2129 lines
74 KiB
C++
2129 lines
74 KiB
C++
/*
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* Copyright © 2003-2021 Dynare Team
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*
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* This file is part of Dynare.
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*
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* Dynare is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* Dynare is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with Dynare. If not, see <https://www.gnu.org/licenses/>.
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*/
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#include "ModelTree.hh"
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#include "VariableDependencyGraph.hh"
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#pragma GCC diagnostic push
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#pragma GCC diagnostic ignored "-Wold-style-cast"
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#pragma GCC diagnostic ignored "-Wsign-compare"
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#pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
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#include <boost/graph/adjacency_list.hpp>
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#include <boost/graph/max_cardinality_matching.hpp>
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#include <boost/graph/topological_sort.hpp>
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#pragma GCC diagnostic pop
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#ifdef __APPLE__
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# include <mach-o/dyld.h>
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#endif
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#include <regex>
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#include <utility>
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void
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ModelTree::copyHelper(const ModelTree &m)
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{
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auto f = [this](expr_t e) { return e->clone(*this); };
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// Equations
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for (const auto &it : m.equations)
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equations.push_back(dynamic_cast<BinaryOpNode *>(f(it)));
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for (const auto &it : m.aux_equations)
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aux_equations.push_back(dynamic_cast<BinaryOpNode *>(f(it)));
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auto convert_deriv_map = [f](map<vector<int>, expr_t> dm)
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{
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map<vector<int>, expr_t> dm2;
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for (const auto &it : dm)
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dm2.emplace(it.first, f(it.second));
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return dm2;
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};
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// Derivatives
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for (const auto &it : m.derivatives)
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derivatives.push_back(convert_deriv_map(it));
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for (const auto &it : m.params_derivatives)
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params_derivatives[it.first] = convert_deriv_map(it.second);
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auto convert_temporary_terms_t = [f](temporary_terms_t tt)
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{
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temporary_terms_t tt2;
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for (const auto &it : tt)
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tt2.insert(f(it));
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return tt2;
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};
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// Temporary terms
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for (const auto &it : m.temporary_terms_mlv)
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temporary_terms_mlv[dynamic_cast<VariableNode *>(f(it.first))] = f(it.second);
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for (const auto &it : m.temporary_terms_derivatives)
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temporary_terms_derivatives.push_back(convert_temporary_terms_t(it));
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for (const auto &it : m.temporary_terms_idxs)
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temporary_terms_idxs[f(it.first)] = it.second;
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for (const auto &it : m.params_derivs_temporary_terms)
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params_derivs_temporary_terms[it.first] = convert_temporary_terms_t(it.second);
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for (const auto &it : m.params_derivs_temporary_terms_idxs)
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params_derivs_temporary_terms_idxs[f(it.first)] = it.second;
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// Other stuff
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for (const auto &it : m.trend_symbols_map)
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trend_symbols_map[it.first] = f(it.second);
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for (const auto &it : m.nonstationary_symbols_map)
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nonstationary_symbols_map[it.first] = {it.second.first, f(it.second.second)};
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for (const auto &it : m.equation_type_and_normalized_equation)
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equation_type_and_normalized_equation.emplace_back(it.first, dynamic_cast<BinaryOpNode *>(f(it.second)));
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for (const auto &it : m.blocks_derivatives)
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{
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map<tuple<int, int, int>, expr_t> v;
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for (const auto &it2 : it)
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v[it2.first] = f(it2.second);
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blocks_derivatives.push_back(v);
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}
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auto convert_vector_tt = [f](vector<temporary_terms_t> vtt)
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{
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vector<temporary_terms_t> vtt2;
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for (const auto &tt : vtt)
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{
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temporary_terms_t tt2;
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for (const auto &it : tt)
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tt2.insert(f(it));
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vtt2.push_back(tt2);
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}
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return vtt2;
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};
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for (const auto &it : m.blocks_temporary_terms)
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blocks_temporary_terms.push_back(convert_vector_tt(it));
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for (const auto &it : m.blocks_temporary_terms_idxs)
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blocks_temporary_terms_idxs[f(it.first)] = it.second;
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}
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ModelTree::ModelTree(SymbolTable &symbol_table_arg,
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NumericalConstants &num_constants_arg,
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ExternalFunctionsTable &external_functions_table_arg,
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bool is_dynamic_arg) :
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DataTree{symbol_table_arg, num_constants_arg, external_functions_table_arg, is_dynamic_arg},
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derivatives(4),
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NNZDerivatives(4, 0),
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temporary_terms_derivatives(4)
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{
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}
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ModelTree::ModelTree(const ModelTree &m) :
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DataTree{m},
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user_set_add_flags{m.user_set_add_flags},
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user_set_subst_flags{m.user_set_subst_flags},
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user_set_add_libs{m.user_set_add_libs},
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user_set_subst_libs{m.user_set_subst_libs},
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user_set_compiler{m.user_set_compiler},
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equations_lineno{m.equations_lineno},
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equation_tags{m.equation_tags},
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computed_derivs_order{m.computed_derivs_order},
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NNZDerivatives{m.NNZDerivatives},
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eq_idx_block2orig{m.eq_idx_block2orig},
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endo_idx_block2orig{m.endo_idx_block2orig},
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eq_idx_orig2block{m.eq_idx_orig2block},
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endo_idx_orig2block{m.endo_idx_orig2block},
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blocks{m.blocks},
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endo2block{m.endo2block},
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eq2block{m.eq2block},
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endo2eq{m.endo2eq},
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cutoff{m.cutoff},
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mfs{m.mfs}
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{
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copyHelper(m);
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}
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ModelTree &
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ModelTree::operator=(const ModelTree &m)
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{
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DataTree::operator=(m);
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equations.clear();
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equations_lineno = m.equations_lineno;
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aux_equations.clear();
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equation_tags = m.equation_tags;
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computed_derivs_order = m.computed_derivs_order;
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NNZDerivatives = m.NNZDerivatives;
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derivatives.clear();
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params_derivatives.clear();
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temporary_terms_mlv.clear();
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temporary_terms_derivatives.clear();
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params_derivs_temporary_terms.clear();
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params_derivs_temporary_terms_idxs.clear();
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trend_symbols_map.clear();
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nonstationary_symbols_map.clear();
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eq_idx_block2orig = m.eq_idx_block2orig;
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endo_idx_block2orig = m.endo_idx_block2orig;
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eq_idx_orig2block = m.eq_idx_orig2block;
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endo_idx_orig2block = m.endo_idx_orig2block;
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equation_type_and_normalized_equation.clear();
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blocks_derivatives.clear();
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blocks = m.blocks;
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endo2block = m.endo2block;
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eq2block = m.eq2block;
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blocks_temporary_terms.clear();
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blocks_temporary_terms_idxs.clear();
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endo2eq = m.endo2eq;
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cutoff = m.cutoff;
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mfs = m.mfs;
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user_set_add_flags = m.user_set_add_flags;
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user_set_subst_flags = m.user_set_subst_flags;
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user_set_add_libs = m.user_set_add_libs;
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user_set_subst_libs = m.user_set_subst_libs;
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user_set_compiler = m.user_set_compiler;
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copyHelper(m);
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return *this;
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}
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bool
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ModelTree::computeNormalization(const jacob_map_t &contemporaneous_jacobian, bool verbose)
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{
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const int n = equations.size();
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assert(n == symbol_table.endo_nbr());
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using BipartiteGraph = boost::adjacency_list<boost::vecS, boost::vecS, boost::undirectedS>;
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/*
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Vertices 0 to n-1 are for endogenous (using type specific ID)
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Vertices n to 2*n-1 are for equations (using equation no.)
