exponential.h

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// -*- C++ -*-
// $Id$
 
/*
 * This generator uses the straightforward transformation
 *  x = - log(y) * m
 *
 * to turn a uniform (0,1) y into an exponentially distributed
 * variable x.  x has density function
 * 
 * f(x) = (1/m) exp(-(1/m)x)  (x > 0)
 *
 * and mean m.
 *
 * NEEDS_WORK: Adapt the method of Ahrens and Dieter.  This will
 * require extending the precision of the constants.
 *
 * Ahrens, J.H. and Dieter, U.  Computer Methods for Sampling From the
 * Exponential and Normal Distributions. Comm. ACM, 15,10 (Oct. 1972), p. 873.
 */
 
#ifndef BZ_RANDOM_EXPONENTIAL
#define BZ_RANDOM_EXPONENTIAL
 
#ifndef BZ_RANDOM_UNIFORM
 #include <random/uniform.h>
#endif
 
namespace ranlib {
 
template<typename T = double, typename IRNG = defaultIRNG, 
    typename stateTag = defaultState>
class ExponentialUnit : public UniformOpen<T,IRNG,stateTag>
{
public:
    typedef T T_numtype;
 
  ExponentialUnit() {}
 
  explicit ExponentialUnit(unsigned int i) :
    UniformOpen<T,IRNG,stateTag>(i) {};
 
    T random()
    {
        return - log(UniformOpen<T,IRNG,stateTag>::random());
    }
};
 
template<typename T = double, typename IRNG = defaultIRNG, 
    typename stateTag = defaultState>
class Exponential : public ExponentialUnit<T,IRNG,stateTag> {
 
public:
    typedef T T_numtype;
 
    Exponential(T mean)
    {
        mean_ = mean;
    }
 
  Exponential(T mean, unsigned int i) :
    UniformOpen<T,IRNG,stateTag>(i) 
  {
        mean_ = mean;
  };
  
    T random()
    {
        return mean_ * ExponentialUnit<T,IRNG,stateTag>::random();
    }
 
private:
    T mean_;
};
 
}
 
#endif // BZ_RANDOM_EXPONENTIAL