Update dataest examples to interface changes

release-packaging/Examples/dataset/demo-dataset-maker-utilities.scd

@@ -150,7 +150,8 @@ FluidBufCompose.process(s,~loader.buffer,a,(b-a),numChans: 1, destination: ~targ
 
 //find its nearest neighbours
 ~friends = Array;
-~tree.kNearest(~flatbuf[0],5,{|x| ~friends = x.postln;})
+~tree.numNeighbours = 5;
+~tree.kNearest(~flatbuf[0],{|x| ~friends = x.postln;})
 
 // play them in a row
 (

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

@@ -16,12 +16,12 @@ Routine{
 ~tree = FluidKDTree.new(s)
 ~tree.fit(~ds,action:{"Done indexing".postln})
 
-k = 5; //play with this
+~tree.numNeighbours = 5; //play with this
 (
 Routine{
 	10.do{|i|
 		~point.set(0,i);
-		~tree.kNearest(~point, k, {|x| "Neighbours for a value of % are ".postf(i); x.postln});
+		~tree.kNearest(~point, {|x| "Neighbours for a value of % are ".postf(i); x.postln});
 		s.sync;
 	}
 }.play
@@ -29,9 +29,9 @@ Routine{
 
 /*** KMEANS ***/
 
-~kmeans = FluidKMeans.new(s)
-~nClusters = 2; //play with this
-~kmeans.fit(~ds,~nClusters,100,action:{|x| "Done fitting with these number of items per cluster ".post;x.postln;})
+~kmeans = FluidKMeans.new(s,maxIter:100)
+~kmeans.numClusters = 2; //play with this
+~kmeans.fit(~ds,action:{|x| "Done fitting with these number of items per cluster ".post;x.postln;})
 
 (
 Routine{

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

@@ -18,13 +18,13 @@ Routine{
 ~tree = FluidKDTree.new(s)
 ~tree.fit(~ds,action:{"Done indexing".postln})
 
-k = 5; //play with this
+~tree.numNeighbours = 5; //play with this
 (
 Routine{
 	15.do{|i|
 		~point.set(0,i);
-		~tree.kNearest(~point, k, {|x| "Neighbours for a value of % are ".postf(i); x.post;" with respective distances of ".post;});
-		~tree.kNearestDist(~point,k,{|x| x.postln});
+		~tree.kNearest(~point, {|x| "Neighbours for a value of % are ".postf(i); x.post;" with respective distances of ".post;});
+		~tree.kNearestDist(~point, {|x| x.postln});
 		s.sync;
 	}
 }.play
@@ -33,9 +33,9 @@ Routine{
 
 /*** KMEANS ***/
 
-~kmeans = FluidKMeans.new(s)
-~nClusters = 2; //play with this
-~kmeans.fit(~ds,~nClusters,100,action:{|x| "Done fitting with these number of items per cluster ".post;x.postln;})
+~kmeans = FluidKMeans.new(s,maxIter:100)
+~kmeans.numClusters = 2; //play with this
+~kmeans.fit(~ds, action:{|x| "Done fitting with these number of items per cluster ".post;x.postln;})
 
 (
 Routine{

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

@@ -3,7 +3,7 @@
 ~simpleOutput = FluidLabelSet(s,\simpleOutput);
 b = Buffer.alloc(s,2);
 ~knn = FluidKNNClassifier(s);
-k = 3
+~knn.numNeighbours = 3
 )
 
 (
@@ -22,7 +22,7 @@ v.mouseDownAction = {|view, x, y|myx=x;myy=y;w.refresh;
 	// myx.postln;myy.postln;
 	Routine{
 		b.setn(0,[myx,myy]);
-		~knn.predictPoint(b, k, action: {|x|x.postln;});
+		~knn.predictPoint(b, action: {|x|x.postln;});
 		s.sync;
 }.play;};
 

release-packaging/Examples/dataset/super-simple-dictionary-json-example.scd

@@ -13,12 +13,11 @@
 ~ds.print
 
 //fun with kdtree to see it actually works
-~kdtree = FluidKDTree.new(s)
+~kdtree = FluidKDTree.new(s,numNeighbours:5)
 ~kdtree.fit(~ds,{\done.postln;})
-
 ~target = Buffer.loadCollection(s,(4).dup(10));
-~kdtree.kNearest(~target,5,{|a|a.postln;})
-~kdtree.kNearestDist(~target,5,{|a|a.postln;})
+~kdtree.kNearest(~target, {|a|a.postln;})
+~kdtree.kNearestDist(~target, {|a|a.postln;})
 
