tidying up

release-packaging/Classes/FluidMLP.sc

@@ -61,7 +61,7 @@ FluidMLPRegressor : FluidRealTimeModel {
 
 	predictPoint { |sourceBuffer, targetBuffer, action|
         actions[\predictPoint] = [nil,{action.value(targetBuffer)}];
-        this.predictPointMsg(sourceBuffer, targetBuffer).postln;
+        this.predictPointMsg(sourceBuffer, targetBuffer);
 		this.prSendMsg(this.predictPointMsg(sourceBuffer, targetBuffer));
 	}
 

release-packaging/Examples/dataset/1-learning examples/10b-weighted-pitch-comparison.scd

@@ -68,7 +68,6 @@ c.free
 //for completion, here is just with rejection of outliers - not as good, but a decent second best!
 FluidBufStats.process(s,~pitches, stats:~stats,outliersCutoff: 1.5)
 ~stats.getn(0,14,{|x|~pitchIQRStats = x;x.reshape(7,2).do{|y| "%\t\t\t%\n".postf(y[0].round(0.1),y[1].round(0.01))}})
-//now that is impressive!
 c = {SinOsc.ar(~pitchIQRStats[0],mul: 0.05)}.play
 b.play
 c.free

release-packaging/Examples/dataset/1-learning examples/12-windowed-clustered-segmentation.scd

@@ -4,7 +4,7 @@
 
 //slightly oversegment with novelty
 //segments should still make sense but might cut a few elements in 2 or 3
-~slicer = FluidSliceCorpus({ |src,start,num,dest| FluidBufNoveltySlice.kr(src,start,num,indices:dest, feature: 1, kernelSize: 29, threshold: 0.1, filterSize: 5, hopSize: 128)});
+~slicer = FluidSliceCorpus({ |src,start,num,dest| FluidBufNoveltySlice.kr(src,start,num,indices:dest, feature: 1, kernelSize: 29, threshold: 0.1, filterSize: 5, hopSize: 128, blocking: 1)});
 ~slicer.play(s, ~loader.buffer,~loader.index);
 
 //test the segmentation by looping them
@@ -45,7 +45,11 @@ a = Slider(w, Rect(10, 20, 330, 20))
 	writer = FluidDataSetWr.kr(~slicesShapes,label, -1, ~flatbuf[voice], Done.kr(flatten),blocking: 1);
 });
 )
-~extractor.play(s,~loader.buffer, ~slicer.index);
+
+(
+t = Main.elapsedTime;
+~extractor.play(s,~loader.buffer, ~slicer.index, action:{(Main.elapsedTime - t).postln;"Analysis done".postln});
+)
 
 ~slicesMFCC.print
 ~slicesShapes.print
@@ -102,22 +106,24 @@ a = Slider(w, Rect(10, 20, 330, 20))
 	//copy the items to a subdataset from hear
 	winSize.do{|i|
 		tempDict.put((i.asString), ~sliceDict["data"][(~orginalkeys[(i+head)]).asString]);//here one could curate which stats to take
-		"whichslices:%\n".postf(i+head);
+		// "whichslices:%\n".postf(i+head);
 	};
 	~windowDS.load(Dictionary.newFrom([\cols, ~sliceDict["cols"].asInteger, \data, tempDict]), action: {
-		"% - loaded\n".postf(head);
+		// "% - loaded\n".postf(head);
 
 		//kmeans 2 and retrieve ordered array of class assignations
 		~kmeans.fitPredict(~windowDS, ~windowLS, action: {|x|
 			nbass = x;
-			"% - fitted1: ".postf(head); nbass.postln;
+			// "% - fitted1: ".postf(head); nbass.postln;
 
 			if (nbass.includes(winSize.asFloat), {
 				~kmeans.fitPredict(~windowDS, ~windowLS, {|x|
-					nbass = x; "% - fitted2: ".postf(head); nbass.postln;
+					nbass = x;
+					// "% - fitted2: ".postf(head); nbass.postln;
 					if (nbass.includes(winSize.asFloat), {
 						~kmeans.fitPredict(~windowDS, ~windowLS, {|x|
-							nbass = x; "% - fitted3: ".postf(head); nbass.postln;
+							nbass = x;
+							// "% - fitted3: ".postf(head); nbass.postln;
 						});
 					});
 				});

release-packaging/Examples/dataset/1-learning examples/13-massive-parallelisation-example.scd

@@ -211,9 +211,7 @@ fork{
 )
 
 //Above we sneakily made a dictionary of slice data for playback (bufnum,start,end). Let's throw it in a dataset
-(
 ~slicedata = FluidDataSet(s);  // will hold slice data (bufnum,start,end) for playback
-)
 
 //dict -> dataset
 (

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

@@ -53,7 +53,7 @@ Routine{
 		~simpleInput.addPoint((i+50).asString,b,{("Added Input" + (i+50)).postln});
 		~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

@@ -19,8 +19,8 @@ c = Buffer.alloc(s,1);
 (
 Routine{
     n.do{|i|
-        b.set(0,~idx[i].postln);
+        b.set(0,~idx[i]);
-        c.set(0,~data[i].postln);
+        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]/61.4).asInteger,~data[i]);
@@ -57,5 +57,3 @@ Routine{
 
 // look at the interpolated values
 ~mappingresult.plot
-
-(31416/61.4).asInteger

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

@@ -113,7 +113,7 @@ Routine{
 ~normalize.transformPoint(~query_buf,~normbuf);
 ~standardize.transformPoint(~query_buf,~stdbuf);
 
-//query the single nearest neighbourg via 3 different data scaling. Depending on the random source at the begining, you will get small to large differences between the 3 answers!
+//query the single nearest neighbourg via 3 different data scaling. Depending on the random source at the begining, you should get (small or large) differences between the 3 answers!
 [~tree,~normtree,~stdtree].do{|t| t.numNeighbours =1  };
 ~tree.kNearest(~query_buf, {|x| ("Original:" + x).post;~tree.kNearestDist(~query_buf, {|x| (" with a distance of " + x).postln});});
 ~normtree.kNearest(~normbuf, {|x| ("Normalized:" + x).post;~normtree.kNearestDist(~normbuf, {|x| (" with a distance of " + x).postln});});