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

release-packaging/Classes/FluidKMeans.sc

@@ -14,8 +14,9 @@ FluidKMeans : FluidManipulationClient {
     fit{|dataset,k, maxIter = 100, buffer, action|
        buffer = buffer ? -1;
         this.k = k;
-        this.prSendMsg(\fit,[dataset.asSymbol, k,maxIter, buffer.asUGenInput],action,[numbers(FluidMessageResponse,_,k,_)]);
-    }
+		// this.prSendMsg(\fit,[dataset.asSymbol, k,maxIter, buffer.asUGenInput],action,[numbers(FluidMessageResponse,_,k,_)]);
+	    this.prSendMsg(\fit,[dataset.asSymbol, k,maxIter],action,[numbers(FluidMessageResponse,_,k,_)]);
+	}
 
     fitPredict{|dataset,labelset, k, maxIter = 100, action|
         this.k = k;

release-packaging/Examples/dataset/myfirstdataset.scd

@@ -22,14 +22,14 @@ FileDialog.new(fileMode:2,okFunc:{|x| ~path = x[0];
 )
 
 //STEP 2: Make a FluidDataSet
-~dataset = FluidDataSet.new(s,"mfccs", 96) //12 dims * 4 stats * 2 derivatives
+~dataset = FluidDataSet.new(s,"mfccs");
 
 //STEP 3A: EITHER populate the dataset like so (and cry about how long the data point assembly takes)
 (
 Routine{
 	var tmpMFCCs = Buffer.new(s);
 	var tmpStats = Buffer.new(s);
-	var tmpFlat = Buffer.alloc(s,12 * 4 * 2, 1);
+	var tmpFlat = Buffer.alloc(s,12 * 4 * 2, 1);//12 dims * 4 stats * 2 derivatives
 	s.sync;
 	~audioBuffers.do{|b|
 		("Analyzing" + b.path).postln;
@@ -61,7 +61,7 @@ Routine{
 	var tmpStats = Buffer.new(s);
 	var langStats;
 	var langFlat;
-	var tmpFlat = Buffer.alloc(s,12 * 4 * 2, 1);
+	var tmpFlat = Buffer.alloc(s,12 * 4 * 2, 1); //12 dims * 4 stats * 2 derivatives
 	s.sync;
 	~audioBuffers.do{|b|
 		("Analyzing" + b.path).postln;

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

@@ -1,5 +1,5 @@
 s.reboot
-~ds = FluidDataSet.new(s,\simple1data,1)
+~ds = FluidDataSet.new(s,\simple1data)
 ~point = Buffer.alloc(s,1,1)
 (
 Routine{
@@ -30,8 +30,8 @@ Routine{
 /*** KMEANS ***/
 
 ~kmeans = FluidKMeans.new(s)
-~nClusters = 4; //play with this
-~kmeans.fit(~ds,~nClusters,100,action:{"Done fitting".postln})
+~nClusters = 2; //play with this
+~kmeans.fit(~ds,~nClusters,100,action:{|x| "Done fitting with these number of items per cluster ".post;x.postln;})
 
 (
 Routine{
@@ -50,11 +50,9 @@ Routine{
 (
 Routine{
     var n;
-    ~labels.size({|x| n = x.asInt});
-    n.asInt.do{|i|
+    ~labels.size({|x|
+		x.asInteger.do{|i|
         ~labels.getLabel(i.asString,action: {|l|("Label for" + i ++ ":" + l).postln});
-    }
+	};});
 }.play
 )
-
-

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

@@ -1,5 +1,5 @@
 s.reboot
-~ds = FluidDataSet.new(s,\simple1data,1)
+~ds = FluidDataSet.new(s,\simple1data)
 ~point = Buffer.alloc(s,1,1)
 (
 Routine{
@@ -34,7 +34,8 @@ Routine{
 
 ~kmeans = FluidKMeans.new(s)
 ~nClusters = 2; //play with this
-~kmeans.fit(~ds,~nClusters,100,action:{"Done fitting".postln})
+~kmeans.fit(~ds,~nClusters,100,action:{|x| "Done fitting with these number of items per cluster ".post;x.postln;})
+
 (
 Routine{
     10.do{|i|
@@ -48,14 +49,13 @@ Routine{
 ~labels = FluidLabelSet(s,\simple1label);
 
 ~kmeans.predict(~ds,~labels, {|x| ("Size of each cluster" +  x).postln})
-(
+
+s(
 Routine{
     var n;
-    ~labels.size({|x| n = x.asInt});
-    n.asInt.do{|i|
+    ~labels.size({|x|
+		x.asInteger.do{|i|
         ~labels.getLabel(i.asString,action: {|l|("Label for" + i ++ ":" + l).postln});
-    }
+	};});
 }.play
 )
-
-

