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StaticModel.cc 31.32 KiB
/*
* Copyright © 2003-2025 Dynare Team
*
* This file is part of Dynare.
*
* Dynare is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* Dynare is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with Dynare. If not, see <https://www.gnu.org/licenses/>.
*/
#include <algorithm>
#include <cassert>
#include <cmath>
#include <cstdlib>
#include <iostream>
#include <numeric>
#include <ranges>
#include <sstream>
#include <unordered_map>
#include "DynamicModel.hh"
#include "StaticModel.hh"
StaticModel::StaticModel(SymbolTable& symbol_table_arg, NumericalConstants& num_constants_arg,
ExternalFunctionsTable& external_functions_table_arg,
HeterogeneityTable& heterogeneity_table_arg) :
ModelTree {symbol_table_arg, num_constants_arg, external_functions_table_arg,
heterogeneity_table_arg}
{
}
void
StaticModel::copyHelper(const StaticModel& m)
{
auto f = [this](const ExprNode* e) { return e->clone(*this); };
for (const auto& it : m.ramsey_multipliers_derivatives_temporary_terms)
ramsey_multipliers_derivatives_temporary_terms.insert(f(it));
for (const auto& it : m.ramsey_multipliers_derivatives_temporary_terms_idxs)
ramsey_multipliers_derivatives_temporary_terms_idxs.emplace(f(it.first), it.second);
}
StaticModel::StaticModel(const StaticModel& m) :
ModelTree {m},
ramsey_multipliers_derivatives {m.ramsey_multipliers_derivatives},
ramsey_multipliers_derivatives_sparse_colptr {m.ramsey_multipliers_derivatives_sparse_colptr},
static_mfs {m.static_mfs}
{
copyHelper(m);
}
StaticModel&
StaticModel::operator=(const StaticModel& m)
{
ModelTree::operator=(m);
ramsey_multipliers_derivatives = m.ramsey_multipliers_derivatives;
ramsey_multipliers_derivatives_sparse_colptr = m.ramsey_multipliers_derivatives_sparse_colptr;
static_mfs = m.static_mfs;
copyHelper(m);
return *this;
}
StaticModel::StaticModel(const DynamicModel& m) :
ModelTree {m.symbol_table, m.num_constants, m.external_functions_table, m.heterogeneity_table}
{
// Convert model local variables (need to be done first)
for (int it : m.local_variables_vector)
AddLocalVariable(it, m.local_variables_table.at(it)->toStatic(*this));
// Convert equations
int static_only_index = 0;
set<int> dynamic_equations = m.equation_tags.getDynamicEqns();
for (int i = 0; i < static_cast<int>(m.equations.size()); i++)
try
{
// If equation is dynamic, replace it by an equation marked [static]
if (dynamic_equations.contains(i))
{
auto [static_only_equations, static_only_equations_lineno,
static_only_complementarity_conditions, static_only_equations_equation_tags]
= m.getStaticOnlyEquationsInfo();
addEquation(static_only_equations[static_only_index]->toStatic(*this),
static_only_equations_lineno[static_only_index],
static_only_complementarity_conditions[static_only_index],
static_only_equations_equation_tags.getTagsByEqn(static_only_index));
static_only_index++;
}
else
addEquation(m.equations[i]->toStatic(*this), m.equations_lineno[i],
m.complementarity_conditions[i], m.equation_tags.getTagsByEqn(i));
}
catch (DataTree::DivisionByZeroException)
{
cerr << "...division by zero error encountered when converting equation " << i
<< " to static" << endl;
exit(EXIT_FAILURE);
}
// Convert auxiliary equations
for (auto aux_eq : m.aux_equations)
addAuxEquation(aux_eq->toStatic(*this));
cutoff = m.cutoff;
user_set_add_flags = m.user_set_add_flags;
user_set_subst_flags = m.user_set_subst_flags;
user_set_add_libs = m.user_set_add_libs;
user_set_subst_libs = m.user_set_subst_libs;
user_set_compiler = m.user_set_compiler;
static_mfs = m.static_mfs;
}
void
StaticModel::writeStaticBytecode(const string& basename) const
{
/* Bytecode only works when there are with as many endogenous as equations.
