Add signatures for laplace #1511
Description
Laplace Signatures
template <typename ThetaMatrix, typename CovarFun, typename CovarArgs,
typename TrainTuple, typename PredTuple, class RNG,
require_eigen_t<ThetaMatrix>* = nullptr>
inline std::vector<double>
laplace_marginal_tol_bernoulli_logit_rng(
const std::vector<int>& y, const std::vector<int>& n_samples,
const ThetaMatrix& theta_0, CovarFun&& covariance_function,
CovarArgs&& covar_args, TrainTuple&& train_tuple, PredTuple&& pred_tuple,
const double tolerance, const int64_t max_num_steps,
const int hessian_block_size, const int solver,
const int max_steps_line_search, RNG& rng, std::ostream* msgs);
template <typename CovarFun, typename ThetaMatrix, class RNG,
typename TrainTuple, typename PredTuple, typename CovarArgs,
require_eigen_t<ThetaMatrix>* = nullptr>
inline std::vector<double>
laplace_marginal_bernoulli_logit_rng(
const std::vector<int>& y, const std::vector<int>& n_samples,
const ThetaMatrix& theta_0, CovarFun&& covariance_function,
CovarArgs&& covar_args, TrainTuple&& train_tuple, PredTuple&& pred_tuple,
RNG& rng, std::ostream* msgs);
template <typename CovarF, typename ThetaMatrix, typename CovarArgs,
require_eigen_t<ThetaMatrix>* = nullptr>
inline auto laplace_marginal_tol_bernoulli_logit_lpmf(
const std::vector<int>& y, const std::vector<int>& n_samples,
const ThetaMatrix& theta_0, CovarF&& covariance_function,
CovarArgs&& covar_args, double tolerance, int64_t max_num_steps,
const int hessian_block_size, const int solver,
const int max_steps_line_search, std::ostream* msgs);
template <typename CovarF, typename ThetaMatrix, typename CovarArgs,
require_eigen_t<ThetaMatrix>* = nullptr>
inline auto laplace_marginal_bernoulli_logit_lpmf(
const std::vector<int>& y, const std::vector<int>& n_samples,
const ThetaMatrix& theta_0, CovarF&& covariance_function,
CovarArgs&& covar_args, std::ostream* msgs);
template <bool propto = false, typename LFun, typename LArgs, typename CovarFun,
typename Theta0, typename CovarArgs,
require_all_eigen_vector_t<Theta0>* = nullptr>
inline auto laplace_marginal_tol_lpdf(
LFun&& L_f, LArgs&& l_args, const Theta0& theta_0, CovarFun&& K_f,
CovarArgs&& covar_args, double tolerance, int64_t max_num_steps,
const int hessian_block_size, const int solver,
const int max_steps_line_search, std::ostream* msgs);
template <bool propto = false, typename LFun, typename LArgs, typename CovarFun,
typename Theta0, typename CovarArgs>
inline auto laplace_marginal_tol_lpmf(
LFun&& L_f, LArgs&& l_args, const Theta0& theta_0, CovarFun&& K_f,
CovarArgs&& covar_args, const double tolerance, const int64_t max_num_steps,
const int hessian_block_size, const int solver,
const int max_steps_line_search, std::ostream* msgs);
template <typename CovarFun, typename Eta, typename ThetaVec,
typename CovarArgs, require_all_eigen_vector_t<ThetaVec>* = nullptr>
inline auto laplace_marginal_tol_neg_binomial_2_log_lpmf(
const std::vector<int>& y, const std::vector<int>& y_index, const Eta& eta,
const ThetaVec& theta_0, CovarFun&& covariance_function,
CovarArgs&& covar_args, double tolerance, int64_t max_num_steps,
const int hessian_block_size, const int solver,
const int max_steps_line_search, std::ostream* msgs);
template <typename CovarFun, typename Eta, typename Theta0, typename CovarArgs>
inline auto laplace_marginal_neg_binomial_2_log_lpmf(
const std::vector<int>& y, const std::vector<int>& y_index, const Eta& eta,
const Theta0& theta_0, CovarFun&& covariance_function,
CovarArgs&& covar_args, std::ostream* msgs);
template <typename CovarFun, typename YeVec, typename ThetaVec,
typename CovarArgs,
require_all_eigen_vector_t<YeVec, ThetaVec>* = nullptr>
inline auto laplace_marginal_tol_poisson_2_log_lpmf(
const std::vector<int>& y, const std::vector<int>& y_index, const YeVec& ye,
const ThetaVec& theta_0, CovarFun&& covariance_function,
