Repackage methods

src/Math-PrincipalComponentAnalysis/PMPrincipalComponentAnalyserSVD.class.st

@@ -10,88 +10,6 @@ Class {
 	#package : 'Math-PrincipalComponentAnalysis'
 }
 
-{ #category : 'examples' }
-PMPrincipalComponentAnalyserSVD class >> example1 [
-	"Extract 4 columns from original data"
-
-	| d m pca m1 d1 irisDataFrame roassalChart scatterPlotShapes |
-
-	irisDataFrame := AIDatasets loadIris.
-	d := irisDataFrame columnsFrom: 1 to: 4.
-
-	"Transform DF  as matrix"
-	m := PMMatrix rows: d asArrayOfRows.
-
-	"Data standardization (mean = 0 and variance = 1)"
-	m := PMStandardizationScaler new fitAndTransform: m.
-
-	"Compute PCA components"
-	pca := PMPrincipalComponentAnalyserSVD new.
-	pca componentsNumber: 2.
-	pca fit: m.
-	pca transformMatrix.
-
-	m1 := pca transform: m.
-
-	d1 := DataFrame withRows: m1 rows.
-	d1
-		addColumn: (irisDataFrame columnsFrom: 5 to: 5) asArrayOfColumns first
-		named: 'type'.
-
-	roassalChart := RSCompositeChart new
-		title: 'PCA of Iris Data Frame';
-		addDecoration: (RSHorizontalTick new doNotUseNiceLabel asFloat: 3);
-		addDecoration: RSVerticalTick new;
-		yourself.
-
-	scatterPlotShapes := (d1 column: 'type') asSet collect: [ : type | 
-		(d1 select: [ : e | e values includes: type ]) 
-			addScatterPlotShapeToChart: roassalChart ].
-
-	roassalChart padding: 10.
-	scatterPlotShapes asOrderedCollection
-		with: { Color red . Color green . Color blue } 
-		do: [ : scatterPlotShape  : scatterPlotDotColor |
-			scatterPlotShape color: scatterPlotDotColor ].
-
-	roassalChart open.
-
-
-]
-
-{ #category : 'examples' }
-PMPrincipalComponentAnalyserSVD class >> example2 [
-	"Extract 4 columns from original data. This example uses Random colors for each flower type"
-
-  | d m pca m1 d1 irisDataFrame |
-
-	irisDataFrame := AIDatasets loadIris.
-	d := irisDataFrame columnsFrom: 1 to: 4.
-
-	"Transform DF  as matrix"
-	m := PMMatrix rows: d asArrayOfRows.
-
-	"Data standardization (mean = 0 and variance = 1)"
-	m := PMStandardizationScaler new fitAndTransform: m.
-
-	"Compute PCA components"
-	pca := PMPrincipalComponentAnalyserSVD new.
-	pca componentsNumber: 2.
-	pca fit: m.
-	pca transformMatrix.
-
-	m1 := pca transform: m.
-
-	d1 := DataFrame withRows: m1 rows.
-	d1
-		addColumn: (irisDataFrame columnsFrom: 5 to: 5) asArrayOfColumns first
-		named: 'type'.
-
-	d1 	
-		openScatterPlotWithTitle: 'PCA of Iris data set' 
-		forColumn: 'type'.
-]
-
 { #category : 'accessing' }
 PMPrincipalComponentAnalyserSVD >> fit: aPMMatrix [
 

src/Math-Visualizations/PMPrincipalComponentAnalyserSVD.extension.st

@@ -0,0 +1,83 @@
+Extension { #name : 'PMPrincipalComponentAnalyserSVD' }
+
+{ #category : '*Math-Visualizations' }
+PMPrincipalComponentAnalyserSVD class >> example1 [
+	"Extract 4 columns from original data"
+
+	| d m pca m1 d1 irisDataFrame roassalChart scatterPlotShapes |
+
+	irisDataFrame := AIDatasets loadIris.
+	d := irisDataFrame columnsFrom: 1 to: 4.
+
+	"Transform DF  as matrix"
+	m := PMMatrix rows: d asArrayOfRows.
+
+	"Data standardization (mean = 0 and variance = 1)"
+	m := PMStandardizationScaler new fitAndTransform: m.
+
+	"Compute PCA components"
+	pca := PMPrincipalComponentAnalyserSVD new.
+	pca componentsNumber: 2.
+	pca fit: m.
+	pca transformMatrix.
+
+	m1 := pca transform: m.
+
+	d1 := DataFrame withRows: m1 rows.
+	d1
+		addColumn: (irisDataFrame columnsFrom: 5 to: 5) asArrayOfColumns first
+		named: 'type'.
+
+	roassalChart := RSCompositeChart new
+		title: 'PCA of Iris Data Frame';
+		addDecoration: (RSHorizontalTick new doNotUseNiceLabel asFloat: 3);
+		addDecoration: RSVerticalTick new;
+		yourself.
+
+	scatterPlotShapes := (d1 column: 'type') asSet collect: [ : type | 
+		(d1 select: [ : e | e values includes: type ]) 
+			addScatterPlotShapeToChart: roassalChart ].
+
+	roassalChart padding: 10.
+	scatterPlotShapes asOrderedCollection
+		with: { Color red . Color green . Color blue } 
+		do: [ : scatterPlotShape  : scatterPlotDotColor |
+			scatterPlotShape color: scatterPlotDotColor ].
+
+	roassalChart open.
+
+
+]
+
+{ #category : '*Math-Visualizations' }
+PMPrincipalComponentAnalyserSVD class >> example2 [
+	"Extract 4 columns from original data. This example uses Random colors for each flower type"
+
+  | d m pca m1 d1 irisDataFrame |
+
+	irisDataFrame := AIDatasets loadIris.
+	d := irisDataFrame columnsFrom: 1 to: 4.
+
+	"Transform DF  as matrix"
+	m := PMMatrix rows: d asArrayOfRows.
+
+	"Data standardization (mean = 0 and variance = 1)"
+	m := PMStandardizationScaler new fitAndTransform: m.
+
+	"Compute PCA components"
+	pca := PMPrincipalComponentAnalyserSVD new.
+	pca componentsNumber: 2.
+	pca fit: m.
+	pca transformMatrix.
+
+	m1 := pca transform: m.
+
+	d1 := DataFrame withRows: m1 rows.
+	d1
+		addColumn: (irisDataFrame columnsFrom: 5 to: 5) asArrayOfColumns first
+		named: 'type'.
+
+	d1 	
+		openScatterPlotWithTitle: 'PCA of Iris data set' 
+		forColumn: 'type'.
+]

src/Math-Visualizations/package.st

@@ -0,0 +1 @@
+Package { #name : 'Math-Visualizations' }