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*/
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BipartiteGraph g(2 * n);
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// Fill in the graph
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for (const auto &[eq_and_endo, val] : contemporaneous_jacobian)
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add_edge(eq_and_endo.first + n, eq_and_endo.second, g);
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// Compute maximum cardinality matching
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vector<int> mate_map(2*n);
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bool check = checked_edmonds_maximum_cardinality_matching(g, &mate_map[0]);
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assert(check);
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#ifdef DEBUG
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for (int i = 0; i < n; i++)
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cout << "Endogenous " << symbol_table.getName(symbol_table.getID(eEndogenous, i))
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<< " matched with equation " << (mate_map[i]-n+1) << endl;
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#endif
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// Create the resulting map, by copying the n first elements of mate_map, and substracting n to them
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endo2eq.resize(equations.size());
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transform(mate_map.begin(), mate_map.begin() + n, endo2eq.begin(), [=](int i) { return i-n; });
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// Check if all variables are normalized
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if (auto it = find(mate_map.begin(), mate_map.begin() + n, boost::graph_traits<BipartiteGraph>::null_vertex());
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it != mate_map.begin() + n)
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{
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if (verbose)
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cerr << "ERROR: Could not normalize the model. Variable "
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<< symbol_table.getName(symbol_table.getID(SymbolType::endogenous, it - mate_map.begin()))
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<< " is not in the maximum cardinality matching." << endl;
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check = false;
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}
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return check;
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}
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void
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ModelTree::computeNonSingularNormalization(const jacob_map_t &contemporaneous_jacobian)
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{
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cout << "Normalizing the model..." << endl;
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int n = equations.size();
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// Compute the maximum value of each row of the contemporaneous Jacobian matrix
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vector<double> max_val(n, 0.0);
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for (const auto &[eq_and_endo, val] : contemporaneous_jacobian)
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max_val[eq_and_endo.first] = max(max_val[eq_and_endo.first], fabs(val));
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// Compute normalized contemporaneous Jacobian
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jacob_map_t normalized_contemporaneous_jacobian(contemporaneous_jacobian);
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for (auto &[eq_and_endo, val] : normalized_contemporaneous_jacobian)
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val /= max_val[eq_and_endo.first];
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// We start with the highest value of the cutoff and try to normalize the model
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double current_cutoff = 0.99999999;
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const double cutoff_lower_limit = 1e-19;
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bool found_normalization = false;
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int last_suppressed = 0;
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while (!found_normalization && current_cutoff > cutoff_lower_limit)
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{
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// Drop elements below cutoff from normalized contemporaneous Jacobian
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jacob_map_t normalized_contemporaneous_jacobian_above_cutoff;
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int suppressed = 0;
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for (const auto &[eq_and_endo, val] : normalized_contemporaneous_jacobian)
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if (fabs(val) > max(current_cutoff, cutoff))
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normalized_contemporaneous_jacobian_above_cutoff[eq_and_endo] = val;
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else
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suppressed++;
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if (suppressed != last_suppressed)
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found_normalization = computeNormalization(normalized_contemporaneous_jacobian_above_cutoff, false);
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last_suppressed = suppressed;
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if (!found_normalization)
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{
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current_cutoff /= 2;
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// In this last case try to normalize with the complete jacobian
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if (current_cutoff <= cutoff_lower_limit)
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found_normalization = computeNormalization(normalized_contemporaneous_jacobian, false);
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}
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}
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if (!found_normalization)
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{
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cout << "Normalization failed with cutoff, trying symbolic normalization..." << endl;
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/* If no non-singular normalization can be found, try to find a
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normalization even with a potential singularity.
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TODO: Explain why symbolic_jacobian is not contemporaneous. */
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auto symbolic_jacobian = computeSymbolicJacobian();
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found_normalization = computeNormalization(symbolic_jacobian, true);
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}
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if (!found_normalization)
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{
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cerr << "No normalization could be computed. Aborting." << endl;
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exit(EXIT_FAILURE);
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}
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}
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ModelTree::jacob_map_t
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ModelTree::evaluateAndReduceJacobian(const eval_context_t &eval_context) const
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{
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jacob_map_t contemporaneous_jacobian;
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for (const auto &[indices, d1] : derivatives[1])
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{
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int deriv_id = indices[1];
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if (getTypeByDerivID(deriv_id) == SymbolType::endogenous)
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{
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int eq = indices[0];
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int symb = getSymbIDByDerivID(deriv_id);
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int var = symbol_table.getTypeSpecificID(symb);
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int lag = getLagByDerivID(deriv_id);
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double val = 0;
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try
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{
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val = d1->eval(eval_context);
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}
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catch (ExprNode::EvalExternalFunctionException &e)
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{
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val = 1;
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}
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catch (ExprNode::EvalException &e)
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{
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cerr << "ERROR: evaluation of Jacobian failed for equation " << eq+1 << " (line " << equations_lineno[eq] << ") and variable " << symbol_table.getName(symb) << "(" << lag << ") [" << symb << "] !" << endl;
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d1->writeOutput(cerr, ExprNodeOutputType::matlabDynamicModel, {}, {});
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cerr << endl;
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exit(EXIT_FAILURE);
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}
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if ((isnan(val) || fabs(val) >= cutoff) && lag == 0)
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contemporaneous_jacobian[{ eq, var }] = val;
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}
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}
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return contemporaneous_jacobian;
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}
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pair<int, int>
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ModelTree::computePrologueAndEpilogue()
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{
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const int n = equations.size();
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/* Initialize “eq_idx_block2orig” and “endo_idx_block2orig” to the identity
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permutation. */
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eq_idx_block2orig.resize(n);
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endo_idx_block2orig.resize(n);
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for (int i = 0; i < n; i++)
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{
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eq_idx_block2orig[i] = i;
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endo_idx_block2orig[endo2eq[i]] = i;
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}
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/* Compute incidence matrix, equations in rows, variables in columns. Row
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(resp. column) indices are to be interpreted according to
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“eq_idx_block2orig” (resp. “endo_idx_block2orig”). Stored in row-major
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order. */
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vector<bool> IM(n*n, false);
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for (int i = 0; i < n; i++)
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{
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set<pair<int, int>> endos_and_lags;
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equations[i]->collectEndogenous(endos_and_lags);
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for (auto [endo, lag] : endos_and_lags)
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IM[i * n + endo2eq[endo]] = true;
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}
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bool something_has_been_done;
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// Find the prologue equations and place first the AR(1) shock equations first
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int prologue = 0;
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do
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{
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something_has_been_done = false;
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int new_prologue = prologue;
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for (int i = prologue; i < n; i++)
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{
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int nze = 0;
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int k = 0;