CovarArgs&& covar_args, double tolerance, int64_t max_num_steps,
const int hessian_block_size, const int solver,
const int max_steps_line_search, std::ostream* msgs);
template <typename CovarFun, typename YeVec, typename ThetaVec,
typename CovarArgs,
require_all_eigen_vector_t<YeVec, ThetaVec>* = nullptr>
inline auto laplace_marginal_poisson_2_log_lpmf(
const std::vector<int>& y, const std::vector<int>& y_index, const YeVec& ye,
const ThetaVec& theta_0, CovarFun&& covariance_function,
CovarArgs&& covar_args, std::ostream* msgs);
template <typename CovarFun, typename ThetaVec, typename CovarArgs,
require_all_eigen_vector_t<ThetaVec>* = nullptr>
inline auto laplace_marginal_tol_poisson_log_lpmf(
const std::vector<int>& y, const std::vector<int>& y_index,
const ThetaVec& theta_0, CovarFun&& covariance_function,
CovarArgs&& covar_args, double tolerance, int64_t max_num_steps,
const int hessian_block_size, const int solver,
const int max_steps_line_search, std::ostream* msgs);
template <typename CovarFun, typename ThetaVec, typename CovarArgs,
require_eigen_vector_t<ThetaVec>* = nullptr>
inline auto laplace_marginal_poisson_log_lpmf(const std::vector<int>& y,
const std::vector<int>& y_index,
const ThetaVec& theta_0,
CovarFun&& covariance_function,
CovarArgs&& covar_args,
std::ostream* msgs)
template <typename CovarFun, typename Eta, typename ThetaMatrix, class RNG,
typename TrainTuple, typename PredTuple, typename CovarArgs,
require_eigen_t<ThetaMatrix>* = nullptr>
inline std::vector<double> laplace_marginal_tol_neg_binomial_2_log_rng(
const std::vector<int>& y, const std::vector<int>& y_index, const Eta& eta,
const ThetaMatrix& theta_0, CovarFun&& covariance_function,
CovarArgs&& covar_args, TrainTuple&& train_tuple, PredTuple&& pred_tuple,
const double tolerance, const int64_t max_num_steps,
const int hessian_block_size, const int solver,
const int max_steps_line_search, RNG& rng, std::ostream* msgs);
template <typename CovarFun, typename Eta, typename ThetaMatrix, class RNG,
typename TrainTuple, typename PredTuple, typename CovarArgs,
require_eigen_t<ThetaMatrix>* = nullptr>
inline std::vector<double> laplace_marginal_neg_binomial_2_log_rng(
const std::vector<int>& y, const std::vector<int>& y_index, const Eta& eta,
const ThetaMatrix& theta_0, CovarFun&& covariance_function,
CovarArgs&& covar_args, TrainTuple&& train_tuple, PredTuple&& pred_tuple,
RNG& rng, std::ostream* msgs);
template <typename ThetaMatrix, typename CovarFun, typename CovarArgs,
typename TrainTuple, typename PredTuple, class RNG,
require_eigen_t<ThetaMatrix>* = nullptr>
inline std::vector<double>
laplace_marginal_tol_poisson_2_log_rng(
const std::vector<int>& y, const std::vector<int>& y_index,
const Eigen::VectorXd& ye, const ThetaMatrix& theta_0,
CovarFun&& covariance_function, CovarArgs&& covar_args,
TrainTuple&& train_tuple, PredTuple&& pred_tuple, const double tolerance,
const int64_t max_num_steps, const int hessian_block_size, const int solver,
const int max_steps_line_search, RNG& rng, std::ostream* msgs);
template <typename ThetaMatrix, typename CovarFun, typename CovarArgs,
typename TrainTuple, typename PredTuple, class RNG,
require_eigen_t<ThetaMatrix>* = nullptr>
inline std::vector<double>
laplace_marginal_poisson_2_log_rng(
const std::vector<int>& y, const std::vector<int>& y_index,
const Eigen::VectorXd& ye, const ThetaMatrix& theta_0,
CovarFun&& covariance_function, CovarArgs&& covar_args,
TrainTuple&& train_tuple, PredTuple&& pred_tuple, RNG& rng,
std::ostream* msgs);
template <typename CovarFun, typename ThetaMatrix, class RNG,
typename TrainTuple, typename PredTuple, typename CovarArgs,
require_eigen_t<ThetaMatrix>* = nullptr>
inline std::vector<double> laplace_marginal_tol_poisson_log_rng(
const std::vector<int>& y, const std::vector<int>& y_index,
const ThetaMatrix& theta_0, CovarFun&& covariance_function,
