comparison src/samer/models/GaussianStatsOnline.java @ 0:bf79fb79ee13

Initial Mercurial check in.

+/*
+ *	Copyright (c) 2000, Samer Abdallah, King's College London.
+ *	All rights reserved.
+ *
+ *	This software is provided AS iS and WITHOUT ANY WARRANTY; 
+ *	without even the implied warranty of MERCHANTABILITY or 
+ *	FITNESS FOR A PARTICULAR PURPOSE.
+ */
+
+package samer.models;
+import  samer.core.*;
+import  samer.maths.*;
+import  samer.core.types.*;
+
+/**
+	Similar to GaussianStats:
+		Collect statistics about a vector: accumulates
+		sums and sums of products, then computes mean
+		and covariance.
+	
+		This is NOT a model! Computing energies and probabilities
+		directly from the first and second moments is horrible, since
+		you need the inverse of the covariance matrix. You could,
+		however, use this to train a Gaussian model.
+
+	BUT uses a moving average with a learning rate to track changes 
+ */
+
+public class GaussianStatsOnline extends GaussianStats
+{
+	VDouble	rate1,rate2;
+
+	public GaussianStatsOnline(VVector vec) throws Exception
+	{
+		super(vec);
+		rate1 = new VDouble("mean.rate",0.0001);
+		rate2 = new VDouble("cov.rate",0.0001);
+		sumx.addSaver();
+		count=1; // should stay that way.
+	}
+
+	public void reset() { super.reset(); count=1; }
+	public void dispose() { rate2.dispose(); rate1.dispose(); super.dispose(); }
+	public void run()
+	{
+		double r;
+		
+		r=rate1.value;
+		for (int i=0; i<n; i++) _sumx[i] += r*(x[i]-_sumx[i]);
+		
+		r=rate2.value;
+		for (int i=0; i<n; i++) {
+			double  [] a=_sumxx[i];
+			double  k=x[i];
+			for (int j=0; j<=i; j++) {
+				a[j] += r*(k*x[j]-a[j]);
+			}
+		}
+		sumx.changed();
+		sumxx.changed();
+	}
+}
+