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for (int j = new_prologue; j < n; j++)
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if (IM[i * n + j])
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{
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nze++;
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k = j;
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}
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if (nze == 1)
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{
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// Swap equations indexed by “new_prologue” and i
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for (int j = 0; j < n; j++)
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swap(IM[new_prologue * n + j], IM[i * n + j]);
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swap(eq_idx_block2orig[new_prologue], eq_idx_block2orig[i]);
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// Swap variables indexed by “new_prologue” and k (in the matching)
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for (int j = 0; j < n; j++)
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swap(IM[j * n + new_prologue], IM[j * n + k]);
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swap(endo_idx_block2orig[new_prologue], endo_idx_block2orig[k]);
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new_prologue++;
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something_has_been_done = true;
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}
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}
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prologue = new_prologue;
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}
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while (something_has_been_done);
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// Find the epilogue equations
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int epilogue = 0;
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do
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{
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something_has_been_done = false;
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int new_epilogue = epilogue;
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for (int i = prologue; i < n - epilogue; i++)
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{
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int nze = 0;
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int k = 0;
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for (int j = prologue; j < n - new_epilogue; j++)
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if (IM[j * n + i])
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{
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nze++;
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k = j;
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}
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if (nze == 1)
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{
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for (int j = 0; j < n; j++)
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swap(IM[(n - 1 - new_epilogue) * n + j], IM[k * n + j]);
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swap(eq_idx_block2orig[n - 1 - new_epilogue], eq_idx_block2orig[k]);
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for (int j = 0; j < n; j++)
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swap(IM[j * n + n - 1 - new_epilogue], IM[j * n + i]);
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swap(endo_idx_block2orig[n - 1 - new_epilogue], endo_idx_block2orig[i]);
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new_epilogue++;
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something_has_been_done = true;
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}
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}
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epilogue = new_epilogue;
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}
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while (something_has_been_done);
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updateReverseVariableEquationOrderings();
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return { prologue, epilogue };
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}
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void
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ModelTree::equationTypeDetermination(const map<tuple<int, int, int>, expr_t> &first_order_endo_derivatives, int mfs)
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{
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equation_type_and_normalized_equation.clear();
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equation_type_and_normalized_equation.resize(equations.size());
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for (int i = 0; i < static_cast<int>(equations.size()); i++)
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{
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int eq = eq_idx_block2orig[i];
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int var = endo_idx_block2orig[i];
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expr_t lhs = equations[eq]->arg1;
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EquationType Equation_Simulation_Type = EquationType::solve;
|
|
BinaryOpNode *normalized_eq = nullptr;
|
|
if (auto it = first_order_endo_derivatives.find({ eq, var, 0 });
|
|
it != first_order_endo_derivatives.end())
|
|
{
|
|
expr_t derivative = it->second;
|
|
// Determine whether the equation can be evaluated rather than solved
|
|
if (lhs->isVariableNodeEqualTo(SymbolType::endogenous, endo_idx_block2orig[i], 0)
|
|
&& derivative->isNumConstNodeEqualTo(1))
|
|
Equation_Simulation_Type = EquationType::evaluate;
|
|
else
|
|
{
|
|
set<pair<int, int>> result;
|
|
derivative->collectEndogenous(result);
|
|
bool variable_not_in_derivative = result.find({ var, 0 }) == result.end();
|
|
|
|
try
|
|
{
|
|
normalized_eq = equations[eq]->normalizeEquation(symbol_table.getID(SymbolType::endogenous, var), 0);
|
|
if ((mfs == 2 && variable_not_in_derivative) || mfs == 3)
|
|
Equation_Simulation_Type = EquationType::evaluateRenormalized;
|
|
}
|
|
catch (ExprNode::NormalizationFailed &e)
|
|
{
|
|
}
|
|
}
|
|
}
|
|
equation_type_and_normalized_equation[eq] = { Equation_Simulation_Type, normalized_eq };
|
|
}
|
|
}
|
|
|
|
void
|
|
ModelTree::computeDynamicStructureOfBlock(int blk)
|
|
{
|
|
vector<pair<int, int>> max_endo_lag_lead(blocks[blk].size, { 0, 0 });
|
|
blocks[blk].max_endo_lag = blocks[blk].max_endo_lead = 0;
|
|
blocks[blk].max_other_endo_lag = blocks[blk].max_other_endo_lead = 0;
|
|
blocks[blk].max_exo_lag = blocks[blk].max_exo_lead = 0;
|
|
blocks[blk].max_exo_det_lag = blocks[blk].max_exo_det_lead = 0;
|
|
for (int eq = 0; eq < blocks[blk].size; eq++)
|
|
{
|
|
set<pair<int, int>> endos_and_lags;
|
|
expr_t e = getBlockEquationExpr(blk, eq);
|
|
|
|
/* Compute max lags/leads for endogenous. Also fill per-variable structure
|
|
for endos belonging to this block */
|
|
e->collectEndogenous(endos_and_lags);
|
|
for (auto [endo, lag] : endos_and_lags)
|
|
if (endo2block[endo] == blk)
|
|
{
|
|
blocks[blk].max_endo_lag = max(blocks[blk].max_endo_lag, -lag);
|
|
blocks[blk].max_endo_lead = max(blocks[blk].max_endo_lead, lag);
|
|
auto &[max_endo_lag, max_endo_lead] = max_endo_lag_lead[getBlockInitialVariableID(blk, endo)];
|
|
max_endo_lag = max(max_endo_lag, -lag);
|
|
max_endo_lead = max(max_endo_lead, lag);
|
|
}
|
|
else
|
|
{
|
|
blocks[blk].max_other_endo_lag = max(blocks[blk].max_other_endo_lag, -lag);
|
|
blocks[blk].max_other_endo_lead = max(blocks[blk].max_other_endo_lead, lag);
|
|
}
|
|
|
|
// Compute max lags/leads for exogenous
|
|
blocks[blk].max_exo_lag = max(e->maxExoLag(), blocks[blk].max_exo_lag);
|
|
blocks[blk].max_exo_lead = max(e->maxExoLead(), blocks[blk].max_exo_lead);
|
|
|
|
// Compute max lags/leads for deterministic exogenous
|
|
set<pair<int, int>> dynvars;
|
|
e->collectDynamicVariables(SymbolType::exogenousDet, dynvars);
|
|
for (auto [symb_id, lag] : dynvars)
|
|
{
|
|
blocks[blk].max_exo_det_lag = max(-lag, blocks[blk].max_exo_det_lag);
|
|
blocks[blk].max_exo_det_lead = max(lag, blocks[blk].max_exo_det_lead);
|
|
}
|
|
}
|
|
|
|
// Compute max lags/leads over all variables
|
|
blocks[blk].max_lag = max(blocks[blk].max_endo_lag, max(blocks[blk].max_other_endo_lag,
|
|
max(blocks[blk].max_exo_lag,
|
|
blocks[blk].max_exo_det_lag)));
|
|
blocks[blk].max_lead = max(blocks[blk].max_endo_lead, max(blocks[blk].max_other_endo_lead,
|
|
max(blocks[blk].max_exo_lead,
|
|
blocks[blk].max_exo_det_lead)));
|
|
|
|
// Categorize endos that belong to the block
|
|
blocks[blk].n_mixed = blocks[blk].n_forward = blocks[blk].n_backward = blocks[blk].n_static = 0;
|
|
for (int var = 0; var < blocks[blk].size; var++)
|
|
{
|
|
auto [max_lag, max_lead] = max_endo_lag_lead[var];
|
|
if (max_lag != 0 && max_lead != 0)
|
|
blocks[blk].n_mixed++;
|
|
else if (max_lag == 0 && max_lead != 0)
|
|
blocks[blk].n_forward++;
|
|
else if (max_lag != 0 && max_lead == 0)
|
|
blocks[blk].n_backward++;
|
|
else
|
|
blocks[blk].n_static++;
|
|
}
|
|
}
|
|
|
|
void
|
|
ModelTree::computeSimulationTypeOfBlock(int blk)
|
|
{
|
|
auto &type = blocks[blk].simulation_type;
|
|
if (blocks[blk].max_endo_lag > 0 && blocks[blk].max_endo_lead > 0)
|
|
{
|
|
if (blocks[blk].size == 1)
|
|
type = BlockSimulationType::solveTwoBoundariesSimple;
|
|
else
|
|
type = BlockSimulationType::solveTwoBoundariesComplete;
|
|
}
|
|
else if (blocks[blk].size > 1)
|
|
{
|
|
if (blocks[blk].max_endo_lead > 0)
|
|
type = BlockSimulationType::solveBackwardComplete;
|
|
else
|
|
type = BlockSimulationType::solveForwardComplete;
|
|
}
|
|
else
|
|
{
|
|
bool can_eval = (getBlockEquationType(blk, 0) == EquationType::evaluate
|
|
|| getBlockEquationType(blk, 0) == EquationType::evaluateRenormalized);
|
|
if (blocks[blk].max_endo_lead > 0)
|
|
type = can_eval ? BlockSimulationType::evaluateBackward :
|
|
BlockSimulationType::solveBackwardSimple;
|
|
else
|
|
type = can_eval ? BlockSimulationType::evaluateForward :
|
|
BlockSimulationType::solveForwardSimple;
|
|
}
|
|
}
|
|
|
|
pair<lag_lead_vector_t, lag_lead_vector_t>
|
|
ModelTree::getVariableLeadLagByBlock() const
|
|
{
|
|
int nb_endo = symbol_table.endo_nbr();
|
|
|
|
lag_lead_vector_t variable_lag_lead(nb_endo, { 0, 0 }), equation_lag_lead(nb_endo, { 0, 0 });
|
|
for (int eq = 0; eq < nb_endo; eq++)
|
|
{
|
|
set<pair<int, int>> endos_and_lags;
|
|
equations[eq]->collectEndogenous(endos_and_lags);
|
|
for (auto [endo, lag] : endos_and_lags)
|
|
if (endo2block[endo] == eq2block[eq])
|
|
{
|
|
variable_lag_lead[endo].first = max(variable_lag_lead[endo].first, -lag);
|
|
variable_lag_lead[endo].second = max(variable_lag_lead[endo].second, lag);
|
|
equation_lag_lead[eq].first = max(equation_lag_lead[eq].first, -lag);
|
|
equation_lag_lead[eq].second = max(equation_lag_lead[eq].second, lag);
|
|
}
|
|
}
|
|
return { equation_lag_lead, variable_lag_lead };
|
|
}
|
|
|
|
void
|
|
ModelTree::computeBlockDecomposition(int prologue, int epilogue)
|
|
{
|
|
int nb_var = symbol_table.endo_nbr();
|
|
int nb_simvars = nb_var - prologue - epilogue;
|
|
|
|
/* Construct the graph representing the dependencies between all
|
|
variables that do not belong to the prologue or the epilogue.
|
|
|
|
For detecting dependencies between variables, use the symbolic adjacency
|
|
matrix */
|
|
VariableDependencyGraph G(nb_simvars);
|
|
for (const auto &[key, value] : computeSymbolicJacobian())
|
|
{
|
|
auto [eq, endo] = key;
|
|
if (eq_idx_orig2block[eq] >= prologue
|
|
&& eq_idx_orig2block[eq] < nb_var - epilogue
|
|
&& endo_idx_orig2block[endo] >= prologue
|
|
&& endo_idx_orig2block[endo] < nb_var - epilogue
|
|
&& eq != endo2eq[endo])
|
|
add_edge(vertex(eq_idx_orig2block[endo2eq[endo]]-prologue, G),
|
|
vertex(eq_idx_orig2block[eq]-prologue, G), G);
|
|
}
|
|
|
|
/* Identify the simultaneous blocks. Each simultaneous block is given an
|
|
index, starting from 0, in recursive order */
|
|
auto [num_simblocks, simvar2simblock] = G.sortedStronglyConnectedComponents();
|
|
|
|
int num_blocks = prologue+num_simblocks+epilogue;
|
|
|
|
blocks.clear();
|
|
blocks.resize(num_blocks);
|
|
endo2block.resize(nb_var);
|
|
eq2block.resize(nb_var);
|
|
|
|
// Initialize size and mfs_size for prologue and epilogue, plus eq/endo→block mappings
|
|
for (int blk = 0; blk < num_blocks; blk++)
|
|
if (blk < prologue || blk >= num_blocks-epilogue)
|
|
{
|
|
int var_eq = (blk < prologue ? blk : blk-num_simblocks+nb_simvars);
|
|
blocks[blk].size = 1;
|
|
blocks[blk].mfs_size = 1;
|
|
blocks[blk].first_equation = var_eq;
|
|
endo2block[endo_idx_block2orig[var_eq]] = blk;
|
|
eq2block[eq_idx_block2orig[var_eq]] = blk;
|
|
}
|
|
|
|
// Initialize size for simultaneous blocks, plus eq/endo→block mappings
|
|
vector<vector<int>> simblock2simvars(num_simblocks);
|
|
for (int i = 0; i < static_cast<int>(simvar2simblock.size()); i++)
|
|
{
|
|
simblock2simvars[simvar2simblock[i]].push_back(i);
|
|
int blk = prologue+simvar2simblock[i];
|
|
blocks[blk].size++;
|
|
endo2block[endo_idx_block2orig[prologue+i]] = blk;
|
|
eq2block[eq_idx_block2orig[prologue+i]] = blk;
|
|
}
|
|
|
|
// Determine the dynamic structure of each block
|
|
auto [equation_lag_lead, variable_lag_lead] = getVariableLeadLagByBlock();
|
|
|
|
/* For each simultaneous block, the minimum set of feedback variable is computed.
