added work-in-progress of 3d scaling example

release-packaging/Examples/dataset/2-various other examples/scaling-dimension-as-weighting/2-3Dscaling.scd

@@ -0,0 +1,161 @@
+// Make:
+// - A kmeans
+// - a datasetquery
+// - a normalizer
+// - a standardizer
+// - 3 DataSets of example points R-G-B descriptions
+// - 3 DataSets for the scaled versions
+// - 1 summative dataset and a LabelSet for predicted labels
+
+(
+~classifier = FluidKMeans(s,5, 1000);
+~query = FluidDataSetQuery(s);
+~stan = FluidStandardize(s);
+~norm = FluidNormalize(s);
+~sourceR = FluidDataSet(s,\a3DsourceR);
+~sourceG = FluidDataSet(s,\a3DsourceG);
+~sourceB = FluidDataSet(s,\a3DsourceB);
+~scaledR = FluidDataSet(s,\a3DscaledR);
+~scaledG = FluidDataSet(s,\a3DscaledG);
+~scaledB = FluidDataSet(s,\a3DscaledB);
+~composited = FluidDataSet(s,\a3Dcomposited);
+~labels = FluidLabelSet(s,\a3Dlabels);
+)
+
+//Make some random, but clustered test points, each descriptor category in a separate dataset
+(
+~sourceR.load(Dictionary.newFrom([\cols, 1, \data, (Dictionary.newFrom(40.collect{|x| [x, 1.0.sum3rand]}.flatten))]));
+~sourceG.load(Dictionary.newFrom([\cols, 1, \data, (Dictionary.newFrom(40.collect{|x| [x, 1.0.rand2]}.flatten))]));
+~sourceB.load(Dictionary.newFrom([\cols, 1, \data, (Dictionary.newFrom(40.collect{|x| [x, (0.5.sum3rand).squared + [0.75,-0.1].choose]}.flatten))]));
+)
+
+//here we manipulate
+
+//assemble the scaled dataset
+(
+~query.addColumn(0, {
+	~query.transformJoin(~sourceB, ~sourceG, ~composited, {
+		~query.transformJoin(~sourceR, ~composited, ~composited);
+	});
+});
+)
+
+~composited.print
+
+//Fit the classifier to the example DataSet and labels, and then run prediction on the test data into our mapping label set
+~classifier.fitPredict(~composited,~labels,{~labels.dump{|x|~labeldict = x;};~composited.dump{|x|~compodict=x;};});
+
+//Visualise:
+(
+w = Window("sourceClasses", Rect(128, 64, 820, 120));
+w.drawFunc = {
+	Pen.use{
+		~compodict["data"].keysValuesDo{|key,  colour|
+			Pen.fillColor = Color.fromArray((colour * 0.5 + 0.5 ).clip(0,1) ++ 1);
+			Pen.fillRect( Rect( (key.asFloat * 20 + 10), (~labeldict["data"].at(key).asInteger[0] * 20 + 10),15,15));
+		};
+	};
+};
+w.refresh;
+w.front;
+)
+
+// standardize our colours and rerun
+(
+~stan.fitTransform(~sourceR, ~scaledR, {
+	~stan.fitTransform(~sourceG, ~scaledG, {
+		~stan.fitTransform(~sourceB, ~scaledB, {
+			//assemble
+			~query.addColumn(0, {
+				~query.transformJoin(~scaledB, ~scaledG, ~composited, {
+					~query.transformJoin(~scaledR, ~composited, ~composited, {
+						//fit
+						~classifier.fitPredict(~composited,~labels,{~labels.dump{|x|~labeldict2 = x;};~composited.dump{|x|~compodict2=x;};});
+					});
+				});
+			});
+		});
+	});
+});
+)
+
+//Visualise:
+(
+w = Window("stanClasses", Rect(128, 64, 820, 120));
+w.drawFunc = {
+	Pen.use{
+		~compodict2["data"].keysValuesDo{|key,  colour|
+			Pen.fillColor = Color.fromArray((colour * 0.25 + 0.5 ).clip(0,1) ++ 1);
+			Pen.fillRect( Rect( (key.asFloat * 20 + 10), (~labeldict2["data"].at(key).asInteger[0] * 20 + 10),15,15));
+		};
+	};
+};
+w.refresh;
+w.front;
+)
+
+//now let's normalise instead
+(
+~norm.fitTransform(~sourceR, ~scaledR, {
+	~norm.fitTransform(~sourceG, ~scaledG, {
+		~norm.fitTransform(~sourceB, ~scaledB, {
+			//assemble
+			~query.addColumn(0, {
+				~query.transformJoin(~scaledB, ~scaledG, ~composited, {
+					~query.transformJoin(~scaledR, ~composited, ~composited, {
+						//fit
+						~classifier.fitPredict(~composited,~labels,{~labels.dump{|x|~labeldict2 = x;};~composited.dump{|x|~compodict2=x;};});
+					});
+				});
+			});
+		});
+	});
+});
+)
+
+//Visualise:
+(
+w = Window("normClasses", Rect(128, 64, 820, 120));
+w.drawFunc = {
+	Pen.use{
+		~compodict2["data"].keysValuesDo{|key,  colour|
+			Pen.fillColor = Color.fromArray((colour * 0.25 + 0.5 ).clip(0,1) ++ 1);
+			Pen.fillRect( Rect( (key.asFloat * 20 + 10), (~labeldict2["data"].at(key).asInteger[0] * 20 + 10),15,15));
+		};
+	};
+};
+w.refresh;
+w.front;
+)
+
+// let's mess up with the scaling of one dimension: let's multiply the range of Red by 10
+~norm.min = -10;
+~norm.max = 10;
+(
+~norm.fitTransform(~sourceR, ~scaledR, {
+	//assemble
+	~query.addColumn(0, {
+		~query.transformJoin(~scaledB, ~scaledG, ~composited, {
+			~query.transformJoin(~scaledR, ~composited, ~composited, {
+				//fit
+				~classifier.fitPredict(~composited,~labels,{~labels.dump{|x|~labeldict2 = x;};~composited.dump{|x|~compodict2=x;};});
+			});
+		});
+	});
+});
+)
+
+//Visualise:
+(
+w = Window("norm10rClasses", Rect(128, 64, 820, 120));
+w.drawFunc = {
+	Pen.use{
+		~compodict2["data"].keysValuesDo{|key,  colour|
+			Pen.fillColor = Color.fromArray((colour * 0.25 + 0.5 ).clip(0,1) ++ 1);
+			Pen.fillRect( Rect( (key.asFloat * 20 + 10), (~labeldict2["data"].at(key).asInteger[0] * 20 + 10),15,15));
+		};
+	};
+};
+w.refresh;
+w.front;
+)