Constrain model

constrain.hpp

// Code generated by stanc 4f8f010
#include <stan/model/model_header.hpp>
namespace constrain_model_namespace {


inline void validate_positive_index(const char* var_name, const char* expr,
                                    int val) {
  if (val < 1) {
    std::stringstream msg;
    msg << "Found dimension size less than one in simplex declaration"
        << "; variable=" << var_name << "; dimension size expression=" << expr
        << "; expression value=" << val;
    std::string msg_str(msg.str());
    throw std::invalid_argument(msg_str.c_str());
  }
}

inline void validate_unit_vector_index(const char* var_name, const char* expr,
                                       int val) {
  if (val <= 1) {
    std::stringstream msg;
    if (val == 1) {
      msg << "Found dimension size one in unit vector declaration."
          << " One-dimensional unit vector is discrete"
          << " but the target distribution must be continuous."
          << " variable=" << var_name << "; dimension size expression=" << expr;
    } else {
      msg << "Found dimension size less than one in unit vector declaration"
          << "; variable=" << var_name << "; dimension size expression=" << expr
          << "; expression value=" << val;
    }
    std::string msg_str(msg.str());
    throw std::invalid_argument(msg_str.c_str());
  }
}


using std::istream;
using std::string;
using std::stringstream;
using std::vector;
using std::pow;
using stan::io::dump;
using stan::math::lgamma;
using stan::model::model_base_crtp;
using stan::model::rvalue;
using stan::model::cons_list;
using stan::model::index_uni;
using stan::model::index_max;
using stan::model::index_min;
using stan::model::index_min_max;
using stan::model::index_multi;
using stan::model::index_omni;
using stan::model::nil_index_list;
using namespace stan::math;
using stan::math::pow; 

stan::math::profile_map profiles__;
static int current_statement__= 0;
static const std::vector<string> locations_array__ = {" (found before start of program)",
                                                      " (in 'constrain.stan', line 10, column 2 to column 29)",
                                                      " (in 'constrain.stan', line 14, column 2 to column 36)",
                                                      " (in 'constrain.stan', line 18, column 2 to column 36)",
                                                      " (in 'constrain.stan', line 2, column 2 to column 29)",
                                                      " (in 'constrain.stan', line 6, column 2 to column 36)"};



class constrain_model final : public model_base_crtp<constrain_model> {

 private:
  Eigen::Matrix<double, -1, 1> x_d;
  Eigen::Matrix<double, -1, 1> x_tf;
 
 public:
  ~constrain_model() { }
  
  inline std::string model_name() const final { return "constrain_model"; }

  inline std::vector<std::string> model_compile_info() const noexcept {
    return std::vector<std::string>{"stanc_version = stanc3 4f8f010", "stancflags = "};
  }
  
  
  constrain_model(stan::io::var_context& context__,
                  unsigned int random_seed__ = 0,
                  std::ostream* pstream__ = nullptr) : model_base_crtp(0) {
    using local_scalar_t__ = double ;
    boost::ecuyer1988 base_rng__ = 
        stan::services::util::create_rng(random_seed__, 0);
    (void) base_rng__;  // suppress unused var warning
    static const char* function__ = "constrain_model_namespace::constrain_model";
    (void) function__;  // suppress unused var warning
    local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
    (void) DUMMY_VAR__;  // suppress unused var warning
    
    try {
      int pos__;
      pos__ = std::numeric_limits<int>::min();
      
      pos__ = 1;
      current_statement__ = 4;
      context__.validate_dims("data initialization","x_d","double",
          context__.to_vec(3));
      x_d = Eigen::Matrix<double, -1, 1>(3);
      stan::math::fill(x_d, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> x_d_flat__;
        current_statement__ = 4;
        assign(x_d_flat__, nil_index_list(), context__.vals_r("x_d"),
          "assigning variable x_d_flat__");
        current_statement__ = 4;
        pos__ = 1;
        current_statement__ = 4;
        for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
          current_statement__ = 4;
          assign(x_d, cons_list(index_uni(sym1__), nil_index_list()),
            x_d_flat__[(pos__ - 1)], "assigning variable x_d");
          current_statement__ = 4;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 4;
      for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
        current_statement__ = 4;
        current_statement__ = 4;
        check_greater_or_equal(function__, "x_d[sym1__]", x_d[(sym1__ - 1)],
                               0.0);}
      current_statement__ = 5;
      x_tf = Eigen::Matrix<double, -1, 1>(3);
      stan::math::fill(x_tf, std::numeric_limits<double>::quiet_NaN());
      
