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62 lines
1.3 KiB
Plaintext
62 lines
1.3 KiB
Plaintext
s.reboot
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~ds = FluidDataSet.new(s,\simple1data,1)
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~point = Buffer.alloc(s,1,1)
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(
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Routine{
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10.do{|i|
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var d;
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if(i<=4,{d=i},{d=i+5});
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~point.set(0,d);
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s.sync;
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~ds.addPoint(i.asString,~point,{("addPoint"+i).postln})
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}
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}.play
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)
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/*** KDTREE ***/
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~tree = FluidKDTree.new(s)
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~tree.fit(~ds,action:{"Done indexing".postln})
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k = 5; //play with this
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(
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Routine{
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10.do{|i|
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~point.set(0,i*2);
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s.sync;
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("Neighbours for point" + (i*2)).postln;
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~tree.kNearest(~point, k, {|x| ("Labels:" + x).postln});
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~tree.kNearestDist(~point,k,{|x| ("Distances:" + x).postln})
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}
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}.play
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)
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/*** KMEANS ***/
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~kmeans = FluidKMeans.new(s)
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~nClusters = 2; //play with this
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~kmeans.fit(~ds,~nClusters,100,action:{"Done fitting".postln})
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(
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Routine{
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10.do{|i|
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~point.set(0,i * 10);
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s.sync;
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~kmeans.predictPoint(~point,{|x| ("Predicted Cluster for point" + (i * 10) ++ ":" + x).postln})
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}
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}.play
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)
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~labels = FluidLabelSet(s,\simple1label);
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~kmeans.predict(~ds,~labels, {|x| ("Size of each cluster" + x).postln})
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(
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Routine{
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var n;
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~labels.size({|x| n = x.asInt});
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n.asInt.do{|i|
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~labels.getLabel(i.asString,action: {|l|("Label for" + i ++ ":" + l).postln});
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}
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}.play
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)
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