linear_regression_model.cpp

// Code generated by Stan version 2.20.0

#include <stan/model/model_header.hpp>

namespace linear_regression_model_namespace {

using std::istream;
using std::string;
using std::stringstream;
using std::vector;
using stan::io::dump;
using stan::math::lgamma;
using stan::model::prob_grad;
using namespace stan::math;

static int current_statement_begin__;

stan::io::program_reader prog_reader__() {
    stan::io::program_reader reader;
    reader.add_event(0, 0, "start", "linear_regression.stan");
    reader.add_event(17, 15, "end", "linear_regression.stan");
    return reader;
}

class linear_regression_model
  : public stan::model::model_base_crtp<linear_regression_model> {
private:
        double observation;
        double covariate;
public:
    linear_regression_model(stan::io::var_context& context__,
        std::ostream* pstream__ = 0)
        : model_base_crtp(0) {
        ctor_body(context__, 0, pstream__);
    }

    linear_regression_model(stan::io::var_context& context__,
        unsigned int random_seed__,
        std::ostream* pstream__ = 0)
        : model_base_crtp(0) {
        ctor_body(context__, random_seed__, pstream__);
    }

    void ctor_body(stan::io::var_context& context__,
                   unsigned int random_seed__,
                   std::ostream* pstream__) {
        typedef double local_scalar_t__;

        boost::ecuyer1988 base_rng__ =
          stan::services::util::create_rng(random_seed__, 0);
        (void) base_rng__;  // suppress unused var warning

        current_statement_begin__ = -1;

        static const char* function__ = "linear_regression_model_namespace::linear_regression_model";
        (void) function__;  // dummy to suppress unused var warning
        size_t pos__;
        (void) pos__;  // dummy to suppress unused var warning
        std::vector<int> vals_i__;
        std::vector<double> vals_r__;
        local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
        (void) DUMMY_VAR__;  // suppress unused var warning

        try {
            // initialize data block variables from context__
            current_statement_begin__ = 2;
            context__.validate_dims("data initialization", "observation", "double", context__.to_vec());
            observation = double(0);
            vals_r__ = context__.vals_r("observation");
            pos__ = 0;
            observation = vals_r__[pos__++];

            current_statement_begin__ = 3;
            context__.validate_dims("data initialization", "covariate", "double", context__.to_vec());
            covariate = double(0);
            vals_r__ = context__.vals_r("covariate");
            pos__ = 0;
            covariate = vals_r__[pos__++];


            // initialize transformed data variables
            // execute transformed data statements

            // validate transformed data

            // validate, set parameter ranges
            num_params_r__ = 0U;
            param_ranges_i__.clear();
            current_statement_begin__ = 7;
            num_params_r__ += 1;
            current_statement_begin__ = 8;
            num_params_r__ += 1;
        } catch (const std::exception& e) {
            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
            // Next line prevents compiler griping about no return
            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
        }
    }

    ~linear_regression_model() { }


    void transform_inits(const stan::io::var_context& context__,
                         std::vector<int>& params_i__,
                         std::vector<double>& params_r__,
                         std::ostream* pstream__) const {
        typedef double local_scalar_t__;
        stan::io::writer<double> writer__(params_r__, params_i__);
        size_t pos__;
        (void) pos__; // dummy call to supress warning
        std::vector<double> vals_r__;
        std::vector<int> vals_i__;

        current_statement_begin__ = 7;
        if (!(context__.contains_r("coefficient")))
            stan::lang::rethrow_located(std::runtime_error(std::string("Variable coefficient missing")), current_statement_begin__, prog_reader__());
        vals_r__ = context__.vals_r("coefficient");
        pos__ = 0U;
        context__.validate_dims("parameter initialization", "coefficient", "double", context__.to_vec());
        double coefficient(0);
        coefficient = vals_r__[pos__++];
        try {
            writer__.scalar_unconstrain(coefficient);
        } catch (const std::exception& e) {
            stan::lang::rethrow_located(std::runtime_error(std::string("Error transforming variable coefficient: ") + e.what()), current_statement_begin__, prog_reader__());
        }

        current_statement_begin__ = 8;
        if (!(context__.contains_r("observation_noise_scale")))
            stan::lang::rethrow_located(std::runtime_error(std::string("Variable observation_noise_scale missing")), current_statement_begin__, prog_reader__());
        vals_r__ = context__.vals_r("observation_noise_scale");
        pos__ = 0U;
        context__.validate_dims("parameter initialization", "observation_noise_scale", "double", context__.to_vec());
        double observation_noise_scale(0);
        observation_noise_scale = vals_r__[pos__++];
        try {
            writer__.scalar_lb_unconstrain(0.0, observation_noise_scale);
        } catch (const std::exception& e) {
            stan::lang::rethrow_located(std::runtime_error(std::string("Error transforming variable observation_noise_scale: ") + e.what()), current_statement_begin__, prog_reader__());
        }

        params_r__ = writer__.data_r();
        params_i__ = writer__.data_i();
    }

    void transform_inits(const stan::io::var_context& context,
                         Eigen::Matrix<double, Eigen::Dynamic, 1>& params_r,
                         std::ostream* pstream__) const {
      std::vector<double> params_r_vec;
      std::vector<int> params_i_vec;
      transform_inits(context, params_i_vec, params_r_vec, pstream__);
      params_r.resize(params_r_vec.size());
      for (int i = 0; i < params_r.size(); ++i)
        params_r(i) = params_r_vec[i];
    }


    template <bool propto__, bool jacobian__, typename T__>
    T__ log_prob(std::vector<T__>& params_r__,
                 std::vector<int>& params_i__,
                 std::ostream* pstream__ = 0) const {

        typedef T__ local_scalar_t__;

        local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
        (void) DUMMY_VAR__;  // dummy to suppress unused var warning

