collision_test.hpp

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//============================================================================
// MCKL/include/mckl/random/collision_test.hpp
//----------------------------------------------------------------------------
// MCKL: Monte Carlo Kernel Library
//----------------------------------------------------------------------------
// Copyright (c) 2013-2018, Yan Zhou
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
//   Redistributions of source code must retain the above copyright notice,
//   this list of conditions and the following disclaimer.
//
//   Redistributions in binary form must reproduce the above copyright notice,
//   this list of conditions and the following disclaimer in the documentation
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//
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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//============================================================================

#ifndef MCKL_RANDOM_COLLISION_TEST_HPP
#define MCKL_RANDOM_COLLISION_TEST_HPP

#include <mckl/random/internal/common.hpp>
#include <mckl/random/poisson_test.hpp>
#include <bitset>

namespace mckl {

template <std::size_t D, std::size_t T>
class CollisionTest : public PoissonTest<CollisionTest<D, T>>
{
    static_assert(D > 1, "**CollisionTest** used with D less than two");

    static_assert(T > 0, "**CollisionTest** used with T equal to zero");

  public:
    CollisionTest(std::size_t n) : n_(n), mean_(0)
    {
        double p = static_cast<double>(n);
        mean_ = K_ * (p / K_ - 1 + std::pow(1 - 1.0 / K_, p));
    }

    MCKL_DEFINE_RANDOM_TEST_OPERATOR(std::size_t)

    template <typename RNGType, typename U01DistributionType>
    std::size_t operator()(RNGType &rng, U01DistributionType &u01)
    {
        using result_type = typename U01DistributionType::result_type;

        const std::size_t k = internal::BufferSize<result_type, T>::value;
        const std::size_t m = n_ / k;
        const std::size_t l = n_ % k;
        Vector<result_type> r(k * T);
        std::size_t s = 0;
        occurs_type occurs;
        for (std::size_t i = 0; i != m; ++i) {
            generate(rng, u01, k, r.data(), s, occurs);
        }
        generate(rng, u01, l, r.data(), s, occurs);

        return s;
    }

    double mean() const { return mean_; }

  private:
    static constexpr std::size_t K_ = internal::Pow<std::size_t, D, T>::value;

    using occurs_type = std::conditional_t<K_ <= (1U << 27), std::bitset<K_>,
        std::set<std::size_t>>;

    std::size_t n_;
    double mean_;

    template <typename RNGType, typename U01DistributionType>
    void generate(RNGType &rng, U01DistributionType &u01, std::size_t n,
        typename U01DistributionType::result_type *r, std::size_t &s,
        occurs_type &occurs) const
    {
        rand(rng, u01, n * T, r);
        mul(n * T, static_cast<typename U01DistributionType::result_type>(D),
            r, r);
        for (std::size_t i = 0; i != n; ++i, r += T) {
            count(internal::serial_index<D, T>(r), s, occurs);
        }
    }

    void count(std::size_t u, std::size_t &s, std::bitset<K_> &occurs) const
    {
        if (occurs.test(u)) {
            ++s;
        } else {
            occurs.set(u);
        }
    }

    void count(
        std::size_t u, std::size_t &s, std::set<std::size_t> &occurs) const
    {
        if (occurs.count(u)) {
            ++s;
        } else {
            occurs.insert(u);
        }
    }
}; // class CollisionTest

} // namespace mckl

#endif // MCKL_RANDOM_COLLISION_TEST_HPP