Merge branch 'clients/inter_client_comms' of https://bitbucket.org/flucoma/flucoma-supercollider into clients/inter_client_comms

release-packaging/HelpSource/Classes/FluidAudioTransport.schelp

@@ -43,5 +43,6 @@ The maximum FFT size for processing
 EXAMPLES::
 
 code::
-(some example code)
+//the mouse X axis interpolates between the two sinewaves
+{FluidAudioTransport.ar(SinOsc.ar(220),SinOsc.ar(440),MouseX.kr())}.play;
 ::

release-packaging/ignore/Examples/dataset/myfirstdataset.scd

@@ -122,18 +122,28 @@ c = Buffer.new(s)
 //FluidKMeans
 ~kMeans= FluidKMeans.new(s)
 ~kMeans.fit(~dataset,k:5,action:{"fit".postln})
+
+
+// predicts in which cluster a point would be
 ~kMeans.predictPoint(c,{|x|x.postln})
+
+// predicts which cluster each points of a dataset would be in, as a label
 ~labels = FluidLabelSet.new(s,"clusters")
 ~kMeans.predict(~dataset,~labels, {|x| x.postln})
+
 ~labels.getLabel(~audioBuffers[2].path,action:{|c| c.postln})
+
+//query each item
+(
 Routine{
 	~labels.size({|x|x.do {|i|
-		forkIfNeeded{
-			~audioBuffers[i].path.postln;
-			~labels.getLabel(~audioBuffers[i].path,action:{|c| c.postln});
-			s.sync;
+		~audioBuffers[i].path.postln;
+		~labels.getLabel(~audioBuffers[i].path,action:{|c| c.postln});
+		s.sync;
 		}
-	}
 	});
 }.play
+)
+
+//labelset can be written as json
 ~labels.write(~path+/+"labels.json")

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

@@ -0,0 +1,64 @@
+(
+~simpleInput = FluidDataSet(s,\simpleInput,2);
+~simpleOutput = FluidLabelSet(s,\simpleOutput,2);
+b = Buffer.alloc(s,2);
+~knn = FluidKNN(s);
+k = 3
+)
+
+(
+var w,v,myx,myy;
+
+//initialise the mouse position holder
+myx=0;
+myy=0;
+
+//make a window and a full size view
+w = Window.new("Viewer",  Rect(100,Window.screenBounds.height - 400, 310, 310)).front;
+v = View.new(w,Rect(0,0, 310, 310));
+
+//creates a function that reacts to mousedown
+// v.mouseMoveAction = {|view, x, y|myx=x;myy=y;w.refresh;};
+v.mouseDownAction = {|view, x, y|myx=x;myy=y;w.refresh;
+	myx.postln;myy.postln;
+	Routine{
+		b.setn(0,[myx,myy]);
+		s.sync;
+		~knn.classifyPoint(b, ~simpleOutput, k, {|x|x.postln;});
+}.play;};
+
+//custom redraw function
+w.drawFunc = {
+	100.do { |i|
+		if (i < 50, {Pen.color = Color.white;} ,{Pen.color = Color.red;});
+		Pen.addRect(Rect(i.div(10)*30+10,i.mod(10)*30+10,20,20));
+		Pen.perform(\fill);
+	};
+	Pen.color = Color.black;
+	Pen.addOval(Rect(myx-5, myy-5,10,10));
+	Pen.perform(\stroke);
+};
+)
+
+(
+//populates a dataset with the same squares as the gui (their centres)
+Routine{
+	50.do{|i|
+	var	x = i.div(10)*30+20;
+			var y = i.mod(10)*30+20;
+	b.setn(0,[x,y]);
+		s.sync;
+		~simpleInput.addPoint(i.asString,b,{("Added Input" + i).postln});
+			~simpleOutput.addLabel(i.asString,"White",{("Added Output" + i).postln});
+			b.setn(0,[x+150,y]);
+		s.sync;
+		~simpleInput.addPoint((i+50).asString,b,{("Added Input" + (i+50)).postln});
+		~simpleOutput.addLabel((i+50).asString,"Red",{("Added Output" + (i+50)).postln});
+	}
+	}.play;
+)
+
+// fit the dataset
+~knn.fit(~simpleInput,action:{"fitting done".postln})
+
+// now click on the grid and read the estimated class according to the nearest K neighbours.