second-super-simple-1D-example

release-packaging/ignore/Examples/dataset/super-simple-1D-example2.scd

@@ -0,0 +1,61 @@
+s.reboot
+~ds = FluidDataSet.new(s,\simple1data,1)
+~point = Buffer.alloc(s,1,1)
+(
+Routine{
+    10.do{|i|
+        var d;
+        if(i<=4,{d=i},{d=i+5});
+        ~point.set(0,d);
+        s.sync;
+        ~ds.addPoint(i.asString,~point,{("addPoint"+i).postln})
+    }
+}.play
+)
+
+/*** KDTREE ***/
+~tree = FluidKDTree.new(s)
+~tree.index(~ds,action:{"Done indexing".postln})
+
+k = 5; //play with this
+(
+Routine{
+    10.do{|i|
+        ~point.set(0,i*10);
+        s.sync;
+        ("Neighbours for point" + (i*10)).postln;
+        ~tree.kNearest(~point, k, {|x| ("Labels:" + x).postln});
+        ~tree.kNearestDist(~point,k,{|x| ("Distances:" + x).postln})
+    }
+}.play
+)
+
+/*** KMEANS ***/
+
+~kmeans = FluidKMeans.new(s)
+~nClusters = 2; //play with this
+~kmeans.fit(~ds,~nClusters,100,action:{"Done fitting".postln})
+(
+Routine{
+    10.do{|i|
+        ~point.set(0,i * 10);
+        s.sync;
+        ~kmeans.predictPoint(~point,{|x| ("Predicted Cluster for point" + (i * 10) ++ ":" + x).postln})
+    }
+}.play
+)
+
+~labels = FluidLabelSet(s,\simple1label);
+
+~kmeans.predict(~ds,~labels, {|x| ("Size of each cluster" +  x).postln})
+(
+Routine{
+    var n;
+    ~labels.size({|x| n = x.asInt});
+    n.asInt.do{|i|
+        ~labels.getLabel(i.asString,action: {|l|("Label for" + i ++ ":" + l).postln});
+    }
+}.play
+)
+
+