added work-in-progress of 3d scaling example
Pierre Alexandre Tremblay
5 years ago
parent
50247dba33
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5cf7e32d0b
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// Make:
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// - A kmeans
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// - a datasetquery
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// - a normalizer
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// - a standardizer
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// - 3 DataSets of example points R-G-B descriptions
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// - 3 DataSets for the scaled versions
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// - 1 summative dataset and a LabelSet for predicted labels
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(
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~classifier = FluidKMeans(s,5, 1000);
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~query = FluidDataSetQuery(s);
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~stan = FluidStandardize(s);
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~norm = FluidNormalize(s);
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~sourceR = FluidDataSet(s,\a3DsourceR);
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~sourceG = FluidDataSet(s,\a3DsourceG);
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~sourceB = FluidDataSet(s,\a3DsourceB);
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~scaledR = FluidDataSet(s,\a3DscaledR);
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~scaledG = FluidDataSet(s,\a3DscaledG);
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~scaledB = FluidDataSet(s,\a3DscaledB);
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~composited = FluidDataSet(s,\a3Dcomposited);
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~labels = FluidLabelSet(s,\a3Dlabels);
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)
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//Make some random, but clustered test points, each descriptor category in a separate dataset
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(
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~sourceR.load(Dictionary.newFrom([\cols, 1, \data, (Dictionary.newFrom(40.collect{|x| [x, 1.0.sum3rand]}.flatten))]));
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~sourceG.load(Dictionary.newFrom([\cols, 1, \data, (Dictionary.newFrom(40.collect{|x| [x, 1.0.rand2]}.flatten))]));
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~sourceB.load(Dictionary.newFrom([\cols, 1, \data, (Dictionary.newFrom(40.collect{|x| [x, (0.5.sum3rand).squared + [0.75,-0.1].choose]}.flatten))]));
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)
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//here we manipulate
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//assemble the scaled dataset
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(
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~query.addColumn(0, {
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~query.transformJoin(~sourceB, ~sourceG, ~composited, {
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~query.transformJoin(~sourceR, ~composited, ~composited);
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});
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});
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)
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~composited.print
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//Fit the classifier to the example DataSet and labels, and then run prediction on the test data into our mapping label set
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~classifier.fitPredict(~composited,~labels,{~labels.dump{|x|~labeldict = x;};~composited.dump{|x|~compodict=x;};});
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//Visualise:
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(
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w = Window("sourceClasses", Rect(128, 64, 820, 120));
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w.drawFunc = {
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Pen.use{
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~compodict["data"].keysValuesDo{|key, colour|
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Pen.fillColor = Color.fromArray((colour * 0.5 + 0.5 ).clip(0,1) ++ 1);
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Pen.fillRect( Rect( (key.asFloat * 20 + 10), (~labeldict["data"].at(key).asInteger[0] * 20 + 10),15,15));
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};
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};
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};
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w.refresh;
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w.front;
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)
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// standardize our colours and rerun
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(
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~stan.fitTransform(~sourceR, ~scaledR, {
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~stan.fitTransform(~sourceG, ~scaledG, {
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~stan.fitTransform(~sourceB, ~scaledB, {
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//assemble
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~query.addColumn(0, {
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~query.transformJoin(~scaledB, ~scaledG, ~composited, {
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~query.transformJoin(~scaledR, ~composited, ~composited, {
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//fit
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~classifier.fitPredict(~composited,~labels,{~labels.dump{|x|~labeldict2 = x;};~composited.dump{|x|~compodict2=x;};});
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});
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});
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});
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});
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});
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});
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)
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//Visualise:
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(
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w = Window("stanClasses", Rect(128, 64, 820, 120));
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w.drawFunc = {
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Pen.use{
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~compodict2["data"].keysValuesDo{|key, colour|
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Pen.fillColor = Color.fromArray((colour * 0.25 + 0.5 ).clip(0,1) ++ 1);
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Pen.fillRect( Rect( (key.asFloat * 20 + 10), (~labeldict2["data"].at(key).asInteger[0] * 20 + 10),15,15));
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};
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};
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};
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w.refresh;
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w.front;
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)
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//now let's normalise instead
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(
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~norm.fitTransform(~sourceR, ~scaledR, {
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~norm.fitTransform(~sourceG, ~scaledG, {
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~norm.fitTransform(~sourceB, ~scaledB, {
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//assemble
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~query.addColumn(0, {
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~query.transformJoin(~scaledB, ~scaledG, ~composited, {
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~query.transformJoin(~scaledR, ~composited, ~composited, {
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//fit
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~classifier.fitPredict(~composited,~labels,{~labels.dump{|x|~labeldict2 = x;};~composited.dump{|x|~compodict2=x;};});
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});
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});
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});
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});
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});
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});
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)
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//Visualise:
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(
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w = Window("normClasses", Rect(128, 64, 820, 120));
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w.drawFunc = {
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Pen.use{
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~compodict2["data"].keysValuesDo{|key, colour|
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Pen.fillColor = Color.fromArray((colour * 0.25 + 0.5 ).clip(0,1) ++ 1);
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Pen.fillRect( Rect( (key.asFloat * 20 + 10), (~labeldict2["data"].at(key).asInteger[0] * 20 + 10),15,15));
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};
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};
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};
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w.refresh;
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w.front;
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)
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// let's mess up with the scaling of one dimension: let's multiply the range of Red by 10
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~norm.min = -10;
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~norm.max = 10;
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(
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~norm.fitTransform(~sourceR, ~scaledR, {
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//assemble
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~query.addColumn(0, {
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~query.transformJoin(~scaledB, ~scaledG, ~composited, {
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~query.transformJoin(~scaledR, ~composited, ~composited, {
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//fit
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~classifier.fitPredict(~composited,~labels,{~labels.dump{|x|~labeldict2 = x;};~composited.dump{|x|~compodict2=x;};});
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});
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});
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});
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});
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)
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//Visualise:
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(
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w = Window("norm10rClasses", Rect(128, 64, 820, 120));
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w.drawFunc = {
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Pen.use{
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~compodict2["data"].keysValuesDo{|key, colour|
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Pen.fillColor = Color.fromArray((colour * 0.25 + 0.5 ).clip(0,1) ++ 1);
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Pen.fillRect( Rect( (key.asFloat * 20 + 10), (~labeldict2["data"].at(key).asInteger[0] * 20 + 10),15,15));
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};
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};
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};
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w.refresh;
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w.front;
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)
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