        T__ lp__(0.0);
        stan::math::accumulator<T__> lp_accum__;
        try {
            stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);

            // model parameters
            current_statement_begin__ = 7;
            local_scalar_t__ coefficient;
            (void) coefficient;  // dummy to suppress unused var warning
            if (jacobian__)
                coefficient = in__.scalar_constrain(lp__);
            else
                coefficient = in__.scalar_constrain();

            current_statement_begin__ = 8;
            local_scalar_t__ observation_noise_scale;
            (void) observation_noise_scale;  // dummy to suppress unused var warning
            if (jacobian__)
                observation_noise_scale = in__.scalar_lb_constrain(0.0, lp__);
            else
                observation_noise_scale = in__.scalar_lb_constrain(0.0);

            // model body

            current_statement_begin__ = 12;
            lp_accum__.add(normal_log<propto__>(coefficient, 0.0, 1.0));
            current_statement_begin__ = 13;
            lp_accum__.add(exponential_log<propto__>(observation_noise_scale, 1.0));
            current_statement_begin__ = 14;
            lp_accum__.add(normal_log<propto__>(observation, (coefficient * covariate), observation_noise_scale));

        } catch (const std::exception& e) {
            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
            // Next line prevents compiler griping about no return
            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
        }

        lp_accum__.add(lp__);
        return lp_accum__.sum();

    } // log_prob()

    template <bool propto, bool jacobian, typename T_>
    T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
               std::ostream* pstream = 0) const {
      std::vector<T_> vec_params_r;
      vec_params_r.reserve(params_r.size());
      for (int i = 0; i < params_r.size(); ++i)
        vec_params_r.push_back(params_r(i));
      std::vector<int> vec_params_i;
      return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
    }


    void get_param_names(std::vector<std::string>& names__) const {
        names__.resize(0);
        names__.push_back("coefficient");
        names__.push_back("observation_noise_scale");
    }


    void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
        dimss__.resize(0);
        std::vector<size_t> dims__;
        dims__.resize(0);
        dimss__.push_back(dims__);
        dims__.resize(0);
        dimss__.push_back(dims__);
    }

    template <typename RNG>
    void write_array(RNG& base_rng__,
                     std::vector<double>& params_r__,
                     std::vector<int>& params_i__,
                     std::vector<double>& vars__,
                     bool include_tparams__ = true,
                     bool include_gqs__ = true,
                     std::ostream* pstream__ = 0) const {
        typedef double local_scalar_t__;

        vars__.resize(0);
        stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);
        static const char* function__ = "linear_regression_model_namespace::write_array";
        (void) function__;  // dummy to suppress unused var warning

        // read-transform, write parameters
        double coefficient = in__.scalar_constrain();
        vars__.push_back(coefficient);

        double observation_noise_scale = in__.scalar_lb_constrain(0.0);
        vars__.push_back(observation_noise_scale);

        double lp__ = 0.0;
        (void) lp__;  // dummy to suppress unused var warning
        stan::math::accumulator<double> lp_accum__;

        local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
        (void) DUMMY_VAR__;  // suppress unused var warning

        if (!include_tparams__ && !include_gqs__) return;

        try {
            if (!include_gqs__ && !include_tparams__) return;
            if (!include_gqs__) return;
        } catch (const std::exception& e) {
            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
            // Next line prevents compiler griping about no return
            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
        }
    }

    template <typename RNG>
    void write_array(RNG& base_rng,
                     Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
                     Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
                     bool include_tparams = true,
                     bool include_gqs = true,
                     std::ostream* pstream = 0) const {
      std::vector<double> params_r_vec(params_r.size());
      for (int i = 0; i < params_r.size(); ++i)
        params_r_vec[i] = params_r(i);
      std::vector<double> vars_vec;
      std::vector<int> params_i_vec;
      write_array(base_rng, params_r_vec, params_i_vec, vars_vec, include_tparams, include_gqs, pstream);
      vars.resize(vars_vec.size());
      for (int i = 0; i < vars.size(); ++i)
        vars(i) = vars_vec[i];
    }

    std::string model_name() const {
        return "linear_regression_model";
    }


    void constrained_param_names(std::vector<std::string>& param_names__,
                                 bool include_tparams__ = true,
                                 bool include_gqs__ = true) const {
        std::stringstream param_name_stream__;
        param_name_stream__.str(std::string());
        param_name_stream__ << "coefficient";
        param_names__.push_back(param_name_stream__.str());
        param_name_stream__.str(std::string());
        param_name_stream__ << "observation_noise_scale";
        param_names__.push_back(param_name_stream__.str());

        if (!include_gqs__ && !include_tparams__) return;

        if (include_tparams__) {
        }

        if (!include_gqs__) return;
    }


    void unconstrained_param_names(std::vector<std::string>& param_names__,
                                   bool include_tparams__ = true,
                                   bool include_gqs__ = true) const {
        std::stringstream param_name_stream__;
        param_name_stream__.str(std::string());
        param_name_stream__ << "coefficient";
        param_names__.push_back(param_name_stream__.str());
        param_name_stream__.str(std::string());
        param_name_stream__ << "observation_noise_scale";
        param_names__.push_back(param_name_stream__.str());

        if (!include_gqs__ && !include_tparams__) return;

        if (include_tparams__) {
        }

        if (!include_gqs__) return;
    }

}; // model

}  // namespace

typedef linear_regression_model_namespace::linear_regression_model stan_model;

stan::model::model_base& new_model(
        stan::io::var_context& data_context,
        unsigned int seed,
        std::ostream* msg_stream) {
  stan_model* m = new stan_model(data_context, seed, msg_stream);
  return *m;
}