edits to learning files with the new syntax

release-packaging/Examples/dataset/1-learning examples/1a-starting-1D-example.scd

@@ -5,9 +5,8 @@ s.reboot
 Routine{
 	10.do{|i|
 		~point.set(0,i);
-		s.sync;
-		~ds.addPoint(i.asString,~point,{("addPoint"+i).postln});
-		s.sync;
+		~ds.addPoint(i.asString,~point,{("addPoint"+i).postln}); //because buffer.set do an immediate update in the RT thread we can take for granted it'll be updated when we call addPoint
+		s.sync; //but we need to sync to make sure everything is done on the DataSet before the next iteration
 	}
 }.play
 )
@@ -22,7 +21,6 @@ Routine{
 Routine{
 	10.do{|i|
 		~point.set(0,i);
-		s.sync;
 		~tree.kNearest(~point, {|x| "Neighbours for a value of % are ".postf(i); x.postln});
 		s.sync;
 	}
@@ -39,7 +37,6 @@ Routine{
 Routine{
 	10.do{|i|
 		~point.set(0,i);
-		s.sync;
 		~kmeans.predictPoint(~point,{|x| ("Predicted Cluster for a value of " + i ++ ":" + x).postln});
 		s.sync;
 	}

release-packaging/Examples/dataset/1-learning examples/2a-starting-1D-example2.scd

@@ -1,5 +1,5 @@
 s.reboot
-~ds = FluidDataSet.new(s,\simple1data)
+~ds = FluidDataSet.new(s)
 ~point = Buffer.alloc(s,1,1)
 (
 Routine{
@@ -41,8 +41,8 @@ Routine{
 Routine{
     15.do{|i|
         ~point.set(0,i);
-        s.sync;
 		~kmeans.predictPoint(~point,{|x| ("Predicted Cluster for a value of " + i ++ ":" + x).postln});
+        s.sync;
     }
 }.play
 )

release-packaging/Examples/dataset/1-learning examples/3a-classifier-example.scd

@@ -1,6 +1,6 @@
 (
-~simpleInput = FluidDataSet(s,\simpleInput);
-~simpleOutput = FluidLabelSet(s,\simpleOutput);
+~simpleInput = FluidDataSet(s);
+~simpleOutput = FluidLabelSet(s);
 b = Buffer.alloc(s,2);
 ~knn = FluidKNNClassifier(s);
 ~knn.numNeighbours = 3
@@ -54,6 +54,7 @@ Routine{
 		~simpleOutput.addLabel((i+50).asString,"Red",{("Added Output" + (i+50)).postln});
 		s.sync;
 	}
+	\done.postln;
 	}.play;
 )
 

release-packaging/Examples/dataset/1-learning examples/4-regressor-example.scd

@@ -9,9 +9,10 @@ n = 200
 
 // creates the dataset with these associated indices and values
 (
-~simpleInput = FluidDataSet(s,\simpleInput);
-~simpleOutput = FluidDataSet(s,\simpleOutput);
-b = Buffer.alloc(s,1,1);
+~simpleInput = FluidDataSet(s);
+~simpleOutput = FluidDataSet(s);
+b = Buffer.alloc(s,1);
+c = Buffer.alloc(s,1);
 ~mappingviz = Buffer.alloc(s,512);
 )
 
@@ -19,12 +20,11 @@ b = Buffer.alloc(s,1,1);
 Routine{
     n.do{|i|
         b.set(0,~idx[i]);
+        c.set(0,~data[i]);
         ~simpleInput.addPoint(i.asString,b,{("Added Input" + i).postln});
+        ~simpleOutput.addPoint(i.asString,c,{("Added Output" + i).postln});
+        ~mappingviz.set((~idx[i]/61).asInteger,~data[i]);
         s.sync;
-        b.set(0,~data[i]);
-        ~simpleOutput.addPoint(i.asString,b,{("Added Output" + i).postln});
-        s.sync;
-        ~mappingviz.set((~idx[i]/61).asInteger,~data[i])
     }
 }.play
 )

release-packaging/Examples/dataset/1-learning examples/5-normalization-and-standardization-example.scd

@@ -1,7 +1,7 @@
 (
 // set some variables
 ~nb_of_dim = 10;
-~dataset = FluidDataSet(s,\test,~nb_of_dim);
+~dataset = FluidDataSet(s);
 )
 
 (
@@ -14,6 +14,7 @@ Routine{
 		s.sync;
 	});
 	buf.free;
+	\done.postln;
 }.play
 )
 
@@ -38,7 +39,7 @@ Routine{
 ~normalize.fit(~dataset,{"done".postln;});
 
 // making an empty 'normed_dataset' which is required for the normalize function
-~normed_dataset = FluidDataSet(s,\normed,~nb_of_dim);
+~normed_dataset = FluidDataSet(s);
 
 // normalize the full dataset
 ~normalize.transform(~dataset,~normed_dataset,{"done".postln;});
@@ -55,7 +56,7 @@ Routine{
 ~standardize.fit(~dataset,{"done".postln;});
 
 // standardize the full dataset
-~standardized_dataset = FluidDataSet(s,\standardized,~nb_of_dim);
+~standardized_dataset = FluidDataSet(s);
 ~standardize.transform(~dataset,~standardized_dataset,{"done".postln;});
 
 // look at a point to see that it has points in it

release-packaging/Examples/dataset/1-learning examples/7a-making-datasets-with-json.scd

@@ -41,4 +41,12 @@
 ~classifier = FluidKNNClassifier.new(s, numNeighbours:2);
 ~classifier.fit(~ds,~ls, {\done.postln;})
 
-~classifier.predictPoint(~target, action: {|x|x.postln;})
+// run many times for random pleasure
+(
+fork{
+	var value = 7.rand;
+	~ds.getPoint(("entry-"++value).asSymbol,~target);
+	s.sync;
+	~classifier.predictPoint(~target, action: {|x|"entry % is an % entry.\n".postf(value,x);})
+}
+)