|
|
Then, the variables within the blocks are reordered so that recursive
|
|
(non-feedback) appear first, to get a sub-recursive block without feedback variables.
|
|
Within each of the two sub-blocks, variables are reordered depending
|
|
on their dynamic status: static first, then backward, mixed and forward. */
|
|
|
|
/* First, add a loop on vertices which could not be normalized or vertices
|
|
related to lead/lag variables. This forces those vertices to belong to the
|
|
feedback set */
|
|
for (int i = 0; i < nb_simvars; i++)
|
|
if (equation_type_and_normalized_equation[eq_idx_block2orig[i+prologue]].first == EquationType::solve
|
|
|| variable_lag_lead[endo_idx_block2orig[i+prologue]].first > 0
|
|
|| variable_lag_lead[endo_idx_block2orig[i+prologue]].second > 0
|
|
|| equation_lag_lead[eq_idx_block2orig[i+prologue]].first > 0
|
|
|| equation_lag_lead[eq_idx_block2orig[i+prologue]].second > 0
|
|
|| mfs == 0)
|
|
add_edge(vertex(i, G), vertex(i, G), G);
|
|
|
|
const vector<int> old_eq_idx_block2orig(eq_idx_block2orig), old_endo_idx_block2orig(endo_idx_block2orig);
|
|
int ordidx = prologue;
|
|
for (int blk = prologue; blk < prologue+num_simblocks; blk++)
|
|
{
|
|
blocks[blk].first_equation = (blk == 0 ? 0 : blocks[blk-1].first_equation + blocks[blk-1].size);
|
|
auto subG = G.extractSubgraph(simblock2simvars[blk-prologue]);
|
|
auto feed_back_vertices = subG.minimalSetOfFeedbackVertices();
|
|
blocks[blk].mfs_size = feed_back_vertices.size();
|
|
auto recursive_vertices = subG.reorderRecursiveVariables(feed_back_vertices);
|
|
auto v_index1 = get(boost::vertex_index1, subG);
|
|
|
|
const vector<pair<int, int>> dynamic_order{ make_pair(0, 0), make_pair(1, 0),
|
|
make_pair(1, 1), make_pair(0, 1) };
|
|
|
|
// First the recursive equations conditional on feedback variables
|
|
for (auto max_lag_lead : dynamic_order)
|
|
for (int vtx : recursive_vertices)
|
|
if (int simvar = v_index1[vertex(vtx, subG)];
|
|
variable_lag_lead[old_endo_idx_block2orig[simvar+prologue]] == max_lag_lead)
|
|
{
|
|
eq_idx_block2orig[ordidx] = old_eq_idx_block2orig[simvar+prologue];
|
|
endo_idx_block2orig[ordidx] = old_endo_idx_block2orig[simvar+prologue];
|
|
ordidx++;
|
|
}
|
|
|
|
// Then the equations related to the feedback variables
|
|
for (auto max_lag_lead : dynamic_order)
|
|
for (int vtx : feed_back_vertices)
|
|
if (int simvar = v_index1[vertex(vtx, subG)];
|
|
variable_lag_lead[old_endo_idx_block2orig[simvar+prologue]] == max_lag_lead)
|
|
{
|
|
eq_idx_block2orig[ordidx] = old_eq_idx_block2orig[simvar+prologue];
|
|
endo_idx_block2orig[ordidx] = old_endo_idx_block2orig[simvar+prologue];
|
|
ordidx++;
|
|
}
|
|
}
|
|
|
|
updateReverseVariableEquationOrderings();
|
|
|
|
for (int blk = 0; blk < static_cast<int>(blocks.size()); blk++)
|
|
{
|
|
computeDynamicStructureOfBlock(blk);
|
|
computeSimulationTypeOfBlock(blk);
|
|
}
|
|
}
|
|
|
|
void
|
|
ModelTree::printBlockDecomposition() const
|
|
{
|
|
int largest_block = 0, Nb_SimulBlocks = 0, Nb_feedback_variable = 0;
|
|
int Nb_TotalBlocks = blocks.size();
|
|
for (int block = 0; block < Nb_TotalBlocks; block++)
|
|
if (BlockSimulationType simulation_type = blocks[block].simulation_type;
|
|
simulation_type == BlockSimulationType::solveForwardComplete
|
|
|| simulation_type == BlockSimulationType::solveBackwardComplete
|
|
|| simulation_type == BlockSimulationType::solveTwoBoundariesComplete)
|
|
{
|
|
Nb_SimulBlocks++;
|
|
if (int size = blocks[block].size;
|
|
size > largest_block)
|
|
{
|
|
largest_block = size;
|
|
Nb_feedback_variable = blocks[block].mfs_size;
|
|
}
|
|
}
|
|
|
|
int Nb_RecursBlocks = Nb_TotalBlocks - Nb_SimulBlocks;
|
|
cout << Nb_TotalBlocks << " block(s) found:" << endl
|
|
<< " " << Nb_RecursBlocks << " recursive block(s) and " << Nb_SimulBlocks << " simultaneous block(s)." << endl
|
|
<< " the largest simultaneous block has " << largest_block << " equation(s)" << endl
|
|
<< " and " << Nb_feedback_variable << " feedback variable(s)." << endl;
|
|
}
|
|
|
|
void
|
|
ModelTree::reduceBlockDecomposition()
|
|
{
|
|
for (int blk = 1; blk < static_cast<int>(blocks.size()); blk++)
|
|
if (blocks[blk].size == 1)
|
|
{
|
|
/* Try to merge this block with the previous one.