      current_statement__ = 5;
      assign(x_tf, nil_index_list(), x_d, "assigning variable x_tf");
      current_statement__ = 5;
      for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
        current_statement__ = 5;
        current_statement__ = 5;
        check_greater_or_equal(function__, "x_tf[sym1__]",
                               x_tf[(sym1__ - 1)], 0.0);}
    } catch (const std::exception& e) {
      stan::lang::rethrow_located(e, locations_array__[current_statement__]);
      // Next line prevents compiler griping about no return
      throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***"); 
    }
    num_params_r__ = 0U;
    
    try {
      num_params_r__ += 3;
    } catch (const std::exception& e) {
      stan::lang::rethrow_located(e, locations_array__[current_statement__]);
      // Next line prevents compiler griping about no return
      throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***"); 
    }
  }
  template <bool propto__, bool jacobian__, typename VecR, typename VecI, stan::require_vector_like_t<VecR>* = nullptr, stan::require_vector_like_vt<std::is_integral, VecI>* = nullptr>
  inline stan::scalar_type_t<VecR> log_prob_impl(VecR& params_r__,
                                                 VecI& params_i__,
                                                 std::ostream* pstream__ = nullptr) const {
    using T__ = stan::scalar_type_t<VecR>;
    using local_scalar_t__ = T__;
    T__ lp__(0.0);
    stan::math::accumulator<T__> lp_accum__;
    static const char* function__ = "constrain_model_namespace::log_prob";
(void) function__;  // suppress unused var warning

    stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);
    local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
    (void) DUMMY_VAR__;  // suppress unused var warning

    
    try {
      Eigen::Matrix<local_scalar_t__, -1, 1> x_p;
      x_p = Eigen::Matrix<local_scalar_t__, -1, 1>(3);
      stan::math::fill(x_p, DUMMY_VAR__);
      
      current_statement__ = 1;
      x_p = in__.vector(3);
      current_statement__ = 1;
      for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
        current_statement__ = 1;
        if (jacobian__) {
          current_statement__ = 1;
          assign(x_p, cons_list(index_uni(sym1__), nil_index_list()),
            stan::math::lb_constrain(x_p[(sym1__ - 1)], 0.0, lp__),
            "assigning variable x_p");
        } else {
          current_statement__ = 1;
          assign(x_p, cons_list(index_uni(sym1__), nil_index_list()),
            stan::math::lb_constrain(x_p[(sym1__ - 1)], 0.0),
            "assigning variable x_p");
        }}
      Eigen::Matrix<local_scalar_t__, -1, 1> x_tp;
      x_tp = Eigen::Matrix<local_scalar_t__, -1, 1>(3);
      stan::math::fill(x_tp, DUMMY_VAR__);
      
      current_statement__ = 2;
      assign(x_tp, nil_index_list(), x_p, "assigning variable x_tp");
      current_statement__ = 2;
      for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
        current_statement__ = 2;
        current_statement__ = 2;
        check_greater_or_equal(function__, "x_tp[sym1__]",
                               x_tp[(sym1__ - 1)], 0.0);}
    } catch (const std::exception& e) {
      stan::lang::rethrow_located(e, locations_array__[current_statement__]);
      // 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_impl() 
    
  template <typename RNG, typename VecR, typename VecI, typename VecVar, stan::require_vector_like_vt<std::is_floating_point, VecR>* = nullptr, stan::require_vector_like_vt<std::is_integral, VecI>* = nullptr, stan::require_std_vector_vt<std::is_floating_point, VecVar>* = nullptr>
  inline void write_array_impl(RNG& base_rng__, VecR& params_r__,
                               VecI& params_i__, VecVar& vars__,
                               const bool emit_transformed_parameters__ = true,
                               const bool emit_generated_quantities__ = true,
                               std::ostream* pstream__ = nullptr) const {
    using local_scalar_t__ = double;
    vars__.resize(0);
    stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);
    static const char* function__ = "constrain_model_namespace::write_array";
(void) function__;  // suppress unused var warning

    (void) function__;  // suppress unused var warning

    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

    
    try {
      Eigen::Matrix<double, -1, 1> x_p;
      x_p = Eigen::Matrix<double, -1, 1>(3);
      stan::math::fill(x_p, std::numeric_limits<double>::quiet_NaN());
      
      current_statement__ = 1;
      x_p = in__.vector(3);
      current_statement__ = 1;
      for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
        current_statement__ = 1;
        assign(x_p, cons_list(index_uni(sym1__), nil_index_list()),
          stan::math::lb_constrain(x_p[(sym1__ - 1)], 0.0),
          "assigning variable x_p");}
      Eigen::Matrix<double, -1, 1> x_tp;
      x_tp = Eigen::Matrix<double, -1, 1>(3);
      stan::math::fill(x_tp, std::numeric_limits<double>::quiet_NaN());
      