CovarArgs&& covar_args, TrainTuple&& train_tuple, PredTuple&& pred_tuple,
const double tolerance, const int64_t max_num_steps,
const int hessian_block_size, const int solver,
const int max_steps_line_search, RNG& rng, std::ostream* msgs);
template <typename CovarFun, typename ThetaMatrix, class RNG,
typename TrainTuple, typename PredTuple, typename CovarArgs,
require_eigen_t<ThetaMatrix>* = nullptr>
inline std::vector<double> laplace_marginal_poisson_log_rng(
const std::vector<int>& y, const std::vector<int>& y_index,
const ThetaMatrix& theta_0, CovarFun&& covariance_function,
CovarArgs&& covar_args, TrainTuple&& train_tuple, PredTuple&& pred_tuple,
RNG& rng, std::ostream* msgs);
template <typename LLFunc, typename LLArgs, typename ThetaMatrix,
typename CovarFun, typename CovarArgs, typename TrainTuple,
typename PredTuple, typename RNG>
inline std::vector<double> laplace_marginal_tol_rng(
LLFunc&& L_f, LLArgs&& l_args, const ThetaMatrix& theta_0,
CovarFun&& covariance_function, CovarArgs&& covar_args,
TrainTuple&& train_tuple, PredTuple&& pred_tuple, const double tolerance,
const int64_t max_num_steps, const int hessian_block_size, const int solver,
const int max_steps_line_search, RNG& rng, std::ostream* msgs);
template <typename LLFunc, typename LLArgs, typename ThetaMatrix,
typename CovarFun, typename CovarArgs, typename TrainTuple,
typename PredTuple, typename RNG>
inline std::vector<double> laplace_marginal_rng(LLFunc&& L_f, LLArgs&& l_args,
const ThetaMatrix& theta_0,
CovarFun&& covariance_function,
CovarArgs&& covar_args,
TrainTuple&& train_tuple,
PredTuple&& pred_tuple, RNG& rng,
std::ostream* msgs);
Here are the stan signatures for each translated to Stan. Some things to note.
- The tuple's for the covariance function need to be the same types as the types in the covariance functors arguments
- Same for the likelihoods in the general laplace
- Each function has a standard version as well as a
tolversion that exposes the tolerance parameters for the laplace approximation - The rng functions have a bit of an odd signature with respect to the covariance function. In those signatures the covariance function is called twice. Once with
covar_argsandtrain_tupleand another time timecovar_argsandtest_tuple. Thetrain_tupleandtest_tuplecan be different types, but valid covariance function signatures should exist for both
covariance_function(covar_args..., train_tuple...);
covariance_function(covar_args..., test_tuple...);
functions {
// 1. Laplace marginal for bernoulli_logit with tolerance _rng
// C++: std::vector<double> laplace_marginal_tol_bernoulli_logit_rng( ... )
vector laplace_marginal_tol_bernoulli_logit_rng(
int[] y,
int[] n_samples,
vector theta_0, // or matrix theta_0 if appropriate
// covariance_function: a user-supplied function (replace ... with its required signature)
matrix covariance_function(...),
tuple covar_args,
tuple train_tuple,
tuple pred_tuple,
real tolerance,
int max_num_steps,
int hessian_block_size,
int solver,
int max_steps_line_search
)
// 2. Laplace marginal for bernoulli_logit without tolerance _rng
vector laplace_marginal_bernoulli_logit_rng(
int[] y,
int[] n_samples,
vector theta_0,
matrix covariance_function(...),
tuple covar_args,
tuple train_tuple,
tuple pred_tuple
)
// 3. Laplace marginal for bernoulli_logit with tolerance lpmf
real laplace_marginal_tol_bernoulli_logit_lpmf(
int[] y,
int[] n_samples,
vector theta_0,
matrix covariance_function(...),
tuple covar_args,
real tolerance,
int max_num_steps,
int hessian_block_size,
int solver,
int max_steps_line_search
)
// 4. Laplace marginal for bernoulli_logit lpmf (without tolerance control)
real laplace_marginal_bernoulli_logit_lpmf(
int[] y,
int[] n_samples,
vector theta_0,
matrix covariance_function(...),
tuple covar_args
)
// 5. Laplace marginal with tolerance lpdf (generic likelihood version)
// Here we include a propto flag as the first parameter.