|
|
This is only possible if the two blocks can simply be evaluated
|
|
(in the same direction), and if the merge does not break the
|
|
restrictions on leads/lags. */
|
|
set<pair<int, int>> endos_and_lags;
|
|
getBlockEquationExpr(blk, 0)->collectEndogenous(endos_and_lags);
|
|
bool is_lead = false, is_lag = false;
|
|
for (int var = 0; var < blocks[blk-1].size; var++)
|
|
{
|
|
is_lag = is_lag || endos_and_lags.find({ getBlockVariableID(blk-1, var), -1 }) != endos_and_lags.end();
|
|
is_lead = is_lead || endos_and_lags.find({ getBlockVariableID(blk-1, var), 1 }) != endos_and_lags.end();
|
|
}
|
|
|
|
if ((blocks[blk-1].simulation_type == BlockSimulationType::evaluateForward
|
|
&& blocks[blk].simulation_type == BlockSimulationType::evaluateForward
|
|
&& !is_lead)
|
|
|| (blocks[blk-1].simulation_type == BlockSimulationType::evaluateBackward
|
|
&& blocks[blk].simulation_type == BlockSimulationType::evaluateBackward
|
|
&& !is_lag))
|
|
{
|
|
// Merge the current block into the previous one
|
|
blocks[blk-1].size++;
|
|
blocks[blk-1].mfs_size = blocks[blk-1].size;
|
|
computeDynamicStructureOfBlock(blk-1);
|
|
blocks.erase(blocks.begin()+blk);
|
|
for (auto &b : endo2block)
|
|
if (b >= blk)
|
|
b--;
|
|
for (auto &b : eq2block)
|
|
if (b >= blk)
|
|
b--;
|
|
blk--;
|
|
continue;
|
|
}
|
|
}
|
|
}
|
|
|
|
void
|
|
ModelTree::determineLinearBlocks()
|
|
{
|
|
// Note that field “linear” in class BlockInfo defaults to true
|
|
for (int blk = 0; blk < static_cast<int>(blocks.size()); blk++)
|
|
switch (blocks[blk].simulation_type)
|
|
{
|
|
case BlockSimulationType::solveBackwardComplete:
|
|
case BlockSimulationType::solveForwardComplete:
|
|
for (const auto &[indices, d1] : blocks_derivatives[blk])
|
|
{
|
|
int lag = get<2>(indices);
|
|
if (lag == 0)
|
|
{
|
|
set<pair<int, int>> endogenous;
|
|
d1->collectEndogenous(endogenous);
|
|
for (int l = 0; l < blocks[blk].size; l++)
|
|
if (endogenous.find({ endo_idx_block2orig[blocks[blk].first_equation+l], 0 })
|
|
!= endogenous.end())
|
|
{
|
|
blocks[blk].linear = false;
|
|
goto the_end;
|
|
}
|
|
}
|
|
}
|
|
the_end:
|
|
break;
|
|
case BlockSimulationType::solveTwoBoundariesComplete:
|
|
case BlockSimulationType::solveTwoBoundariesSimple:
|
|
for (const auto &[indices, d1] : blocks_derivatives[blk])
|
|
{
|
|
int lag = get<2>(indices);
|
|
set<pair<int, int>> endogenous;
|
|
d1->collectEndogenous(endogenous);
|
|
for (int l = 0; l < blocks[blk].size; l++)
|
|
if (endogenous.find({ endo_idx_block2orig[blocks[blk].first_equation+l], lag })
|
|
!= endogenous.end())
|
|
{
|
|
blocks[blk].linear = false;
|
|
goto the_end2;
|
|
}
|
|
}
|
|
the_end2:
|
|
break;
|
|
default:
|
|
break;
|
|
}
|
|
}
|
|
|
|
int
|
|
ModelTree::equation_number() const
|
|
{
|
|
return (equations.size());
|
|
}
|
|
|
|
void
|
|
ModelTree::writeDerivative(ostream &output, int eq, int symb_id, int lag,
|
|
ExprNodeOutputType output_type,
|
|
const temporary_terms_t &temporary_terms) const
|
|
{
|
|
if (auto it = derivatives[1].find({ eq, getDerivID(symb_id, lag) });
|
|
it != derivatives[1].end())
|
|
it->second->writeOutput(output, output_type, temporary_terms, {});
|
|
else
|
|
output << 0;
|
|
}
|
|
|
|
void
|
|
ModelTree::computeDerivatives(int order, const set<int> &vars)
|
|
{
|
|
assert(order >= 1);
|
|
|
|
computed_derivs_order = order;
|
|
|
|
// Do not shrink the vectors, since they have a minimal size of 4 (see constructor)
|
|
derivatives.resize(max(static_cast<size_t>(order+1), derivatives.size()));
|
|
NNZDerivatives.resize(max(static_cast<size_t>(order+1), NNZDerivatives.size()), 0);
|
|
|
|
// First-order derivatives
|
|
for (int var : vars)
|
|
for (int eq = 0; eq < static_cast<int>(equations.size()); eq++)
|
|
{
|
|
expr_t d1 = equations[eq]->getDerivative(var);
|
|
if (d1 == Zero)
|
|
continue;
|
|
derivatives[1][{ eq, var }] = d1;
|
|
++NNZDerivatives[1];
|
|
}
|
|
|
|
// Higher-order derivatives
|
|
for (int o = 2; o <= order; o++)
|
|
for (const auto &it : derivatives[o-1])
|
|
for (int var : vars)
|
|
{
|
|
if (it.first.back() > var)
|
|
continue;
|
|
|
|
expr_t d = it.second->getDerivative(var);
|
|
if (d == Zero)
|
|
continue;
|
|
|
|
vector<int> indices{it.first};
|
|
indices.push_back(var);
|
|
// At this point, indices of endogenous variables are sorted in non-decreasing order
|
|
derivatives[o][indices] = d;
|
|
// We output symmetric elements at order = 2
|
|
if (o == 2 && indices[1] != indices[2])
|
|
NNZDerivatives[o] += 2;
|
|
else
|
|
NNZDerivatives[o]++;
|
|
}
|
|
}
|
|
|
|
void
|
|
ModelTree::computeTemporaryTerms(bool is_matlab, bool no_tmp_terms)
|
|
{
|
|
/* Collect all model local variables appearing in equations (and only those,
|
|
because printing unused model local variables can lead to a crash,
|
|
see Dynare/dynare#101).
|
|
Then store them in a dedicated structure (temporary_terms_mlv), that will
|
|
be treated as the rest of temporary terms. */
|
|
temporary_terms_mlv.clear();
|
|
set<int> used_local_vars;
|
|
for (auto &equation : equations)
|
|
equation->collectVariables(SymbolType::modelLocalVariable, used_local_vars);
|
|
for (int used_local_var : used_local_vars)
|
|
{
|
|
VariableNode *v = AddVariable(used_local_var);
|
|
temporary_terms_mlv[v] = local_variables_table.find(used_local_var)->second;
|
|
}
|
|
|
|
// Compute the temporary terms in equations and derivatives
|
|
map<pair<int, int>, temporary_terms_t> temp_terms_map;
|
|
map<expr_t, pair<int, pair<int, int>>> reference_count;
|
|
|
|
for (auto &equation : equations)
|
|
equation->computeTemporaryTerms({ 0, 0 },
|
|
temp_terms_map,
|
|
reference_count,
|
|
is_matlab);
|
|
|
|
for (int order = 1; order < static_cast<int>(derivatives.size()); order++)
|
|
for (const auto &it : derivatives[order])
|
|
it.second->computeTemporaryTerms({ 0, order },
|
|
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)
|
|
// The following loop can be simplified with std::erase_if() in C++20
|
|
for (auto it2 = it.second.begin(); it2 != it.second.end();)
|
|
if (!dynamic_cast<AbstractExternalFunctionNode *>(*it2))
|
|
it2 = it.second.erase(it2);
|
|
else
|
|
++it2;
|
|
|
|
// Fill the structures
|
|
temporary_terms_derivatives.clear();
|
|
temporary_terms_derivatives.resize(derivatives.size());
|
|
for (int order = 0; order < static_cast<int>(derivatives.size()); order++)
|
|
temporary_terms_derivatives[order] = move(temp_terms_map[{ 0, order }]);
|
|
|
|
// Compute indices in MATLAB/Julia vector
|
|
int idx = 0;
|
|
for (auto [mlv, value] : temporary_terms_mlv)
|
|
temporary_terms_idxs[mlv] = idx++;
|
|
for (int order = 0; order < static_cast<int>(derivatives.size()); order++)
|
|
for (auto it : temporary_terms_derivatives[order])
|
|
temporary_terms_idxs[it] = idx++;
|
|
}
|
|
|
|
void
|
|
