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65 lines
1.7 KiB
Plaintext
65 lines
1.7 KiB
Plaintext
(
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~simpleInput = FluidDataSet(s);
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~simpleOutput = FluidLabelSet(s);
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b = Buffer.alloc(s,2);
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~knn = FluidKNNClassifier(s);
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~knn.numNeighbours = 3
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)
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(
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var w,v,myx,myy;
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//initialise the mouse position holder
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myx=0;
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myy=0;
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//make a window and a full size view
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w = Window.new("Viewer", Rect(100,Window.screenBounds.height - 400, 310, 310)).front;
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v = View.new(w,Rect(0,0, 310, 310));
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//creates a function that reacts to mousedown
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v.mouseDownAction = {|view, x, y|myx=x;myy=y;w.refresh;
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// myx.postln;myy.postln;
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Routine{
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b.setn(0,[myx,myy]);
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~knn.predictPoint(b, action: {|x|x.postln;});
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s.sync;
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}.play;};
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//custom redraw function
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w.drawFunc = {
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100.do { |i|
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if (i < 50, {Pen.color = Color.white;} ,{Pen.color = Color.red;});
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Pen.addRect(Rect(i.div(10)*30+10,i.mod(10)*30+10,20,20));
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Pen.perform(\fill);
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};
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Pen.color = Color.black;
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Pen.addOval(Rect(myx-5, myy-5,10,10));
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Pen.perform(\stroke);
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};
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)
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(
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//populates a dataset with the same squares as the gui (their centres) (old method, iterating over buffers. A dictionary approach would be more efficient, see the example in this folder)
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Routine{
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50.do{|i|
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var x = i.div(10)*30+20;
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var y = i.mod(10)*30+20;
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b.setn(0,[x,y]);
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~simpleInput.addPoint(i.asString,b,{("Added Input" + i).postln});
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~simpleOutput.addLabel(i.asString,"White",{("Added Output" + i).postln});
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s.sync;
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b.setn(0,[x+150,y]);
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~simpleInput.addPoint((i+50).asString,b,{("Added Input" + (i+50)).postln});
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~simpleOutput.addLabel((i+50).asString,"Red",{("Added Output" + (i+50)).postln});
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s.sync;
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};
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\done.postln;
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}.play;
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)
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// fit the dataset
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~knn.fit(~simpleInput,~simpleOutput, action:{"fitting done".postln})
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// now click on the grid and read the estimated class according to the nearest K neighbours.
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