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);
@@ -66,59 +71,44 @@ 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.fit(~source,~labels);
-	~classifier = FluidKNNClassifier(s);
+~classifier.predict(~test, ~mapping, 1);
-	s.sync;
-	~classifier.fit(~source,~labels);
-	~classifier.predict(~test, ~mapping, 1);
-    s.sync;
-}
 )
 
 //Return labels of clustered points
 (
 ~assignments = Array.new(~testpoints.size);
 fork{
-	~testpoints.do{|x,i|
+    ~testpoints.do{|x,i|
-		~mapping.getLabel(i,action:{|l|
+        ~mapping.getLabel(i, action:{|l|
-			~assignments.add(l);
+            ~assignments.add(l);
-		});
+        });
-		s.sync;
+        s.sync;
-		if(i==(~testpoints.size - 1)){"Got assignments".postln;}
+        if(i==(~testpoints.size - 1)){"Got assignments".postln;}
-	};
+    };
-	~assignments.postln;
+    ~assignments.postln;
 }
 )
 
@@ -137,24 +127,23 @@ d = ((~testpoints + 1) * 0.5).flatten(1).unlace;
 w = Window("scatter", Rect(128, 64, 200, 200));
 ~colours = [Color.blue,Color.red,Color.green,Color.magenta];
 w.drawFunc = {
-	Pen.use {
+    Pen.use {
-		e[0].size.do{|i|
+        e[0].size.do{|i|
-			var r = Rect(e[0][i],e[1][i],10,10);
+            var r = Rect(e[0][i],e[1][i],10,10);
-			Pen.fillColor = c[~examplelabels[i]];
+            Pen.fillColor = c[~examplelabels[i]];
-			Pen.fillOval(r);
+            Pen.fillOval(r);
-		};
+        };
-		d[0].size.do{|i|
+        d[0].size.do{|i|
-			var x = (d[0][i]*200);
+            var x = (d[0][i]*200);
-			var y = (d[1][i]*200);
+            var y = (d[1][i]*200);
-			var r = Rect(x,y,5,5);
+            var r = Rect(x,y,5,5);
-			Pen.fillColor = c[~assignments[i].asSymbol].alpha_(0.3);
+            Pen.fillColor = c[~assignments[i].asSymbol].alpha_(0.3);
-			Pen.fillOval(r);
+            Pen.fillOval(r);
-		}
+        }
-	}
+    }
 };
 w.refresh;
 w.front;
 )
 
-
 ::