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76 lines
1.3 KiB
Plaintext
76 lines
1.3 KiB
Plaintext
s.reboot
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~ds = FluidDataSet.new(s)
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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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~point.set(0,i);
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s.sync;
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~ds.addPoint(i.asString,~point,{("addPoint"+i).postln});
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s.sync;
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}
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}.play
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)
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~ds.print;
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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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~tree.numNeighbours = 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);
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s.sync;
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~tree.kNearest(~point, {|x| "Neighbours for a value of % are ".postf(i); x.postln});
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s.sync;
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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,maxIter:100);
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~kmeans.numClusters = 2; //play with this
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~kmeans.fit(~ds,action:{|x| "Done fitting with these number of items per cluster ".post;x.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);
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s.sync;
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~kmeans.predictPoint(~point,{|x| ("Predicted Cluster for a value of " + i ++ ":" + x).postln});
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s.sync;
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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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~labels.size{|x|
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Routine{x.asInteger.do{|i|
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~labels.getLabel(i,action: {|l|
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("Label for entry " + i ++ ":" + l).postln;
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});
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s.sync;
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}
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}.play;
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};
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)
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// or simply print it
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~labels.print
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// or dump and format
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(
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~labels.dump{|x|
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var keys = x["data"].keys.asArray.sort;
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keys.do{|key|
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"Label for entry % is %\n".postf(key, x["data"][key][0]);
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}
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}
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) |