(e.g. the constructor of FBEGINBLOCK_ makes this assumption) */
assert(static_cast<int>(equations.size()) == symbol_table.endo_nbr());
// First write the .bin file
int u_count_int {writeBytecodeBinFile(basename + "/model/bytecode/static.bin", false)};
Bytecode::Writer code_file {basename + "/model/bytecode/static.cod"};
auto eq_idx = views::iota(0, static_cast<int>(equations.size()));
auto endo_idx = views::iota(0, symbol_table.endo_nbr());
// Declare temporary terms and the (single) block
code_file << Bytecode::FDIMST {static_cast<int>(temporary_terms_derivatives[0].size()
+ temporary_terms_derivatives[1].size())}
<< Bytecode::FBEGINBLOCK {symbol_table.endo_nbr(),
BlockSimulationType::solveForwardComplete,
0,
symbol_table.endo_nbr(),
{endo_idx.begin(), endo_idx.end()},
{eq_idx.begin(), eq_idx.end()},
false,
u_count_int,
symbol_table.endo_nbr()};
writeBytecodeHelper<false>(code_file);
}
void
StaticModel::writeStaticBlockBytecode(const string& basename) const
{
Bytecode::Writer code_file {basename + "/model/bytecode/block/static.cod"};
const filesystem::path bin_filename {basename + "/model/bytecode/block/static.bin"};
ofstream bin_file {bin_filename, ios::out | ios::binary};
if (!bin_file.is_open())
{
cerr << R"(Error : Can't open file ")" << bin_filename.string() << R"(" for writing)" << endl;
exit(EXIT_FAILURE);
}
// Temporary variables declaration
code_file << Bytecode::FDIMST {static_cast<int>(blocks_temporary_terms_idxs.size())};
temporary_terms_t temporary_terms_written;
for (int block {0}; block < static_cast<int>(blocks.size()); block++)
{
const BlockSimulationType simulation_type {blocks[block].simulation_type};
const int block_size {blocks[block].size};
const int u_count {simulation_type == BlockSimulationType::solveBackwardComplete
|| simulation_type == BlockSimulationType::solveForwardComplete
? writeBlockBytecodeBinFile(bin_file, block)
: 0};
code_file << Bytecode::FBEGINBLOCK {blocks[block].mfs_size,
simulation_type,
blocks[block].first_equation,
block_size,
endo_idx_block2orig,
eq_idx_block2orig,
blocks[block].linear,
u_count,
block_size};
writeBlockBytecodeHelper<false>(code_file, block, temporary_terms_written);
}
code_file << Bytecode::FEND {};
}
void
StaticModel::computingPass(int derivsOrder, int paramsDerivsOrder,
const eval_context_t& eval_context, bool no_tmp_terms, bool block,
bool use_dll)
{
initializeVariablesAndEquations();
/* In both MATLAB and Julia, tensors for higher-order derivatives are stored
in matrices whose columns correspond to variable multi-indices. Since we
currently are limited to 32-bit signed integers (hence 31 bits) for matrix indices, check that we will not overflow (see #89). Note that such a check
is not needed for parameter derivatives, since tensors for those are not
stored as matrices. This check is implemented at this place for symmetry
with DynamicModel::computingPass(). */
if (log2(symbol_table.endo_nbr()) * derivsOrder >= numeric_limits<int>::digits)
{
cerr << "ERROR: The derivatives matrix of the " << modelClassName()
<< " is too large. Please decrease the approximation order." << endl;
exit(EXIT_FAILURE);
}
// Compute derivatives w.r. to all endogenous
set<int> vars;
for (int i = 0; i < symbol_table.endo_nbr(); i++)
{
int id = symbol_table.getID(SymbolType::endogenous, i);
vars.insert(getDerivID(id, 0));
}
// Launch computations
cout << "Computing " << modelClassName() << " derivatives (order " << derivsOrder << ")." << endl;
computeDerivatives(derivsOrder, vars);
if (paramsDerivsOrder > 0)
{
cout << "Computing " << modelClassName() << " derivatives w.r.t. parameters (order "
<< paramsDerivsOrder << ")." << endl;
computeParamsDerivatives(paramsDerivsOrder);
}
computeTemporaryTerms(!use_dll, no_tmp_terms);
if (paramsDerivsOrder > 0 && !no_tmp_terms)
computeParamsDerivativesTemporaryTerms();
computingPassBlock(eval_context, no_tmp_terms);
if (!block_decomposed && block)
{
cerr << "ERROR: Block decomposition requested but failed. If your model does not have a "
"steady state, you may want to try the 'no_static' option of the 'model' block."