 
 /////////////////////////////////////////////
@@ -39,7 +38,7 @@
 ~ls.print
 
 // testin with a classifier toy example
-~classifier = FluidKNNClassifier.new(s);
+~classifier = FluidKNNClassifier.new(s, numNeighbours:2);
 ~classifier.fit(~ds,~ls, {\done.postln;})
 
-~classifier.predictPoint(~target,2,action: {|x|x.postln;})
+~classifier.predictPoint(~target, action: {|x|x.postln;})

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

@@ -73,10 +73,10 @@ Routine{
 ~dataset.getPoint("point-7",~query_buf);
 
 // find the 2 points with the shortest distances in the dataset
-~tree = FluidKDTree.new(s);
+~tree = FluidKDTree.new(s,numNeighbours:2);
 ~tree.fit(~dataset)
-~tree.kNearest(~query_buf,2, {|x| ("Labels:" + x).postln});
-~tree.kNearestDist(~query_buf,2, {|x| ("Distances:" + x).postln});
+~tree.kNearest(~query_buf, {|x| ("Labels:" + x).postln});
+~tree.kNearestDist(~query_buf, {|x| ("Distances:" + x).postln});
 // its nearest neighbourg is itself: it should be itself and the distance should be 0. The second point is depending on your input dataset.
 
 // normalise that point (~query_buf) to be at the right scale
@@ -85,10 +85,10 @@ Routine{
 ~normbuf.getn(0,~nb_of_dim,{arg vec;vec.postln;});
 
 // make a tree of the normalized database and query with the normalize buffer
-~normtree = FluidKDTree.new(s);
+~normtree = FluidKDTree.new(s,numNeighbours:2);
 ~normtree.fit(~normed_dataset)
-~normtree.kNearest(~normbuf,2, {|x| ("Labels:" + x).postln});
-~normtree.kNearestDist(~normbuf,2, {|x| ("Distances:" + x).postln});
+~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
 
 // standardize that same point (~query_buf) to be at the right scale
@@ -97,10 +97,10 @@ Routine{
 ~stdbuf.getn(0,~nb_of_dim,{arg vec;vec.postln;});
 
 // make a tree of the standardized database and query with the normalize buffer
-~stdtree = FluidKDTree.new(s);
+~stdtree = FluidKDTree.new(s, numNeighbours: 2);
 ~stdtree.fit(~standardized_dataset)
-~stdtree.kNearest(~stdbuf,2, {|x| ("Labels:" + x).postln});
-~stdtree.kNearestDist(~stdbuf,2, {|x| ("Distances:" + x).postln});
+~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
 
 // where it starts to be interesting is when we query points that are not in our original dataset
@@ -113,6 +113,7 @@ Routine{
 ~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});});
-~normtree.kNearest(~normbuf,1, {|x| ("Normalized:" + x).post;~normtree.kNearestDist(~normbuf,1, {|x| (" with a distance of " + x).postln});});
-~stdtree.kNearest(~stdbuf,1, {|x| ("Standardized:" + x).post; ~stdtree.kNearestDist(~stdbuf,1, {|x| (" with a distance of " + x).postln});});
+[~tree,~normtree,~stdtree].do{|t| t.numNeighbours =1  };
+~tree.kNearest(~query_buf, {|x| ("Original:" + x).post;~tree.kNearestDist(~query_buf,1, {|x| (" with a distance of " + x).postln});});
+~normtree.kNearest(~normbuf, {|x| ("Normalized:" + x).post;~normtree.kNearestDist(~normbuf,1, {|x| (" with a distance of " + x).postln});});
+~stdtree.kNearest(~stdbuf, {|x| ("Standardized:" + x).post; ~stdtree.kNearestDist(~stdbuf,1, {|x| (" with a distance of " + x).postln});});

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

@@ -44,11 +44,11 @@ Routine{
 
 // query 512 points along the line (slow because of all that sync'ing)
 (
-k = 1; // change to see how many points the system uses to regress
+~knn.numNeighbours = 1; // change to see how many points the system uses to regress
 Routine{
 	512.do{|i|
 		b.set(0,i*61);
-		~knn.predictPoint(b,k,action:{|d|~mappingresult.set(i,d);});
+		~knn.predictPoint(b,action:{|d|~mappingresult.set(i,d);});
 		s.sync;
 		i.postln;
 	}