KNNClassifier example update

release-packaging/HelpSource/Classes/FluidKNNClassifier.schelp

@@ -52,10 +52,15 @@ Run when done, passes predicted label as argument
 EXAMPLES::
 
 code::
-//A dataset of example points, and a label set of corresponding labels
+
-//+
+
-//A dataset of test data and a labelset for predicted labels
+// Make:
+// - A KNN Classifier
+// - A dataset of example points, and a label set of corresponding labels
+// - A dataset of test data and a labelset for predicted labels
+
 (
+~classifier = FluidKNNClassifier(s);
 ~source= FluidDataSet(s,\knnclassify_help_examples);
 ~labels = FluidLabelSet(s,\knnclassify_help_labels);
 ~test = FluidDataSet(s,\knnclassify_help_test);
@@ -66,45 +71,30 @@ code::
 (
 ~examplepoints = [[0.5,0.5],[-0.5,0.5],[0.5,-0.5],[-0.5,-0.5]];
 ~examplelabels = [\red,\orange,\green,\blue];
-~source.clear;
+d = Dictionary.new;
-~labels.clear;
+d.add(\cols -> 2);
-~tmpbuf = Buffer.alloc(s,2);
+d.add(\data -> Dictionary.newFrom(~examplepoints.collect{|x, i|[i.asString, x]}.flatten));
-fork{
+~source.load(d);
-    s.sync;
+~examplelabels.collect{|x,i| ~labels.addLabel(i, x);};
-    ~examplepoints.do{|x,i|
-        (""++(i+1)++"/4").postln;
-        ~tmpbuf.setn(0,x);
-        ~source.addPoint(i,~tmpbuf);
-		~labels.addLabel(i,~examplelabels[i]);
-        s.sync
-    }
-}
 )
 
 //Make some random, but clustered test points
 (
-~testpoints = (4.collect{64.collect{(1.sum3rand) + [1,-1].choose}.clump(2)}).flatten(1) * 0.5;
+~testpoints = (4.collect{
-~test.clear;
+               64.collect{(1.sum3rand) + [1,-1].choose}.clump(2)
-fork {
+           }).flatten(1) * 0.5;
-	s.sync;
+d = Dictionary.with(
-	~testpoints.do{|x,i|
+        *[\cols -> 2,\data -> Dictionary.newFrom(
-		~tmpbuf.setn(0,x);
+            ~testpoints.collect{|x, i| [i, x]}.flatten)]);
-		~test.addPoint(i,~tmpbuf);
+ ~test.load(d);
-		s.sync;
+
-		if(i==(~testpoints.size - 1)){"Generated test data".postln;}
-	}
-}
 )
 
-//Make a new KNN classifier model, fit it to the example dataset and labels, and then run preduction on the test data into our mapping label set
+
+//Fit the classifier to the example dataset and labels, and then run prediction on the test data into our mapping label set
 (
-fork{
-	~classifier = FluidKNNClassifier(s);
-	s.sync;
 ~classifier.fit(~source,~labels);
 ~classifier.predict(~test, ~mapping, 1);
-    s.sync;
-}
 )
 
 //Return labels of clustered points
@@ -156,5 +146,4 @@ w.refresh;
 w.front;
 )
 
-
 ::