<< endl;
exit(EXIT_FAILURE);
}
computeMCPEquationsReordering();
}
void
StaticModel::writeStaticFile(const string& basename, bool use_dll, const string& mexext,
const filesystem::path& matlabroot, bool julia) const
{
filesystem::path model_dir {basename};
model_dir /= "model";
if (use_dll)
{
create_directories(model_dir / "src" / "sparse");
if (block_decomposed)
create_directories(model_dir / "src" / "sparse" / "block");
}
if (julia)
create_directories(model_dir / "julia");
else
{
auto plusfolder {packageDir(basename)};
/* The following is not a duplicate of the same call from
ModFile::writeMOutput(), because of planner_objective which needs its
+objective subdirectory */
create_directories(plusfolder);
auto sparsefolder {plusfolder / "+sparse"};
create_directories(sparsefolder);
if (block_decomposed)
create_directories(sparsefolder / "+block");
create_directories(plusfolder / "+debug");
}
create_directories(model_dir / "bytecode" / "block");
/* PlannerObjective subclass or discretionary optimal policy models don’t
have as many variables as equations; bytecode does not support that
case */
if (static_cast<int>(equations.size()) == symbol_table.endo_nbr())
writeStaticBytecode(basename);
if (block_decomposed)
writeStaticBlockBytecode(basename);
// Sparse representation
if (use_dll)
writeSparseModelCFiles<false>(basename, mexext, matlabroot);
else if (julia)
writeSparseModelJuliaFiles<false>(basename);
else // MATLAB/Octave
writeSparseModelMFiles<false>(basename);
writeSetAuxiliaryVariablesFile<false>(basename, julia);
if (!julia)
{
writeComplementarityConditionsFile<false>(basename);
// Support for model debugging
writeDebugModelMFiles<false>(basename);
}
}
bool
StaticModel::exoPresentInEqs() const
{
for (auto equation : equations)
if (equation->hasExogenous())
return true;
return false;
}
void
StaticModel::writeDriverOutput(ostream& output) const
{
output << "M_.static_tmp_nbr = [";
for (const auto& temporary_terms_derivative : temporary_terms_derivatives)
output << temporary_terms_derivative.size() << "; ";
output << "];" << endl;
if (block_decomposed)
writeBlockDriverOutput(output);
writeDriverSparseIndicesHelper("static", output);
output << "M_.static_mcp_equations_reordering = [";
for (auto i : mcp_equations_reordering)
output << i + 1 << "; ";
output << "];" << endl;
}
void
StaticModel::writeBlockDriverOutput(ostream& output) const
{
for (int blk = 0; blk < static_cast<int>(blocks.size()); blk++)
{
output << "M_.block_structure_stat.block(" << blk + 1
<< ").Simulation_Type = " << static_cast<int>(blocks[blk].simulation_type) << ";" << endl
<< "M_.block_structure_stat.block(" << blk + 1 << ").endo_nbr = " << blocks[blk].size
<< ";" << endl
<< "M_.block_structure_stat.block(" << blk + 1 << ").mfs = " << blocks[blk].mfs_size
<< ";" << endl
<< "M_.block_structure_stat.block(" << blk + 1 << ").equation = [";
for (int eq = 0; eq < blocks[blk].size; eq++)
output << " " << getBlockEquationID(blk, eq) + 1;
output << "];" << endl << "M_.block_structure_stat.block(" << blk + 1 << ").variable = [";
for (int var = 0; var < blocks[blk].size; var++)
output << " " << getBlockVariableID(blk, var) + 1;
output << "];" << endl;
}
output << "M_.block_structure_stat.variable_reordered = [";