ModelTree::computeBlockTemporaryTerms()
|
|
{
|
|
int nb_blocks = blocks.size();
|
|
blocks_temporary_terms.resize(nb_blocks);
|
|
|
|
map<expr_t, tuple<int, int, int>> reference_count;
|
|
for (int blk = 0; blk < nb_blocks; blk++)
|
|
{
|
|
blocks_temporary_terms[blk].resize(blocks[blk].size + 1);
|
|
for (int eq = 0; eq < blocks[blk].size; eq++)
|
|
{
|
|
if (eq < blocks[blk].getRecursiveSize() && isBlockEquationRenormalized(blk, eq))
|
|
getBlockEquationRenormalizedExpr(blk, eq)->computeBlockTemporaryTerms(blk, eq, blocks_temporary_terms, reference_count);
|
|
else
|
|
getBlockEquationExpr(blk, eq)->computeBlockTemporaryTerms(blk, eq, blocks_temporary_terms, reference_count);
|
|
}
|
|
for (const auto &[ignore, d] : blocks_derivatives[blk])
|
|
d->computeBlockTemporaryTerms(blk, blocks[blk].size, blocks_temporary_terms, reference_count);
|
|
|
|
additionalBlockTemporaryTerms(blk, blocks_temporary_terms, reference_count);
|
|
}
|
|
|
|
// Compute indices in the temporary terms vector
|
|
int idx = 0;
|
|
blocks_temporary_terms_idxs.clear();
|
|
for (auto &blk_tt : blocks_temporary_terms)
|
|
for (auto &eq_tt : blk_tt)
|
|
for (auto tt : eq_tt)
|
|
blocks_temporary_terms_idxs[tt] = idx++;
|
|
}
|
|
|
|
void
|
|
ModelTree::additionalBlockTemporaryTerms(int blk,
|
|
vector<vector<temporary_terms_t>> &blocks_temporary_terms,
|
|
map<expr_t, tuple<int, int, int>> &reference_count) const
|
|
{
|
|
}
|
|
|
|
void
|
|
ModelTree::writeModelLocalVariableTemporaryTerms(temporary_terms_t &temp_term_union,
|
|
const temporary_terms_idxs_t &tt_idxs,
|
|
ostream &output, ExprNodeOutputType output_type,
|
|
deriv_node_temp_terms_t &tef_terms) const
|
|
{
|
|
temporary_terms_t tto;
|
|
for (auto [mlv, value] : temporary_terms_mlv)
|
|
tto.insert(mlv);
|
|
|
|
for (auto [mlv, value] : temporary_terms_mlv)
|
|
{
|
|
value->writeExternalFunctionOutput(output, output_type, temp_term_union, tt_idxs, tef_terms);
|
|
|
|
if (isJuliaOutput(output_type))
|
|
output << " @inbounds const ";
|
|
|
|
mlv->writeOutput(output, output_type, tto, tt_idxs, tef_terms);
|
|
output << " = ";
|
|
value->writeOutput(output, output_type, temp_term_union, tt_idxs, tef_terms);
|
|
|
|
if (isCOutput(output_type) || isMatlabOutput(output_type))
|
|
output << ";";
|
|
output << endl;
|
|
|
|
/* We put in temp_term_union the VariableNode corresponding to the MLV,
|
|
not its definition, so that when equations use the MLV,
|
|
T(XXX) is printed instead of the MLV name */
|
|
temp_term_union.insert(mlv);
|
|
}
|
|
}
|
|
|
|
void
|
|
ModelTree::writeTemporaryTerms(const temporary_terms_t &tt,
|
|
temporary_terms_t &temp_term_union,
|
|
const temporary_terms_idxs_t &tt_idxs,
|
|
ostream &output, ExprNodeOutputType output_type, deriv_node_temp_terms_t &tef_terms) const
|
|
{
|
|
for (auto it : tt)
|
|
{
|
|
if (dynamic_cast<AbstractExternalFunctionNode *>(it))
|
|
it->writeExternalFunctionOutput(output, output_type, temp_term_union, tt_idxs, tef_terms);
|
|
|
|
if (isJuliaOutput(output_type))
|
|
output << " @inbounds ";
|
|
|
|
it->writeOutput(output, output_type, tt, tt_idxs, tef_terms);
|
|
output << " = ";
|
|
it->writeOutput(output, output_type, temp_term_union, tt_idxs, tef_terms);
|
|
|
|
if (isCOutput(output_type) || isMatlabOutput(output_type))
|
|
output << ";";
|
|
output << endl;
|
|
|
|
temp_term_union.insert(it);
|
|
}
|
|
}
|
|
|
|
void
|
|
ModelTree::writeJsonTemporaryTerms(const temporary_terms_t &tt,
|
|
temporary_terms_t &temp_term_union,
|
|
ostream &output,
|
|
deriv_node_temp_terms_t &tef_terms, const string &concat) const
|
|
{
|
|
// Local var used to keep track of temp nodes already written
|
|
bool wrote_term = false;
|
|
temporary_terms_t tt2 = temp_term_union;
|
|
|
|
output << R"("external_functions_temporary_terms_)" << concat << R"(": [)";
|
|
for (auto it : tt)
|
|
{
|
|
if (dynamic_cast<AbstractExternalFunctionNode *>(it))
|
|
{
|
|
if (wrote_term)
|
|
output << ", ";
|
|
vector<string> efout;
|
|
it->writeJsonExternalFunctionOutput(efout, tt2, tef_terms);
|
|
for (auto it1 = efout.begin(); it1 != efout.end(); ++it1)
|
|
{
|
|
if (it1 != efout.begin())
|
|
output << ", ";
|
|
output << *it1;
|
|
}
|
|
wrote_term = true;
|
|
}
|
|
tt2.insert(it);
|
|
}
|
|
|
|
wrote_term = false;
|
|
output << "]"
|
|
<< R"(, "temporary_terms_)" << concat << R"(": [)";
|
|
for (const auto &it : tt)
|
|
{
|
|
if (wrote_term)
|
|
output << ", ";
|
|
output << R"({"temporary_term": ")";
|
|
it->writeJsonOutput(output, tt, tef_terms);
|
|
output << R"(")"
|
|
<< R"(, "value": ")";
|
|
it->writeJsonOutput(output, temp_term_union, tef_terms);
|
|
output << R"("})" << endl;
|
|
wrote_term = true;
|
|
|
|
temp_term_union.insert(it);
|
|
}
|
|
output << "]";
|
|
}
|
|
|
|
void
|
|
ModelTree::fixNestedParenthesis(ostringstream &output, map<string, string> &tmp_paren_vars, bool &message_printed) const
|
|
{
|
|
string str = output.str();
|
|
if (!testNestedParenthesis(str))
|
|
return;
|
|
int open = 0;
|
|
int first_open_paren = 0;
|
|
int matching_paren = 0;
|
|
bool hit_limit = false;
|
|
int i1 = 0;
|
|
for (size_t i = 0; i < str.length(); i++)
|
|
{
|
|
if (str.at(i) == '(')
|
|
{
|
|
if (open == 0)
|
|
first_open_paren = i;
|
|
open++;
|
|
}
|
|
else if (str.at(i) == ')')
|
|
{
|
|
open--;
|
|
if (open == 0)
|
|
matching_paren = i;
|
|
}
|
|
if (open > 32)
|
|
hit_limit = true;
|
|
|
|
if (hit_limit && open == 0)
|
|
{
|
|
if (!message_printed)
|
|
{
|
|
cerr << "Warning: A .m file created by Dynare will have more than 32 nested parenthesis. MATLAB cannot support this. " << endl
|
|
<< " We are going to modify, albeit inefficiently, this output to have fewer than 32 nested parenthesis. " << endl
|
|
<< " It would hence behoove you to use the use_dll option of the model block to circumnavigate this problem." << endl
|
|
<< " If you have not yet set up a compiler on your system, see the MATLAB documentation for doing so." << endl
|
|
<< " For Windows, see: https://www.mathworks.com/help/matlab/matlab_external/install-mingw-support-package.html" << endl << endl;
|
|
message_printed = true;
|
|
}
|
|
string str1 = str.substr(first_open_paren, matching_paren - first_open_paren + 1);
|
|
string repstr, varname;
|
|
while (testNestedParenthesis(str1))
|
|
{
|
|
size_t open_paren_idx = string::npos;
|
|
size_t match_paren_idx = string::npos;
|
|
size_t last_open_paren = string::npos;
|
|
for (size_t j = 0; j < str1.length(); j++)
|
|
{
|
|
if (str1.at(j) == '(')
|
|
{
|
|
// don't match, e.g. y(1)
|
|
if (size_t idx = str1.find_last_of("*/-+", j - 1);
|
|
j == 0 || (idx != string::npos && idx == j - 1))
|
|
open_paren_idx = j;
|
|
last_open_paren = j;
|
|
}
|
|
else if (str1.at(j) == ')')
|
|
{
|
|
// don't match, e.g. y(1)
|
|
if (size_t idx = str1.find_last_not_of("0123456789", j - 1);
