/*
 * 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,
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 * 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 common.Util;

public class UnivarGaussian extends AbstractCondProbDistrib{
    public static final UnivarGaussian STANDARD = new UnivarGaussian(0, 1);

    

    public UnivarGaussian(double mean, double variance){
	fixedMean = true;
	setMean(new Double(mean));

	fixedVariance = true;
	setVariance(new Double(variance));
    }

    

    public UnivarGaussian(List params){
	if (params.size() == 0) {
	    fixedMean = false;
	    fixedVariance = false;
	} else if (params.size() == 1) {
	    fixedMean = false;
	    fixedVariance = true;
	    setVariance(params.get(0));
	} else if (params.size() == 2) {
	    fixedMean = true;
	    setMean(params.get(0));
	    fixedVariance = true;
	    setVariance(params.get(1));
	} else {
	    throw new IllegalArgumentException
		("UnivarGaussian CPD expects at most 2 parameters, not " 
		 + params.size());
	}
    }


    public double getProb(List args, Object value){
	initParams(args);
	if(!(value instanceof Number))
	    throw new IllegalArgumentException
		("The value passed to the univariate Gaussian distribution's "
		 + "getProb "
		 + "method must be of type Number, not "+value.getClass()+".");
	return getProb(((Number) value).doubleValue());
    }

    public double getLogProb(List args, Object value){
	initParams(args);
	if(!(value instanceof Number))
	    throw new IllegalArgumentException
		("The value passed to the univariate Gaussian distribution's "
		 + "getLogProb "
		 + "method must be of type Number, not "+value.getClass()+".");
	return getLogProb(((Number) value).doubleValue());
    }

    

    public double getProb(double x) {
	return (Math.exp(-Math.pow((x - mu), 2)/(2*sigmaSquared))
		/ normConst);
    }

    

    public double getLogProb(double x) {
	return (-Math.pow((x - mu), 2)/(2*sigmaSquared)) - logNormConst;
    }

    public Object sampleVal(List args, Type childType){
	initParams(args);
	return new Double(sampleVal());
    }

    

    public double sampleVal() {
	double U = Util.random();
        double V = Util.random();
        return (mu + (sigma * Math.sin(2 * Math.PI * V) *
		      Math.sqrt((-2 * Math.log(U)))));	
    }

    private void initParams(List args) {
	if (fixedMean) {
	    if (args.size() > 0) {
		throw new IllegalArgumentException
		    ("UnivarGaussian CPD with fixed mean expects no "
		     + "arguments.");
	    }
	} else {
	    if (args.size() < 1) {
		throw new IllegalArgumentException
		    ("UnivarGaussian CPD created without a fixed mean; "
		     + "requires mean as an argument.");
	    }
	    setMean(args.get(0));

	    if (fixedVariance) {
		if (args.size() > 1) {
		    throw new IllegalArgumentException
			("UnivarGaussian CPD with fixed variance expects "
			 + "only one argument.");
		}
	    } else {
		if (args.size() < 2) {
		    throw new IllegalArgumentException
			("UnivarGaussian CPD created without a fixed "
			 + "variance; requires variance as argument.");
		}
		setVariance(args.get(1));
	    }
	}
    }
	
    private void setMean(Object mean) {
	if (!(mean instanceof Number)) {
	    throw new IllegalArgumentException
		("Mean of UnivarGaussian distribution must be a number, "
		 + "not " + mean + " of " + mean.getClass());
	}
	mu = ((Number) mean).doubleValue();
    }

    private void setVariance(Object variance) {
	if (!(variance instanceof Number)) {
	    throw new IllegalArgumentException
		("Variance of UnivarGaussian distribution must be a number, "
		 + "not " + variance + " of " + variance.getClass());
	}
	sigmaSquared = ((Number) variance).doubleValue();

	if (sigmaSquared <= 0) {
	    throw new IllegalArgumentException
		("Variance of UnivarGaussian distribution must be positive, "
		 + "not " + sigmaSquared);
	}
	sigma = Math.sqrt(sigmaSquared);
	normConst = sigma * ROOT_2PI;
	logNormConst = (0.5 * Math.log(sigmaSquared)) + LOG_ROOT_2PI;
    }

    private boolean fixedMean;
    private boolean fixedVariance;

    private double mu;
    private double sigmaSquared;
    private double sigma;
    private double normConst;
    private double logNormConst;

    private static final double ROOT_2PI = Math.sqrt(2 * Math.PI);
    private static final double LOG_ROOT_2PI 
	= 0.5 * (Math.log(2) + Math.log(Math.PI));
}

This file was generated on Tue Jun 08 17:53:48 PDT 2010 from file UnivarGaussian.java
by the ilog.language.tools.Hilite Java tool written by Hassan Aït-Kaci