for (int i = 0; i < symbol_table.endo_nbr(); i++)
output << " " << endo_idx_block2orig[i] + 1;
output << "];" << endl << "M_.block_structure_stat.equation_reordered = [";
for (int i = 0; i < symbol_table.endo_nbr(); i++)
output << " " << eq_idx_block2orig[i] + 1;
output << "];" << endl;
set<pair<int, int>> row_incidence;
for (const auto& [indices, d1] : derivatives[1])
if (int deriv_id = indices[1]; getTypeByDerivID(deriv_id) == SymbolType::endogenous)
{
int eq = indices[0];
int var {getTypeSpecificIDByDerivID(deriv_id)};
row_incidence.emplace(eq, var);
}
output << "M_.block_structure_stat.incidence.sparse_IM = [" << endl;
for (auto [eq, var] : row_incidence)
output << " " << eq + 1 << " " << var + 1 << ";" << endl;
output << "];" << endl
<< "M_.block_structure_stat.tmp_nbr = " << blocks_temporary_terms_idxs.size() << ";"
<< endl;
writeBlockDriverSparseIndicesHelper<false>(output);
}
SymbolType
StaticModel::getTypeByDerivID(int deriv_id) const noexcept(false)
{
if (deriv_id < symbol_table.endo_nbr())
return SymbolType::endogenous;
else if (deriv_id < symbol_table.endo_nbr() + symbol_table.param_nbr())
return SymbolType::parameter;
else
throw UnknownDerivIDException();
}
int
StaticModel::getLagByDerivID([[maybe_unused]] int deriv_id) const noexcept(false)
{
return 0;
}
int
StaticModel::getSymbIDByDerivID(int deriv_id) const noexcept(false)
{
if (deriv_id < symbol_table.endo_nbr())
return symbol_table.getID(SymbolType::endogenous, deriv_id);
else if (deriv_id < symbol_table.endo_nbr() + symbol_table.param_nbr())
return symbol_table.getID(SymbolType::parameter, deriv_id - symbol_table.endo_nbr());
else
throw UnknownDerivIDException();
}
int
StaticModel::getTypeSpecificIDByDerivID(int deriv_id) const
{ if (deriv_id < symbol_table.endo_nbr())
return deriv_id;
else if (deriv_id < symbol_table.endo_nbr() + symbol_table.param_nbr())
return deriv_id - symbol_table.endo_nbr();
else
throw UnknownDerivIDException();
}
int
StaticModel::getDerivID(int symb_id, [[maybe_unused]] int lag) const noexcept(false)
{
if (symbol_table.getType(symb_id) == SymbolType::endogenous)
return symbol_table.getTypeSpecificID(symb_id);
else if (symbol_table.getType(symb_id) == SymbolType::parameter)
return symbol_table.getTypeSpecificID(symb_id) + symbol_table.endo_nbr();
else
/* See the special treatment in VariableNode::prepareForDerivation(),
VariableNode::computeDerivative() and VariableNode::getChainRuleDerivative() */
throw UnknownDerivIDException {};
}
void
StaticModel::addAllParamDerivId(set<int>& deriv_id_set)
{
for (int i = 0; i < symbol_table.param_nbr(); i++)
deriv_id_set.insert(i + symbol_table.endo_nbr());
}
void
StaticModel::computeChainRuleJacobian()
{
int nb_blocks = blocks.size();
blocks_derivatives.resize(nb_blocks);
blocks_jacobian_sparse_column_major_order.resize(nb_blocks);
blocks_jacobian_sparse_colptr.resize(nb_blocks);
for (int blk = 0; blk < nb_blocks; blk++)
{
int nb_recursives = blocks[blk].getRecursiveSize();
BlockSimulationType simulation_type {blocks[blk].simulation_type};
map<int, BinaryOpNode*> recursive_vars;
for (int i = 0; i < nb_recursives; i++)
{
int deriv_id = getDerivID(
symbol_table.getID(SymbolType::endogenous, getBlockVariableID(blk, i)), 0);
if (getBlockEquationType(blk, i) == EquationType::evaluateRenormalized)
