annotate src/samer/units/NoisyLinearSource.java @ 8:5e3cbbf173aa tip

Reorganise some more

/*
 *	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.units;
import  samer.core.*;
import  samer.maths.*;
import  samer.tools.*;

/** 
	A class for generating random vectors according
	to a particular generative model. If the output
	is y, then
		y = Bx + e
	where x is a vector of indepedent identitically
	distributed random variables, e is another vector of
	iid random variables, and B is an arbitrary matrix.
	The elements of x are the 'sources' and are drawn from
	the random number generator rnd. The elements of e
	are the 'noise' and are drawn from a different generator,
	which defaults to Gaussian.
  */

public class NoisyLinearSource extends AnonymousTask
{
	public 	VGenerator	src, noise;
	
	VVector				s;		// sources
	VVector				x;		// mixtures
	VVector				e;		// noise
	Matrix					A;		// mixing matrix

	double []	xa, ea;
	Task			t1, t2;

	public NoisyLinearSource( int m, int n)	throws Exception
	{
		// super("source");

		// Shell.push(node);
		s   = new VVector("s",m);
		x   = new VVector("x",n);
		e   = new VVector("e",n);
		A   = new Matrix("A",n,m);
		A.identity();

		src = new VGenerator("source");
		noise = new VGenerator("noise");
		// Shell.pop();

		xa=s.array();
		ea=e.array();
		t1 = new MatrixTimesVector(x,A,s);
		t2 = new VectorPlusEqualsVector(x,e);
 	}

	public VVector	output() { return x; }
	public Matrix	getMixingMatrix() { return A; }

	public void run() throws Exception { 
		src.next(xa); 
		noise.next(ea); 
		t1.run();
		t2.run();
		s.changed(); 
		x.changed(); 
		e.changed(); 
	}

	public void dispose() 
	{
		src.dispose();
		noise.dispose();
		s.dispose();
		x.dispose();
		e.dispose();
		A.dispose();
		t1.dispose();
		t2.dispose();
		super.dispose();
	}
}