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Hernán Morales Durand
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Repackage methods
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‎src/Math-PrincipalComponentAnalysis/PMPrincipalComponentAnalyserSVD.class.st‎

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#package : 'Math-PrincipalComponentAnalysis'
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}
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{ #category : 'examples' }
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PMPrincipalComponentAnalyserSVD class >> example1 [
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"Extract 4 columns from original data"
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| d m pca m1 d1 irisDataFrame roassalChart scatterPlotShapes |
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irisDataFrame := AIDatasets loadIris.
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d := irisDataFrame columnsFrom: 1 to: 4.
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"Transform DF as matrix"
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m := PMMatrix rows: d asArrayOfRows.
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"Data standardization (mean = 0 and variance = 1)"
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m := PMStandardizationScaler new fitAndTransform: m.
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"Compute PCA components"
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pca := PMPrincipalComponentAnalyserSVD new.
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pca componentsNumber: 2.
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pca fit: m.
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pca transformMatrix.
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m1 := pca transform: m.
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d1 := DataFrame withRows: m1 rows.
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d1
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addColumn: (irisDataFrame columnsFrom: 5 to: 5) asArrayOfColumns first
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named: 'type'.
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roassalChart := RSCompositeChart new
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title: 'PCA of Iris Data Frame';
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addDecoration: (RSHorizontalTick new doNotUseNiceLabel asFloat: 3);
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addDecoration: RSVerticalTick new;
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yourself.
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scatterPlotShapes := (d1 column: 'type') asSet collect: [ : type |
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(d1 select: [ : e | e values includes: type ])
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addScatterPlotShapeToChart: roassalChart ].
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roassalChart padding: 10.
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scatterPlotShapes asOrderedCollection
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with: { Color red . Color green . Color blue }
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do: [ : scatterPlotShape : scatterPlotDotColor |
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scatterPlotShape color: scatterPlotDotColor ].
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roassalChart open.
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]
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{ #category : 'examples' }
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PMPrincipalComponentAnalyserSVD class >> example2 [
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"Extract 4 columns from original data. This example uses Random colors for each flower type"
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| d m pca m1 d1 irisDataFrame |
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irisDataFrame := AIDatasets loadIris.
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d := irisDataFrame columnsFrom: 1 to: 4.
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"Transform DF as matrix"
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m := PMMatrix rows: d asArrayOfRows.
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"Data standardization (mean = 0 and variance = 1)"
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m := PMStandardizationScaler new fitAndTransform: m.
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"Compute PCA components"
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pca := PMPrincipalComponentAnalyserSVD new.
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pca componentsNumber: 2.
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pca fit: m.
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pca transformMatrix.
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m1 := pca transform: m.
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d1 := DataFrame withRows: m1 rows.
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d1
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addColumn: (irisDataFrame columnsFrom: 5 to: 5) asArrayOfColumns first
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named: 'type'.
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d1
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openScatterPlotWithTitle: 'PCA of Iris data set'
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forColumn: 'type'.
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]
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{ #category : 'accessing' }
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PMPrincipalComponentAnalyserSVD >> fit: aPMMatrix [
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‎src/Math-Visualizations/PMPrincipalComponentAnalyserSVD.extension.st‎

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Extension { #name : 'PMPrincipalComponentAnalyserSVD' }
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{ #category : '*Math-Visualizations' }
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PMPrincipalComponentAnalyserSVD class >> example1 [
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"Extract 4 columns from original data"
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| d m pca m1 d1 irisDataFrame roassalChart scatterPlotShapes |
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irisDataFrame := AIDatasets loadIris.
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d := irisDataFrame columnsFrom: 1 to: 4.
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"Transform DF as matrix"
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m := PMMatrix rows: d asArrayOfRows.
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"Data standardization (mean = 0 and variance = 1)"
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m := PMStandardizationScaler new fitAndTransform: m.
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"Compute PCA components"
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pca := PMPrincipalComponentAnalyserSVD new.
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pca componentsNumber: 2.
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pca fit: m.
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pca transformMatrix.
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m1 := pca transform: m.
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d1 := DataFrame withRows: m1 rows.
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d1
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addColumn: (irisDataFrame columnsFrom: 5 to: 5) asArrayOfColumns first
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named: 'type'.
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roassalChart := RSCompositeChart new
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title: 'PCA of Iris Data Frame';
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addDecoration: (RSHorizontalTick new doNotUseNiceLabel asFloat: 3);
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addDecoration: RSVerticalTick new;
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yourself.
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scatterPlotShapes := (d1 column: 'type') asSet collect: [ : type |
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(d1 select: [ : e | e values includes: type ])
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addScatterPlotShapeToChart: roassalChart ].
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roassalChart padding: 10.
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scatterPlotShapes asOrderedCollection
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with: { Color red . Color green . Color blue }
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do: [ : scatterPlotShape : scatterPlotDotColor |
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scatterPlotShape color: scatterPlotDotColor ].
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roassalChart open.
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]
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{ #category : '*Math-Visualizations' }
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PMPrincipalComponentAnalyserSVD class >> example2 [
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"Extract 4 columns from original data. This example uses Random colors for each flower type"
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| d m pca m1 d1 irisDataFrame |
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irisDataFrame := AIDatasets loadIris.
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d := irisDataFrame columnsFrom: 1 to: 4.
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"Transform DF as matrix"
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m := PMMatrix rows: d asArrayOfRows.
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"Data standardization (mean = 0 and variance = 1)"
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m := PMStandardizationScaler new fitAndTransform: m.
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"Compute PCA components"
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pca := PMPrincipalComponentAnalyserSVD new.
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pca componentsNumber: 2.
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pca fit: m.
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pca transformMatrix.
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m1 := pca transform: m.
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d1 := DataFrame withRows: m1 rows.
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d1
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addColumn: (irisDataFrame columnsFrom: 5 to: 5) asArrayOfColumns first
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named: 'type'.
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d1
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openScatterPlotWithTitle: 'PCA of Iris data set'
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forColumn: 'type'.
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]

‎src/Math-Visualizations/package.st‎

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Package { #name : 'Math-Visualizations' }

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