real laplace_marginal_tol_lpdf(
bool propto, // typically false by default
real likelihood_function(...), // likelihood function; adjust its signature as needed
tuple l_args,
vector theta_0, // constrained to be an Eigen vector equivalent
real K_f(real, tuple), // covariance (or second-order) function pointer
tuple covar_args,
real tolerance,
int max_num_steps,
int hessian_block_size,
int solver,
int max_steps_line_search
)
// 6. Laplace marginal with tolerance lpmf (generic likelihood version)
real laplace_marginal_tol_lpmf(
bool propto,
real likelihood_function(...),
tuple l_args,
vector theta_0,
real K_f(real, tuple),
tuple covar_args,
real tolerance,
int max_num_steps,
int hessian_block_size,
int solver,
int max_steps_line_search
)
// 7. Laplace marginal tol for neg_binomial_2_log lpmf
real laplace_marginal_tol_neg_binomial_2_log_lpmf(
int[] y,
int[] y_index,
vector eta,
vector theta_0,
matrix covariance_function(...),
tuple covar_args,
real tolerance,
int max_num_steps,
int hessian_block_size,
int solver,
int max_steps_line_search
)
// 8. Laplace marginal for neg_binomial_2_log lpmf (without tolerance)
real laplace_marginal_neg_binomial_2_log_lpmf(
int[] y,
int[] y_index,
vector eta,
vector theta_0,
matrix covariance_function(...),
tuple covar_args
)
// 9. Laplace marginal tol for poisson_2_log lpmf
real laplace_marginal_tol_poisson_2_log_lpmf(
int[] y,
int[] y_index,
vector ye,
vector theta_0,
matrix covariance_function(...),
tuple covar_args,
real tolerance,
int max_num_steps,
int hessian_block_size,
int solver,
int max_steps_line_search
)
// 10. Laplace marginal for poisson_2_log lpmf (without tolerance)
real laplace_marginal_poisson_2_log_lpmf(
int[] y,
int[] y_index,
vector ye,
vector theta_0,
matrix covariance_function(...),
tuple covar_args
)
// 11. Laplace marginal tol for poisson log lpmf
real laplace_marginal_tol_poisson_log_lpmf(
int[] y,
int[] y_index,
vector theta_0,
matrix covariance_function(...),
tuple covar_args,
real tolerance,
int max_num_steps,
int hessian_block_size,
int solver,
int max_steps_line_search
)
// 12. Laplace marginal for poisson log lpmf (without tolerance)
real laplace_marginal_poisson_log_lpmf(
int[] y,
int[] y_index,
vector theta_0,
matrix covariance_function(...),
tuple covar_args
)
// 13. Laplace marginal tol for neg_binomial_2_log _rng version
vector laplace_marginal_tol_neg_binomial_2_log_rng(
int[] y,
int[] y_index,
vector eta,
matrix theta_0, // here ThetaMatrix is represented as a matrix
matrix covariance_function(...),
tuple covar_args,
tuple train_tuple,
tuple pred_tuple,
real tolerance,
int max_num_steps,
int hessian_block_size,
int solver,
int max_steps_line_search
)
// 14. Laplace marginal for neg_binomial_2_log _rng version (without tolerance)
vector laplace_marginal_neg_binomial_2_log_rng(
int[] y,
int[] y_index,
vector eta,
matrix theta_0,
matrix covariance_function(...),
tuple covar_args,
tuple train_tuple,
tuple pred_tuple
)
// 15. Laplace marginal tol for poisson_2_log _rng version
vector laplace_marginal_tol_poisson_2_log_rng(
int[] y,
int[] y_index,
vector ye,
matrix theta_0,
matrix covariance_function(...),
tuple covar_args,
tuple train_tuple,
tuple pred_tuple,
real tolerance,
int max_num_steps,
int hessian_block_size,
int solver,
int max_steps_line_search
)
// 16. Laplace marginal for poisson_2_log _rng version (without tolerance)
vector laplace_marginal_poisson_2_log_rng(
int[] y,
int[] y_index,
vector ye,
matrix theta_0,
matrix covariance_function(...),
tuple covar_args,
tuple train_tuple,
tuple pred_tuple
)
// 17. Laplace marginal tol for poisson log _rng version
vector laplace_marginal_tol_poisson_log_rng(
int[] y,
int[] y_index,
matrix theta_0,
matrix covariance_function(...),
tuple covar_args,
tuple train_tuple,
tuple pred_tuple,
real tolerance,
int max_num_steps,
int hessian_block_size,
int solver,
int max_steps_line_search
)
// 18. Laplace marginal for poisson log _rng version (without tolerance)
vector laplace_marginal_poisson_log_rng(
int[] y,
int[] y_index,
matrix theta_0,
matrix covariance_function(...),
tuple covar_args,
tuple train_tuple,
tuple pred_tuple
)
// 19. Laplace marginal tol _rng for a generic likelihood function
vector laplace_marginal_tol_rng(
real likelihood_function(...), // generic likelihood function
tuple l_args,
matrix theta_0,
matrix covariance_function(...),
tuple covar_args,
tuple train_tuple,
tuple pred_tuple,
real tolerance,
int max_num_steps,
int hessian_block_size,
int solver,
int max_steps_line_search
)
// 20. Laplace marginal _rng for a generic likelihood function (without tolerance)
vector laplace_marginal_rng(
real likelihood_function(...),
tuple l_args,
matrix theta_0,
matrix covariance_function(...),
tuple covar_args,
tuple train_tuple,
tuple pred_tuple
)
}