a few defers here and there, and removing the names of the datasets

release-packaging/Examples/dataset/1-learning examples/10a-weighted-MFCCs-comparison.scd

@@ -1,8 +1,8 @@
 // define a few processes
 (
-~ds = FluidDataSet(s,\ds10);
-~dsW = FluidDataSet(s,\ds10W);
-~dsL = FluidDataSet(s,\ds10L);
+~ds = FluidDataSet(s);
+~dsW = FluidDataSet(s);
+~dsL = FluidDataSet(s);
 //define as many buffers as we have parallel voices/threads in the extractor processing (default is 4)
 ~loudbuf = 4.collect{Buffer.new};
 ~weightbuf = 4.collect{Buffer.new};

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

@@ -90,7 +90,7 @@ Routine{
 ~normtree.fit(~normed_dataset)
 ~normtree.kNearest(~normbuf,  {|x| ("Labels:" + x).postln});
 ~normtree.kNearestDist(~normbuf, {|x| ("Distances:" + x).postln});
-// its nearest neighbourg is still itself as it should be, but the 2nd neighbourg will probably have changed. The distance is now different too
+// its nearest neighbourg is still itself as it should be, but the 2nd neighbourg might have changed. The distance is now different too
 
 // standardize that same point (~query_buf) to be at the right scale
 ~stdbuf = Buffer.alloc(s,~nb_of_dim);
@@ -102,7 +102,7 @@ Routine{
 ~stdtree.fit(~standardized_dataset)
 ~stdtree.kNearest(~stdbuf, {|x| ("Labels:" + x).postln});
 ~stdtree.kNearestDist(~stdbuf, {|x| ("Distances:" + x).postln});
-// its nearest neighbourg is still itself as it should be, but the 2nd neighbourg will probably have changed yet again. The distance is also different too
+// its nearest neighbourg is still itself as it should be, but the 2nd neighbourg might have changed yet again. The distance is also different too
 
 // where it starts to be interesting is when we query points that are not in our original dataset
 

release-packaging/Examples/dataset/1-learning examples/8a-mlp-didactic.scd

@@ -8,13 +8,13 @@ Routine{
 	d = Dictionary.new;
 	d.add(\cols -> 2);
 	d.add(\data -> Dictionary.newFrom(["f-f", [0,0], "f-t", [0,1], "t-f", [1,0], "t-t", [1,1]]));
-    ~mlpHelpSource = FluidDataSet.new(s,\mlpHelpSource);
+    ~mlpHelpSource = FluidDataSet.new(s);
 	s.sync;
     ~mlpHelpSource.load(d);
 	s.sync;
 	d.add(\cols -> 1);
 	d.add(\data -> Dictionary.newFrom(["f-f", [0], "f-t", [1], "t-f", [1], "t-t", [0]]));
-    ~mlpHelpTarget = FluidDataSet.new(s,\mlpHelpTarget);
+    ~mlpHelpTarget = FluidDataSet.new(s);
 	s.sync;
     ~mlpHelpTarget.load(d);
 	s.sync;
@@ -27,7 +27,7 @@ Routine{
 
 // make an MLPregressor
 ~mlp = FluidMLPRegressor(s, [3], FluidMLPRegressor.sigmoid, FluidMLPRegressor.sigmoid,maxIter:1000,learnRate: 0.1,momentum: 0.1,batchSize: 1,validation: 0);//1000 epoch at a time
-//train on it and observe the error
+//train it by executing the following line multiple time, and observe the error
 ~mlp.fit(~mlpHelpSource,~mlpHelpTarget,{|x|x.postln;});
 
 //to make a plot of the error let's do a classic 'shades of truth' (a grid of 11 x 11 with each values of truth between 0 and 1
@@ -37,7 +37,7 @@ Routine{
 
 	d.add(\cols -> 2);
 	d.add(\data -> Dictionary.newFrom(121.collect{|x|[x.asString, [x.div(10)/10,x.mod(10)/10]]}.flatten));
-    ~mlpHelpShades = FluidDataSet.new(s,\mlpHelpShades);
+    ~mlpHelpShades = FluidDataSet.new(s);
 	s.sync;
     ~mlpHelpShades.load(d);
 	s.sync;
@@ -48,7 +48,7 @@ Routine{
 ~mlpHelpShades.print
 
 // let's make a destination for our regressions
-~mlpHelpRegressed = FluidDataSet.new(s,\mlpHelpRegressed);
+~mlpHelpRegressed = FluidDataSet.new(s);
 
 // then predict the full DataSet in our trained network
 ~mlp.predict(~mlpHelpShades,~mlpHelpRegressed);
@@ -77,6 +77,7 @@ w.refresh;
 w.front;
 )
 
+~mlp.free
 ~mlpHelpShades.free
 ~mlpHelpSource.free
 ~mlpHelpTarget.free

release-packaging/Examples/dataset/1-learning examples/8b-mlp-synth-control.scd

@@ -7,8 +7,8 @@ var output = Buffer.alloc(s,10);
 var mlp = FluidMLPRegressor(s,[6],activation: 1,outputActivation: 1,maxIter: 1000,learnRate: 0.1,momentum: 0,batchSize: 1,validation: 0);
 var entry = 0;
 