      for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
        vars__.emplace_back(x_p[(sym1__ - 1)]);}
      if (logical_negation((primitive_value(emit_transformed_parameters__) ||
            primitive_value(emit_generated_quantities__)))) {
        return ;
      } 
      current_statement__ = 2;
      assign(x_tp, nil_index_list(), x_p, "assigning variable x_tp");
      current_statement__ = 2;
      for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
        current_statement__ = 2;
        current_statement__ = 2;
        check_greater_or_equal(function__, "x_tp[sym1__]",
                               x_tp[(sym1__ - 1)], 0.0);}
      if (emit_transformed_parameters__) {
        for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
          vars__.emplace_back(x_tp[(sym1__ - 1)]);}
      } 
      if (logical_negation(emit_generated_quantities__)) {
        return ;
      } 
      Eigen::Matrix<double, -1, 1> x_gq;
      x_gq = Eigen::Matrix<double, -1, 1>(3);
      stan::math::fill(x_gq, std::numeric_limits<double>::quiet_NaN());
      
      current_statement__ = 3;
      assign(x_gq, nil_index_list(), x_p, "assigning variable x_gq");
      current_statement__ = 3;
      for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
        current_statement__ = 3;
        current_statement__ = 3;
        check_greater_or_equal(function__, "x_gq[sym1__]",
                               x_gq[(sym1__ - 1)], 0.0);}
      for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
        vars__.emplace_back(x_gq[(sym1__ - 1)]);}
    } catch (const std::exception& e) {
      stan::lang::rethrow_located(e, locations_array__[current_statement__]);
      // Next line prevents compiler griping about no return
      throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***"); 
    }
    } // write_array_impl() 
    
  template <typename VecVar, typename VecI, stan::require_std_vector_t<VecVar>* = nullptr, stan::require_vector_like_vt<std::is_integral, VecI>* = nullptr>
  inline void transform_inits_impl(const stan::io::var_context& context__,
                                   VecI& params_i__, VecVar& vars__,
                                   std::ostream* pstream__ = nullptr) const {
    using local_scalar_t__ = double;
    vars__.clear();
    vars__.reserve(num_params_r__);
    
    try {
      int pos__;
      pos__ = std::numeric_limits<int>::min();
      
      pos__ = 1;
      Eigen::Matrix<double, -1, 1> x_p;
      x_p = Eigen::Matrix<double, -1, 1>(3);
      stan::math::fill(x_p, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> x_p_flat__;
        current_statement__ = 1;
        assign(x_p_flat__, nil_index_list(), context__.vals_r("x_p"),
          "assigning variable x_p_flat__");
        current_statement__ = 1;
        pos__ = 1;
        current_statement__ = 1;
        for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
          current_statement__ = 1;
          assign(x_p, cons_list(index_uni(sym1__), nil_index_list()),
            x_p_flat__[(pos__ - 1)], "assigning variable x_p");
          current_statement__ = 1;
          pos__ = (pos__ + 1);}
      }
      Eigen::Matrix<double, -1, 1> x_p_free__;
      x_p_free__ = Eigen::Matrix<double, -1, 1>(3);
      stan::math::fill(x_p_free__, std::numeric_limits<double>::quiet_NaN());
      
      current_statement__ = 1;
      for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
        current_statement__ = 1;
        assign(x_p_free__, cons_list(index_uni(sym1__), nil_index_list()),
          stan::math::lb_free(x_p[(sym1__ - 1)], 0.0),
          "assigning variable x_p_free__");}
      for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
        vars__.emplace_back(x_p_free__[(sym1__ - 1)]);}
    } catch (const std::exception& e) {
      stan::lang::rethrow_located(e, locations_array__[current_statement__]);
      // Next line prevents compiler griping about no return
      throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***"); 
    }
    } // transform_inits_impl() 
    
  inline void get_param_names(std::vector<std::string>& names__) const {
    
    names__.clear();
    names__.emplace_back("x_p");
    names__.emplace_back("x_tp");
    names__.emplace_back("x_gq");
    } // get_param_names() 
    
  inline void get_dims(std::vector<std::vector<size_t>>& dimss__) const {
    dimss__.clear();
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(3)});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(3)});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(3)});
    
    } // get_dims() 
    
  inline void constrained_param_names(
                                      std::vector<std::string>& param_names__,
                                      bool emit_transformed_parameters__ = true,
                                      bool emit_generated_quantities__ = true) const
    final {
    
    for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
      {
        param_names__.emplace_back(std::string() + "x_p" + '.' + std::to_string(sym1__));
      }}
    if (emit_transformed_parameters__) {
      for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "x_tp" + '.' + std::to_string(sym1__));
        }}
    }
    
    if (emit_generated_quantities__) {
      for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "x_gq" + '.' + std::to_string(sym1__));
        }}
    }
    