recursive_vars[deriv_id] = getBlockEquationRenormalizedExpr(blk, i);
else
recursive_vars[deriv_id] = getBlockEquationExpr(blk, i);
}
assert(simulation_type != BlockSimulationType::solveTwoBoundariesSimple
&& simulation_type != BlockSimulationType::solveTwoBoundariesComplete);
int size = blocks[blk].size;
unordered_map<expr_t, set<int>> non_null_chain_rule_derivatives;
unordered_map<expr_t, map<int, expr_t>> chain_rule_deriv_cache;
for (int eq = nb_recursives; eq < size; eq++)
{
int eq_orig = getBlockEquationID(blk, eq);
for (int var = nb_recursives; var < size; var++)
{
int var_orig = getBlockVariableID(blk, var);
expr_t d1 = equations[eq_orig]->getChainRuleDerivative(
getDerivID(symbol_table.getID(SymbolType::endogenous, var_orig), 0),
recursive_vars, non_null_chain_rule_derivatives, chain_rule_deriv_cache);
if (d1 != Zero)
blocks_derivatives[blk][{eq, var, 0}] = d1; }
}
// Compute the sparse representation of the Jacobian
if (simulation_type != BlockSimulationType::evaluateForward
&& simulation_type != BlockSimulationType::evaluateBackward)
{
for (const auto& [indices, d1] : blocks_derivatives[blk])
{
auto& [eq, var, lag] {indices};
assert(eq >= nb_recursives && var >= nb_recursives && lag == 0);
blocks_jacobian_sparse_column_major_order[blk].try_emplace(
{eq - nb_recursives, var - nb_recursives}, d1);
}
blocks_jacobian_sparse_colptr[blk] = computeCSCColPtr(
blocks_jacobian_sparse_column_major_order[blk], blocks[blk].mfs_size);
}
}
}
void
StaticModel::writeLatexFile(const string& basename, bool write_equation_tags) const
{
writeLatexModelFile(basename, "static", ExprNodeOutputType::latexStaticModel,
write_equation_tags);
}
void
StaticModel::writeAuxVarInitval(ostream& output, ExprNodeOutputType output_type) const
{
for (auto aux_equation : aux_equations)
{
dynamic_cast<ExprNode*>(aux_equation)->writeOutput(output, output_type);
output << ";" << endl;
}
}
void
StaticModel::writeLatexAuxVarRecursiveDefinitions(ostream& output) const
{
deriv_node_temp_terms_t tef_terms;
temporary_terms_t temporary_terms;
temporary_terms_idxs_t temporary_terms_idxs;
for (auto aux_equation : aux_equations)
if (aux_equation->containsExternalFunction())
aux_equation->writeExternalFunctionOutput(output, ExprNodeOutputType::latexStaticModel,
temporary_terms, temporary_terms_idxs, tef_terms);
for (auto aux_equation : aux_equations)
{
output << R"(\begin{dmath})" << endl;
dynamic_cast<ExprNode*>(aux_equation)
->writeOutput(output, ExprNodeOutputType::latexStaticModel);
output << endl << R"(\end{dmath})" << endl;
}
}
void
StaticModel::writeJsonAuxVarRecursiveDefinitions(ostream& output) const
{
deriv_node_temp_terms_t tef_terms;
temporary_terms_t temporary_terms;
for (auto aux_equation : aux_equations)
if (aux_equation->containsExternalFunction())
{
vector<string> efout;
aux_equation->writeJsonExternalFunctionOutput(efout, temporary_terms, tef_terms, false);
for (bool printed_something {false}; const auto& it : efout)
{
if (exchange(printed_something, true)) output << ", ";
output << it;
}
}
for (auto aux_equation : aux_equations)
{
output << R"(, {"lhs": ")";
aux_equation->arg1->writeJsonOutput(output, temporary_terms, tef_terms, false);
output << R"(", "rhs": ")";