-~inData = FluidDataSet(s,\indata);
-~outData = FluidDataSet(s,\outdata);
+~inData = FluidDataSet(s);
+~outData = FluidDataSet(s);
 
 w = Window("ChaosSynth", Rect(10, 10, 790, 320)).front;
 a = MultiSliderView(w,Rect(10, 10, 400, 300)).elasticMode_(1).isFilled_(1);
@@ -42,8 +42,10 @@ f.action = {arg x,y; //if trained, predict the point f.x f.y
 c = Button(w, Rect(730,240,50, 20)).states_([["train", Color.red, Color.white], ["trained", Color.white, Color.grey]]).action_{
 	mlp.fit(~inData,~outData,{|x|
 		trained = 1;
-		c.value = 1;
-		e.value = x.round(0.001).asString;
+		{
+			c.value = 1;
+			e.value = x.round(0.001).asString;
+		}.defer;
 	});//train the network
 };
 d = Button(w, Rect(730,10,50, 20)).states_([["entry", Color.white, Color.grey], ["entry",  Color.red, Color.white]]).action_{

release-packaging/Examples/dataset/1-learning examples/8c-mlp-regressor-as-dim-redux.scd

@@ -1,9 +1,9 @@
 s.reboot;
 //Preliminaries: we want some audio, a couple of FluidDataSets, some Buffers
 (
-~raw = FluidDataSet(s,\Mel40);
-~norm = FluidDataSet(s,\Mel40n);
-~retrieved = FluidDataSet(s,\ae2);
+~raw = FluidDataSet(s);
+~norm = FluidDataSet(s);
+~retrieved = FluidDataSet(s);
 ~audio = Buffer.read(s,File.realpath(FluidBufMelBands.class.filenameSymbol).dirname +/+ "../AudioFiles/Tremblay-ASWINE-ScratchySynth-M.wav");
 ~melfeatures = Buffer.new(s);
 ~stats = Buffer.alloc(s, 7, 40);
@@ -56,7 +56,7 @@ FluidBufMelBands.process(s,~audio, features: ~melfeatures,action: {\done.postln;
 ~retrieved.print;
 
 //let's normalise it for display
-~normData = FluidDataSet(s,\ae2N);
+~normData = FluidDataSet(s);
 ~reducedarray = Array.new(100);
 ~normalView = FluidNormalize(s,0.1,0.9);
 (

release-packaging/Examples/dataset/1-learning examples/9-regressor-comparison.scd

@@ -2,9 +2,9 @@
 
 //Here we make a 3-points pair of dataset.
 
-~dsIN = FluidDataSet(s,\dsIN);
+~dsIN = FluidDataSet(s);
 ~dsIN.load(Dictionary.newFrom([\cols, 1, \data, Dictionary.newFrom([\point1, [10], \point2, [20], \point3, [30]])]));
-~dsOUT = FluidDataSet(s,\dsOUT);
+~dsOUT = FluidDataSet(s);
 ~dsOUT.load(Dictionary.newFrom([\cols, 1, \data, Dictionary.newFrom([\point1, [0.8], \point2, [0.2], \point3, [0.5]])]));
 
 //check the values
@@ -13,13 +13,13 @@
 (0.dup(10) ++ 0.8 ++ 0.dup(9) ++ 0.2 ++ 0.dup(9) ++ 0.5 ++ 0.dup(10)).plot(\source,discrete: true, minval:0, maxval: 1).plotMode=\bars;
 
 //Let's make a complete dataset to predict each points in our examples:
-~dsALLin = FluidDataSet(s,\dsALLin);
+~dsALLin = FluidDataSet(s);
 ~dsALLin.load(Dictionary.newFrom([\cols, 1, \data, Dictionary.newFrom(Array.fill(41,{|x| [x.asSymbol, [x]];}).flatten(1);)]));
 ~dsALLin.print
 
 //We'll regress these values via KNN and plot
 ~regK = FluidKNNRegressor(s,numNeighbours: 2,weight: 1);
-~dsALLknn = FluidDataSet(s,\dsALLknn);
+~dsALLknn = FluidDataSet(s);
 ~regK.fit(~dsIN,~dsOUT);
 ~regK.predict(~dsALLin,~dsALLknn);
 
@@ -36,7 +36,7 @@
 
 //Let's do the same process with MLP
 ~regM = FluidMLPRegressor(s,hidden: [4],activation: 1,outputActivation: 1,maxIter: 10000,learnRate: 0.1,momentum: 0,batchSize: 1,validation: 0);
-~dsALLmlp = FluidDataSet(s,\dsALLmlp);
+~dsALLmlp = FluidDataSet(s);
 ~regM.fit(~dsIN,~dsOUT,{|x|x.postln;});
 ~regM.predict(~dsALLin,~dsALLmlp);
 
@@ -86,7 +86,7 @@
 					~dsALLknn.dump{|x| 41.do{|i|
 						~knnALLval.add((x["data"][i.asString]));
 					};//draw everything
-					[~source, ~knnALLval.flatten(1), ~mlpALLval.flatten(1)].flop.flatten(1).plot(\source,numChannels: 3, discrete: false, minval:0, maxval: 1).plotMode=\bars;
+					{[~source, ~knnALLval.flatten(1), ~mlpALLval.flatten(1)].flop.flatten(1).plot(\source,numChannels: 3, discrete: false, minval:0, maxval: 1).plotMode=\bars;}.defer;
 					};
 				});
 			});