Google summer of code'24, Week 8.

template<typename WalkTypePolicy, typename PointType, typename RNGType, typename MT>
void tune_walkL(const std::vector<unsigned int>& walkL_values, const std::vector<unsigned int>& dimensions, const unsigned int num_points,
            	const unsigned int nburns, const unsigned int num_matrices){
    for (unsigned int n : dimensions) {
        std::list<MT> randCorMatrices;

        for (unsigned int walkL : walkL_values) {
            std::chrono::steady_clock::time_point start, end;
            double time;
            start = std::chrono::steady_clock::now();

            uniform_correlation_sampling_MT<WalkTypePolicy, PointType, RNGType>(n, randCorMatrices, walkL, num_points, 0);

            end = std::chrono::steady_clock::now();
            time = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count();
            std::cout << "Elapsed time : " << time << " (ms) for dimension: " << n << std::endl;

            int d = n*(n-1)/2;
            MT samples(d, num_points);
            unsigned int jj = 0;
            for(auto& mat : randCorMatrices){
                samples.col(jj) = getCoefficientsFromMatrix<NT, MT>(mat);
            	jj++;
            }

            //calculate psrf
            VT psrf = univariate_psrf<NT, VT, MT>(samples);
            double max_psrf = psrf.maxCoeff();
       	    std::cout << "PSRF = " << max_psrf << std::endl;

    	    //calculate ess
            unsigned int min_ess = 0;
            VT ess_vector = effective_sample_size<NT, VT, MT>(samples, min_ess);
	    std::cout << "Effective Sample Size = " << min_ess << std::endl;
            std::cout << "Average Effective Sample Size = " << min_ess/num_matrices << std::endl;

            // Clear the matrices for the next iteration
            randCorMatrices.clear();
    	}
    }
}