If[k < 3,*) 154 | (*k++;*) 155 | (*Print[region["Properties"]];*) 156 | (*Print[region["GetSamplingPoints"[]]];*) 157 | (*Print[region["EvaluateTransformedIntegrand"[#]] & /@ absc];*) 158 | (*Print[region["Integrand"]];*) 159 | (*Print[region["Boundaries"]];*) 160 | (*Print[region["OriginalBoundaries"]];*) 161 | (*];*) 162 | {integrals, errNormFunc[errors], 1} 163 | ]; 164 | End[] (* `Private` *) 165 | 166 | EndPackage[] -------------------------------------------------------------------------------- /Misc/LeftAlignedPresentationReStyling.m: -------------------------------------------------------------------------------- 1 | (* 2 | Left Aligned Presentation Re-styling Mode Mathematica package 3 | 4 | BSD 3-Clause License 5 | 6 | Copyright (c) 2022, Anton Antonov 7 | All rights reserved. 8 | 9 | Redistribution and use in source and binary forms, with or without 10 | modification, are permitted provided that the following conditions are met: 11 | 12 | * Redistributions of source code must retain the above copyright notice, this 13 | list of conditions and the following disclaimer. 14 | 15 | * Redistributions in binary form must reproduce the above copyright notice, 16 | this list of conditions and the following disclaimer in the documentation 17 | and/or other materials provided with the distribution. 18 | 19 | * Neither the name of the copyright holder nor the names of its 20 | contributors may be used to endorse or promote products derived from 21 | this software without specific prior written permission. 22 | 23 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 24 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 25 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 26 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE 27 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 28 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 29 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 30 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 31 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 32 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 33 | 34 | Written by Anton Antonov, 35 | ʇǝu˙oǝʇsod@ǝqnɔuouoʇuɐ, 36 | Windermere, Florida, USA. 37 | *) 38 | (* Created by the Wolfram Language Plugin for IntelliJ, see http://wlplugin.halirutan.de/ *) 39 | 40 | (* :Title: LeftAlignedPresentationReStyling *) 41 | (* :Context: LeftAlignedPresentationReStyling` *) 42 | (* :Author: Anton Antonov *) 43 | (* :Date: 2022-02-21 *) 44 | 45 | (* :Package Version: 0.1 *) 46 | (* :Mathematica Version: 12.1 *) 47 | (* :Copyright: (c) 2022 Anton Antonov *) 48 | (* :Keywords: *) 49 | (* :Discussion: *) 50 | 51 | BeginPackage["LeftAlignedPresentationReStyling`"]; 52 | (* Exported symbols added here with SymbolName::usage *) 53 | LeftAlignedPresentationMode::usage = "Restyle the notebook to use the left border aligned presentation cell styles."; 54 | 55 | Begin["`Private`"]; 56 | 57 | nbReStyle = 58 | Notebook[{ 59 | Cell[StyleData[StyleDefinitions -> "Default.nb"]], 60 | 61 | (* Original *) 62 | (* Cell[StyleData["Input", "SlideShow"],*) 63 | (* CellMargins->{{*) 64 | (* 0.135 FrontEnd`AbsoluteCurrentValue[{WindowSize, 1}], 0.01*) 65 | (* FrontEnd`AbsoluteCurrentValue[{WindowSize, 1}]}, {8, 15}},*) 66 | (* LinebreakAdjustments->{1, 2., 12., 1., 1.},*) 67 | (* FontSize->20],*) 68 | Cell[StyleData["Text", "SlideShow"], CellMargins->{{66, 10}, {5, 8}}], 69 | Cell[StyleData["Item", "SlideShow"], CellMargins->{{81, 10}, {4, 8}}], 70 | Cell[StyleData["ItemParagraph", "SlideShow"], CellMargins->{{81, 10}, {4, 1}}], 71 | Cell[StyleData["Subitem", "SlideShow"], CellMargins->{{105, 12}, {4, 4}}], 72 | Cell[StyleData["Subsubitem", "SlideShow"], CellMargins->{{129, 12}, {4, 4}}], 73 | Cell[StyleData["SubsubitemParagraph", "SlideShow"], CellMargins->{{129, 12}, {4, 4}}], 74 | 75 | Cell[StyleData["ItemNumbered", "SlideShow"], CellMargins->{{81, 10}, {4, 8}}], 76 | Cell[StyleData["SubitemNumbered", "SlideShow"], CellMargins->{{105, 12}, {4, 4}}], 77 | Cell[StyleData["SubsubitemNumbered", "SlideShow"], CellMargins->{{129, 12}, {4, 4}}], 78 | 79 | Cell[StyleData["Section", "SlideShow"], CellMargins->{{27, Inherited}, {8, 18}}], 80 | Cell[StyleData["Subsection", "SlideShow"], CellMargins->{{50.34765625, 3.}, {8., 20.}}], 81 | Cell[StyleData["Subsubsection", "SlideShow"], CellMargins->{{66, Inherited}, {8, 12}}], 82 | 83 | Cell[StyleData["Input", "SlideShow"], CellMargins->{{66, 10}, {5, 8}}], 84 | 85 | Cell[StyleData["Output", "SlideShow"], CellMargins->{{66, 10}, {5, 8}}], 86 | 87 | Cell[StyleData["Echo", "SlideShow"], CellMargins->{{66, 10}, {5, 8}}], 88 | 89 | Cell[StyleData["ExternalLanguage", "SlideShow"], CellMargins->{{66, 10}, {5, 8}}], 90 | 91 | Cell[StyleData["Code", "SlideShow"], CellMargins->{{66, 10}, {5, 8}}] 92 | }, 93 | WindowSize -> {857, 887}, 94 | WindowMargins -> {{373, Automatic}, {Automatic, 219}}, 95 | FrontEndVersion -> "12.1 for Mac OS X x86 (64-bit) (June 19, 2020)", 96 | StyleDefinitions -> "PrivateStylesheetFormatting.nb" 97 | ]; 98 | 99 | Clear[LeftAlignedPresentationMode] ; 100 | LeftAlignedPresentationMode[True] = LeftAlignedPresentationMode[]; 101 | 102 | LeftAlignedPresentationMode[] := LeftAlignedPresentationMode[EvaluationNotebook[]]; 103 | 104 | LeftAlignedPresentationMode[nb_NotebookObject, True] := LeftAlignedPresentationMode[nb]; 105 | 106 | LeftAlignedPresentationMode[nb_NotebookObject] := 107 | Block[{}, 108 | SetOptions[nb, StyleDefinitions -> BinaryDeserialize[BinarySerialize[nbReStyle]]] 109 | ]; 110 | 111 | LeftAlignedPresentationMode[ False] := SetOptions[EvaluationNotebook[], StyleDefinitions -> "Default.nb"]; 112 | 113 | LeftAlignedPresentationMode[nb_NotebookObject, False] := SetOptions[nb, StyleDefinitions -> "Default.nb"]; 114 | 115 | End[]; (* `Private` *) 116 | 117 | EndPackage[] -------------------------------------------------------------------------------- /Misc/NeuralNetworkGraph.m: -------------------------------------------------------------------------------- 1 | (* 2 | Neural network graph Mathematica package 3 | Copyright (C) 2018-2022 Anton Antonov 4 | 5 | This program is free software: you can redistribute it and/or modify 6 | it under the terms of the GNU General Public License as published by 7 | the Free Software Foundation, either version 3 of the License, or 8 | (at your option) any later version. 9 | 10 | This program is distributed in the hope that it will be useful, 11 | but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | GNU General Public License for more details. 14 | 15 | You should have received a copy of the GNU General Public License 16 | along with this program. If not, see . 17 | 18 | Written by Anton Antonov, 19 | ʇǝu˙oǝʇsod@ǝqnɔuouoʇuɐ 20 | Windermere, Florida, USA. 21 | *) 22 | 23 | (* 24 | Mathematica is (C) Copyright 1988-2022 Wolfram Research, Inc. 25 | 26 | Protected by copyright law and international treaties. 27 | 28 | Unauthorized reproduction or distribution subject to severe civil 29 | and criminal penalties. 30 | 31 | Mathematica is a registered trademark of Wolfram Research, Inc. 32 | *) 33 | 34 | (* Mathematica Package *) 35 | (* Created by the Wolfram Language Plugin for IntelliJ, see http://wlplugin.halirutan.de/ *) 36 | 37 | (* :Title: NeuralNetworkGraph *) 38 | (* :Context: NeuralNetworkGraph` *) 39 | (* :Author: Anton Antonov *) 40 | (* :Date: 2022-09-16 *) 41 | 42 | (* :Package Version: 0.1 *) 43 | (* :Mathematica Version: 13.1 *) 44 | (* :Copyright: (c) 2022 Anton Antonov *) 45 | (* :Keywords: *) 46 | (* :Discussion: 47 | 48 | Based on codes in the discussions: 49 | 50 | - ["Neural network illustrations"](https://mathematica.stackexchange.com/q/175686/34008) 51 | - ["Please share neural network illustrations"](https://community.wolfram.com/groups/-/m/t/1360660) 52 | 53 | *) 54 | 55 | BeginPackage["NeuralNetworkGraph`"]; 56 | 57 | NeuralNetworkGraph::usage = "NeuralNetworkGraph[layerCounts : ( {_Integer...} | _Association )] \ 58 | produces a graph that illustrates a neural network."; 59 | 60 | Begin["`Private`"]; 61 | 62 | Clear[NeuralNetworkGraph]; 63 | 64 | Options[NeuralNetworkGraph] = Options[Graph]; 65 | 66 | NeuralNetworkGraph[layerCounts : {_Integer ..}, opts:OptionsPattern[]] := 67 | NeuralNetworkGraph[AssociationThread[Row[{"layer ", #}] & /@ Range@Length[layerCounts], layerCounts], opts]; 68 | 69 | NeuralNetworkGraph[namedLayerCounts_Association, opts:OptionsPattern[]] := 70 | Block[{graphUnion, graph, vstyle, 71 | layerCounts = Values[namedLayerCounts], 72 | layerCountsNames = Keys[namedLayerCounts]}, 73 | graphUnion[g_?GraphQ] := g; 74 | graphUnion[g__?GraphQ] := GraphUnion[g]; 75 | graph = 76 | graphUnion @@ 77 | MapThread[ 78 | IndexGraph, {CompleteGraph /@ Partition[layerCounts, 2, 1], 79 | FoldList[Plus, 0, layerCounts[[;; -3]]]}]; 80 | vstyle = 81 | Catenate[ 82 | Thread /@ 83 | Thread[TakeList[VertexList[graph], layerCounts] -> 84 | ColorData[97] /@ Range@Length[layerCounts]]]; 85 | graph = 86 | Graph[graph, opts, 87 | GraphLayout -> {"MultipartiteEmbedding", 88 | "VertexPartition" -> layerCounts}, GraphStyle -> "BasicBlack", 89 | VertexSize -> 0.5, VertexStyle -> vstyle]; 90 | Legended[graph, 91 | Placed[PointLegend[ColorData[97] /@ Range@Length[layerCounts], 92 | layerCountsNames, LegendMarkerSize -> 30, LegendLayout -> "Row"], Below]] 93 | ]; 94 | 95 | End[]; (* `Private` *) 96 | 97 | EndPackage[] -------------------------------------------------------------------------------- /Misc/Soundex.m: -------------------------------------------------------------------------------- 1 | (* 2 | Soundex Mathematica package 3 | Copyright (C) 2017 Anton Antonov 4 | 5 | This program is free software: you can redistribute it and/or modify 6 | it under the terms of the GNU General Public License as published by 7 | the Free Software Foundation, either version 3 of the License, or 8 | (at your option) any later version. 