only train on 10% of the points

src/ImageJ/Demo_Pixel_Replication.java

@@ -9,6 +9,7 @@ import java.nio.FloatBuffer;
 import java.util.ArrayList;
 import java.util.HashSet;
 import java.util.Random;
+import java.util.Collections;
 
 import static org.bytedeco.javacpp.opencv_core.*;
 import static org.bytedeco.javacpp.opencv_imgproc.*;
@@ -147,6 +148,14 @@ public class Demo_Pixel_Replication implements PlugInFilter {
         return iplOut.asCvMat();
     }
 
+    private Mat PrPts2Mat(ArrayList<Point2f> PrPts, double pct) {
+        ArrayList<Point2f> ShufPts = new ArrayList<Point2f>(PrPts);
+        Collections.shuffle(ShufPts);
+        // ArrayList<Point2f> SampPts = new ArrayList<Point2f>(ShufPts.subList(0, (int) (ShufPts.size() * pct)));
+        // return PrPts2Mat(SampPts);
+        return PrPts2Mat(new ArrayList<Point2f>(ShufPts.subList(0, (int) (ShufPts.size() * pct))));
+    }
+
     private Mat PrPts2Mat(ArrayList<Point2f> PrPts) {
         Mat emMat = new Mat(PrPts.size(), 2, CV_32FC1);
         FloatIndexer emMatIndexer = emMat.createIndexer();
@@ -171,9 +180,9 @@ public class Demo_Pixel_Replication implements PlugInFilter {
             em.set("nclusters", k);
             em.set("covMatType", EM.COV_MAT_GENERIC);
             em.set("maxIters", 1000);
-            // em.set("epsilon", 1e-6);
+            em.set("epsilon", 1e-6);
 
-            Mat emMat = PrPts2Mat(PrPts);
+            Mat emMat = PrPts2Mat(PrPts, 0.10);
             Mat initMeans = randomInitMeans(k);
             // em.trainE(emMat, initMeans, noArray());
             em.trainE(emMat, initMeans, new Mat(), new Mat(), logLikelihoods, labels, new Mat());