mmpp2.java

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/*
 * Created on Oct 31, 2006
 *
 * TODO To change the template for this generated file go to
 * Window - Preferences - Java - Code Style - Code Templates
 */
package jmt.engine.random;

import jmt.common.exception.IncorrectDistributionParameterException;
import jmt.engine.random.engine.RandomEngine;

/**
 * @author casale
 *
 * TODO To change the template for this generated type comment go to
 * Window - Preferences - Java - Code Style - Code Templates
 */
public class MMPP2 extends Distribution {
	private int curState;
	protected Exponential expDistr;

	public MMPP2(){
		curState=0;
		engine = RandomEngine.makeDefault();
		expDistr = new Exponential();
	}

	public int getCurState()
	{
		return curState;
	}

	public void setCurState(int newState)
	{
		curState=newState;
	}

	public double pdf(double x, Parameter p) { //other implementation may use p.check()
		return 0.0;
	}

	public double cdf(double x, Parameter p) { //other implementation may use p.check()
		return 0.0;
	}

	public double theorMean(Parameter p)
	        throws IncorrectDistributionParameterException {
		return 0.0;
	}

	public double theorVariance(Parameter p)
	        throws IncorrectDistributionParameterException {
		return 0.0;
	}

	/**
	 * it returns the new random number.
	 * This method is used to obtain from the distribution the next number distributed
	 * according to the distribution parameter.
	 *
	 * @param p parameter of the hyper exponential distribution.
	 * @throws IncorrectDistributionParameterException
	 * @return double with the next random number of this distribution.
	 */

	public double nextRand(Parameter p) throws IncorrectDistributionParameterException {
		if (p.check()) {
			if (this.getCurState()==0)
			{
				if(engine.nextDouble() <= ((MMPP2Par) p).getR0())
					this.setCurState(1);
				return expDistr.nextRand(((MMPP2Par) p).getExpParam1());
			}
			else
			{
					if(engine.nextDouble() <= ((MMPP2Par) p).getR1())
						this.setCurState(0);
					return expDistr.nextRand(((MMPP2Par) p).getExpParam2());
			}
		} else {
			throw new IncorrectDistributionParameterException("Remember: parameter mean, variance, lambda1 and lambda 2 must be gtz; p must be a number betwen 0 and 1");
		}		
	}

}