9 | 10 | This program is distributed in the hope that it will be useful, 11 | but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | GNU General Public License for more details. 14 | 15 | You should have received a copy of the GNU General Public License 16 | along with this program. If not, see . 17 | 18 | Written by Anton Antonov, 19 | ʇǝu˙oǝʇsod@ǝqnɔuouoʇuɐ 20 | Windermere, Florida, USA. 21 | *) 22 | 23 | (* 24 | Mathematica is (C) Copyright 1988-2022 Wolfram Research, Inc. 25 | 26 | Protected by copyright law and international treaties. 27 | 28 | Unauthorized reproduction or distribution subject to severe civil 29 | and criminal penalties. 30 | 31 | Mathematica is a registered trademark of Wolfram Research, Inc. 32 | *) 33 | 34 | (* :Title: Soundex *) 35 | (* :Context: Soundex` *) 36 | (* :Author: Anton Antonov *) 37 | (* :Date: 2017-11-11 *) 38 | 39 | (* :Package Version: 1.0 *) 40 | (* :Mathematica Version: *) 41 | (* :Copyright: (c) 2017 Anton Antonov *) 42 | (* :Keywords: *) 43 | (* :Discussion: 44 | 45 | # Introduction 46 | 47 | For the Soundex general idea and algorithm see [1]. Here is the opening paragraph of [1]: 48 | 49 | "Soundex is a phonetic algorithm for indexing names by sound, as pronounced in English. 50 | The goal is for homophones to be encoded to the same representation so that they can be matched 51 | despite minor differences in spelling. The algorithm mainly encodes consonants; 52 | a vowel will not be encoded unless it is the first letter. Soundex is the most widely known 53 | of all phonetic algorithms (in part because it is a standard feature of popular database 54 | software such as DB2, PostgreSQL, MySQL, SQLite, Ingres, MS SQL Server and Oracle). 55 | Improvements to Soundex are the basis for many modern phonetic algorithms." 56 | 57 | 58 | # Algorithm steps 59 | 60 | The code below is based on the second variant in [1]. 61 | 62 | 1. Save the first letter. Remove all occurrences of 'h' and 'w' except first letter. 63 | 64 | 2. Replace all consonants (including the first letter) with digits as follows: 65 | 66 | b, f, p, v → 1 67 | c, g, j, k, q, s, x, z → 2 68 | d, t → 3 69 | l → 4 70 | m, n → 5 71 | r → 6 72 | 73 | 3. Replace all adjacent same digits with one digit. 74 | 75 | 4. Remove all occurrences of a, e, i, o, u, y except the first letter. 76 | 77 | 5. If the first symbol is a digit replace it with the letter saved on step 1. 78 | 79 | 6. Append 3 zeros if the result contains less than 3 digits. 80 | Remove all characters except the first letter and 3 digits after it. 81 | 82 | 83 | # References 84 | 85 | [1] Wikipedia entry, Soundex, https://en.wikipedia.org/wiki/Soundex . 86 | 87 | *) 88 | 89 | BeginPackage["Soundex`"]; 90 | 91 | Soundex::usage = "Soundex[word] finds the Soundex phonetic index of a given string."; 92 | 93 | 94 | Begin["`Private`"]; 95 | 96 | Clear[Soundex]; 97 | Soundex[word_String] := 98 | Block[{sword = ToLowerCase[word], f, res}, 99 | (*1*) 100 | f = StringTake[sword, {1, 1}]; 101 | res = StringDelete[StringTake[sword, {2, -1}], RegularExpression["[hw]"]]; 102 | (*2*) 103 | res = 104 | StringReplace[ 105 | f <> res, 106 | {RegularExpression["[bfpv]"] -> "1", 107 | RegularExpression["[cgjkqsxz]"] -> "2", 108 | RegularExpression["[dt]"] -> "3", 109 | "l" -> "4", 110 | RegularExpression["[mn]"] -> "5", 111 | "r" -> "6"}]; 112 | (*3*) 113 | res = StringReplace[res, (x_ ~~ x_) :> x]; 114 | (*4*) 115 | res = StringDelete[res, RegularExpression["[aeiouy]"]]; 116 | (*5*) 117 | If[StringLength[res] == 0, res = f]; 118 | res = StringReplace[res, StartOfString ~~ DigitCharacter :> f]; 119 | (*6*) 120 | If[StringLength[res] < 4, 121 | res = res <> "000"; 122 | ]; 123 | res = StringTake[res, 4]; 124 | res 125 | ]; 126 | 127 | End[]; (* `Private` *) 128 | 129 | EndPackage[] -------------------------------------------------------------------------------- /Misc/TileGraph.m: -------------------------------------------------------------------------------- 1 | (* 2 | Tile graph Mathematica package 3 | Copyright (C) 2022 Anton Antonov 4 | 5 | This program is free software: you can redistribute it and/or modify 6 | it under the terms of the GNU General Public License as published by 7 | the Free Software Foundation, either version 3 of the License, or 8 | (at your option) any later version. 9 | 10 | This program is distributed in the hope that it will be useful, 11 | but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | GNU General Public License for more details. 14 | 15 | You should have received a copy of the GNU General Public License 16 | along with this program. If not, see . 17 | 18 | Written by Anton Antonov, 19 | ʇǝu˙oǝʇsod@ǝqnɔuouoʇuɐ, 20 | Windermere, Florida, USA. 21 | *) 22 | 23 | (* 24 | Mathematica is (C) Copyright 1988-2022 Wolfram Research, Inc. 25 | 26 | Protected by copyright law and international treaties. 27 | 28 | Unauthorized reproduction or distribution subject to severe civil 29 | and criminal penalties. 30 | 31 | Mathematica is a registered trademark of Wolfram Research, Inc. 32 | *) 33 | 34 | (* :Title: TileGraph *) 35 | (* :Context: TileGraph` *) 36 | (* :Author: Anton Antonov *) 37 | (* :Date: 2022-05-01 *) 38 | 39 | (* :Package Version: 0.8 *) 40 | (* :Mathematica Version: 13.0 *) 41 | (* :Copyright: (c) 2022 Anton Antonov *) 42 | (* :Keywords: Tile, Rectangle, Binning, Histogram, Graph, Mathematica, Wolfram Language, WL *) 43 | (* :Discussion: 44 | 45 | # In brief 46 | 47 | This package has a function that makes graphs that correspond to rectangular tile binning. 48 | 49 | It is a modified version of the package HextileGraph.m : 50 | https://github.com/antononcube/MathematicaForPrediction/blob/master/Misc/HextileGraph.m 51 | *) 52 | 53 | (**************************************************************) 54 | (* Importing packages (if needed) *) 55 | (**************************************************************) 56 | 57 | If[Length[DownValues[TileBins`TileBins]] == 0, 58 | Echo["TileBins.m", "Importing from GitHub:"]; 59 | Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/Misc/TileBins.m"] 60 | ]; 61 | 62 | 63 | (**************************************************************) 64 | (* Package definition *) 65 | (**************************************************************) 66 | 67 | BeginPackage["TileGraph`"]; 68 | 69 | TileGraph::usage = "TileGraph[aLonLatValue, cellRadius, opts] \ 70 | makes a rectangular tiling graph for specified data."; 71 | 72 | Begin["`Private`"]; 73 | 74 | Needs["TileBins`"]; 75 | 76 | (***********************************************************) 77 | (* TileGraph *) 78 | (***********************************************************) 79 | 80 | Clear[TileGraph]; 81 | 82 | TileGraph::"nbm" = "The value of the option \"BinMethod\" is expected to be one of \"TileGraph\" or \"GeoHistogram\"."; 83 | 84 | TileGraph::"mr" = "If the value of the option \"BinMethod\" is `1` then the second argument is expected to be `2`."; 85 | 86 | Options[TileGraph] := 87 | Join[ 88 | {"BinMethod" -> "TileGraph", "RemoveLoneCells" -> False}, 89 | Options[TileBins], 90 | Options[GeoHistogram], 91 | Options[NearestNeighborGraph] 92 | ]; 93 | 94 | TileGraph[ 95 | aLonLatValue : Association[({_?NumberQ, _?NumberQ} -> _?NumberQ) ..], 96 | cellRadius : (_?NumberQ | _Quantity), 97 | opts : OptionsPattern[] ] := 98 | 99 | Block[{binMethod, removeLoneCellsQ, aPolygonValues, lsCells, aCells, 100 | nc, lsDistances, pos, grSquareCellsNetwork, grSquareCells}, 101 | 102 | binMethod = OptionValue[TileGraph, "BinMethod"]; 103 | removeLoneCellsQ = TrueQ[OptionValue[TileGraph, "RemoveLoneCells"]]; 104 | 105 | Which[ 106 | ToLowerCase["TileGraph"] == ToLowerCase[binMethod] && NumberQ[cellRadius], 107 | aPolygonValues = TileBins[aLonLatValue, cellRadius, FilterRules[{opts}, Options[TileBins]]], 108 | 109 | ToLowerCase["TileGraph"] == ToLowerCase[binMethod] && !NumberQ[cellRadius], 110 | Message[TileGraph::"mr", "\"TileGraph\"", "a number"]; 111 | Return[$Failed], 112 | 113 | True, 114 | Message[TileGraph::"nbm"]; 115 | Return[$Failed] 116 | ]; 117 | 118 | (* Make cell objects *) 119 | 120 | lsCells = KeyValueMap[<|"Value" -> #2, "Cell" -> #1, "Center" -> Mean[PolygonCoordinates[#1]]|> &, aPolygonValues]; 121 | lsCells = SortBy[lsCells, #["Center"] &]; 122 | aCells = AssociationThread[Range[Length[lsCells]], lsCells]; 123 | aCells = Association@KeyValueMap[#1 -> Prepend[#2, "ID" -> #1] &, aCells]; 124 | 125 | (* Create a function to find the nearest cell to a given position *) 126 | nc = Nearest[Values[aCells] -> Keys[aCells], DistanceFunction -> (EuclideanDistance[#1["Center"], #2["Center"]] &)]; 127 | 128 | lsDistances = Select[Flatten@DistanceMatrix[Values[#["Center"] & /@ aCells]], # > 0 &]; 129 | 130 | (* Identify outlier(s) and drop them *) 131 | If[removeLoneCellsQ, 132 | pos = Select[nc[#, {8, 1.1 * Min[lsDistances] * Sqrt[2]}] & /@ aCells, Length[#] == 1 &]; 133 | aCells = KeyDrop[aCells, Keys[pos]]; 134 | ]; 135 | 136 | (* Reassign cell ID's *) 137 | aCells = AssociationThread[Range[Length[aCells]], Values[aCells]]; 138 | aCells = Association@KeyValueMap[#1 -> Prepend[#2, "ID" -> #1] &, aCells]; 139 | 140 | (* Make neighbors graph *) 141 | grSquareCellsNetwork = 142 | NearestNeighborGraph[ 143 | Keys[aCells], {9, Min[lsDistances] * Sqrt[2]}, 144 | DistanceFunction -> (EuclideanDistance[aCells[#1]["Center"], aCells[#2]["Center"]] &), 145 | VertexCoordinates -> KeyValueMap[#1 -> #2["Center"] &, aCells], 146 | FilterRules[{opts}, Options[NearestNeighborGraph]] 147 | ]; 148 | 149 | (* Make final graph *) 150 | grSquareCells = 151 | Graph[ 152 | DirectedEdge @@@ 153 | Join[ 154 | EdgeList[grSquareCellsNetwork], 155 | Reverse /@ EdgeList[grSquareCellsNetwork] 156 | ], 157 | DirectedEdges -> True, 158 | VertexCoordinates -> KeyValueMap[#1 -> #2["Center"] &, aCells], 159 | FilterRules[{opts}, Options[Graph]] 160 | ] 161 | ]; 162 | 163 | 164 | End[]; (* `Private` *) 165 | 166 | EndPackage[] -------------------------------------------------------------------------------- /Misc/TilingUtilizationFunctions.m: -------------------------------------------------------------------------------- 1 | (* 2 | Tiling utilization functions Mathematica package 3 | Copyright (C) 2020 Anton Antonov 4 | 5 | This program is free software: you can redistribute it and/or modify 6 | it under the terms of the GNU General Public License as published by 7 | the Free Software Foundation, either version 3 of the License, or 8 | (at your option) any later version. 9 | 10 | This program is distributed in the hope that it will be useful, 11 | but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | GNU General Public License for more details. 14 | 15 | You should have received a copy of the GNU General Public License 16 | along with this program. If not, see . 17 | 18 | Written by Anton Antonov, 19 | ʇǝu˙oǝʇsod@ǝqnɔuouoʇuɐ, 20 | Windermere, Florida, USA. 21 | *) 22 | 23 | (* 24 | Mathematica is (C) Copyright 1988-2022 Wolfram Research, Inc. 25 | 26 | Protected by copyright law and international treaties. 27 | 28 | Unauthorized reproduction or distribution subject to severe civil 29 | and criminal penalties. 30 | 31 | Mathematica is a registered trademark of Wolfram Research, Inc. 32 | *) 33 | 34 | (* :Title: TilingUtilizationFunctions *) 35 | (* :Context: TilingUtilizationFunctions` *) 36 | (* :Author: Anton Antonov *) 37 | (* :Date: 2020-05-04 *) 38 | 39 | (* :Package Version: 0.1 *) 40 | (* :Mathematica Version: 12.1 *) 41 | (* :Copyright: (c) 2020 Anton Antonov *) 42 | (* :Keywords: Hextile, Hexagon, Tile, Rectangle, Binning, Histogram, Polygon, Mathematica, Wolfram Language, WL *) 43 | (* :Discussion: *) 44 | 45 | (*********************************************************) 46 | (* Load needed packages *) 47 | (*********************************************************) 48 | 49 | If[Length[DownValues[HextileBins`HextileBins]] == 0, 50 | Echo["HextileBins.m", "Importing from GitHub:"]; 51 | Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/Misc/HextileBins.m"] 52 | ]; 53 | 54 | If[Length[DownValues[TileBins`TileBins]] == 0, 55 | Echo["TileBins.m", "Importing from GitHub:"]; 56 | Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/Misc/TileBins.m"] 57 | ]; 58 | 59 | 60 | (*********************************************************) 61 | (* Package definition *) 62 | (*********************************************************) 63 | 64 | BeginPackage["TilingUtilizationFunctions`"]; 65 | (* Exported symbols added here with SymbolName::usage *) 66 | 67 | TileTagging::usage = "TileTagging[ aPoints : Association[ ( _ -> { _?NumericQ, _?NumericQ} ).. ], cellSize_?NumericQ, ___] \ 68 | produces a tile tagging system for specified 2D points with ID's."; 69 | 70 | Begin["`Private`"]; 71 | 72 | Needs["HextileBins`"]; 73 | Needs["TileBins`"]; 74 | 75 | (*********************************************************) 76 | (* TileTagging *) 77 | (*********************************************************) 78 | 79 | Clear[TileTagging]; 80 | 81 | SyntaxInformation[TileTagging] = { "ArgumentsPattern" -> { _, _, OptionsPattern[] } }; 82 | 83 | Options[TileTagging] = { "TilingFunction" -> HextileBins }; 84 | 85 | TileTagging[ aPoints : Association[ ( _ -> { _?NumericQ, _?NumericQ } ).. ], cellSize_?NumericQ, opts : OptionsPattern[] ] := 86 | Block[{tilingFunc, aTileBins, aTileBinCenters, nf, aCellIDs}, 87 | 88 | tilingFunc = OptionValue[TileTagging, "TilingFunction"]; 89 | 90 | aTileBins = KeySortBy[tilingFunc[Values@aPoints, cellSize], Mean@*PolygonCoordinates]; 91 | 92 | aTileBinCenters = KeyMap[Mean@*PolygonCoordinates, aTileBins]; 93 | 94 | nf = Nearest[Keys[aTileBinCenters] -> "Index"]; 95 | 96 | aCellIDs = Map[First[nf[#]] &, aPoints]; 97 | 98 | <| "TileTagging" -> aCellIDs, "TileBins" -> aTileBins, "NearestTileCenterFunction" -> nf |> 99 | 100 | ] /; NumberQ[cellSize] && cellSize > 0; 101 | 102 | TileTagging[___] := 103 | Block[{}, 104 | $Failed 105 | ]; 106 | 107 | End[]; (* `Private` *) 108 | 109 | EndPackage[] -------------------------------------------------------------------------------- /Misc/WeatherEventRecords.m: -------------------------------------------------------------------------------- 1 | (* 2 | Weather event records data Mathematica package 3 | Copyright (C) 2018 Anton Antonov 4 | 5 | This program is free software: you can redistribute it and/or modify 6 | it under the terms of the GNU General Public License as published by 7 | the Free Software Foundation, either version 3 of the License, or 8 | (at your option) any later version. 9 | 10 | This program is distributed in the hope that it will be useful, 11 | but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | GNU General Public License for more details. 14 | 15 | You should have received a copy of the GNU General Public License 16 | along with this program. If not, see . 17 | 18 | Written by Anton Antonov, 19 | antononcube @ gmail . com, 20 | Windermere, Florida, USA. 21 | *) 22 | 23 | (* 24 | Mathematica is (C) Copyright 1988-2018 Wolfram Research, Inc. 25 | 26 | Protected by copyright law and international treaties. 27 | 28 | Unauthorized reproduction or distribution subject to severe civil 29 | and criminal penalties. 30 | 31 | Mathematica is a registered trademark of Wolfram Research, Inc. 32 | *) 33 | 34 | (* :Title: WeatherEventRecords *) 35 | (* :Context: WeatherEventRecords` *) 36 | (* :Author: Anton Antonov *) 37 | (* :Date: 2018-09-20 *) 38 | 39 | (* :Package Version: 0.1 *) 40 | (* :Mathematica Version: 11.3 *) 41 | (* :Copyright: (c) 2018 Anton Antonov *) 42 | (* :Keywords: weather data, long form, database, star schema, even data, time series *) 43 | (* :Discussion: 44 | 45 | 46 | # In brief 47 | 48 | This package provides a function that extracts weather data using specifications for: 49 | cities, date range, weather data variables, and number of stations. 50 | 51 | The main inspiration to make this package is need for example data in order to document 52 | the ERTMon package [1]; see [2]. 53 | 54 | 55 | # Usage example 56 | 57 | citiesSpec = {{"Miami", "USA"}, {"Chicago", "USA"}, {"London", "UK"}, {"Melbourne", "Australia"}}; 58 | wProps = {"Temperature", "Pressure", "Humidity", "WindSpeed"}; 59 | 60 | res = WeatherEventRecords[citiesSpec, {{2018, 5, 1}, {2018, 8, 31}}, wProps, 2] 61 | 62 | RecordsSummary[res] 63 | 64 | DateListPlot[res["eventRecords"][Select[#EntityID == "KMFL" && #Variable == "Pressure" &], {"ObservationTime", "Value"}]] 65 | 66 | 67 | # References 68 | 69 | [1] Anton Antonov, Monadic Event Records Transformations Mathematica package, (2018), MathematicaForPrediction at GitHub. 70 | URL: https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MonadicEventRecordsTransformations.m . 71 | 72 | [2] Anton Antonov, Parametrized event records data transformations, (2018), MathematicaForPrediction at GitHub. 73 | URL: https://github.com/antononcube/MathematicaForPrediction/blob/master/MarkdownDocuments/Parametrized-event-records-data-transformations.md 74 | 75 | 76 | Anton Antonov 77 | 2018-09-20 78 | Windermere, FL, USA 79 | 80 | *) 81 | 82 | (**************************************************************) 83 | (* Importing packages (if needed) *) 84 | (**************************************************************) 85 | 86 | If[Length[DownValues[MathematicaForPredictionUtilities`RecordsSummary]] == 0, 87 | Echo["MathematicaForPredictionUtilities.m", "Importing from GitHub:"]; 88 | Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/MathematicaForPredictionUtilities.m"] 89 | ]; 90 | 91 | If[Length[DownValues[DataReshape`ToLongForm]] == 0, 92 | Echo["DataReshape.m", "Importing from GitHub:"]; 93 | Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/DataReshape.m"] 94 | ]; 95 | 96 | 97 | (**************************************************************) 98 | (* Package definition *) 99 | (**************************************************************) 100 | 101 | BeginPackage["WeatherEventRecords`"]; 102 | (* Exported symbols added here with SymbolName::usage *) 103 | 104 | WeatherEventRecords::usage = "\ 105 | WeatherEventRecords[ \ 106 | citiesSpec_: {{_String, _String}..