/
command.hpp
833 lines (794 loc) · 40.3 KB
/
command.hpp
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#ifndef CMDSTAN_COMMAND_HPP
#define CMDSTAN_COMMAND_HPP
#include <cmdstan/arguments/arg_data.hpp>
#include <cmdstan/arguments/arg_id.hpp>
#include <cmdstan/arguments/arg_init.hpp>
#include <cmdstan/arguments/arg_output.hpp>
#include <cmdstan/arguments/arg_random.hpp>
#include <cmdstan/arguments/argument_parser.hpp>
#include <cmdstan/io/json/json_data.hpp>
#include <cmdstan/write_model.hpp>
#include <cmdstan/write_opencl_device.hpp>
#include <cmdstan/write_parallel_info.hpp>
#include <cmdstan/write_stan.hpp>
#include <stan/callbacks/interrupt.hpp>
#include <stan/callbacks/logger.hpp>
#include <stan/callbacks/stream_logger.hpp>
#include <stan/callbacks/stream_writer.hpp>
#include <stan/callbacks/writer.hpp>
#include <stan/io/dump.hpp>
#include <stan/io/ends_with.hpp>
#include <stan/io/stan_csv_reader.hpp>
#include <stan/math/prim/fun/Eigen.hpp>
#include <stan/model/model_base.hpp>
#include <stan/services/diagnose/diagnose.hpp>
#include <stan/services/experimental/advi/fullrank.hpp>
#include <stan/services/experimental/advi/meanfield.hpp>
#include <stan/services/optimize/bfgs.hpp>
#include <stan/services/optimize/lbfgs.hpp>
#include <stan/services/optimize/newton.hpp>
#include <stan/services/sample/fixed_param.hpp>
#include <stan/services/sample/hmc_nuts_dense_e.hpp>
#include <stan/services/sample/hmc_nuts_dense_e_adapt.hpp>
#include <stan/services/sample/hmc_nuts_diag_e.hpp>
#include <stan/services/sample/hmc_nuts_diag_e_adapt.hpp>
#include <stan/services/sample/hmc_nuts_unit_e.hpp>
#include <stan/services/sample/hmc_nuts_unit_e_adapt.hpp>
#include <stan/services/sample/hmc_static_dense_e.hpp>
#include <stan/services/sample/hmc_static_dense_e_adapt.hpp>
#include <stan/services/sample/hmc_static_diag_e.hpp>
#include <stan/services/sample/hmc_static_diag_e_adapt.hpp>
#include <stan/services/sample/hmc_static_unit_e.hpp>
#include <stan/services/sample/hmc_static_unit_e_adapt.hpp>
#include <stan/services/sample/standalone_gqs.hpp>
#include <fstream>
#include <memory>
#include <sstream>
#include <stdexcept>
#include <string>
#include <vector>
#include <stan/math/prim/core/init_threadpool_tbb.hpp>
#ifdef STAN_MPI
#include <stan/math/prim/functor/mpi_cluster.hpp>
#include <stan/math/prim/functor/mpi_command.hpp>
#include <stan/math/prim/functor/mpi_distributed_apply.hpp>
#endif
// forward declaration for function defined in another translation unit
stan::model::model_base &new_model(stan::io::var_context &data_context,
unsigned int seed, std::ostream *msg_stream);
namespace cmdstan {
#ifdef STAN_MPI
stan::math::mpi_cluster &get_mpi_cluster() {
static stan::math::mpi_cluster cluster;
return cluster;
}
#endif
std::shared_ptr<stan::io::var_context> get_var_context(const std::string file) {
std::fstream stream(file.c_str(), std::fstream::in);
if (file != "" && (stream.rdstate() & std::ifstream::failbit)) {
std::stringstream msg;
msg << "Can't open specified file, \"" << file << "\"" << std::endl;
throw std::invalid_argument(msg.str());
}
if (stan::io::ends_with(".json", file)) {
cmdstan::json::json_data var_context(stream);
stream.close();
std::shared_ptr<stan::io::var_context> result
= std::make_shared<cmdstan::json::json_data>(var_context);
return result;
}
stan::io::dump var_context(stream);
stream.close();
std::shared_ptr<stan::io::var_context> result
= std::make_shared<stan::io::dump>(var_context);
return result;
}
static int hmc_fixed_cols = 7; // hmc sampler outputs columns __lp + 6
int command(int argc, const char *argv[]) {
stan::callbacks::stream_writer info(std::cout);
stan::callbacks::stream_writer err(std::cout);
stan::callbacks::stream_logger logger(std::cout, std::cout, std::cout,
std::cerr, std::cerr);
#ifdef STAN_MPI
stan::math::mpi_cluster &cluster = get_mpi_cluster();
cluster.listen();
if (cluster.rank_ != 0)
return 0;
#endif
stan::math::init_threadpool_tbb();
// Read arguments
std::vector<argument *> valid_arguments;
valid_arguments.push_back(new arg_id());
valid_arguments.push_back(new arg_data());
valid_arguments.push_back(new arg_init());
