added a learning example of dictionary -> dataset / labelset

release-packaging/Examples/dataset/super-simple-dictionary-json-example.scd

@@ -0,0 +1,45 @@
+// create the data dictionary
+~data = Dictionary.new
+7.do{|i| ~data.add(("entry-"++i).asSymbol -> 10.collect{|j|j/10 + i})}
+
+// nest that dictionary in the dataset format, adding the number of columns
+~dict = Dictionary.new
+~dict.add(\data -> ~data)
+~dict.add(\cols -> 10)
+
+//create a dataset, then loading the dictionary
+~ds = FluidDataSet.new(s,\simple1data);
+~ds.load(~dict)
+~ds.print
+
+//fun with kdtree to see it actually works
+~kdtree = FluidKDTree.new(s)
+~kdtree.fit(~ds,{\done.postln;})
+
+~target = Buffer.loadCollection(s,(4).dup(10));
+~kdtree.kNearest(~target,5,{|a|a.postln;})
+~kdtree.kNearestDist(~target,5,{|a|a.postln;})
+
+
+/////////////////////////////////////////////
+// creating a labelset the same way
+
+// creating the data dictionary
+~data2 = Dictionary.new
+7.do{|i| ~data2.add(("entry-"++i).asSymbol -> (if( i.odd, {["odd"]},{["even"]})))}
+
+// nesting again
+~dict2 = Dictionary.new
+~dict2.add(\data -> ~data2)
+~dict2.add(\cols -> 1)
+
+// creating a labelset and loading the dictionary
+~ls = FluidLabelSet.new(s,\simplelabel);
+~ls.load(~dict2)
+~ls.print
+
+// testin with a classifier toy example
+~classifier = FluidKNNClassifier.new(s);
+~classifier.fit(~ds,~ls, {\done.postln;})
+
+~classifier.predictPoint(~target,2,action: {|x|x.postln;})