}, \ 107 | dateRange:{{_Integer, _Integer, _Integer}, {_Integer, _Integer, _Integer}}, \ 108 | wProps:{_String..} : {\"Temperature\"}, \ 109 | nStations_Integer : 1 ] \ 110 | gives an association with event records data." 111 | 112 | Begin["`Private`"]; 113 | 114 | Needs["DataReshape`"]; 115 | 116 | Clear[WeatherEventRecords]; 117 | 118 | WeatherEventRecords[ 119 | citiesSpec_: {{_String, _String}..}, 120 | dateRange:{{_Integer, _Integer, _Integer}, {_Integer, _Integer, _Integer}}, 121 | wProps:{_String..} : {"Temperature"}, 122 | nStations_Integer : 1 ] := 123 | 124 | Block[{wStations, aWStations, tsData, XXX, eventRecords, entityAttributes}, 125 | 126 | wStations = WeatherData[{#, nStations}] & /@ citiesSpec; 127 | wStations = Map[#[[2]] &, wStations, {2}]; 128 | 129 | aWStations = 130 | Join @@ MapThread[ 131 | AssociationThread[#1, XXX] /. XXX -> #2 &, {wStations, citiesSpec}]; 132 | 133 | tsData = 134 | Association@ 135 | Flatten@Outer[{#1, #2} -> 136 | WeatherData[#1, #2, {dateRange[[1]], dateRange[[2]], "Day"}] &, 137 | Keys[aWStations], wProps, 1]; 138 | 139 | If[ !AssociationQ[tsData], Return[$Failed]]; 140 | 141 | tsData = Select[tsData, MatchQ[#, _TemporalData] &]; 142 | 143 | If[ Length[tsData] == 0, Return[$Failed]]; 144 | 145 | eventRecords = 146 | Dataset[ Flatten[#, 1] &@ KeyValueMap[Thread[List[Sequence @@ #1, #2["Times"], #2["Values"]]] &, tsData] ]; 147 | 148 | eventRecords = 149 | eventRecords[All, AssociationThread[{"EntityID", "Variable", "ObservationTime", "Value"} -> #] &]; 150 | 151 | eventRecords = 152 | eventRecords[All, Join[#, <|"LocationID" -> aWStations[#EntityID][[1]]|>] &]; 153 | 154 | eventRecords = 155 | eventRecords[All, {"EntityID", "LocationID", "ObservationTime", "Variable", "Value"}]; 156 | 157 | eventRecords = DeleteMissing[eventRecords, 1, 2]; 158 | eventRecords = eventRecords[Select[NumberQ[#Value] &]]; 159 | 160 | entityAttributes = 161 | Dataset[KeyValueMap[Flatten[{#1, #2[[1]], #2[[-1]]}] &, aWStations]][All, AssociationThread[{"Station", "City", "Country"} -> #] &]; 162 | 163 | entityAttributes = 164 | ToLongForm[entityAttributes, "Station", {"City", "Country"}]; 165 | 166 | entityAttributes = 167 | entityAttributes[All, Association[{"EntityID" -> #Station, "Attribute" -> #Variable, "Value" -> #Value}] &]; 168 | 169 | <| "eventRecords"->eventRecords, "entityAttributes"->entityAttributes |> 170 | ]; 171 | 172 | End[]; (* `Private` *) 173 | 174 | EndPackage[] -------------------------------------------------------------------------------- /MonadicProgramming/MonadicNeuralNetworks.m: -------------------------------------------------------------------------------- 1 | (* 2 | Monadic Neural Networks Mathematica package 3 | Copyright (C) 2018 Anton Antonov 4 | 5 | This program is free software: you can redistribute it and/or modify 6 | it under the terms of the GNU General Public License as published by 7 | the Free Software Foundation, either version 3 of the License, or 8 | (at your option) any later version. 9 | 10 | This program is distributed in the hope that it will be useful, 11 | but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | GNU General Public License for more details. 14 | 15 | You should have received a copy of the GNU General Public License 16 | along with this program. If not, see . 17 | 18 | Written by Anton Antonov, 19 | antononcube @ gmail . com, 20 | Windermere, Florida, USA. 21 | *) 22 | 23 | (* 24 | Mathematica is (C) Copyright 1988-2018 Wolfram Research, Inc. 25 | 26 | Protected by copyright law and international treaties. 27 | 28 | Unauthorized reproduction or distribution subject to severe civil 29 | and criminal penalties. 30 | 31 | Mathematica is a registered trademark of Wolfram Research, Inc. 32 | *) 33 | 34 | (* :Title: MonadicNeuralNetworks *) 35 | (* :Context: MonadicNeuralNetworks` *) 36 | (* :Author: Anton Antonov *) 37 | (* :Date: 2018-08-18 *) 38 | 39 | (* :Package Version: 0.1 *) 40 | (* :Mathematica Version: 11.3 *) 41 | (* :Copyright: (c) 2018 Anton Antonov *) 42 | (* :Keywords: neural net, monad *) 43 | (* :Discussion: 44 | * 45 | * 46 | * *) 47 | 48 | (**************************************************************) 49 | (* Importing packages (if needed) *) 50 | (**************************************************************) 51 | 52 | If[Length[DownValues[MathematicaForPredictionUtilities`RecordsSummary]] == 0, 53 | Echo["MathematicaForPredictionUtilities.m", "Importing from GitHub:"]; 54 | Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/MathematicaForPredictionUtilities.m"] 55 | ]; 56 | 57 | If[Length[DownValues[StateMonadCodeGenerator`GenerateStateMonadCode]] == 0, 58 | Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/MonadicProgramming/StateMonadCodeGenerator.m"] 59 | ]; 60 | 61 | 62 | (**************************************************************) 63 | (* Package definition *) 64 | (**************************************************************) 65 | 66 | BeginPackage["MonadicNeuralNetworks`"]; 67 | 68 | $NetMonFailure::usage = "Failure symbol for the monad NetMon." 69 | 70 | NetMonSetNet::usage = "Assigns the argument to the key \"net\" in the monad context." 71 | 72 | NetMonTakeNet::usage = "Gives the value of the key \"net\" from the monad context." 73 | 74 | NetMonSetEncoder::usage = "Assigns the argument to the key \"encoder\" in the monad context." 75 | 76 | NetMonTakeEncoder::usage = "Gives the value of the key \"encoder\" from the monad context." 77 | 78 | NetMonSetDecoder::usage = "Assigns the argument to the key \"decoder\" in the monad context." 79 | 80 | NetMonTakeDecoder::usage = "Gives the value of the key \"decoder\" from the monad context." 81 | 82 | NetMonSetTrainedNet::usage = "Assigns the argument to the key \"trainedNet\" in the monad context." 83 | 84 | NetMonTakeTrainedNet::usage = "Gives the value of the key \"trainedNet\" from the monad context." 85 | 86 | NetMonTakeNetTrainResultsObject::usage = "Gives the value of the key \"netTrainResultsObject\" from the monad context." 87 | 88 | NetMonTrain::usage = "Train the network. If no data is given the pipeline value is used." 89 | 90 | Begin["`Private`"]; 91 | 92 | Needs["MathematicaForPredictionUtilities`"] 93 | Needs["StateMonadCodeGenerator`"] 94 | 95 | 96 | (**************************************************************) 97 | (* Generation *) 98 | (**************************************************************) 99 | 100 | (* Generate base functions of NetMon monad (through StMon.) *) 101 | 102 | GenerateStateMonadCode[ "MonadicNeuralNetworks`NetMon", "FailureSymbol" -> $NetMonFailure, "StringContextNames" -> False ] 103 | 104 | (**************************************************************) 105 | (* Setters / getters *) 106 | (**************************************************************) 107 | 108 | 109 | ClearAll[NetMonSetNet] 110 | NetMonSetNet[$NetMonFailure] := $NetMonFailure; 111 | NetMonSetNet[xs_, context_] := NetMonSetNet[][xs, context]; 112 | NetMonSetNet[][xs_, context_] := $NetMonFailure; 113 | NetMonSetNet[net:(_NetChain|_NetGraph)][xs_, context_] := NetMonUnit[ xs, Join[ context, <|"net"->net|> ] ]; 114 | NetMonSetNet[__][___] := $NetMonFailure; 115 | 116 | 117 | ClearAll[NetMonTakeNet] 118 | NetMonTakeNet[$NetMonFailure] := $NetMonFailure; 119 | NetMonTakeNet[][$NetMonFailure] := $NetMonFailure; 120 | NetMonTakeNet[xs_, context_] := NetMonTakeNet[][xs, context]; 121 | NetMonTakeNet[][xs_, context_] := Lookup[context, "net", $NetMonFailure]; 122 | NetMonTakeNet[__][___] := $NetMonFailure; 123 | 124 | 125 | 126 | (**************************************************************) 127 | (* Training *) 128 | (**************************************************************) 129 | 130 | ClearAll[NetMonTrain]; 131 | 132 | NetMonTrain[$NetMonFailure] := $NetMonFailure; 133 | 134 | NetMonTrain[xs_, context_] := NetMonTrain[][xs, context]; 135 | 136 | NetMonTrain[opts:OptionsPattern[]][xs_, context_] := 137 | Block[{}, 138 | If[DataRulesForClassifyQ[xs], 139 | NetMonTrain[xs, opts][xs, context], 140 | $NetMonFailure 141 | ] 142 | ]; 143 | 144 | NetMonTrain[trainingData_?DataRulesForClassifyQ, opts:OptionsPattern[]][xs_, context_] := 145 | Block[{res}, 146 | res = NetTrain[context["netChain"], trainingData, All, opts]; 147 | NetMonUnit[res, <||>] 148 | ]; 149 | 150 | NetMonTrain[___][__] := $NetMonFailure; 151 | 152 | (**************************************************************) 153 | (* Setters / getters *) 154 | (**************************************************************) 155 | 156 | 157 | End[]; (* `Private` *) 158 | 159 | EndPackage[] -------------------------------------------------------------------------------- /MonadicProgramming/Package-structure-and-development-workflow.md: -------------------------------------------------------------------------------- 1 | # Package structure and develoment workflow 2 | 3 | ## Introduction 4 | 5 | Because the development of some the monad packages can be large and complex, 6 | a more flexible and powerful structure documented Mathematica/WL is required. 