generate_theta.hpp

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// SPDX-License-Identifier: BSD-2-Clause
 
#ifndef cMHN_UTILITY_GENERATE_THETA_H
#define cMHN_UTILITY_GENERATE_THETA_H
 
#include <prc.hpp>
 
namespace cMHN
{
    template<pRC::Size D, class T = pRC::Float<>>
    static inline auto generateTheta(pRC::RandomEngine &rng,
        pRC::Float<> const &fullness = 0.5, T const &diagonalMean = -2,
        T const &diagonalStd = 2, T const &offDiagonalMean = 0,
        T const &offDiagonalB = 0.75)
    {
        // diagonal
        pRC::GaussianDistribution<T> diagonalDist(diagonalMean, diagonalStd);
        auto diagonalTheta =
            pRC::random<pRC::Tensor<T, D, D>>(rng, diagonalDist);
 
        // off diagonal
        pRC::LaplaceDistribution<T> offDiagonalDist(offDiagonalMean,
            offDiagonalB);
        auto offDiagonalTheta =
            pRC::random<pRC::Tensor<T, D, D>>(rng, offDiagonalDist);
 
        // combine both
        auto theta =
            eval(diagonal(diagonalTheta) + offDiagonal(offDiagonalTheta));
 
        // add sparsity
        pRC::UniformDistribution<pRC::Float<>> sparsityDist;
        auto allowedPlaces = offDiagonal(
            pRC::random<pRC::Tensor<pRC::Float<>, D, D>>(rng, sparsityDist));
        for(pRC::Index i = 0; i < D; ++i)
        {
            for(pRC::Index j = 0; j < D; ++j)
            {
                if(allowedPlaces(i, j) > fullness)
                {
                    theta(i, j) = pRC::zero();
                }
            }
        }
 
        return theta;
    }
 
    template<pRC::Size D, class T = pRC::Float<>>
    static inline auto generateTheta(pRC::Float<> const &fullness = 0.5,
        T const &diagonalMean = -2, T const &diagonalStd = 2,
        T const &offDiagonalMean = 0, T const &offDiagonalB = 0.75)
    {
        // rng setup
        pRC::SeedSequence seq(8, 16);
        pRC::RandomEngine rng(seq);
 
        return generateTheta<D>(rng, fullness, diagonalMean, diagonalStd,
            offDiagonalMean, offDiagonalB);
    }
}
 
#endif // cMHN_UTILITY_GENERATE_THETA_H