valid_arguments.push_back(new arg_random());
valid_arguments.push_back(new arg_output());
argument_parser parser(valid_arguments);
int err_code = parser.parse_args(argc, argv, info, err);
if (err_code != 0) {
std::cout << "Failed to parse arguments, terminating Stan" << std::endl;
return err_code;
}
if (parser.help_printed())
return err_code;
arg_seed *random_arg
= dynamic_cast<arg_seed *>(parser.arg("random")->arg("seed"));
unsigned int random_seed = random_arg->random_value();
parser.print(info);
write_parallel_info(info);
write_opencl_device(info);
info();
stan::callbacks::writer init_writer;
stan::callbacks::interrupt interrupt;
std::fstream output_stream(
dynamic_cast<string_argument *>(parser.arg("output")->arg("file"))
->value()
.c_str(),
std::fstream::out);
stan::callbacks::stream_writer sample_writer(output_stream, "# ");
std::fstream diagnostic_stream(
dynamic_cast<string_argument *>(
parser.arg("output")->arg("diagnostic_file"))
->value()
.c_str(),
std::fstream::out);
stan::callbacks::stream_writer diagnostic_writer(diagnostic_stream, "# ");
//////////////////////////////////////////////////
// Initialize Model //
//////////////////////////////////////////////////
std::string filename(
dynamic_cast<string_argument *>(parser.arg("data")->arg("file"))
->value());
std::shared_ptr<stan::io::var_context> var_context
= get_var_context(filename);
stan::model::model_base &model
= new_model(*var_context, random_seed, &std::cout);
write_stan(sample_writer);
write_model(sample_writer, model.model_name());
parser.print(sample_writer);
write_parallel_info(sample_writer);
write_opencl_device(sample_writer);
write_stan(diagnostic_writer);
write_model(diagnostic_writer, model.model_name());
parser.print(diagnostic_writer);
int refresh
= dynamic_cast<int_argument *>(parser.arg("output")->arg("refresh"))
->value();
unsigned int id = dynamic_cast<int_argument *>(parser.arg("id"))->value();
std::string init
= dynamic_cast<string_argument *>(parser.arg("init"))->value();
double init_radius = 2.0;
// argument "init" can be non-negative number of filename
try {
init_radius = boost::lexical_cast<double>(init);
init = "";
} catch (const boost::bad_lexical_cast &e) {
}
std::shared_ptr<stan::io::var_context> init_context = get_var_context(init);
int return_code = stan::services::error_codes::CONFIG;
if (parser.arg("method")->arg("generate_quantities")) {
// read sample from cmdstan csv output file
string_argument *fitted_params_file = dynamic_cast<string_argument *>(
parser.arg("method")->arg("generate_quantities")->arg("fitted_params"));
if (fitted_params_file->is_default()) {
info(
"Must specify argument fitted_params which is a csv file containing "
"the sample.");
return_code = stan::services::error_codes::CONFIG;
}
std::string fname(fitted_params_file->value());
std::ifstream stream(fname.c_str());
if (fname != "" && (stream.rdstate() & std::ifstream::failbit)) {
std::stringstream msg;
msg << "Can't open specified file, \"" << fname << "\"" << std::endl;
throw std::invalid_argument(msg.str());
}
stan::io::stan_csv fitted_params;
std::stringstream msg;
stan::io::stan_csv_reader::read_metadata(stream, fitted_params.metadata,
&msg);
if (!stan::io::stan_csv_reader::read_header(stream, fitted_params.header,
&msg)) {
msg << "Error reading fitted param names from sample csv file \"" << fname
<< "\"" << std::endl;
throw std::invalid_argument(msg.str());
}
stan::io::stan_csv_reader::read_adaptation(stream, fitted_params.adaptation,
&msg);
fitted_params.timing.warmup = 0;
fitted_params.timing.sampling = 0;
stan::io::stan_csv_reader::read_samples(stream, fitted_params.samples,
fitted_params.timing, &msg);
stream.close();
std::vector<std::string> param_names;
model.constrained_param_names(param_names, false, false);
size_t num_cols = param_names.size();
size_t num_rows = fitted_params.metadata.num_samples;
// check that all parameter names are in sample, in order
if (num_cols + hmc_fixed_cols > fitted_params.header.size()) {
std::stringstream msg;
msg << "Mismatch between model and fitted_parameters csv file \"" << fname
<< "\"" << std::endl;
throw std::invalid_argument(msg.str());
}
for (size_t i = 0; i < num_cols; ++i) {