|
|
idx != string::npos && idx != last_open_paren)
|
|
match_paren_idx = j;
|
|
}
|
|
|
|
if (open_paren_idx != string::npos && match_paren_idx != string::npos)
|
|
{
|
|
string val = str1.substr(open_paren_idx, match_paren_idx - open_paren_idx + 1);
|
|
if (auto it = tmp_paren_vars.find(val);
|
|
it == tmp_paren_vars.end())
|
|
{
|
|
ostringstream ptvstr;
|
|
ptvstr << i1++;
|
|
varname = "paren32_tmp_var_" + ptvstr.str();
|
|
repstr = repstr + varname + " = " + val + ";\n";
|
|
tmp_paren_vars[val] = varname;
|
|
}
|
|
else
|
|
varname = it->second;
|
|
str1.replace(open_paren_idx, match_paren_idx - open_paren_idx + 1, varname);
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
if (auto it = tmp_paren_vars.find(str1);
|
|
it == tmp_paren_vars.end())
|
|
{
|
|
ostringstream ptvstr;
|
|
ptvstr << i1++;
|
|
varname = "paren32_tmp_var_" + ptvstr.str();
|
|
repstr = repstr + varname + " = " + str1 + ";\n";
|
|
}
|
|
else
|
|
varname = it->second;
|
|
str.replace(first_open_paren, matching_paren - first_open_paren + 1, varname);
|
|
size_t insertLoc = str.find_last_of("\n", first_open_paren);
|
|
str.insert(insertLoc + 1, repstr);
|
|
hit_limit = false;
|
|
i = -1;
|
|
first_open_paren = matching_paren = open = 0;
|
|
}
|
|
}
|
|
output.str(str);
|
|
}
|
|
|
|
bool
|
|
ModelTree::testNestedParenthesis(const string &str) const
|
|
{
|
|
int open = 0;
|
|
for (char i : str)
|
|
{
|
|
if (i == '(')
|
|
open++;
|
|
else if (i == ')')
|
|
open--;
|
|
if (open > 32)
|
|
return true;
|
|
}
|
|
return false;
|
|
}
|
|
|
|
void
|
|
ModelTree::compileTemporaryTerms(ostream &code_file, unsigned int &instruction_number, bool dynamic, bool steady_dynamic, temporary_terms_t &temporary_terms_union, const temporary_terms_idxs_t &temporary_terms_idxs) const
|
|
{
|
|
// To store the functions that have already been written in the form TEF* = ext_fun();
|
|
deriv_node_temp_terms_t tef_terms;
|
|
for (auto [tt, idx] : temporary_terms_idxs)
|
|
{
|
|
if (dynamic_cast<AbstractExternalFunctionNode *>(tt))
|
|
tt->compileExternalFunctionOutput(code_file, instruction_number, false, temporary_terms_union, temporary_terms_idxs, dynamic, steady_dynamic, tef_terms);
|
|
|
|
FNUMEXPR_ fnumexpr(ExpressionType::TemporaryTerm, idx);
|
|
fnumexpr.write(code_file, instruction_number);
|
|
tt->compile(code_file, instruction_number, false, temporary_terms_union, temporary_terms_idxs, dynamic, steady_dynamic, tef_terms);
|
|
if (dynamic)
|
|
{
|
|
FSTPT_ fstpt(idx);
|
|
fstpt.write(code_file, instruction_number);
|
|
}
|
|
else
|
|
{
|
|
FSTPST_ fstpst(idx);
|
|
fstpst.write(code_file, instruction_number);
|
|
}
|
|
}
|
|
}
|
|
|
|
void
|
|
ModelTree::writeJsonModelLocalVariables(ostream &output, bool write_tef_terms, deriv_node_temp_terms_t &tef_terms) const
|
|
{
|
|
/* Collect all model local variables appearing in equations, and print only
|
|
them. Printing unused model local variables can lead to a crash (see
|
|
ticket #101). */
|
|
set<int> used_local_vars;
|
|
|
|
for (auto equation : equations)
|
|
equation->collectVariables(SymbolType::modelLocalVariable, used_local_vars);
|
|
|
|
output << R"("model_local_variables": [)";
|
|
bool printed = false;
|
|
for (int id : local_variables_vector)
|
|
if (used_local_vars.find(id) != used_local_vars.end())
|
|
{
|
|
if (printed)
|
|
output << ", ";
|
|
else
|
|
printed = true;
|
|
|
|
expr_t value = local_variables_table.find(id)->second;
|
|
if (write_tef_terms)
|
|
{
|
|
vector<string> efout;
|
|
value->writeJsonExternalFunctionOutput(efout, {}, tef_terms);
|
|
for (auto it1 = efout.begin(); it1 != efout.end(); ++it1)
|
|
{
|
|
if (it1 != efout.begin())
|
|
output << ", ";
|
|
output << *it1;
|
|
}
|
|
|
|
if (!efout.empty())
|
|
output << ", ";
|
|
}
|
|
|
|
output << R"({"variable": ")" << symbol_table.getName(id)
|
|
<< R"(", "value": ")";
|
|
value->writeJsonOutput(output, {}, tef_terms);
|
|
output << R"("})" << endl;
|
|
}
|
|
output << "]";
|
|
}
|
|
|
|
void
|
|
ModelTree::writeModelEquations(ostream &output, ExprNodeOutputType output_type) const
|
|
{
|
|
temporary_terms_t tt;
|
|
temporary_terms_idxs_t ttidxs;
|
|
writeModelEquations(output, output_type, tt);
|
|
}
|
|
|
|
void
|
|
ModelTree::writeModelEquations(ostream &output, ExprNodeOutputType output_type,
|
|
const temporary_terms_t &temporary_terms) const
|
|
{
|
|
for (int eq = 0; eq < static_cast<int>(equations.size()); eq++)
|
|
{
|
|
BinaryOpNode *eq_node = equations[eq];
|
|
expr_t lhs = eq_node->arg1;
|
|
expr_t rhs = eq_node->arg2;
|
|
|
|
// Test if the right hand side of the equation is empty.
|
|
double vrhs = 1.0;
|
|
try
|
|
{
|
|
vrhs = rhs->eval(eval_context_t());
|
|
}
|
|
catch (ExprNode::EvalException &e)
|
|
{
|
|
}
|
|
|
|
if (vrhs != 0) // The right hand side of the equation is not empty ==> residual=lhs-rhs;
|
|
if (isJuliaOutput(output_type))
|
|
{
|
|
output << " @inbounds residual" << LEFT_ARRAY_SUBSCRIPT(output_type)
|
|
<< eq + ARRAY_SUBSCRIPT_OFFSET(output_type)
|
|
<< RIGHT_ARRAY_SUBSCRIPT(output_type)
|
|
<< " = (";
|
|
lhs->writeOutput(output, output_type, temporary_terms, temporary_terms_idxs);
|
|
output << ") - (";
|
|
rhs->writeOutput(output, output_type, temporary_terms, temporary_terms_idxs);
|
|
output << ")" << endl;
|
|
}
|
|
else
|
|
{
|
|
output << "lhs = ";
|
|
lhs->writeOutput(output, output_type, temporary_terms, temporary_terms_idxs);
|
|
output << ";" << endl
|
|
<< "rhs = ";
|
|
rhs->writeOutput(output, output_type, temporary_terms, temporary_terms_idxs);
|
|
output << ";" << endl
|
|
<< "residual" << LEFT_ARRAY_SUBSCRIPT(output_type)
|
|
<< eq + ARRAY_SUBSCRIPT_OFFSET(output_type)
|
|
<< RIGHT_ARRAY_SUBSCRIPT(output_type)
|
|
<< " = lhs - rhs;" << endl;
|
|
}
|
|
else // The right hand side of the equation is empty ==> residual=lhs;
|
|
{
|
|
if (isJuliaOutput(output_type))
|
|
output << " @inbounds ";
|
|
output << "residual" << LEFT_ARRAY_SUBSCRIPT(output_type)
|
|
<< eq + ARRAY_SUBSCRIPT_OFFSET(output_type)
|
|
<< RIGHT_ARRAY_SUBSCRIPT(output_type)
|
|
<< " = ";
|
|
lhs->writeOutput(output, output_type, temporary_terms, temporary_terms_idxs);
|
|
output << ";" << endl;
|
|
}
|
|
}
|
|
}
|
|
|
|
void
|
|
ModelTree::compileModelEquations(ostream &code_file, unsigned int &instruction_number, bool dynamic, bool steady_dynamic, const temporary_terms_t &temporary_terms_union, const temporary_terms_idxs_t &temporary_terms_idxs) const
|
|
{
|
|
for (int eq = 0; eq < static_cast<int>(equations.size()); eq++)
|
|
{
|
|
BinaryOpNode *eq_node = equations[eq];
|
|
expr_t lhs = eq_node->arg1;
|
|
expr_t rhs = eq_node->arg2;
|
|
FNUMEXPR_ fnumexpr(ExpressionType::ModelEquation, eq);
|
|
fnumexpr.write(code_file, instruction_number);
|
|
// Test if the right hand side of the equation is empty.
