package umontreal.iro.lecuyer.randvar;
import umontreal.iro.lecuyer.rng.*;
import umontreal.iro.lecuyer.probdist.*;


/**
 * This class implements noncentral chi square random variate generators 
 * using the additive property of the noncentral chi square distribution. It uses the following algorithm: generate a real
 *  
 * X∼N((λ)1/2, 1) from a normal distribution with variance 1, 
 * generate a real 
 * Y∼Γ((ν - 1)/2, 1/2) from a gamma distribution,
 * then return X2 + Y. Here ν is the number of degrees of freedom and 
 * λ is the noncentrality parameter.
 * 
 * 

 * To generate the normal variates, one uses the fast 
 * acceptance-complement ratio method in
 * (see class {@link umontreal.iro.lecuyer.randvar.NormalACRGen NormalACRGen}).
 * To generate the gamma variates, one uses acceptance-rejection for α < 1,
 * and acceptance-complement for 
 * α >= 1, as proposed in
 * (see class {@link umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen GammaAcceptanceRejectionGen}).
 * 
 * 

 * This noncentral chi square generator is faster than the generator 
 * {@link umontreal.iro.lecuyer.randvar.ChiSquareNoncentralPoisGen ChiSquareNoncentralPoisGen} .
 * For small λ, it is nearly twice as fast. As λ increases, 
 * it is still faster but not as much.
 * 
 */
public class ChiSquareNoncentralGamGen extends ChiSquareNoncentralGen  {
   private double racLam = -1.0;



   /**
    * Creates a noncentral chi square random variate generator with 
    *  with ν = nu degrees of freedom and noncentrality parameter
    * λ = lambda using stream stream, as described above.
    * 
    */
   public ChiSquareNoncentralGamGen (RandomStream stream,
                                     double nu, double lambda)  {
      super (stream, null);
      setParams (nu, lambda);
      racLam = Math.sqrt(lambda);
   }
 
  
   public double nextDouble() {
      return gamGen (stream, nu, racLam);
   }

   /**
    * Generates a variate from the noncentral chi square
    *    distribution with parameters ν = nu and λ = lambda using stream stream, as described above.
    * 
    */
   public static double nextDouble (RandomStream stream,
                                    double nu, double lambda)  {
      double racLam = Math.sqrt(lambda);
      return gamGen (stream, nu, racLam);
   }


//>>>>>>>>>>>>>>>>>>>>  P R I V A T E    M E T H O D S   <<<<<<<<<<<<<<<<<<<< 

   private static double gamGen (RandomStream s, double nu, double racLam) {
      // racLam = sqrt(lambda)
      double x = NormalACRGen.nextDouble (s, racLam, 1.0);
      double y = GammaAcceptanceRejectionGen.nextDouble(s, 0.5*(nu - 1.0), 0.5);
      return x*x + y;
   }

}