if (param_names[i].compare(fitted_params.header[i + hmc_fixed_cols])
!= 0) {
std::stringstream msg;
msg << "Mismatch between model and fitted_parameters csv file \""
<< fname << "\"" << std::endl;
throw std::invalid_argument(msg.str());
}
}
return_code = stan::services::standalone_generate(
model,
fitted_params.samples.block(0, hmc_fixed_cols, num_rows, num_cols),
random_seed, interrupt, logger, sample_writer);
} else if (parser.arg("method")->arg("diagnose")) {
list_argument *test = dynamic_cast<list_argument *>(
parser.arg("method")->arg("diagnose")->arg("test"));
if (test->value() == "gradient") {
double epsilon
= dynamic_cast<real_argument *>(test->arg("gradient")->arg("epsilon"))
->value();
double error
= dynamic_cast<real_argument *>(test->arg("gradient")->arg("error"))
->value();
return_code = stan::services::diagnose::diagnose(
model, *init_context, random_seed, id, init_radius, epsilon, error,
interrupt, logger, init_writer, sample_writer);
}
} else if (parser.arg("method")->arg("optimize")) {
list_argument *algo = dynamic_cast<list_argument *>(
parser.arg("method")->arg("optimize")->arg("algorithm"));
int num_iterations = dynamic_cast<int_argument *>(
parser.arg("method")->arg("optimize")->arg("iter"))
->value();
bool save_iterations
= dynamic_cast<bool_argument *>(
parser.arg("method")->arg("optimize")->arg("save_iterations"))
->value();
if (algo->value() == "newton") {
return_code = stan::services::optimize::newton(
model, *init_context, random_seed, id, init_radius, num_iterations,
save_iterations, interrupt, logger, init_writer, sample_writer);
} else if (algo->value() == "bfgs") {
double init_alpha
= dynamic_cast<real_argument *>(algo->arg("bfgs")->arg("init_alpha"))
->value();
double tol_obj
= dynamic_cast<real_argument *>(algo->arg("bfgs")->arg("tol_obj"))
->value();
double tol_rel_obj
= dynamic_cast<real_argument *>(algo->arg("bfgs")->arg("tol_rel_obj"))
->value();
double tol_grad
= dynamic_cast<real_argument *>(algo->arg("bfgs")->arg("tol_grad"))
->value();
double tol_rel_grad = dynamic_cast<real_argument *>(
algo->arg("bfgs")->arg("tol_rel_grad"))
->value();
double tol_param
= dynamic_cast<real_argument *>(algo->arg("bfgs")->arg("tol_param"))
->value();
return_code = stan::services::optimize::bfgs(
model, *init_context, random_seed, id, init_radius, init_alpha,
tol_obj, tol_rel_obj, tol_grad, tol_rel_grad, tol_param,
num_iterations, save_iterations, refresh, interrupt, logger,
init_writer, sample_writer);
} else if (algo->value() == "lbfgs") {
int history_size = dynamic_cast<int_argument *>(
algo->arg("lbfgs")->arg("history_size"))
->value();
double init_alpha
= dynamic_cast<real_argument *>(algo->arg("lbfgs")->arg("init_alpha"))
->value();
double tol_obj
= dynamic_cast<real_argument *>(algo->arg("lbfgs")->arg("tol_obj"))
->value();
double tol_rel_obj = dynamic_cast<real_argument *>(
algo->arg("lbfgs")->arg("tol_rel_obj"))
->value();
double tol_grad
= dynamic_cast<real_argument *>(algo->arg("lbfgs")->arg("tol_grad"))
->value();
double tol_rel_grad = dynamic_cast<real_argument *>(
algo->arg("lbfgs")->arg("tol_rel_grad"))
->value();
double tol_param
= dynamic_cast<real_argument *>(algo->arg("lbfgs")->arg("tol_param"))
->value();
return_code = stan::services::optimize::lbfgs(
model, *init_context, random_seed, id, init_radius, history_size,
init_alpha, tol_obj, tol_rel_obj, tol_grad, tol_rel_grad, tol_param,
num_iterations, save_iterations, refresh, interrupt, logger,
init_writer, sample_writer);
}
} else if (parser.arg("method")->arg("sample")) {
int num_warmup = dynamic_cast<int_argument *>(
parser.arg("method")->arg("sample")->arg("num_warmup"))
->value();
int num_samples
= dynamic_cast<int_argument *>(
parser.arg("method")->arg("sample")->arg("num_samples"))
->value();
int num_thin = dynamic_cast<int_argument *>(
parser.arg("method")->arg("sample")->arg("thin"))
->value();
bool save_warmup
= dynamic_cast<bool_argument *>(
parser.arg("method")->arg("sample")->arg("save_warmup"))
->value();
list_argument *algo = dynamic_cast<list_argument *>(
parser.arg("method")->arg("sample")->arg("algorithm"));
categorical_argument *adapt = dynamic_cast<categorical_argument *>(