7 | 8 | For the monad package implementation we take a minimalistic approach. 9 | The basic idea is to have: 10 | 11 | - **different modules of project-independent full-fledged packages**, 12 | 13 | - a **custom loader**, and 14 | 15 | - an **interface section.** 16 | 17 | Below is (briefly) described the multi-package dependency, loading, and code generation of the package 18 | ["MonadicContextualClassification.m"](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MonadicContextualClassification.m), \[1\]. 19 | 20 | The goal of the package \[1\] is to provide a Domain Specific Language (DSL) for rapid specification of machine learning classification workflows. 21 | 22 | ## Package file structure 23 | 24 | 1. Packages import code section. 25 | 26 | - Here packages are imported from GitHub with `Import`. 27 | 28 | + (If the corresponding definitions are not already in the context.) 29 | 30 | - These are the mentioned above **"project-independent full-fledged packages"**. 31 | 32 | + (They were developed before the package \[1\].) 33 | 34 | + (The package \[1\] provides a DSL that combines those "full-fledged packages.") 35 | 36 | 2. Package interface definitions code section. 37 | 38 | - Here after `BeginPackage` the `::usage` definitions are given. 39 | 40 | - This is the **"interface section"** mentioned above. 41 | 42 | 3. The private context declaration. 43 | 44 | 4. The declaration of the package contexts with `Needs` code section. 45 | 46 | - (For the contexts of the packages in the imports code section 1.) 47 | 48 | 5. Code generation section. 49 | 50 | - In this case with 51 | `` GenerateStateMonadCode["MonadicContextualClassification`ClCon",...]`` 52 | 53 | - This code generation code section together with the imports code section 1, 54 | correspond to the **"custom loader"** mentioned above. 55 | 56 | + (The package \[2\] generates the definitions of some of the functions of the package \[1\].) 57 | 58 | 6. Function implementations code section. 59 | 60 | ## Testing 61 | 62 | The development of the package \[1\] was/is done with two unit test files. 63 | (One with [hand-written unit tests](https://github.com/antononcube/MathematicaForPrediction/blob/master/UnitTests/MonadicContextualClassification-Unit-Tests.wlt), the other with [randomly generated tests](https://github.com/antononcube/MathematicaForPrediction/blob/master/UnitTests/MonadicContextualClassificationRandomPipelinesUnitTests.m). The latter is specific to the methodology behind the package functionality.) 64 | 65 | The point is that unit tests are crucial when dealing with this kind of complex package dependencies. 66 | (And packages that deal with complex subjects.) 67 | 68 | 69 | ## Summary diagram 70 | 71 | Below is given a diagram that summarizes the development of ["MonadicContextualClassification.m"](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MonadicContextualClassification.m). For more details see the end sections of the document ["A monad for classification workflows"](https://github.com/antononcube/MathematicaForPrediction/blob/master/MarkdownDocuments/A-monad-for-classification-workflows.md). 72 | 73 | [![ClCon-development-cycle](https://i.imgur.com/hmMPfCrl.png)](https://i.imgur.com/hmMPfCr.png) 74 | 75 | ## Example run 76 | 77 | The following screenshot is of an example run that demonstrates the package import prompts and the utilization of 78 | the loaded packages in a classification pipeline. 79 | (The packages have functionalities for training classifier ensembles, making ROC plots, 80 | and finding importance of variables.) 81 | 82 | [![ClCon-example-run-with-Import](https://imgur.com/X2Nephgh.png)](https://imgur.com/X2Nephg.png) 83 | 84 | ## References 85 | 86 | \[1\] Anton Antonov, [Monadic contextual classification Mathematica package](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MonadicContextualClassification.m), (2017), [MathematicaForPrediction at GitHub](https://github.com/antononcube/MathematicaForPrediction/). 87 | 88 | \[2\] Anton Antonov, [State monad code generator Mathematica package](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/StateMonadCodeGenerator.m), (2017), [MathematicaForPrediction at GitHub](https://github.com/antononcube/MathematicaForPrediction/). 89 | -------------------------------------------------------------------------------- /MonadicProgramming/README.md: -------------------------------------------------------------------------------- 1 | # Monadic programming 2 | 3 | 4 | This folder has two kinds of packages: 5 | 6 | 1. for implementing, code generation of monads, 7 | 8 | 2. utilization of the monad pipeline design pattern for different tasks. 9 | 10 | 11 | ## Monad code generation 12 | 13 | The approach taken here treats the Monadic programming pipeline as a [Software design pattern](https://en.wikipedia.org/wiki/Software_design_pattern). 14 | 15 | The monads are obtained through code generation -- see the packages: 16 | [MaybeMonadCodeGenerator](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MaybeMonadCodeGenerator.m), 17 | [StateMonadCodeGenerator](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/StateMonadCodeGenerator.m), 18 | and the article \[[1](https://github.com/antononcube/MathematicaForPrediction/blob/master/MarkdownDocuments/Monad-code-generation-and-extension.md)\]. 19 | 20 | 21 | ## Applications 22 | 23 | Using the [State Monad package](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/StateMonadCodeGenerator.m) 24 | several packages are developed for different tasks in Machine Learning and Natural Language Processing. 25 | 26 | - Classifier creation and testing, [MonadicContextualClassification.m](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MonadicContextualClassification.m). 27 | 28 | - Text analysis, [MonadicTextAnalyzer.m](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MonadicTextAnalyzer.m). 29 | 30 | - Latent semantic analysis, [MonadicLatentSemanticAnalysis.m](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MonadicLatentSemanticAnalysis.m). 31 | 32 | - Phrase completion, [MonadicPhraseCompletion.m](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MonadicPhraseCompletion.m). 33 | 34 | - Quantile regression, [MonadicQuantileRegression.m](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MonadicQuantileRegression.m). 35 | 36 | The monad tracing package [MonadicTracing.m](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MonadicTracing.m) 37 | demonstrates how a monad can act as Decorator in the [Decorator design pattern](https://en.wikipedia.org/wiki/Decorator_pattern). 38 | 39 | ## Package structure and development workflow 40 | 41 | The document 42 | ["Package structure and develoment workflow"](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/Package-structure-and-development-workflow.md) 43 | gives details of the structure and typical development workflow of the packages in this folder. 44 | 45 | 46 | ## Presentations 47 | 48 | The WTC 2017 presentation 49 | ["Monadic Programming: With Application to Data Analysis, Machine Learning and Language Processing"](https://www.wolfram.com/broadcast/video.php?v=2050), 50 | \[2\], gives a good introduction and overview of the approach taken. 51 | 52 | ## References 53 | 54 | \[1\] Anton Antonov, 55 | ["Monad code generation and extension"](https://github.com/antononcube/MathematicaForPrediction/blob/master/MarkdownDocuments/Monad-code-generation-and-extension.md), 56 | (2017), 57 | [MathematicaForPrediction at GitHub](https://github.com/antononcube/). 58 | 59 | \[2\] Anton Antonov, 60 | ["Monadic Programming: With Application to Data Analysis, Machine Learning and Language Processing"](http://wac.36f4.edgecastcdn.net/0036F4/pub/www.wolfram.com/technology-conference/2017/Antonov.zip), 61 | [Wolfram Technology Conference 2017](https://www.wolfram.com/events/technology-conference/2017/presentations/#wednesday). 62 | ([YouTube video](https://m.youtube.com/watch?v=_cIFA5GHF58).) 63 | 64 | \[3\] Anton Antonov, 65 | ["A monad for classification workflows"](https://github.com/antononcube/MathematicaForPrediction/blob/master/MarkdownDocuments/A-monad-for-classification-workflows.md), 66 | (2018), 67 | [MathematicaForPrediction at GitHub](https://github.com/antononcube/). 68 | -------------------------------------------------------------------------------- /OPL/Quantile regression.mod: -------------------------------------------------------------------------------- 1 | /* 2 | Quantile regression using B-splines OPL model 3 | Copyright (C) 2014 Anton Antonov 4 | 5 | This program is free software: you can redistribute it and/or modify 6 | it under the terms of the GNU General Public License as published by 7 | the Free Software Foundation, either version 3 of the License, or 8 | (at your option) any later version. 9 | 10 | This program is distributed in the hope that it will be useful, 11 | but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | GNU General Public License for more details. 