aux_equation->arg2->writeJsonOutput(output, temporary_terms, tef_terms, false);
output << R"("})";
}
}
void
StaticModel::writeJsonOutput(ostream& output) const
{
output << R"("static_tmp_nbr": [)";
for (bool printed_something {false}; const auto& tts : temporary_terms_derivatives)
{
if (exchange(printed_something, true))
output << ", ";
output << tts.size();
}
output << "], ";
writeJsonSparseIndicesHelper<false>(output);
}
void
StaticModel::writeJsonComputingPassOutput(ostream& output, bool writeDetails) const
{
auto [mlv_output, d_output] {writeJsonComputingPassOutputHelper<false>(writeDetails)};
if (writeDetails)
output << R"("static_model": {)";
else
output << R"("static_model_simple": {)";
output << mlv_output.str();
for (const auto& it : d_output)
output << ", " << it.str();
output << "}";
}
void
StaticModel::writeJsonParamsDerivatives(ostream& output, bool writeDetails) const
{
if (!params_derivatives.size())
return;
auto [mlv_output, tt_output, rp_output, gp_output, rpp_output, gpp_output, hp_output,
g3p_output] {writeJsonParamsDerivativesHelper<false>(writeDetails)};
// g3p_output is ignored
if (writeDetails)
output << R"("static_model_params_derivative": {)";
else
output << R"("static_model_params_derivatives_simple": {)";
output << mlv_output.str() << ", " << tt_output.str() << ", " << rp_output.str() << ", "
<< gp_output.str() << ", " << rpp_output.str() << ", " << gpp_output.str() << ", "
<< hp_output.str() << "}";
}
void
StaticModel::computeRamseyMultipliersDerivatives(int ramsey_orig_endo_nbr, bool is_matlab,
bool no_tmp_terms)
{
// Compute derivation IDs of Lagrange multipliers
set<int> mult_symb_ids {symbol_table.getLagrangeMultipliers()};
vector<int> mult_deriv_ids; mult_deriv_ids.reserve(mult_symb_ids.size());
for (int symb_id : mult_symb_ids)
mult_deriv_ids.push_back(getDerivID(symb_id, 0));
// Compute the list of aux vars for which to apply the chain rule derivation
map<int, BinaryOpNode*> recursive_variables;
for (auto aux_eq : aux_equations)
{
auto varexpr {dynamic_cast<VariableNode*>(aux_eq->arg1)};
assert(varexpr && symbol_table.isAuxiliaryVariable(varexpr->symb_id));
/* Determine whether the auxiliary variable has been added after the last
Lagrange multiplier. We use the guarantee given by SymbolTable that
symbol IDs are increasing. */
if (varexpr->symb_id > *mult_symb_ids.crbegin())
recursive_variables.emplace(varexpr->getDerivID(), aux_eq);
}
// Compute the chain rule derivatives w.r.t. multipliers
unordered_map<expr_t, set<int>> non_null_chain_rule_derivatives;
unordered_map<expr_t, map<int, expr_t>> cache;
for (int eq {0}; eq < ramsey_orig_endo_nbr; eq++)
for (int mult {0}; mult < static_cast<int>(mult_deriv_ids.size()); mult++)
if (expr_t d {equations[eq]->getChainRuleDerivative(mult_deriv_ids[mult], recursive_variables,
non_null_chain_rule_derivatives, cache)};
d != Zero)
ramsey_multipliers_derivatives.try_emplace({eq, mult}, d);
// Compute the temporary terms
map<pair<int, int>, unordered_set<expr_t>> temp_terms_map;
unordered_map<expr_t, pair<int, pair<int, int>>> reference_count;
for (const auto& [row_col, d] : ramsey_multipliers_derivatives)
d->computeTemporaryTerms({1, 0}, temp_terms_map, reference_count, is_matlab);
/* If the user has specified the notmpterms option, clear all temporary
