/*
 * Copyright (c) 2005, Regents of the University of California
 * All rights reserved.
 * 
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions
 * are met:
 *
 * * Redistributions of source code must retain the above copyright
 *   notice, this list of conditions and the following disclaimer.
 *
 * * Redistributions in binary form must reproduce the above copyright
 *   notice, this list of conditions and the following disclaimer in
 *   the documentation and/or other materials provided with the
 *   distribution.  
 *
 * * Neither the name of the University of California, Berkeley nor
 *   the names of its contributors may be used to endorse or promote
 *   products derived from this software without specific prior 
 *   written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
 * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
 * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
 * STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
 * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
 * OF THE POSSIBILITY OF SUCH DAMAGE.
 */

package blog.distrib;

import blog.*;
import java.util.*;
import Jama.*;

public class LinearGaussian extends AbstractCondProbDistrib {
    

    public LinearGaussian(List params) {
	if (params.size() != 3) {
	    throw new IllegalArgumentException
		("LinearGaussian CPD takes three parameters: mu, W, Sigma.");
	}

	for (int i = 0; i < 3; ++i) {
	    if (!(params.get(i) instanceof Matrix)) {
		throw new IllegalArgumentException
		    ("All parameters to LinearGaussian CPD should be "
		     + "matrices.");
	    }
	}

	mu = (Matrix) params.get(0);
	if (mu.getColumnDimension() != 1) {
	    throw new IllegalArgumentException
		("First parameter to LinearGaussian CPD (mean mu) should "
		 + "be a column vector.");
	}
	d = mu.getRowDimension();

	W = (Matrix) params.get(1);
	if (W.getRowDimension() != d) {
	    throw new IllegalArgumentException
		("Second parameter to LinearGaussian CPD (weight matrix W) "
		 + "should have same number of rows as first parameter.");
	}
	c = W.getColumnDimension();

	Sigma = (Matrix) params.get(2);
	if (!((Sigma.getRowDimension() == d)
	      && (Sigma.getColumnDimension() == d))) {
	    throw new IllegalArgumentException
		("Third parameter to LinearGaussian CPD (covariance matrix "
		 + "sigma) should be square matrix of same dimension as "
		 + "first parameter.");
	}
	
	// Make sure Sigma is symmetric
	for (int i = 0; i < Sigma.getRowDimension(); ++i) {
	    for (int j = 0; j < i; ++j) {
		if (Sigma.get(i, j) != Sigma.get(j, i)) {
		    throw new IllegalArgumentException
			("Covariance matrix for LinearGaussian CPD must "
			 + "be symmetric.");
		}
	    }
	}
    }

    public double getProb(List args, Object value) {
	Matrix v = getParentVector(args);

	if (value instanceof Matrix) {
	    MultivarGaussian distrib = new MultivarGaussian
		(mu.plus(W.times(v)), Sigma);
	    return distrib.getProb((Matrix) value);
	}
	return 0;
    }

    public Object sampleVal(List args, Type childType) {
	Matrix v = getParentVector(args);
	MultivarGaussian distrib = new MultivarGaussian
	    (mu.plus(W.times(v)), Sigma);
	return distrib.sampleVal();
    }

    private Matrix getParentVector(List args) {
	Matrix v = new Matrix(c, 1);
	int curRow = 0;
	for (Iterator iter = args.iterator(); iter.hasNext(); ) {
	    Object arg = iter.next();
	    if (!((arg instanceof Matrix)
		  && (((Matrix) arg).getColumnDimension() == 1))) {
		throw new IllegalArgumentException
		    ("Arguments for LinearGaussian CPD must be "
		     + "column vectors.");
	    }

	    Matrix parent = (Matrix) arg;
	    if (curRow + parent.getRowDimension() > c) {
		throw new IllegalArgumentException
		    ("Error in LinearGaussian CPD: sum of dimensions of "
		     + "parents exceeds number of columns in W.");
	    }
	    v.setMatrix(curRow, curRow + parent.getRowDimension() - 1, 
			0, 0, parent);
	    curRow += parent.getRowDimension();
	}

	if (curRow < c) {
	    throw new IllegalArgumentException
		("Error in LinearGaussian CPD: sum of dimensions of "
		 + "parents is less than number of columns in W.");
	}

	return v;
    }
    
    private int d; // dimension of child vector
    private int c; // sum of dimensions of parent vectors

    private Matrix mu;
    private Matrix W;
    private Matrix Sigma;
}

This file was generated on Tue Jun 08 17:53:47 PDT 2010 from file LinearGaussian.java
by the ilog.language.tools.Hilite Java tool written by Hassan Aït-Kaci