/* * 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>#endifBZ_NAMESPACE(ranlib)template<typename T = double, typename IRNG = defaultIRNG,     typename stateTag = defaultState>class ExponentialUnit : public UniformOpen<T,IRNG,stateTag>{public:    typedef T T_numtype;    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;    }    T random()    {        return mean_ * ExponentialUnit<T,IRNG,stateTag>::random();    }private:    T mean_;};BZ_NAMESPACE_END#endif // BZ_RANDOM_EXPONENTIAL