terms, except those that correspond to external functions (since they are
not optional) */
if (no_tmp_terms)
for (auto& it : temp_terms_map)
erase_if(it.second, [](expr_t e) { return !dynamic_cast<AbstractExternalFunctionNode*>(e); });
ranges::copy(temp_terms_map[{1, 0}],
inserter(ramsey_multipliers_derivatives_temporary_terms,
ramsey_multipliers_derivatives_temporary_terms.begin()));
for (int idx {0}; auto it : ramsey_multipliers_derivatives_temporary_terms)
ramsey_multipliers_derivatives_temporary_terms_idxs[it] = idx++;
// Compute the CSC format
ramsey_multipliers_derivatives_sparse_colptr
= computeCSCColPtr(ramsey_multipliers_derivatives, mult_deriv_ids.size());
}
void
StaticModel::writeDriverRamseyMultipliersDerivativesSparseIndices(ostream& output) const
{
output << "M_.ramsey_multipliers_static_g1_sparse_rowval = int32([";
for (auto& [row_col, d] : ramsey_multipliers_derivatives)
output << row_col.first + 1 << ' ';
output << "]);" << endl << "M_.ramsey_multipliers_static_g1_sparse_colval = int32([";
for (auto& [row_col, d] : ramsey_multipliers_derivatives)
output << row_col.second + 1 << ' ';
output << "]);" << endl << "M_.ramsey_multipliers_static_g1_sparse_colptr = int32([";
for (int it : ramsey_multipliers_derivatives_sparse_colptr)
output << it + 1 << ' ';
output << "]);" << endl;
}
void
StaticModel::writeRamseyMultipliersDerivativesMFile(const string& basename,
int ramsey_orig_endo_nbr) const
{
constexpr auto output_type {ExprNodeOutputType::matlabStaticModel};
filesystem::path filename {packageDir(basename) / "ramsey_multipliers_static_g1.m"}; ofstream output_file {filename, ios::out | ios::binary};
if (!output_file.is_open())
{
cerr << "ERROR: Can't open file " << filename.string() << " for writing" << endl;
exit(EXIT_FAILURE);
}
output_file << "function g1m = ramsey_multipliers_static_g1(y, x, params, sparse_rowval, "
"sparse_colval, sparse_colptr)"
<< endl
<< "g1m_v=NaN(" << ramsey_multipliers_derivatives.size() << ",1);" << endl;
writeRamseyMultipliersDerivativesHelper<output_type>(output_file);
output_file << "g1m = sparse(sparse_rowval, sparse_colval, g1m_v, " << ramsey_orig_endo_nbr
<< ", " << symbol_table.getLagrangeMultipliers().size() << ");" << endl
<< "end" << endl;
output_file.close();
}
void
StaticModel::writeRamseyMultipliersDerivativesCFile(const string& basename, const string& mexext,
const filesystem::path& matlabroot,
int ramsey_orig_endo_nbr) const
{
constexpr auto output_type {ExprNodeOutputType::CStaticModel};
const filesystem::path model_src_dir {filesystem::path {basename} / "model" / "src"};
const int xlen {symbol_table.exo_nbr() + symbol_table.exo_det_nbr()};
const int nzval {static_cast<int>(ramsey_multipliers_derivatives.size())};
const int ncols {static_cast<int>(symbol_table.getLagrangeMultipliers().size())};
const filesystem::path p {model_src_dir / "ramsey_multipliers_static_g1.c"};
ofstream output {p, ios::out | ios::binary};
if (!output.is_open())
{
cerr << "ERROR: Can't open file " << p.string() << " for writing" << endl;
exit(EXIT_FAILURE);
}
output << "#include <math.h>" << endl
<< endl
<< R"(#include "mex.h")" << endl // Needed for calls to external functions
<< endl;
writeCHelpersDefinition(output);