14 | 15 | You should have received a copy of the GNU General Public License 16 | along with this program. If not, see . 17 | 18 | Written by Anton Antonov, 19 | antononcube@gmail.com, 20 | Windermere, Florida, USA. 21 | */ 22 | 23 | /* 24 | The full IBM ILOG CPLEX Optimization Studio consists of the CPLEX Optimizer for mathematical programming, the IBM ILOG CPLEX CP Optimizer for constraint programming, the Optimization Programming Language (OPL), and a tightly integrated IDE. 25 | 26 | IBM and ILOG are trademarks or registered trademarks of International Business Machines Corp., in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the Web at Copyright and trademark information. 27 | 28 | */ 29 | 30 | /* Version 1.0 */ 31 | /* 32 | OPL code for the calculation of a regression quantile given with the parameter q with first order B-spline basis using k equaly spaced knots. 33 | The knots are spaced between [ min(d in data) d.x, max(d in data) d.x] . The first and last end points of the interval are B-spline knots. 34 | The data is supplied by an external file with extension .dat, e.g. 35 | data = {<10 12.2> <10.2 13.3> ...} 36 | 37 | A good possible extension is to give the user the possibility to specify the B-spline basis knots. 38 | I am not sure how easy is to program using higher order B-spline basis. The only piecewise functions supported by OPL are piecewise linear. 39 | */ 40 | 41 | tuple Point { 42 | float x; 43 | float y; 44 | }; 45 | 46 | // Data points provided by an external file 47 | {Point} data = ...; 48 | 49 | // Number of knots parameter 50 | int k = 7; 51 | 52 | // Quantile parameter 53 | float q = 0.75; 54 | 55 | float dataMin = min(d in data) d.x; 56 | float dataMax = max(d in data) d.x; 57 | float cLen = ( dataMax - dataMin ) / (k-1); 58 | 59 | dvar float+ u[data]; 60 | dvar float+ v[data]; 61 | dvar float+ bs[1..k]; 62 | 63 | 64 | minimize sum(d in data) q*u[d] + sum(d in data) (1-q)*v[d]; 65 | 66 | subject to { 67 | 68 | forall ( d in data ) { 69 | (sum ( i in 1..k ) bs[i] * (piecewise{ 0-> dataMin + (i-2)*cLen; 1/cLen -> dataMin + (i-1)*cLen; -1/cLen -> dataMin + i*cLen; 0}( dataMin + (i-2)*cLen, 0) d.x) ) + u[d] - v[d] == d.y; 70 | } 71 | } 72 | 73 | execute postProcess{ 74 | writeln("\n=== Regression quantile display ==="); 75 | writeln("Quantile = " + q); 76 | writeln("B-Spline functions and weights:"); 77 | 78 | for( var i =1; i <= k; i ++ ) { 79 | var v1 = dataMin + (i-2)*cLen; 80 | var v2 = dataMin + (i-1)*cLen; 81 | var v3 = dataMin + i*cLen; 82 | var v4 = 1/cLen; 83 | var v5 = -1/cLen; 84 | writeln(bs[i] + " piecewise{ 0 -> " + v1 + "; " + v4 + " -> " + v2 + "; " + v5 + " -> " + v3 + "; 0 } ( " + v1 + ", 0 )" ); 85 | } 86 | } 87 | -------------------------------------------------------------------------------- /R/OutlierIdentifiers.R: -------------------------------------------------------------------------------- 1 | ##======================================================================================= 2 | ## Implementation of one dimensional outlier identifying algorithms in R 3 | ## 4 | ## BSD 3-Clause License 5 | ## 6 | ## Copyright (c) 2013, Anton Antonov 7 | ## All rights reserved. 8 | ## 9 | ## Redistribution and use in source and binary forms, with or without 10 | ## modification, are permitted provided that the following conditions are met: 11 | ## 12 | ## * Redistributions of source code must retain the above copyright notice, this 13 | ## list of conditions and the following disclaimer. 14 | ## 15 | ## * Redistributions in binary form must reproduce the above copyright notice, 16 | ## this list of conditions and the following disclaimer in the documentation 17 | ## and/or other materials provided with the distribution. 18 | ## 19 | ## * Neither the name of the copyright holder nor the names of its 20 | ## contributors may be used to endorse or promote products derived from 21 | ## this software without specific prior written permission. 22 | ## 23 | ## THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 24 | ## AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 25 | ## IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 26 | ## DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE 27 | ## FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 28 | ## DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 29 | ## SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 30 | ## CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 31 | ## OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 32 | ## OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 33 | ## 34 | ## Written by Anton Antonov, 35 | ## antononcube@gmail.com, 36 | ## Windermere, Florida, USA. 37 | ## 38 | ##======================================================================================= 39 | ## 40 | ## This script of R functions re-implements this Mathematica package: 41 | ## 42 | ## [1] Anton Antonov, Implementation of one dimensional outlier identifying algorithms in Mathematica, 43 | ## Mathematica package OutlierIdentifiers.m, (2013), MathematicaForPrediction project at GitHub, 44 | ## https://github.com/antononcube/MathematicaForPrediction/blob/master/OutlierIdentifiers.m . 45 | ## 46 | ## It was easier for me to implement in R the one-dimensional outlier detection functions 47 | ## in [1] than to comprehend the signatures of the R different libraries. 48 | ##======================================================================================= 49 | 50 | #' @description Find an Hampel outlier threshold for a data vector 51 | #' @param dataVec data vector 52 | HampelIdentifierParameters <- function( dataVec ) { 53 | x0 <- median(dataVec) 54 | md <- 1.4826 * median(abs(dataVec - x0)); 55 | c(x0 - md, x0 + md) 56 | } 57 | 58 | #' @description Find an Quartile outlier for a data vector 59 | #' @param dataVec dataVec vector 60 | QuartileIdentifierParameters <- function( dataVec ) { 61 | res <- quantile( dataVec, c( 1/4, 1/2, 3/4 ) ) 62 | xL <- res[[1]] 63 | x0 <- res[[2]] 64 | xU <- res[[3]] 65 | c( x0 - (xU - xL), x0 + (xU - xL) ) 66 | } 67 | 68 | 69 | #' @description Find an SPLUS Quartile outlier for a data vector 70 | #' @param dataVec dataVec vector 71 | SPLUSQuartileIdentifierParameters <- function( dataVec ) { 72 | if ( length(dataVec) <=4 ) { 73 | xL <- min(dataVec) 74 | xU <- max(dataVec) 75 | } else { 76 | res <- quantile( dataVec, c( 1/4, 3/4 ) ) 77 | xL <- res[[1]] 78 | xU <- res[[2]] 79 | } 80 | c( xL - 1.5*(xU-xL), xU + 1.5*(xU-xL) ) 81 | } 82 | 83 | 84 | #' @description Find an outlier threshold for a data vector 85 | #' @param dataVec data vector 86 | #' @param lowerAndUpperThresholds outlier identifier parameters 87 | OutlierIdentifier <- function( dataVec, lowerAndUpperThresholds ) { 88 | dataVec[ dataVec <= lowerAndUpperThresholds[[1]] | dataVec >= lowerAndUpperThresholds[[2]] ] 89 | } 90 | 91 | #' @description Find the top outliers for a data vector 92 | #' @param dataVec data vector 93 | #' @param lowerAndUpperThresholds outlier identifier parameters 94 | TopOutlierIdentifier <- function( dataVec, lowerAndUpperThresholds ) { 95 | dataVec[dataVec >= lowerAndUpperThresholds[[2]] ] 96 | } 97 | 98 | #' @description Find the bottom outliers for a data vector 99 | #' @param dataVec data vector 100 | #' @param lowerAndUpperThresholds outlier identifier parameters 101 | BottomOutlierIdentifier <- function( dataVec, lowerAndUpperThresholds ) { 102 | dataVec[dataVec <= lowerAndUpperThresholds[[1]] ] 103 | } 104 | 105 | #' @description Find the outlier positions in a data vector 106 | #' @param dataVec data vector 107 | #' @param outlierIdentifier outlier identifier function 108 | OutlierPosition <- function( dataVec, outlierIdentifier = HampelIdentifierParameters ) { 109 | cls <- outlierIdentifier(dataVec) 110 | which( dataVec <= cls[[1]] | dataVec >= cls[[2]] ) 111 | } 112 | 113 | #' @description Find the top outlier positions in a data vector 114 | #' @param dataVec data vector 115 | #' @param outlierIdentifier outlier identifier function 116 | TopOutlierPosition <- function( dataVec, outlierIdentifier = HampelIdentifierParameters ) { 117 | cls <- outlierIdentifier(dataVec) 118 | which( dataVec >= cls[[2]] ) 119 | } 120 | 121 | #' @description Find the bottom outlier positions in a data vector 122 | #' @param dataVec data vector 123 | #' @param outlierIdentifier outlier identifier function 124 | BottomOutlierPosition <- function( dataVec, outlierIdentifier = HampelIdentifierParameters ) { 125 | cls <- outlierIdentifier(dataVec) 126 | which( dataVec <= cls[[1]] ) 127 | } 128 | 129 | 130 | 131 | -------------------------------------------------------------------------------- /R/SMRInterfaceGeneral/ui.R: -------------------------------------------------------------------------------- 1 | ##======================================================================================= 2 | ## General Sparse Matrix Recommender Interface, ui side 3 | ## Copyright (C) 2017 Anton Antonov 4 | ## 5 | ## This program is free software: you can redistribute it and/or modify 6 | ## it under the terms of the GNU General Public License as published by 7 | ## the Free Software Foundation, either version 3 of the License, or 8 | ## (at your option) any later version. 