parser.arg("method")->arg("sample")->arg("adapt"));
bool adapt_engaged
= dynamic_cast<bool_argument *>(adapt->arg("engaged"))->value();
if (model.num_params_r() == 0 && algo->value() != "fixed_param") {
info("Must use algorithm=fixed_param for model that has no parameters.");
return_code = stan::services::error_codes::CONFIG;
} else if (algo->value() == "fixed_param") {
return_code = stan::services::sample::fixed_param(
model, *init_context, random_seed, id, init_radius, num_samples,
num_thin, refresh, interrupt, logger, init_writer, sample_writer,
diagnostic_writer);
} else if (algo->value() == "hmc") {
list_argument *engine
= dynamic_cast<list_argument *>(algo->arg("hmc")->arg("engine"));
list_argument *metric
= dynamic_cast<list_argument *>(algo->arg("hmc")->arg("metric"));
string_argument *metric_file = dynamic_cast<string_argument *>(
algo->arg("hmc")->arg("metric_file"));
bool metric_supplied = !metric_file->is_default();
std::string metric_filename(
dynamic_cast<string_argument *>(algo->arg("hmc")->arg("metric_file"))
->value());
std::shared_ptr<stan::io::var_context> metric_context
= get_var_context(metric_filename);
categorical_argument *adapt = dynamic_cast<categorical_argument *>(
parser.arg("method")->arg("sample")->arg("adapt"));
categorical_argument *hmc
= dynamic_cast<categorical_argument *>(algo->arg("hmc"));
double stepsize
= dynamic_cast<real_argument *>(hmc->arg("stepsize"))->value();
double stepsize_jitter
= dynamic_cast<real_argument *>(hmc->arg("stepsize_jitter"))->value();
if (adapt_engaged == true && num_warmup == 0) {
info(
"The number of warmup samples (num_warmup) must be greater than "
"zero if adaptation is enabled.");
return_code = stan::services::error_codes::CONFIG;
} else if (engine->value() == "nuts" && metric->value() == "dense_e"
&& adapt_engaged == false && metric_supplied == false) {
int max_depth = dynamic_cast<int_argument *>(
dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("nuts"))
->arg("max_depth"))
->value();
return_code = stan::services::sample::hmc_nuts_dense_e(
model, *init_context, random_seed, id, init_radius, num_warmup,
num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, max_depth, interrupt, logger, init_writer,
sample_writer, diagnostic_writer);
} else if (engine->value() == "nuts" && metric->value() == "dense_e"
&& adapt_engaged == false && metric_supplied == true) {
int max_depth = dynamic_cast<int_argument *>(
dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("nuts"))
->arg("max_depth"))
->value();
return_code = stan::services::sample::hmc_nuts_dense_e(
model, *init_context, *metric_context, random_seed, id, init_radius,
num_warmup, num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, max_depth, interrupt, logger, init_writer,
sample_writer, diagnostic_writer);
} else if (engine->value() == "nuts" && metric->value() == "dense_e"
&& adapt_engaged == true && metric_supplied == false) {
int max_depth = dynamic_cast<int_argument *>(
dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("nuts"))
->arg("max_depth"))
->value();
double delta
= dynamic_cast<real_argument *>(adapt->arg("delta"))->value();
double gamma
= dynamic_cast<real_argument *>(adapt->arg("gamma"))->value();
double kappa
= dynamic_cast<real_argument *>(adapt->arg("kappa"))->value();
double t0 = dynamic_cast<real_argument *>(adapt->arg("t0"))->value();
unsigned int init_buffer
= dynamic_cast<u_int_argument *>(adapt->arg("init_buffer"))
->value();
unsigned int term_buffer
= dynamic_cast<u_int_argument *>(adapt->arg("term_buffer"))
->value();
unsigned int window
= dynamic_cast<u_int_argument *>(adapt->arg("window"))->value();
return_code = stan::services::sample::hmc_nuts_dense_e_adapt(
model, *init_context, random_seed, id, init_radius, num_warmup,
num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, max_depth, delta, gamma, kappa, t0, init_buffer,
term_buffer, window, interrupt, logger, init_writer, sample_writer,
diagnostic_writer);
} else if (engine->value() == "nuts" && metric->value() == "dense_e"
&& adapt_engaged == true && metric_supplied == true) {
int max_depth = dynamic_cast<int_argument *>(
dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("nuts"))
->arg("max_depth"))
->value();
double delta
= dynamic_cast<real_argument *>(adapt->arg("delta"))->value();
double gamma
= dynamic_cast<real_argument *>(adapt->arg("gamma"))->value();
double kappa
= dynamic_cast<real_argument *>(adapt->arg("kappa"))->value();
double t0 = dynamic_cast<real_argument *>(adapt->arg("t0"))->value();
unsigned int init_buffer
= dynamic_cast<u_int_argument *>(adapt->arg("init_buffer"))
->value();
unsigned int term_buffer
= dynamic_cast<u_int_argument *>(adapt->arg("term_buffer"))
->value();
unsigned int window
= dynamic_cast<u_int_argument *>(adapt->arg("window"))->value();
return_code = stan::services::sample::hmc_nuts_dense_e_adapt(
model, *init_context, *metric_context, random_seed, id, init_radius,
num_warmup, num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, max_depth, delta, gamma, kappa, t0, init_buffer,
term_buffer, window, interrupt, logger, init_writer, sample_writer,
diagnostic_writer);
} else if (engine->value() == "nuts" && metric->value() == "diag_e"
&& adapt_engaged == false && metric_supplied == false) {
categorical_argument *base = dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("nuts"));
int max_depth
= dynamic_cast<int_argument *>(base->arg("max_depth"))->value();
return_code = stan::services::sample::hmc_nuts_diag_e(
model, *init_context, random_seed, id, init_radius, num_warmup,
num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, max_depth, interrupt, logger, init_writer,
sample_writer, diagnostic_writer);
} else if (engine->value() == "nuts" && metric->value() == "diag_e"
&& adapt_engaged == false && metric_supplied == true) {
categorical_argument *base = dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("nuts"));
int max_depth
= dynamic_cast<int_argument *>(base->arg("max_depth"))->value();
return_code = stan::services::sample::hmc_nuts_diag_e(
model, *init_context, *metric_context, random_seed, id, init_radius,
num_warmup, num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, max_depth, interrupt, logger, init_writer,
sample_writer, diagnostic_writer);
} else if (engine->value() == "nuts" && metric->value() == "diag_e"
&& adapt_engaged == true && metric_supplied == false) {
categorical_argument *base = dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("nuts"));
int max_depth
= dynamic_cast<int_argument *>(base->arg("max_depth"))->value();
double delta
= dynamic_cast<real_argument *>(adapt->arg("delta"))->value();
double gamma
= dynamic_cast<real_argument *>(adapt->arg("gamma"))->value();
double kappa
= dynamic_cast<real_argument *>(adapt->arg("kappa"))->value();
double t0 = dynamic_cast<real_argument *>(adapt->arg("t0"))->value();
unsigned int init_buffer
= dynamic_cast<u_int_argument *>(adapt->arg("init_buffer"))
->value();
unsigned int term_buffer
= dynamic_cast<u_int_argument *>(adapt->arg("term_buffer"))
->value();
unsigned int window
= dynamic_cast<u_int_argument *>(adapt->arg("window"))->value();
return_code = stan::services::sample::hmc_nuts_diag_e_adapt(
model, *init_context, random_seed, id, init_radius, num_warmup,
num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, max_depth, delta, gamma, kappa, t0, init_buffer,
term_buffer, window, interrupt, logger, init_writer, sample_writer,
diagnostic_writer);
} else if (engine->value() == "nuts" && metric->value() == "diag_e"
&& adapt_engaged == true && metric_supplied == true) {
categorical_argument *base = dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("nuts"));
int max_depth
= dynamic_cast<int_argument *>(base->arg("max_depth"))->value();
double delta
= dynamic_cast<real_argument *>(adapt->arg("delta"))->value();
double gamma
= dynamic_cast<real_argument *>(adapt->arg("gamma"))->value();
double kappa
= dynamic_cast<real_argument *>(adapt->arg("kappa"))->value();
double t0 = dynamic_cast<real_argument *>(adapt->arg("t0"))->value();
unsigned int init_buffer
= dynamic_cast<u_int_argument *>(adapt->arg("init_buffer"))
->value();
unsigned int term_buffer
= dynamic_cast<u_int_argument *>(adapt->arg("term_buffer"))
->value();
unsigned int window
= dynamic_cast<u_int_argument *>(adapt->arg("window"))->value();