    } // constrained_param_names() 
    
  inline void unconstrained_param_names(
                                        std::vector<std::string>& param_names__,
                                        bool emit_transformed_parameters__ = true,
                                        bool emit_generated_quantities__ = true) const
    final {
    
    for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
      {
        param_names__.emplace_back(std::string() + "x_p" + '.' + std::to_string(sym1__));
      }}
    if (emit_transformed_parameters__) {
      for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "x_tp" + '.' + std::to_string(sym1__));
        }}
    }
    
    if (emit_generated_quantities__) {
      for (int sym1__ = 1; sym1__ <= 3; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "x_gq" + '.' + std::to_string(sym1__));
        }}
    }
    
    } // unconstrained_param_names() 
    
  inline std::string get_constrained_sizedtypes() const {
    stringstream s__;
    s__ << "[{\"name\":\"x_p\",\"type\":{\"name\":\"vector\",\"length\":" << 3 << "},\"block\":\"parameters\"},{\"name\":\"x_tp\",\"type\":{\"name\":\"vector\",\"length\":" << 3 << "},\"block\":\"transformed_parameters\"},{\"name\":\"x_gq\",\"type\":{\"name\":\"vector\",\"length\":" << 3 << "},\"block\":\"generated_quantities\"}]";
    return s__.str();
    } // get_constrained_sizedtypes() 
    
  inline std::string get_unconstrained_sizedtypes() const {
    stringstream s__;
    s__ << "[{\"name\":\"x_p\",\"type\":{\"name\":\"vector\",\"length\":" << 3 << "},\"block\":\"parameters\"},{\"name\":\"x_tp\",\"type\":{\"name\":\"vector\",\"length\":" << 3 << "},\"block\":\"transformed_parameters\"},{\"name\":\"x_gq\",\"type\":{\"name\":\"vector\",\"length\":" << 3 << "},\"block\":\"generated_quantities\"}]";
    return s__.str();
    } // get_unconstrained_sizedtypes() 
    
  
    // Begin method overload boilerplate
    template <typename RNG>
    inline void write_array(RNG& base_rng,
                            Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
                            Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
                            const bool emit_transformed_parameters = true,
                            const bool emit_generated_quantities = true,
                            std::ostream* pstream = nullptr) const {
      std::vector<double> vars_vec(vars.size());
      std::vector<int> params_i;
      write_array_impl(base_rng, params_r, params_i, vars_vec,
          emit_transformed_parameters, emit_generated_quantities, pstream);
      vars.resize(vars_vec.size());
      for (int i = 0; i < vars.size(); ++i) {
        vars.coeffRef(i) = vars_vec[i];
      }
    }

    template <typename RNG>
    inline void write_array(RNG& base_rng, std::vector<double>& params_r,
                            std::vector<int>& params_i,
                            std::vector<double>& vars,
                            bool emit_transformed_parameters = true,
                            bool emit_generated_quantities = true,
                            std::ostream* pstream = nullptr) const {
      write_array_impl(base_rng, params_r, params_i, vars, emit_transformed_parameters, emit_generated_quantities, pstream);
    }

    template <bool propto__, bool jacobian__, typename T_>
    inline T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
                       std::ostream* pstream = nullptr) const {
      Eigen::Matrix<int, -1, 1> params_i;
      return log_prob_impl<propto__, jacobian__>(params_r, params_i, pstream);
    }

    template <bool propto__, bool jacobian__, typename T__>
    inline T__ log_prob(std::vector<T__>& params_r,
                        std::vector<int>& params_i,
                        std::ostream* pstream = nullptr) const {
      return log_prob_impl<propto__, jacobian__>(params_r, params_i, pstream);
    }
  

    inline void transform_inits(const stan::io::var_context& context,
                         Eigen::Matrix<double, Eigen::Dynamic, 1>& params_r,
                         std::ostream* pstream = nullptr) const final {
      std::vector<double> params_r_vec(params_r.size());
      std::vector<int> params_i;
      transform_inits_impl(context, params_i, params_r_vec, pstream);
      params_r.resize(params_r_vec.size());
      for (int i = 0; i < params_r.size(); ++i) {
        params_r.coeffRef(i) = params_r_vec[i];
      }
    }
    inline void transform_inits(const stan::io::var_context& context,
                                std::vector<int>& params_i,
                                std::vector<double>& vars,
                                std::ostream* pstream = nullptr) const final {
      transform_inits_impl(context, params_i, vars, pstream);
    }        

};
}
using stan_model = constrain_model_namespace::constrain_model;

#ifndef USING_R

// Boilerplate
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;
}

stan::math::profile_map& get_stan_profile_data() {
  return constrain_model_namespace::profiles__;
}

#endif

constrain.stan

data {
  vector<lower = 0.0>[3] x_d;
}

transformed data {
  vector<lower = 0.0>[3] x_tf = x_d;
}

parameters {
  vector<lower = 0.0>[3] x_p;
}

transformed parameters {
  vector<lower = 0.0>[3] x_tp = x_p;
}

generated quantities {
  vector<lower = 0.0>[3] x_gq = x_p;
}