9 | ## 10 | ## This program is distributed in the hope that it will be useful, 11 | ## but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | ## GNU General Public License for more details. 14 | ## 15 | ## You should have received a copy of the GNU General Public License 16 | ## along with this program. If not, see . 17 | ## 18 | ## Written by Anton Antonov, 19 | ## antononcube @ gmail . com, 20 | ## Windermere, Florida, USA. 21 | ## 22 | ##======================================================================================= 23 | ## 24 | ## This Shiny interface is made to be used with the recommender system implemented in: 25 | ## https://github.com/antononcube/MathematicaForPrediction/blob/master/R/SparseMatrixRecommender.R 26 | ## 27 | ##======================================================================================= 28 | 29 | library(shiny) 30 | 31 | shinyUI( 32 | fluidPage( 33 | 34 | titlePanel("Item recommendations interface"), 35 | 36 | fluidRow( 37 | 38 | column( 3, 39 | submitButton( "Update", icon = icon("refresh") ), 40 | 41 | textInput("search", "Search items with:", "A.*"), 42 | 43 | textInput("itemList", "Item list:", rownames(itemSMR$M)[1] ), 44 | 45 | textInput("itemRatings", "Item star ratings:", "3"), 46 | 47 | numericInput("nrecs", "Number of recommendations:", 20) 48 | 49 | ), 50 | 51 | column( 6, 52 | tabsetPanel( 53 | tabPanel( "Search results", 54 | h4("Search results"), 55 | DT::dataTableOutput("view") 56 | ), 57 | 58 | tabPanel( "Consumed items list", 59 | h4("Consumed items list"), 60 | tabsetPanel( 61 | tabPanel( "short", DT::dataTableOutput("itemList") ), 62 | tabPanel( "extended", DT::dataTableOutput("itemListData") ) 63 | ) 64 | ) 65 | ) 66 | ), 67 | 68 | column( 3, 69 | textInput("tagTypeSFactors", "Tag type significance factors:", 70 | paste( "c(", paste( SMRCurrentTagTypeSignificanceFactors( itemSMR ), collapse = ", " ), ")" ) ), 71 | DT::dataTableOutput("significanceFactors") 72 | ) 73 | ), 74 | 75 | fluidRow( 76 | column( 9, 77 | h4("Recommendations"), 78 | tabsetPanel( 79 | tabPanel( "main", DT::dataTableOutput("recs") ), 80 | tabPanel( "proofs", DT::dataTableOutput("recsProofs") ), 81 | tabPanel( "scores plot", plotOutput("recsScoresPlot") ) 82 | ) 83 | ), 84 | column( 3, 85 | tabsetPanel( 86 | tabPanel( "Profile", 87 | textInput("selectedProfileTags", "Selected tags:", ""), 88 | h4("Profile"), 89 | DT::dataTableOutput("uprofile") ), 90 | tabPanel( "Detailed proof", 91 | h4("Detailed proof"), 92 | DT::dataTableOutput("uproof") ) 93 | ) 94 | ) 95 | ) 96 | ) 97 | ) 98 | 99 | -------------------------------------------------------------------------------- /R/TimeSeriesSMRInterfaceGeneral/ui.R: -------------------------------------------------------------------------------- 1 | ##======================================================================================= 2 | ## Time series recommender interface, ui side 3 | ## Copyright (C) 2017 Anton Antonov 4 | ## 5 | ## This program is free software: you can redistribute it and/or modify 6 | ## it under the terms of the GNU General Public License as published by 7 | ## the Free Software Foundation, either version 3 of the License, or 8 | ## (at your option) any later version. 9 | ## 10 | ## This program is distributed in the hope that it will be useful, 11 | ## but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | ## GNU General Public License for more details. 14 | ## 15 | ## You should have received a copy of the GNU General Public License 16 | ## along with this program. If not, see . 17 | ## 18 | ## Written by Anton Antonov, 19 | ## antononcube @ gmail . com, 20 | ## Windermere, Florida, USA. 21 | ## 22 | ##======================================================================================= 23 | ## 24 | ## The code in this interface is to be used with [1]. 25 | ## 26 | ##======================================================================================= 27 | ## References 28 | ##======================================================================================= 29 | ## [1] Anton Antonov, Time series recommender framework in R, (2017), GitHub, 30 | ## URL: https://github.com/antononcube/MathematicaForPrediction/blob/master/R/TimeSeriesRecommender.R . 31 | ##======================================================================================= 32 | 33 | library(shinydashboard) 34 | library(DT) 35 | library(arules) 36 | 37 | dashboardPage( 38 | dashboardHeader(title = "Time series dashboard"), 39 | dashboardSidebar( 40 | sidebarMenu( 41 | menuItem("Nearest neighbors", tabName = "NNs"), 42 | menuItem("Trend finding", tabName = "TrendFinding") 43 | ) 44 | ), 45 | 46 | dashboardBody( 47 | 48 | tabItems( 49 | 50 | tabItem( tabName = "NNs", 51 | 52 | selectInput( "searchID", "Entety:", rownames(tsSMR$M) ), 53 | 54 | numericInput( "numberOfNNs", "Number of NNs:", 12 ), 55 | 56 | selectInput( "nnsValueColName", "Value plotting:", c( "Raw" = "Value", "Smoothed" = "Value.ma" ) ), 57 | 58 | hr(), 59 | 60 | plotOutput( "entetyNNsPlot", height = "1000px" ) 61 | 62 | ), 63 | 64 | tabItem( tabName = "TrendFinding", 65 | 66 | selectInput( "searchVectorName", "Search vector type:", names(tsSearchVectors) ), 67 | 68 | numericInput( "numberOfSearchResults", "Number of search results:", 12 ), 69 | 70 | selectInput( "svecValueColName", "Value plotting:", c( "Raw" = "Value", "Smoothed" = "Value.ma" ) ), 71 | 72 | selectInput( "searchVectorColor", "Search vector color:", c("blue", "lightblue", "black", "gray10", "gray25", "gray50", "gray75", "gray90" ), selected = "gray75" ), 73 | 74 | hr(), 75 | 76 | plotOutput( "searchVectorPlot", height = "150px", width = "550px" ), 77 | 78 | plotOutput( "searchVectorNNsPlot", width = "1200px", height = "800px" ) 79 | 80 | 81 | ) 82 | ) 83 | ) 84 | ) -------------------------------------------------------------------------------- /README: -------------------------------------------------------------------------------- 1 | 2 | (This README is OBSOLETE; see the Markdown one: https://github.com/antononcube/MathematicaForPrediction/blob/master/README.md . ) 3 | 4 | This open source project is for Mathematica implementations of machine learning algorithms that are used or can be used for prediction and personalization systems. 5 | (For prediction and facilitation of the behaviour of users, customers, clients, etc.) 6 | 7 | The algorithms implementations are given in Mathematica package files ("*.m"). 8 | Explanations or presentations about the algorithms are given in Mathematica notebook files ("*.nb"). 9 | 10 | The original set of algorithms is: 11 | 1. k-means and bisecting k-means; 12 | 2. associative rules finding; 13 | 3. decision trees and random forests; 14 | 4. non-negative matrix factorization; 15 | 5. prefix trees; 16 | 6. naive Bayesian classifiers generator; 17 | 7. a framework for linear vector space representations of document collections; 18 | 8. an item-item recommender framework based on sparse linear algebra. 19 | 20 | In the future are going to be added algorithms for 21 | 9. quantile regression, 22 | 10. self-organizing maps, 23 | 11. hierarchical clustering, 24 | 12. n-gram language models. 25 | 26 | Also in this repository are going to be placed example or demonstrations notebooks for different applications of the algorithms listed above. 27 | 28 | There is a blog associated with this project: http://mathematicaforprediction.wordpress.com . 29 | 30 | I have implemented and extensively used these algorithms in search and prediction engines work in the last 5.5 years. 31 | 32 | Anton Antonov 33 | 04.07.2013, Florida, USA 34 | 35 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | ## Mission statement 3 | 4 | This open source project is for Mathematica (Wolfram Language) implementations of statistical and Machine Learning 5 | algorithms that can be used for data analysis, forecast, prediction, and recommendation systems. 6 | 7 | ## License matters 8 | 9 | All code files and executable documents are with the license GPL 3.0. For details see 10 | http://www.gnu.org/licenses/ . 11 | 12 | All documents are with the license Creative Commons Attribution 4.0 International (CC BY 4.0). 13 | For details see https://creativecommons.org/licenses/by/4.0/ . 14 | 15 | ## Organization 16 | 17 | The algorithms implementations are given in Mathematica package files ("*.m"). 18 | 19 | Explanations or presentations about the algorithms are given in Mathematica notebook files ("*.nb"), in 20 | [PDF files](https://github.com/antononcube/MathematicaForPrediction/tree/master/Documentation), 21 | or in 22 | [Markdown files](https://github.com/antononcube/MathematicaForPrediction/tree/master/MarkdownDocuments). 