src/tests/libxrpl/csf/random.h

#pragma once
 
#include <algorithm>
#include <cstddef>
#include <iterator>
#include <random>
#include <vector>
 
namespace xrpl::test::csf {

template <class T, class G>
std::vector<T>
randomWeightedShuffle(std::vector<T> v, std::vector<double> w, G& g)
{
    using std::swap;
 
    for (auto i = 0uz; i + 1 < v.size(); ++i)
    {
        // Pick a random item from the unplaced tail, weighted by w.
        // NOLINTNEXTLINE(misc-const-correctness)
        std::discrete_distribution<std::size_t> dd(w.begin() + i, w.end());
        auto const idx = i + dd(g);
        std::swap(v[i], v[idx]);
        std::swap(w[i], w[idx]);
    }
    return v;
}

template <class RandomNumberDistribution, class Generator>
std::vector<typename RandomNumberDistribution::result_type>
sample(std::size_t size, RandomNumberDistribution dist, Generator& g)
{
    std::vector<typename RandomNumberDistribution::result_type> res(size);
    std::ranges::generate(res, [&dist, &g]() { return dist(g); });
    return res;
}

template <class RAIter, class Generator>
class Selector
{
    RAIter first_, last_;
    std::discrete_distribution<> dd_;
    Generator g_;
 
public:
    Selector(RAIter first, RAIter last, std::vector<double> const& w, Generator& g)
        : first_{first}, last_{last}, dd_{w.begin(), w.end()}, g_{g}
    {
        using Tag = std::iterator_traits<RAIter>::iterator_category;
        static_assert(
            std::is_same_v<Tag, std::random_access_iterator_tag>,
            "Selector only supports random access iterators.");
        // TODO: Allow for forward iterators
    }
 
    std::iterator_traits<RAIter>::value_type
    operator()()
    {
        auto idx = dd_(g_);
        return *(first_ + idx);
    }
};
 
template <typename Iter, typename Generator>
Selector<Iter, Generator>
makeSelector(Iter first, Iter last, std::vector<double> const& w, Generator& g)
{
    return Selector<Iter, Generator>(first, last, w, g);
}
 
//------------------------------------------------------------------------------
// Additional distributions of interest not defined in <random>

class ConstantDistribution
{
    double t_;
 
public:
    ConstantDistribution(double const& t) : t_{t}
    {
    }
 
    template <class Generator>
    double
    operator()(Generator&)
    {
        return t_;
    }
};

class PowerLawDistribution
{
    double xmin_;
    double a_;
    double inv_{1.0 / (1.0 - a_)};
    std::uniform_real_distribution<double> uf_{0, 1};
 
public:
    using result_type = double;
 
    PowerLawDistribution(double xmin, double a) : xmin_{xmin}, a_{a}
    {
    }
 
    template <class Generator>
    double
    operator()(Generator& g)
    {
        // use inverse transform of CDF to sample
        // CDF is P(X <= x): 1 - (x/xmin)^(1-a)
        return xmin_ * std::pow(1 - uf_(g), inv_);
    }
};
 
}  // namespace xrpl::test::csf