writeCHelpersDeclaration(output); // Provide external definition of helpers
output << endl
<< "void ramsey_multipliers_static_g1(const double *restrict y, const double *restrict x, "
"const double *restrict params, double *restrict T, double *restrict g1m_v)"
<< endl
<< "{" << endl;
writeRamseyMultipliersDerivativesHelper<output_type>(output);
output << "}" << endl
<< endl
<< "void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])" << endl
<< "{" << endl
<< " if (nrhs != 6)" << endl
<< R"( mexErrMsgTxt("Accepts exactly 6 input arguments");)" << endl
<< " if (nlhs != 1)" << endl
<< R"( mexErrMsgTxt("Accepts exactly 1 output argument");)" << endl
<< " if (!(mxIsDouble(prhs[0]) && !mxIsComplex(prhs[0]) && !mxIsSparse(prhs[0]) && "
"mxGetNumberOfElements(prhs[0]) == "
<< symbol_table.endo_nbr() << "))" << endl
<< R"( mexErrMsgTxt("y must be a real dense numeric array with )"
<< symbol_table.endo_nbr() << R"( elements");)" << endl
<< " const double *restrict y = mxGetPr(prhs[0]);" << endl
<< " if (!(mxIsDouble(prhs[1]) && !mxIsComplex(prhs[1]) && !mxIsSparse(prhs[1]) && "
"mxGetNumberOfElements(prhs[1]) == "
<< xlen << "))" << endl
<< R"( mexErrMsgTxt("x must be a real dense numeric array with )" << xlen << R"( elements");)" << endl
<< " const double *restrict x = mxGetPr(prhs[1]);" << endl
<< " if (!(mxIsDouble(prhs[2]) && !mxIsComplex(prhs[2]) && !mxIsSparse(prhs[2]) && "
"mxGetNumberOfElements(prhs[2]) == "
<< symbol_table.param_nbr() << "))" << endl
<< R"( mexErrMsgTxt("params must be a real dense numeric array with )"
<< symbol_table.param_nbr() << R"( elements");)" << endl
<< " const double *restrict params = mxGetPr(prhs[2]);" << endl
<< " if (!(mxIsInt32(prhs[3]) && mxGetNumberOfElements(prhs[3]) == " << nzval << "))"
<< endl
<< R"( mexErrMsgTxt("sparse_rowval must be an int32 array with )" << nzval
<< R"( elements");)" << endl
<< " if (!(mxIsInt32(prhs[5]) && mxGetNumberOfElements(prhs[5]) == " << ncols + 1 << "))"
<< endl
<< R"( mexErrMsgTxt("sparse_colptr must be an int32 array with )" << ncols + 1
<< R"( elements");)" << endl
<< "#if MX_HAS_INTERLEAVED_COMPLEX" << endl
<< " const int32_T *restrict sparse_rowval = mxGetInt32s(prhs[3]);" << endl
<< " const int32_T *restrict sparse_colptr = mxGetInt32s(prhs[5]);" << endl
<< "#else" << endl
<< " const int32_T *restrict sparse_rowval = (int32_T *) mxGetData(prhs[3]);" << endl
<< " const int32_T *restrict sparse_colptr = (int32_T *) mxGetData(prhs[5]);" << endl
<< "#endif" << endl
<< " plhs[0] = mxCreateSparse(" << ramsey_orig_endo_nbr << ", " << ncols << ", " << nzval
<< ", mxREAL);" << endl
<< " mwIndex *restrict ir = mxGetIr(plhs[0]), *restrict jc = mxGetJc(plhs[0]);" << endl
<< " for (mwSize i = 0; i < " << nzval << "; i++)" << endl
<< " *ir++ = *sparse_rowval++ - 1;" << endl
<< " for (mwSize i = 0; i < " << ncols + 1 << "; i++)" << endl
<< " *jc++ = *sparse_colptr++ - 1;" << endl
<< " mxArray *T_mx = mxCreateDoubleMatrix("
<< ramsey_multipliers_derivatives_temporary_terms.size() << ", 1, mxREAL);" << endl
<< " ramsey_multipliers_static_g1(y, x, params, mxGetPr(T_mx), mxGetPr(plhs[0]));" << endl
<< " mxDestroyArray(T_mx);" << endl
<< "}" << endl;
output.close();
compileMEX(packageDir(basename), "ramsey_multipliers_static_g1", mexext, {p}, matlabroot);
}