return_code = stan::services::sample::hmc_nuts_diag_e_adapt(
model, *init_context, *metric_context, random_seed, id, init_radius,
num_warmup, num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, max_depth, delta, gamma, kappa, t0, init_buffer,
term_buffer, window, interrupt, logger, init_writer, sample_writer,
diagnostic_writer);
} else if (engine->value() == "nuts" && metric->value() == "unit_e"
&& adapt_engaged == false) {
categorical_argument *base = dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("nuts"));
int max_depth
= dynamic_cast<int_argument *>(base->arg("max_depth"))->value();
return_code = stan::services::sample::hmc_nuts_unit_e(
model, *init_context, random_seed, id, init_radius, num_warmup,
num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, max_depth, interrupt, logger, init_writer,
sample_writer, diagnostic_writer);
} else if (engine->value() == "nuts" && metric->value() == "unit_e"
&& adapt_engaged == true) {
categorical_argument *base = dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("nuts"));
int max_depth
= dynamic_cast<int_argument *>(base->arg("max_depth"))->value();
double delta
= dynamic_cast<real_argument *>(adapt->arg("delta"))->value();
double gamma
= dynamic_cast<real_argument *>(adapt->arg("gamma"))->value();
double kappa
= dynamic_cast<real_argument *>(adapt->arg("kappa"))->value();
double t0 = dynamic_cast<real_argument *>(adapt->arg("t0"))->value();
return_code = stan::services::sample::hmc_nuts_unit_e_adapt(
model, *init_context, random_seed, id, init_radius, num_warmup,
num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, max_depth, delta, gamma, kappa, t0, interrupt,
logger, init_writer, sample_writer, diagnostic_writer);
} else if (engine->value() == "static" && metric->value() == "dense_e"
&& adapt_engaged == false && metric_supplied == false) {
categorical_argument *base = dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("static"));
double int_time
= dynamic_cast<real_argument *>(base->arg("int_time"))->value();
return_code = stan::services::sample::hmc_static_dense_e(
model, *init_context, random_seed, id, init_radius, num_warmup,
num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, int_time, interrupt, logger, init_writer,
sample_writer, diagnostic_writer);
} else if (engine->value() == "static" && metric->value() == "dense_e"
&& adapt_engaged == false && metric_supplied == true) {
categorical_argument *base = dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("static"));
double int_time
= dynamic_cast<real_argument *>(base->arg("int_time"))->value();
return_code = stan::services::sample::hmc_static_dense_e(
model, *init_context, *metric_context, random_seed, id, init_radius,
num_warmup, num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, int_time, interrupt, logger, init_writer,
sample_writer, diagnostic_writer);
} else if (engine->value() == "static" && metric->value() == "dense_e"
&& adapt_engaged == true && metric_supplied == false) {
categorical_argument *base = dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("static"));
double int_time
= dynamic_cast<real_argument *>(base->arg("int_time"))->value();
double delta
= dynamic_cast<real_argument *>(adapt->arg("delta"))->value();
double gamma
= dynamic_cast<real_argument *>(adapt->arg("gamma"))->value();
double kappa
= dynamic_cast<real_argument *>(adapt->arg("kappa"))->value();
double t0 = dynamic_cast<real_argument *>(adapt->arg("t0"))->value();
unsigned int init_buffer
= dynamic_cast<u_int_argument *>(adapt->arg("init_buffer"))
->value();
unsigned int term_buffer
= dynamic_cast<u_int_argument *>(adapt->arg("term_buffer"))
->value();
unsigned int window
= dynamic_cast<u_int_argument *>(adapt->arg("window"))->value();
return_code = stan::services::sample::hmc_static_dense_e_adapt(
model, *init_context, random_seed, id, init_radius, num_warmup,
num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, int_time, delta, gamma, kappa, t0, init_buffer,
term_buffer, window, interrupt, logger, init_writer, sample_writer,
diagnostic_writer);
} else if (engine->value() == "static" && metric->value() == "dense_e"
