package JSci.maths.statistics;

/**
* The ProbabilityDistribution superclass provides an object for encapsulating probability distributions.
* @version 1.0
* @author Jaco van Kooten
*/
public abstract class ProbabilityDistribution extends Object {
        /**
        * Constructs a probability distribution.
        */
        public ProbabilityDistribution() {}
        /**
        * Probability density function.
        * @return the probability that a stochastic variable x has the value X, i.e. P(x=X).
        */
        public abstract double probability(double X);
        /**
        * Cumulative distribution function.
	* @return the probability that a stochastic variable x is less then X, i.e. P(x&lt;X).
        */
        public abstract double cumulative(double X);
        /**
	* Inverse of the cumulative distribution function.
        * @return the value X for which P(x&lt;X).
        */
        public abstract double inverse(double probability);
        /**
	* Check if the range of the argument of the distribution method is between <code>lo</code> and <code>hi</code>.
        * @exception OutOfRangeException If the argument is out of range.
        */
        protected final void checkRange(double x, double lo, double hi) {
                if(x<lo || x>hi)
                        throw new OutOfRangeException("The argument of the distribution method should be between "+lo+" and "+hi+".");
        }
        /**
	* Check if the range of the argument of the distribution method is between 0.0 and 1.0.
        * @exception OutOfRangeException If the argument is out of range.
        */
        protected final void checkRange(double x) {
        	if(x<0.0 || x>1.0)
        		throw new OutOfRangeException("The argument of the distribution method should be between 0.0 and 1.0.");
        }
        /**
        * This method approximates the value of X for which P(x&lt;X)=<I>prob</I>.
        * It applies a combination of a Newton-Raphson procedure and bisection method
        * with the value <I>guess</I> as a starting point. Furthermore, to ensure convergency
        * and stability, one should supply an inverval [<I>xLo</I>,<I>xHi</I>] in which the probalility
        * distribution reaches the value <I>prob</I>. The method does no checking, it will produce
        * bad results if wrong values for the parameters are supplied - use it with care.
        */
        protected final double findRoot(double prob,double guess,double xLo,double xHi) {
                final double accuracy=1.0e-10;
                final int maxIteration=150;
                double x=guess,xNew=guess;
                double error,pdf,dx=1000.0;
                int i=0;
                while(Math.abs(dx)>accuracy && i++<maxIteration) {
// Apply Newton-Raphson step
                        error=cumulative(x)-prob;
                        if(error<0.0)
                                xLo=x;
                        else
                                xHi=x;
                        pdf=probability(x);
                        if(pdf!=0.0) {          // Avoid division by zero
                                dx=error/pdf;
                                xNew=x-dx;
                        }
// If the NR fails to converge (which for example may be the
// case if the initial guess is to rough) we apply a bisection
// step to determine a more narrow interval around the root.
                        if(xNew<xLo || xNew>xHi || pdf==0.0) {
                                xNew=(xLo+xHi)/2.0;
                                dx=xNew-x;
                        }
                        x=xNew;
                }
                return x;
        }
}

