rayleigh_distribution.hpp

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//============================================================================
// MCKL/include/mckl/random/rayleigh_distribution.hpp
//----------------------------------------------------------------------------
// MCKL: Monte Carlo Kernel Library
//----------------------------------------------------------------------------
// Copyright (c) 2013-2018, Yan Zhou
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#ifndef MCKL_RANDOM_RAYLEIGH_DISTRIBUTION_HPP
#define MCKL_RANDOM_RAYLEIGH_DISTRIBUTION_HPP

#include <mckl/random/internal/common.hpp>
#include <mckl/random/u01_distribution.hpp>

namespace mckl {

namespace internal {

template <typename RealType>
inline bool rayleigh_distribution_check_param(RealType sigma)
{
    return sigma > 0;
}

template <std::size_t, typename RealType, typename RNGType>
inline void rayleigh_distribution_impl(
    RNGType &rng, std::size_t n, RealType *r, RealType sigma)
{
    u01_oc_distribution(rng, n, r);
    log(n, r, r);
    mul(n, -2 * sigma * sigma, r, r);
    sqrt(n, r, r);
}

} // namespace internal

MCKL_DEFINE_RANDOM_DISTRIBUTION_BATCH_1(
    Rayleigh, rayleigh, RealType, RealType, sigma)

template <typename RealType>
class RayleighDistribution
{
    MCKL_DEFINE_RANDOM_DISTRIBUTION_ASSERT_REAL_TYPE(Rayleigh)
    MCKL_DEFINE_RANDOM_DISTRIBUTION_1(
        Rayleigh, rayleigh, RealType, result_type, sigma, 1)
    MCKL_DEFINE_RANDOM_DISTRIBUTION_MEMBER_0

  public:
    result_type min() const { return 0; }

    result_type max() const { return std::numeric_limits<result_type>::max(); }

    void reset() {}

  private:
    template <typename RNGType>
    result_type generate(RNGType &rng, const param_type &param)
    {
        U01OCDistribution<RealType> u01;

        return param.sigma() * std::sqrt(-2 * std::log(u01(rng)));
    }
}; // class RayleighDistribution

MCKL_DEFINE_RANDOM_DISTRIBUTION_RAND(Rayleigh, RealType)

} // namespace mckl

#endif // MCKL_RANDOM_RAYLEIGH_DISTRIBUTION_HPP