&& adapt_engaged == true && metric_supplied == true) {
categorical_argument *base = dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("static"));
double int_time
= dynamic_cast<real_argument *>(base->arg("int_time"))->value();
double delta
= dynamic_cast<real_argument *>(adapt->arg("delta"))->value();
double gamma
= dynamic_cast<real_argument *>(adapt->arg("gamma"))->value();
double kappa
= dynamic_cast<real_argument *>(adapt->arg("kappa"))->value();
double t0 = dynamic_cast<real_argument *>(adapt->arg("t0"))->value();
unsigned int init_buffer
= dynamic_cast<u_int_argument *>(adapt->arg("init_buffer"))
->value();
unsigned int term_buffer
= dynamic_cast<u_int_argument *>(adapt->arg("term_buffer"))
->value();
unsigned int window
= dynamic_cast<u_int_argument *>(adapt->arg("window"))->value();
return_code = stan::services::sample::hmc_static_dense_e_adapt(
model, *init_context, *metric_context, random_seed, id, init_radius,
num_warmup, num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, int_time, delta, gamma, kappa, t0, init_buffer,
term_buffer, window, interrupt, logger, init_writer, sample_writer,
diagnostic_writer);
} else if (engine->value() == "static" && metric->value() == "diag_e"
&& adapt_engaged == false && metric_supplied == false) {
categorical_argument *base = dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("static"));
double int_time
= dynamic_cast<real_argument *>(base->arg("int_time"))->value();
return_code = stan::services::sample::hmc_static_diag_e(
model, *init_context, random_seed, id, init_radius, num_warmup,
num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, int_time, interrupt, logger, init_writer,
sample_writer, diagnostic_writer);
} else if (engine->value() == "static" && metric->value() == "diag_e"
&& adapt_engaged == false && metric_supplied == true) {
categorical_argument *base = dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("static"));
double int_time
= dynamic_cast<real_argument *>(base->arg("int_time"))->value();
return_code = stan::services::sample::hmc_static_diag_e(
model, *init_context, *metric_context, random_seed, id, init_radius,
num_warmup, num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, int_time, interrupt, logger, init_writer,
sample_writer, diagnostic_writer);
} else if (engine->value() == "static" && metric->value() == "diag_e"
&& adapt_engaged == true && metric_supplied == false) {
categorical_argument *base = dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("static"));
double int_time
= dynamic_cast<real_argument *>(base->arg("int_time"))->value();
double delta
= dynamic_cast<real_argument *>(adapt->arg("delta"))->value();
double gamma
= dynamic_cast<real_argument *>(adapt->arg("gamma"))->value();
double kappa
= dynamic_cast<real_argument *>(adapt->arg("kappa"))->value();
double t0 = dynamic_cast<real_argument *>(adapt->arg("t0"))->value();
unsigned int init_buffer
= dynamic_cast<u_int_argument *>(adapt->arg("init_buffer"))
->value();
unsigned int term_buffer
= dynamic_cast<u_int_argument *>(adapt->arg("term_buffer"))
->value();
unsigned int window
= dynamic_cast<u_int_argument *>(adapt->arg("window"))->value();
return_code = stan::services::sample::hmc_static_diag_e_adapt(
model, *init_context, random_seed, id, init_radius, num_warmup,
num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, int_time, delta, gamma, kappa, t0, init_buffer,
term_buffer, window, interrupt, logger, init_writer, sample_writer,
diagnostic_writer);
} else if (engine->value() == "static" && metric->value() == "diag_e"
&& adapt_engaged == true && metric_supplied == true) {
categorical_argument *base = dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("static"));
double int_time
= dynamic_cast<real_argument *>(base->arg("int_time"))->value();
double delta
= dynamic_cast<real_argument *>(adapt->arg("delta"))->value();
double gamma
= dynamic_cast<real_argument *>(adapt->arg("gamma"))->value();
double kappa
= dynamic_cast<real_argument *>(adapt->arg("kappa"))->value();
double t0 = dynamic_cast<real_argument *>(adapt->arg("t0"))->value();
unsigned int init_buffer
= dynamic_cast<u_int_argument *>(adapt->arg("init_buffer"))
