samer@0: package samer.models;
samer@0: 
samer@0: import samer.core.*;
samer@0: import samer.core.types.*;
samer@0: import samer.maths.*;
samer@0: import samer.functions.*;
samer@0: import samer.maths.opt.*;
samer@0: import samer.tools.*;
samer@0: 
samer@0: /**
samer@0: 	Non adaptive model of vector in which each component is
samer@0: 	independent of the others and all are identically distributed
samer@0: 	according to a given prior
samer@0: 	*/
samer@0: 
samer@0: public class IIDPrior extends AnonymousTask implements Model
samer@0: {
samer@0: 	Vec 		x;
samer@0: 	VVector	e; // , g;
samer@0: 	Function	logprior, phi;
samer@0: 	VDouble	E;
samer@0: 
samer@0: 	double [] _x, _e, _g;
samer@0: 
samer@0: 	// default model is Gaussian
samer@0: 	public IIDPrior(int n) { this(n,new HalfSquare()); }
samer@0: 	public IIDPrior(Vec in) { this(in.size(),new HalfSquare()); setInput(in); }
samer@0: 	public IIDPrior(Vec in, Function E) { this(in.size(),E); setInput(in); }
samer@0: 	public IIDPrior(int N, Function f)
samer@0: 	{
samer@0: 		int n=N;
samer@0: 		e=new VVector("e",n);
samer@0: 		// g=new VVector("phi",n);
samer@0: 		E=new VDouble("E");
samer@0: 		logprior = f; phi = f.derivative();
samer@0: 		_e = e.array();
samer@0: 		_g = new double[n]; // g.array();
samer@0: 	}
samer@0: 
samer@0: 	public int getSize() { return e.size(); }
samer@0: 
samer@0: 	public String toString() { return "IIDPrior:"+logprior.format("x"); }
samer@0: 
samer@0: 	public void dispose() {
samer@0: 		e.dispose();
samer@0: 		// g.dispose();
samer@0: 		E.dispose();
samer@0: 		logprior.dispose();
samer@0: 		phi.dispose();
samer@0: 	}
samer@0: 
samer@0: 	public void setInput(Vec input) {	x=input; _x=x.array(); }
samer@0: 	public void setLogPrior(Function f) {
samer@0: 		phi.dispose(); logprior.dispose();
samer@0: 		logprior=f; phi=f.derivative();
samer@0: 	}
samer@0: 
samer@0: 	public VVector	getEnergyVector() { return e; }
samer@0: 	public VDouble	getEnergySignal() { return E; }
samer@0: 	public double	getEnergy() { return E.value; }
samer@0: 	public double [] getGradient() { return _g; 	}
samer@0: 	public void infer() {}
samer@0: 	public void compute()
samer@0: 	{
samer@0: 	  	phi.apply(_x,_g); // g.changed();
samer@0: 		logprior.apply(_x,_e);	e.changed();
samer@0: 		E.set(Mathx.sum(_e));
samer@0: 	}
samer@0: 
samer@0: 	public Functionx functionx() {
samer@0: 		return new Functionx() {
samer@0: 			public void dispose() {}
samer@0: 			public void evaluate(Datum P) { P.f=evaluate(P.x,P.g); }
samer@0: 			public double evaluate(double [] x, double [] g) {
samer@0: 				logprior.apply(x,g);
samer@0: 				double E=Mathx.sum(g);
samer@0: 				phi.apply(x,g);
samer@0: 				return E;
samer@0: 			}
samer@0: 		};
samer@0: 	}
samer@0: 
samer@0: 	public void run() { compute(); }
samer@0: }
samer@0: