UMAP: removed batchSize, added fit and transform method and examples

release-packaging/Classes/FluidUMAP.sc

@@ -1,15 +1,14 @@
 FluidUMAP : FluidModelObject {
 
-    var <>numDimensions, <>numNeighbours, <>minDist, <>iterations, <>learnRate, <>batchSize;
+    var <>numDimensions, <>numNeighbours, <>minDist, <>iterations, <>learnRate;
 
-	*new {|server,numDimensions = 2, numNeighbours = 15, minDist = 0.1, iterations = 200, learnRate = 0.1, batchSize = 50|
+	*new {|server,numDimensions = 2, numNeighbours = 15, minDist = 0.1, iterations = 200, learnRate = 0.1|
-		^super.new(server,[numDimensions, numNeighbours, minDist, iterations, learnRate, batchSize])
+		^super.new(server,[numDimensions, numNeighbours, minDist, iterations, learnRate])
         .numDimensions_(numDimensions)
         .numNeighbours_(numNeighbours)
         .minDist_(minDist)
         .iterations_(iterations)
-        .learnRate_(learnRate)
+        .learnRate_(learnRate);
-        .batchSize_(batchSize);
 	}
 
     prGetParams{
@@ -18,8 +17,7 @@ FluidUMAP : FluidModelObject {
             this.numNeighbours,
             this.minDist,
             this.iterations,
-            this.learnRate,
+            this.learnRate
-            this.batchSize
         ]
     }
 
@@ -32,6 +30,24 @@ FluidUMAP : FluidModelObject {
 		this.prSendMsg(this.fitTransformMsg(sourceDataSet,destDataSet));
 	}
 
+	fitMsg{|dataSet|
+        ^this.prMakeMsg(\fit,id, dataSet.id);
+    }
+
+    fit{|dataSet, action|
+        actions[\fit] = [nil, action];
+        this.prSendMsg(this.fitMsg(dataSet));
+    }
+
+    transformMsg{|sourceDataSet, destDataSet|
+        ^this.prMakeMsg(\transform, id, sourceDataSet.id, destDataSet.id);
+    }
+
+    transform{|sourceDataSet, destDataSet, action|
+        actions[\transform] = [nil, action];
+        this.prSendMsg(this.transformMsg(sourceDataSet,destDataSet));
+    }
+
 	// not implemented
 	cols {|action|}
 	read{|filename,action|}

release-packaging/HelpSource/Classes/FluidUMAP.schelp

@@ -1,7 +1,7 @@
 TITLE:: FluidUMAP
 summary:: Dimensionality Reduction with Uniform Manifold Approximation and Projection
 categories:: Dimensionality Reduction, Data Processing
-related:: Classes/FluidMDS, Classes/FluidDataSet
+related:: Classes/FluidMDS, Classes/FluidPCA, Classes/FluidDataSet
 
 DESCRIPTION::
 
@@ -53,7 +53,7 @@ code::
 ~normalized = FluidDataSet(s);
 ~standardizer  = FluidStandardize(s);
 ~normalizer = FluidNormalize(s);
-~umap = FluidUMAP(s).numDimensions_(2).numNeighbours_(5).minDist_(0.2).iterations_(50). learnRate_(0.2).batchSize_(50);
+~umap = FluidUMAP(s).numDimensions_(2).numNeighbours_(5).minDist_(0.2).iterations_(50).learnRate_(0.2);
 )
 
 
@@ -78,15 +78,15 @@ code::
 
 //Visualise the 2D projection of our original 4D data
 (
-w = Window("scatter", Rect(128, 64, 200, 200));
+w = Window("a perspective", Rect(128, 64, 200, 200));
 w.drawFunc = {
-    Pen.use {
+	Pen.use {
-        ~normalizedDict.keysValuesDo{|key, val|
+		~normalizedDict.keysValuesDo{|key, val|
 			Pen.fillColor = Color.new(~colours[key.asSymbol][0], ~colours[key.asSymbol][1],~colours[key.asSymbol][2]);
 			Pen.fillOval(Rect((val[0] * 200), (val[1] * 200), 5, 5));
-			~colours[key.asSymbol].flat.postln;
+			~colours[key.asSymbol].flat;
-        }
+		}
-    }
+	}
 };
 w.refresh;
 w.front;
@@ -95,5 +95,75 @@ w.front;
 //play with parameters
 ~umap.numNeighbours = 10;
 ~umap.minDist =5;
-~umap.batchSize = 10;
+
+//rerun the UMAP
+~umap.fitTransform(~standardized,~reduced,action:{"Finished UMAP".postln});
+
+//draw to compare
+(
+~normalizer.fitTransform(~reduced,~normalized,action:{
+	"Normalized Output".postln;
+	~normalized.dump{|x|
+		~normalizedDict = x["data"];
+
+		{
+			w = Window("another perspective", Rect(328, 64, 200, 200));
+			w.drawFunc = {
+				Pen.use {
+					~normalizedDict.keysValuesDo{|key, val|
+						Pen.fillColor = Color.new(~colours[key.asSymbol][0], ~colours[key.asSymbol][1],~colours[key.asSymbol][2]);
+						Pen.fillOval(Rect((val[0] * 200), (val[1] * 200), 5, 5));
+						~colours[key.asSymbol].flat;
+					};
+				};
+			};
+			w.refresh;
+			w.front;
+
+		}.defer;
+
+	};
+});
+)
+
+// now run new random points on the same training material. Colours should be scattered around the same space (BUT THEY DON'T RIGHT NOW)
+
+~newDS = FluidDataSet(s);
+~colours2 = Dictionary.newFrom(400.collect{|i|[("entry"++i).asSymbol, 3.collect{1.0.rand}]}.flatten(1));
+~newDS.load(Dictionary.newFrom([\cols, 3, \data, ~colours2]));
+
+//we need to standardize to the same space
+~newDSstan = FluidDataSet(s);
+~standardizer.transform(~newDS, ~newDSstan);
+
+//then we can run the umap
+~newDSmap = FluidDataSet(s);
+~umap.transform(~newDSstan, ~newDSmap);
+
+//then we can draw and look
+(
+~normalizer.transform(~newDSmap,~normalized,action:{
+	"Normalized Output".postln;
+	~normalized.dump{|x|
+		~normalizedDict = x["data"];
+		{
+			w = Window("new material", Rect(528, 64, 200, 200));
+			w.drawFunc = {
+				Pen.use {
+					~normalizedDict.keysValuesDo{|key, val|
+						Pen.fillColor = Color.new(~colours2[key.asSymbol][0], ~colours2[key.asSymbol][1],~colours2[key.asSymbol][2]);
+						Pen.fillOval(Rect((val[0] * 200), (val[1] * 200), 5, 5));
+						~colours2[key.asSymbol].flat;
+					};
+				};
+			};
+			w.refresh;
+			w.front;
+		}.defer;
+	};
+});
+)
+
+//same process as above in 2 passes maybe?
+
 ::