23 | 24 | Here are some fairly unique to the Mathematica / WL landscape algorithms: 25 | - [Mosaic plots](https://mathematicaforprediction.wordpress.com/?s=mosaicplot) 26 | - [Outlier identifiers](https://github.com/antononcube/MathematicaForPrediction/blob/master/OutlierIdentifiers.m) 27 | - [Associative rules finding](https://github.com/antononcube/MathematicaForPrediction/blob/master/EclatAlgorithm.m) 28 | - [Prefix trees (Tries)](https://mathematicaforprediction.wordpress.com/2013/12/06/tries-with-frequencies-for-data-mining/) 29 | - [Quantile Regression](https://mathematicaforprediction.wordpress.com/?s=quantile+regression) 30 | - [Chernoff faces](https://mathematicaforprediction.wordpress.com/2016/06/03/making-chernoff-faces-for-data-visualization/) 31 | - Non-Negative Matrix Factorization (NNMF) 32 | - [Independent Component Analysis (ICA)](https://mathematicaforprediction.wordpress.com/2016/05/23/independent-component-analysis-for-multidimensional-signals/) 33 | - [Receiver Operating Characteristic (ROC)](https://github.com/antononcube/MathematicaForPrediction/blob/master/MarkdownDocuments/ROCFunctions-Example-Usage.md) 34 | - [Classifier ensembles](https://github.com/antononcube/MathematicaForPrediction/blob/master/MarkdownDocuments/ROC-for-Classifier-Ensembles-Bootstrapping-Damaging-and-Interpolation.md) 35 | - Framework for Linear vector space representations of document collections 36 | - Item-item recommender framework based on Sparse linear algebra 37 | - Generator of Naive Bayesian Classifiers (NBC's) 38 | - [Functional parsers](https://mathematicaforprediction.wordpress.com/2014/02/13/natural-language-processing-with-functional-parsers/) 39 | - [Software monad generator](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/StateMonadCodeGenerator.m) 40 | 41 | The implemented algorithms are (usually) well documented. There is a fair amount of documents with related applications. 42 | There are also 43 | [monadic programming implementations](https://github.com/antononcube/MathematicaForPrediction/tree/master/MonadicProgramming) 44 | closely related to the "main directory" packages. 45 | 46 | Some of the packages listed above have: 47 | 48 | - Counterpart implementations in [Python](https://pypi.org/user/antononcube/), [R](https://github.com/antononcube/R-packages), or other languages 49 | - Related [Wolfram Function Repository functions](https://resources.wolframcloud.com/publishers/resources?PublisherID=antononcube) 50 | 51 | (The code in the R directory in this repository though is not updated, it is just kept for references. 52 | See the corresponding, actively worked on, dedicated repository 53 | [R-packages](https://github.com/antononcube/R-packages).) 54 | 55 | ## Associated blog (at WordPress) 56 | 57 | There is a blog associated with this project, see 58 | [MathematicaForPrediction at WordPress](http://mathematicaforprediction.wordpress.com). 59 | 60 | ------ 61 | 62 | ## Support & appreciation 63 | 64 | [!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/antonov70) 65 | 66 | 67 | 😅 ☕️ 😀 68 | 69 | ------ 70 | 71 | Anton Antonov 72 | 04.07.2013, Florida, USA 73 | 11.01.2017, Florida, USA (updated) 74 | 09.17.2019, Florida, USA (updated) 75 | 29.10.2022, Florida, USA (updated) 76 | -------------------------------------------------------------------------------- /UnitTests/HextileBins-Unit-Tests.mt: -------------------------------------------------------------------------------- 1 | (* 2 | HextileBins Mathematica unit tests 3 | Copyright (C) 2020 Anton Antonov 4 | 5 | This program is free software: you can redistribute it and/or modify 6 | it under the terms of the GNU General Public License as published by 7 | the Free Software Foundation, either version 3 of the License, or 8 | (at your option) any later version. 9 | 10 | This program is distributed in the hope that it will be useful, 11 | but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | GNU General Public License for more details. 14 | 15 | You should have received a copy of the GNU General Public License 16 | along with this program. If not, see . 17 | 18 | Written by Anton Antonov, 19 | antononcube @ gmai l . c om, 20 | Windermere, Florida, USA. 21 | *) 22 | 23 | (* 24 | Mathematica is (C) Copyright 1988-2020 Wolfram Research, Inc. 25 | 26 | Protected by copyright law and international treaties. 27 | 28 | Unauthorized reproduction or distribution subject to severe civil 29 | and criminal penalties. 30 | 31 | Mathematica is a registered trademark of Wolfram Research, Inc. 32 | *) 33 | 34 | (* :Title: HextileBins-Unit-Tests *) 35 | (* :Author: Anton Antonov *) 36 | (* :Date: 2020-04-01 *) 37 | 38 | (* :Package Version: 0.1 *) 39 | (* :Mathematica Version: 12.0 *) 40 | (* :Copyright: (c) 2020 Anton Antonov *) 41 | (* :Keywords: Hextile, Hexagon, Binning, Histogram, Mathematica, Wolfram Language, unit test *) 42 | (* :Discussion: 43 | 44 | This file has unit tests of the functions HextileBins and HextileHistogram implemented in the file: 45 | 46 | https://github.com/antononcube/MathematicaForPrediction/blob/master/Misc/HextileBins.m 47 | 48 | *) 49 | BeginTestSection["HextileBins-Unit-Tests.mt"]; 50 | 51 | VerificationTest[(* 1 *) 52 | CompoundExpression[ 53 | Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/Misc/HextileBins.m"], 54 | Greater[Length[DownValues[HextileBins`HextileBins]], 0] 55 | ] 56 | , 57 | True 58 | , 59 | TestID -> "LoadPackage" 60 | ]; 61 | 62 | 63 | (***********************************************************) 64 | (* Generate data *) 65 | (***********************************************************) 66 | 67 | VerificationTest[(* 2 *) 68 | SeedRandom[1295]; 69 | 70 | data = RandomVariate[MultinormalDistribution[{10, 10}, 7 IdentityMatrix[2]], 100]; 71 | 72 | data2 = Map[# -> RandomInteger[{1, 10}] &, data]; 73 | 74 | MatrixQ[data, NumericQ] && MatrixQ[ data2[[All,1]], NumericQ ] 75 | , 76 | True 77 | , 78 | TestID -> "Generated-2D-data-1" 79 | ]; 80 | 81 | 82 | (***********************************************************) 83 | (* HextileBins standard calls *) 84 | (***********************************************************) 85 | 86 | VerificationTest[ 87 | 88 | res = HextileBins[data, 2, "PolygonKeys" -> True ]; 89 | 90 | MatchQ[ res, Association[ ( _Polygon -> _?NumericQ ).. ] ] 91 | , 92 | True 93 | , 94 | TestID -> "HextileBins-matrix-1" 95 | ]; 96 | 97 | 98 | VerificationTest[ 99 | 100 | res = HextileBins[data, 2, MinMax /@ Transpose[data], "PolygonKeys" -> True ]; 101 | 102 | MatchQ[ res, Association[ ( _Polygon -> _?NumericQ ).. ] ] 103 | , 104 | True 105 | , 106 | TestID -> "HextileBins-matrix-2" 107 | ]; 108 | 109 | 110 | VerificationTest[ 111 | 112 | res = HextileBins[data, 2, "PolygonKeys" -> False ]; 113 | 114 | MatchQ[ res, Association[ ( {_?NumericQ, _?NumericQ} -> _?NumericQ ).. ] ] 115 | , 116 | True 117 | , 118 | TestID -> "HextileBins-matrix-3" 119 | ]; 120 | 121 | 122 | VerificationTest[ 123 | 124 | res = HextileBins[data, 2, MinMax /@ Transpose[data], "PolygonKeys" -> False ]; 125 | 126 | MatchQ[ res, Association[ ( {_?NumericQ, _?NumericQ} -> _?NumericQ ).. ] ] 127 | , 128 | True 129 | , 130 | TestID -> "HextileBins-matrix-4" 131 | ]; 132 | 133 | 134 | VerificationTest[ 135 | 136 | res = HextileBins[data2, 2 ]; 137 | 138 | MatchQ[ res, Association[ ( _Polygon -> _?NumericQ ).. ] ] 139 | , 140 | True 141 | , 142 | TestID -> "HextileBins-rules-1" 143 | ]; 144 | 145 | 146 | VerificationTest[ 147 | 148 | res = HextileBins[data2, 2, MinMax /@ Transpose[ data2[[All,1]] ] ]; 149 | 150 | MatchQ[ res, Association[ ( _Polygon -> _?NumericQ ).. ] ] 151 | , 152 | True 153 | , 154 | TestID -> "HextileBins-rules-2" 155 | ]; 156 | 157 | 158 | VerificationTest[ 159 | 160 | HextileBins[ data2, 2 ] == HextileBins[ Association @ data2, 2 ] 161 | , 162 | True 163 | , 164 | TestID -> "HextileBins-signature-1" 165 | ]; 166 | 167 | 168 | VerificationTest[ 169 | 170 | HextileBins[ data2, 2, "AggregationFunction" -> Total ] == HextileBins[ data2, 2 ] 171 | , 172 | True 173 | , 174 | TestID -> "HextileBins-signature-2" 175 | ]; 176 | 177 | 178 | VerificationTest[ 179 | 180 | HextileBins[ data, 2, "AggregationFunction" -> Mean ] == HextileBins[ data, 2 ] 181 | , 182 | True 183 | , 184 | TestID -> "HextileBins-signature-3" 185 | ]; 186 | 187 | 188 | VerificationTest[ 189 | 190 | HextileBins[ data, 2, Automatic ] == HextileBins[ data, 2 ] && 191 | HextileBins[ data, 2, Automatic ] == HextileBins[ data, 2, MinMax /@ Transpose[ data ] ] 192 | , 193 | True 194 | , 195 | TestID -> "HextileBins-signature-4" 196 | ]; 197 | 198 | 199 | VerificationTest[ 200 | 201 | HextileBins[ data2, 2, Automatic ] == HextileBins[ data2, 2 ] && 202 | HextileBins[ data2, 2, Automatic ] == HextileBins[ data2, 2, MinMax /@ Transpose[ data2[[All, 1]] ] ] 203 | , 204 | True 205 | , 206 | TestID -> "HextileBins-signature-5" 207 | ]; 208 | 209 | 210 | VerificationTest[ 211 | 212 | HextileBins[ data2, 2, Automatic, "AggregationFunction" -> Mean ] == HextileBins[ data2, 2, "AggregationFunction" -> Mean ] 213 | , 214 | True 215 | , 216 | TestID -> "HextileBins-signature-6" 217 | ]; 218 | 219 | 220 | (***********************************************************) 221 | (* Messages *) 222 | (***********************************************************) 223 | 224 | VerificationTest[ 225 | 226 | HextileBins[ data2, 2, "OverlapFactor" -> -1 ] 227 | , 228 | $Failed 229 | , 230 | {HextileBins::"nof"} 231 | , 232 | TestID -> "HextileBins-OverlapFactor-wrong-1" 233 | ]; 234 | 235 | 236 | EndTestSection[] 237 | --------------------------------------------------------------------------------