diag_e_metric.hpp

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#ifndef STAN__MCMC__DIAG__E__METRIC__BETA
#define STAN__MCMC__DIAG__E__METRIC__BETA

#include <boost/random/variate_generator.hpp>
#include <boost/random/normal_distribution.hpp>

#include <stan/mcmc/hmc/hamiltonians/base_hamiltonian.hpp>
#include <stan/mcmc/hmc/hamiltonians/diag_e_point.hpp>

namespace stan {
  
  namespace mcmc {
    
    // Euclidean manifold with diagonal metric
    template <typename M, typename BaseRNG>
    class diag_e_metric: public base_hamiltonian<M, diag_e_point, BaseRNG> {
      
    public:
      
      diag_e_metric(M& m, std::ostream* e):
      base_hamiltonian<M, diag_e_point, BaseRNG>(m, e) {};
      ~diag_e_metric() {};
      
      double T(diag_e_point& z) {
        return 0.5 * z.p.dot( z.mInv.cwiseProduct(z.p) );
      }
      
      double tau(diag_e_point& z) { return T(z); }
      double phi(diag_e_point& z) { return this->V(z); }
      
      const Eigen::VectorXd dtau_dq(diag_e_point& z) {
        return Eigen::VectorXd::Zero(this->model_.num_params_r());
      }

      const Eigen::VectorXd dtau_dp(diag_e_point& z) {
        return z.mInv.cwiseProduct(z.p);
      }
      
      const Eigen::VectorXd dphi_dq(diag_e_point& z) {
        return z.g;
      }
      
      void sample_p(diag_e_point& z, BaseRNG& rng) {
        
        boost::variate_generator<BaseRNG&, boost::normal_distribution<> > 
          rand_diag_gaus(rng, boost::normal_distribution<>());
        
        for (int i = 0; i < z.p.size(); ++i) 
          z.p(i) = rand_diag_gaus() / sqrt(z.mInv(i));

      }
      
    };
    
  } // mcmc
  
} // stan


#endif