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

@@ -1,6 +1,6 @@
 (
-~simpleInput = FluidDataSet(s,\simpleInput,2);
-~simpleOutput = FluidLabelSet(s,\simpleOutput,2);
+~simpleInput = FluidDataSet(s,\simpleInput);
+~simpleOutput = FluidLabelSet(s,\simpleOutput);
 b = Buffer.alloc(s,2);
 ~knn = FluidKNNClassifier(s);
 k = 3
@@ -23,7 +23,7 @@ v.mouseDownAction = {|view, x, y|myx=x;myy=y;w.refresh;
 	Routine{
 		b.setn(0,[myx,myy]);
 		s.sync;
-		~knn.predictPoint(b, k, {|x|x.postln;});
+		~knn.predictPoint(b, k, action: {|x|x.postln;});
 }.play;};
 
 //custom redraw function

release-packaging/Examples/dataset/super-simple-normalization-standardization-example.scd

@@ -39,7 +39,7 @@
 ~normed_dataset = FluidDataSet(s,\normed,~nb_of_dim);
 
 // normalize the full dataset
-~normalize.normalize(~dataset,~normed_dataset,{"done".postln;});
+~normalize.transform(~dataset,~normed_dataset,{"done".postln;});
 
 // look at a point to see that it has points in it
 ~normed_dataset.getPoint("point-0",~query_buf,{~query_buf.getn(0,~nb_of_dim,{|x|x.postln;});});
@@ -54,7 +54,7 @@
 
 // standardize the full dataset
 ~standardized_dataset = FluidDataSet(s,\standardized,~nb_of_dim);
-~standardize.standardize(~dataset,~standardized_dataset,{"done".postln;});
+~standardize.transform(~dataset,~standardized_dataset,{"done".postln;});
 
 // look at a point to see that it has points in it
 ~standardized_dataset.getPoint("point-0",~query_buf,{~query_buf.getn(0,~nb_of_dim,{|x|x.postln;});});
@@ -79,7 +79,7 @@
 
 // normalise that point (~query_buf) to be at the right scale
 ~normbuf = Buffer.alloc(s,~nb_of_dim);
-~normalize.normalizePoint(~query_buf,~normbuf);
+~normalize.transformPoint(~query_buf,~normbuf);
 ~normbuf.getn(0,~nb_of_dim,{arg vec;vec.postln;});
 
 // make a tree of the normalized database and query with the normalize buffer
@@ -91,7 +91,7 @@
 
 // standardize that same point (~query_buf) to be at the right scale
 ~stdbuf = Buffer.alloc(s,~nb_of_dim);
-~standardize.standardizePoint(~query_buf,~stdbuf);
+~standardize.transformPoint(~query_buf,~stdbuf);
 ~stdbuf.getn(0,~nb_of_dim,{arg vec;vec.postln;});
 
 // make a tree of the standardized database and query with the normalize buffer
@@ -107,8 +107,8 @@
 ~query_buf.fill(0,~nb_of_dim,50);
 
 // normalize and standardize the query buffer. Note that we do not need to fit since we have not added a point to our reference dataset
-~normalize.normalizePoint(~query_buf,~normbuf);
-~standardize.standardizePoint(~query_buf,~stdbuf);
+~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!
 ~tree.kNearest(~query_buf,1, {|x| ("Original:" + x).post;~tree.kNearestDist(~query_buf,1, {|x| (" with a distance of " + x).postln});});

release-packaging/HelpSource/Classes/FluidDataSet.schelp

@@ -144,4 +144,16 @@ Routine{
     }
 }.play
 )
+
+//Inspect the dataset using print (abbreviated output) or dump (JSON output)
+
+~ds.print //to post window by default, but you can supply a custom action instead
+~ds.dump //likewise
+//for example
+~ds.dump{|j|
+	~dict = j.parseJSON
+}
+//Now we have a Dictionary of our data and IDs
+~dict.postcs
+
 ::

release-packaging/HelpSource/Classes/FluidKNNClassifier.schelp

@@ -105,9 +105,6 @@ fork{
 }
 )
 
-//Dims of kmeans should match dataset
-~kmeans.cols
-
 //Return labels of clustered points
 (
 ~assignments = Array.new(~testpoints.size);

release-packaging/HelpSource/Classes/FluidKNNRegressor.schelp

@@ -15,7 +15,7 @@ The server to run this model on.
 INSTANCEMETHODS::
 
 METHOD:: fit
-Map a source link::Classes/FluidDataSet:: to a target; they must be the same size, but can have different dimesionality
+Map a source link::Classes/FluidDataSet:: to a target; they must be the same size, but can have different dimensionality
 ARGUMENT:: sourceDataset
 Source data
 ARGUMENT:: targetDataset