updated super simple examples with comments and new interface

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

@@ -13,7 +13,7 @@ Routine{
 
 /*** KDTREE ***/
 ~tree = FluidKDTree.new(s)
-~tree.index(~ds,action:{"Done indexing".postln})
+~tree.fit(~ds,action:{"Done indexing".postln})
 
 k = 5; //play with this
 (
@@ -32,6 +32,7 @@ Routine{
 ~kmeans = FluidKMeans.new(s)
 ~nClusters = 4; //play with this
 ~kmeans.fit(~ds,~nClusters,100,action:{"Done fitting".postln})
+
 (
 Routine{
     10.do{|i|
@@ -45,6 +46,7 @@ Routine{
 ~labels = FluidLabelSet(s,\simple1label);
 
 ~kmeans.predict(~ds,~labels, {|x| ("Size of each cluster" +  x).postln})
+
 (
 Routine{
     var n;

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

@@ -15,15 +15,15 @@ Routine{
 
 /*** KDTREE ***/
 ~tree = FluidKDTree.new(s)
-~tree.index(~ds,action:{"Done indexing".postln})
+~tree.fit(~ds,action:{"Done indexing".postln})
 
 k = 5; //play with this
 (
 Routine{
     10.do{|i|
-        ~point.set(0,i*10);
+        ~point.set(0,i*2);
         s.sync;
-        ("Neighbours for point" + (i*10)).postln;
+        ("Neighbours for point" + (i*2)).postln;
         ~tree.kNearest(~point, k, {|x| ("Labels:" + x).postln});
         ~tree.kNearestDist(~point,k,{|x| ("Distances:" + x).postln})
     }

release-packaging/ignore/Examples/dataset/super-simple-regressor-example.scd

@@ -1,46 +1,63 @@
 s.reboot
 
-
 ~urn = { |n=31416, min=0,max=31415| (min..max).scramble.keep(n) };
 
+// creates 200 indices, then values of the output of a fundion with a predictable shape of a sinewave
 n = 200
 ~idx = ~urn.value(n)
 ~data = n.collect{|i|sin(~idx[i]/5000)}
+
+// creates the dataset with these associated indices and values
 (
 ~simpleInput = FluidDataSet(s,\simpleInput,1);
 ~simpleOutput = FluidDataSet(s,\simpleOutput,1);
 b = Buffer.alloc(s,1,1);
-~mappingviz = Buffer.alloc(s,31416,1);
+~mappingviz = Buffer.alloc(s,512);
 )
+
 (
 Routine{
-n.do{|i|
-        b.set(0,~idx[i]);
-        s.sync;
-        ~simpleInput.addPoint(i.asString,b,{("Added Input" + i).postln});
-        b.set(0,~data[i]);
-        s.sync;
-        ~simpleOutput.addPoint(i.asString,b,{("Added Output" + i).postln});
-        ~mappingviz.set(~idx[i].asInt,~data[i])
-    }
+	n.do{|i|
+		b.set(0,~idx[i]);
+		s.sync;
+		~simpleInput.addPoint(i.asString,b,{("Added Input" + i).postln});
+		b.set(0,~data[i]);
+		s.sync;
+		~simpleOutput.addPoint(i.asString,b,{("Added Output" + i).postln});
+		~mappingviz.set((~idx[i]/61).asInt,~data[i])
+	}
 }.play
 )
-(
-~simpleInput.clear;
-~simpleOutput.clear;
-)
-3%2
+
+//look at the seeing material
 ~mappingviz.plot(minval:-1,maxval:1)
 
-~mappingresult = Buffer.alloc(s,31416,1);
+//create a buffer to query
+~mappingresult = Buffer.alloc(s,512);
 
+//make the process then fit the data
 ~knn = FluidKNN(s)
-~knn.index(~simpleInput,action:{"index done".postln})
+~knn.fit(~simpleInput,action:{"fitting done".postln})
+
+// query 512 points along the line (slow because of all that sync'ing)
 (
+k = 1; // change to see how many points the system uses to regress
+Routine{
+	512.do{|i|
+		b.set(0,i*61);
+		s.sync;
+		~knn.regressPoint(b,~simpleOutput,k,action:{|d|~mappingresult.set(i,d);});
+		s.sync;
+		i.postln;
+	}
+}.play
+)
+
+// look at the interpolated values
+~mappingresult.plot
 
-    512.do{|i|
-        b.set(0,i);
-        ~knn.regress(b,~simpleOutput,1,action:{|d|~mappingresult.set(i,d)});
-    // if(i%512 == 0, {i.postln; s.sync},{});
-}
-)
+// clears both datasets
+(
+~simpleInput.clear;
+~simpleOutput.clear;
+)