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45 lines
1.3 KiB
Markdown
45 lines
1.3 KiB
Markdown
// create the data dictionary
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~data = Dictionary.new
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7.do{|i| ~data.add(("entry-"++i).asSymbol -> 10.collect{|j|j/10 + i})}
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// nest that dictionary in the dataset format, adding the number of columns
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~dict = Dictionary.new
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~dict.add(\data -> ~data)
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~dict.add(\cols -> 10)
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//create a dataset, then loading the dictionary
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~ds = FluidDataSet.new(s,\simple1data);
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~ds.load(~dict)
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~ds.print
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//fun with kdtree to see it actually works
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~kdtree = FluidKDTree.new(s,numNeighbours:5)
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~kdtree.fit(~ds,{\done.postln;})
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~target = Buffer.loadCollection(s,(4).dup(10));
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~kdtree.kNearest(~target, {|a|a.postln;})
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~kdtree.kNearestDist(~target, {|a|a.postln;})
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/////////////////////////////////////////////
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// creating a labelset the same way
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// creating the data dictionary
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~data2 = Dictionary.new
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7.do{|i| ~data2.add(("entry-"++i).asSymbol -> (if( i.odd, {["odd"]},{["even"]})))}
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// nesting again
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~dict2 = Dictionary.new
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~dict2.add(\data -> ~data2)
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~dict2.add(\cols -> 1)
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// creating a labelset and loading the dictionary
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~ls = FluidLabelSet.new(s,\simplelabel);
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~ls.load(~dict2)
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~ls.print
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// testin with a classifier toy example
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~classifier = FluidKNNClassifier.new(s, numNeighbours:2);
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~classifier.fit(~ds,~ls, {\done.postln;})
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~classifier.predictPoint(~target, action: {|x|x.postln;})
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