->value();
unsigned int term_buffer
= dynamic_cast<u_int_argument *>(adapt->arg("term_buffer"))
->value();
unsigned int window
= dynamic_cast<u_int_argument *>(adapt->arg("window"))->value();
return_code = stan::services::sample::hmc_static_diag_e_adapt(
model, *init_context, *metric_context, random_seed, id, init_radius,
num_warmup, num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, int_time, delta, gamma, kappa, t0, init_buffer,
term_buffer, window, interrupt, logger, init_writer, sample_writer,
diagnostic_writer);
} else if (engine->value() == "static" && metric->value() == "unit_e"
&& adapt_engaged == false) {
categorical_argument *base = dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("static"));
double int_time
= dynamic_cast<real_argument *>(base->arg("int_time"))->value();
return_code = stan::services::sample::hmc_static_unit_e(
model, *init_context, random_seed, id, init_radius, num_warmup,
num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, int_time, interrupt, logger, init_writer,
sample_writer, diagnostic_writer);
} else if (engine->value() == "static" && metric->value() == "unit_e"
&& adapt_engaged == true) {
categorical_argument *base = dynamic_cast<categorical_argument *>(
algo->arg("hmc")->arg("engine")->arg("static"));
double int_time
= dynamic_cast<real_argument *>(base->arg("int_time"))->value();
double delta
= dynamic_cast<real_argument *>(adapt->arg("delta"))->value();
double gamma
= dynamic_cast<real_argument *>(adapt->arg("gamma"))->value();
double kappa
= dynamic_cast<real_argument *>(adapt->arg("kappa"))->value();
double t0 = dynamic_cast<real_argument *>(adapt->arg("t0"))->value();
return_code = stan::services::sample::hmc_static_unit_e_adapt(
model, *init_context, random_seed, id, init_radius, num_warmup,
num_samples, num_thin, save_warmup, refresh, stepsize,
stepsize_jitter, int_time, delta, gamma, kappa, t0, interrupt,
logger, init_writer, sample_writer, diagnostic_writer);
}
}
} else if (parser.arg("method")->arg("variational")) {
list_argument *algo = dynamic_cast<list_argument *>(
parser.arg("method")->arg("variational")->arg("algorithm"));
int grad_samples
= dynamic_cast<int_argument *>(
parser.arg("method")->arg("variational")->arg("grad_samples"))
->value();
int elbo_samples
= dynamic_cast<int_argument *>(
parser.arg("method")->arg("variational")->arg("elbo_samples"))
->value();
int max_iterations
= dynamic_cast<int_argument *>(
parser.arg("method")->arg("variational")->arg("iter"))
->value();
double tol_rel_obj
= dynamic_cast<real_argument *>(
parser.arg("method")->arg("variational")->arg("tol_rel_obj"))
->value();
double eta = dynamic_cast<real_argument *>(
parser.arg("method")->arg("variational")->arg("eta"))
->value();
bool adapt_engaged = dynamic_cast<bool_argument *>(parser.arg("method")
->arg("variational")
->arg("adapt")
->arg("engaged"))
->value();
int adapt_iterations = dynamic_cast<int_argument *>(parser.arg("method")
->arg("variational")
->arg("adapt")
->arg("iter"))
->value();
int eval_elbo
= dynamic_cast<int_argument *>(
parser.arg("method")->arg("variational")->arg("eval_elbo"))
->value();
int output_samples
= dynamic_cast<int_argument *>(
parser.arg("method")->arg("variational")->arg("output_samples"))
->value();
if (algo->value() == "fullrank") {
return_code = stan::services::experimental::advi::fullrank(
model, *init_context, random_seed, id, init_radius, grad_samples,
elbo_samples, max_iterations, tol_rel_obj, eta, adapt_engaged,
adapt_iterations, eval_elbo, output_samples, interrupt, logger,
init_writer, sample_writer, diagnostic_writer);
} else if (algo->value() == "meanfield") {
return_code = stan::services::experimental::advi::meanfield(
model, *init_context, random_seed, id, init_radius, grad_samples,
elbo_samples, max_iterations, tol_rel_obj, eta, adapt_engaged,
adapt_iterations, eval_elbo, output_samples, interrupt, logger,
init_writer, sample_writer, diagnostic_writer);
}
}
output_stream.close();
diagnostic_stream.close();
for (size_t i = 0; i < valid_arguments.size(); ++i)
delete valid_arguments.at(i);
#ifdef STAN_MPI
cluster.stop_listen();
#endif
return return_code;
}
} // namespace cmdstan
#endif