|
|
double vrhs = 1.0;
|
|
try
|
|
{
|
|
vrhs = rhs->eval(eval_context_t());
|
|
}
|
|
catch (ExprNode::EvalException &e)
|
|
{
|
|
}
|
|
|
|
if (vrhs != 0) // The right hand side of the equation is not empty ==> residual=lhs-rhs;
|
|
{
|
|
lhs->compile(code_file, instruction_number, false, temporary_terms_union, temporary_terms_idxs, dynamic, steady_dynamic);
|
|
rhs->compile(code_file, instruction_number, false, temporary_terms_union, temporary_terms_idxs, dynamic, steady_dynamic);
|
|
|
|
FBINARY_ fbinary{static_cast<int>(BinaryOpcode::minus)};
|
|
fbinary.write(code_file, instruction_number);
|
|
|
|
FSTPR_ fstpr(eq);
|
|
fstpr.write(code_file, instruction_number);
|
|
}
|
|
else // The right hand side of the equation is empty ==> residual=lhs;
|
|
{
|
|
lhs->compile(code_file, instruction_number, false, temporary_terms_union, temporary_terms_idxs, dynamic, steady_dynamic);
|
|
FSTPR_ fstpr(eq);
|
|
fstpr.write(code_file, instruction_number);
|
|
}
|
|
}
|
|
}
|
|
|
|
void
|
|
ModelTree::writeBytecodeBinFile(const string &filename, int &u_count_int, bool &file_open,
|
|
bool is_two_boundaries) const
|
|
{
|
|
int j;
|
|
std::ofstream SaveCode;
|
|
if (file_open)
|
|
SaveCode.open(filename, ios::out | ios::in | ios::binary | ios::ate);
|
|
else
|
|
SaveCode.open(filename, ios::out | ios::binary);
|
|
if (!SaveCode.is_open())
|
|
{
|
|
cerr << R"(Error : Can't open file ")" << filename << R"(" for writing)" << endl;
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
u_count_int = 0;
|
|
for (const auto & [indices, d1] : derivatives[1])
|
|
{
|
|
int deriv_id = indices[1];
|
|
if (getTypeByDerivID(deriv_id) == SymbolType::endogenous)
|
|
{
|
|
int eq = indices[0];
|
|
int symb = getSymbIDByDerivID(deriv_id);
|
|
int var = symbol_table.getTypeSpecificID(symb);
|
|
int lag = getLagByDerivID(deriv_id);
|
|
SaveCode.write(reinterpret_cast<char *>(&eq), sizeof(eq));
|
|
int varr = var + lag * symbol_table.endo_nbr();
|
|
SaveCode.write(reinterpret_cast<char *>(&varr), sizeof(varr));
|
|
SaveCode.write(reinterpret_cast<char *>(&lag), sizeof(lag));
|
|
int u = u_count_int + symbol_table.endo_nbr();
|
|
SaveCode.write(reinterpret_cast<char *>(&u), sizeof(u));
|
|
u_count_int++;
|
|
}
|
|
}
|
|
if (is_two_boundaries)
|
|
u_count_int += symbol_table.endo_nbr();
|
|
for (j = 0; j < symbol_table.endo_nbr(); j++)
|
|
SaveCode.write(reinterpret_cast<char *>(&j), sizeof(j));
|
|
for (j = 0; j < symbol_table.endo_nbr(); j++)
|
|
SaveCode.write(reinterpret_cast<char *>(&j), sizeof(j));
|
|
SaveCode.close();
|
|
}
|
|
|
|
void
|
|
ModelTree::writeLatexModelFile(const string &mod_basename, const string &latex_basename, ExprNodeOutputType output_type, bool write_equation_tags) const
|
|
{
|
|
filesystem::create_directories(mod_basename + "/latex");
|
|
|
|
ofstream output, content_output;
|
|
string filename = mod_basename + "/latex/" + latex_basename + ".tex";
|
|
string content_filename = mod_basename + "/latex/" + latex_basename + "_content" + ".tex";
|
|
output.open(filename, ios::out | ios::binary);
|
|
if (!output.is_open())
|
|
{
|
|
cerr << "ERROR: Can't open file " << filename << " for writing" << endl;
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
|
|
content_output.open(content_filename, ios::out | ios::binary);
|
|
if (!content_output.is_open())
|
|
{
|
|
cerr << "ERROR: Can't open file " << content_filename << " for writing" << endl;
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
|
|
output << R"(\documentclass[10pt,a4paper]{article})" << endl
|
|
<< R"(\usepackage[landscape]{geometry})" << endl
|
|
<< R"(\usepackage{fullpage})" << endl
|
|
<< R"(\usepackage{amsfonts})" << endl
|
|
<< R"(\usepackage{breqn})" << endl
|
|
<< R"(\begin{document})" << endl
|
|
<< R"(\footnotesize)" << endl;
|
|
|
|
// Write model local variables
|
|
for (int id : local_variables_vector)
|
|
{
|
|
expr_t value = local_variables_table.find(id)->second;
|
|
|
|
content_output << R"(\begin{dmath*})" << endl
|
|
<< symbol_table.getTeXName(id) << " = ";
|
|
// Use an empty set for the temporary terms
|
|
value->writeOutput(content_output, output_type);
|
|
content_output << endl << R"(\end{dmath*})" << endl;
|
|
}
|
|
|
|
for (int eq = 0; eq < static_cast<int>(equations.size()); eq++)
|
|
{
|
|
content_output << "% Equation " << eq + 1 << endl;
|
|
if (write_equation_tags)
|
|
equation_tags.writeLatexOutput(content_output, eq);
|
|
|
|
content_output << R"(\begin{dmath})" << endl;
|
|
// Here it is necessary to cast to superclass ExprNode, otherwise the overloaded writeOutput() method is not found
|
|
dynamic_cast<ExprNode *>(equations[eq])->writeOutput(content_output, output_type);
|
|
content_output << endl << R"(\end{dmath})" << endl;
|
|
}
|
|
|
|
output << R"(\include{)" << latex_basename + "_content" << "}" << endl
|
|
<< R"(\end{document})" << endl;
|
|
|
|
output.close();
|
|
content_output.close();
|
|
}
|
|
|
|
void
|
|
ModelTree::addEquation(expr_t eq, int lineno)
|
|
{
|
|
auto beq = dynamic_cast<BinaryOpNode *>(eq);
|
|
assert(beq && beq->op_code == BinaryOpcode::equal);
|
|
|
|
equations.push_back(beq);
|
|
equations_lineno.push_back(lineno);
|
|
}
|
|
|
|
vector<int>
|
|
ModelTree::includeExcludeEquations(set<pair<string, string>> &eqs, bool exclude_eqs,
|
|
vector<BinaryOpNode *> &equations, vector<int> &equations_lineno,
|
|
EquationTags &equation_tags, bool static_equations) const
|
|
{
|
|
vector<int> excluded_vars;
|
|
if (equations.empty())
|
|
return excluded_vars;
|
|
|
|
// Get equation numbers of tags
|
|
set<int> tag_eqns;
|
|
for (auto it = eqs.begin(); it != eqs.end();)
|
|
if (auto tmp = equation_tags.getEqnsByTag(it->first, it->second);
|
|
!tmp.empty())
|
|
{
|
|
tag_eqns.insert(tmp.begin(), tmp.end());
|
|
it = eqs.erase(it);
|
|
}
|
|
else
|
|
++it;
|
|
|
|
if (tag_eqns.empty())
|
|
return excluded_vars;
|
|
|
|
set<int> eqns;
|
|
if (exclude_eqs)
|
|
eqns = tag_eqns;
|
|
else
|
|
for (size_t i = 0; i < equations.size(); i++)
|
|
if (tag_eqns.find(i) == tag_eqns.end())
|
|
eqns.insert(i);
|
|
|
|
// remove from equations, equations_lineno, equation_tags
|
|
vector<BinaryOpNode *> new_eqns;
|
|
vector<int> new_equations_lineno;
|
|
map<int, int> old_eqn_num_2_new;
|
|
for (size_t i = 0; i < equations.size(); i++)
|
|
if (eqns.find(i) != eqns.end())
|
|
{
|
|
if (auto tmp = equation_tags.getTagValueByEqnAndKey(i, "endogenous"); !tmp.empty())
|
|
excluded_vars.push_back(symbol_table.getID(tmp));
|
|
else
|
|
{
|
|
set<pair<int, int>> result;
|
|
equations[i]->arg1->collectDynamicVariables(SymbolType::endogenous, result);
|
|
if (result.size() == 1)
|
|
excluded_vars.push_back(result.begin()->first);
|
|
else
|
|
{
|
|
cerr << "ERROR: Equation " << i
|
|
<< " has been excluded but does not have a single variable on LHS or `endogenous` tag" << endl;
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
new_eqns.emplace_back(equations[i]);
|
|
old_eqn_num_2_new[i] = new_eqns.size() - 1;
|
|
new_equations_lineno.emplace_back(equations_lineno[i]);
|
|
}
|
|
int n_excl = equations.size() - new_eqns.size();
|
|
|
|
equations = new_eqns;
|
|
equations_lineno = new_equations_lineno;
|
|
|
|
equation_tags.erase(eqns, old_eqn_num_2_new);
|
|
|
|
if (!static_equations)
|
|
for (size_t i = 0; i < excluded_vars.size(); i++)
|
|
for (size_t j = i+1; j < excluded_vars.size(); j++)
|
|
if (excluded_vars[i] == excluded_vars[j])
|
|
{
|
|
cerr << "Error: Variable " << symbol_table.getName(i) << " was excluded twice"
|
|
<< " via in/exclude_eqs option" << endl;
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
|
|
cout << "Excluded " << n_excl << (static_equations ? " static " : " dynamic ")
|
|
<< "equation" << (n_excl > 1 ? "s" : "") << " via in/exclude_eqs option" << endl;
|
|
|
|
return excluded_vars;
|
|
}
|
|
|
|
void
|
|
ModelTree::simplifyEquations()
|
|
{
|
|
size_t last_subst_table_size = 0;
|
|
map<VariableNode *, NumConstNode *> subst_table;
|
|
// Equations with “mcp” tag are excluded, see dynare#1697
|
|
findConstantEquationsWithoutMcpTag(subst_table);
|
|
while (subst_table.size() != last_subst_table_size)
|
|
{
|
|
last_subst_table_size = subst_table.size();
|
|
for (auto &[id, definition] : local_variables_table)
|
|
definition = definition->replaceVarsInEquation(subst_table);
|
|
for (auto &equation : equations)
|
|
equation = dynamic_cast<BinaryOpNode *>(equation->replaceVarsInEquation(subst_table));
|
|
subst_table.clear();
|
|
findConstantEquationsWithoutMcpTag(subst_table);
|
|
}
|
|
}
|
|
|
|
void
|
|
ModelTree::findConstantEquationsWithoutMcpTag(map<VariableNode *, NumConstNode *> &subst_table) const
|
|
{
|
|
for (size_t i = 0; i < equations.size(); i++)
|
|
if (auto tags = getEquationTags(i);
|
|
tags.find("mcp") == tags.end())
|
|
equations[i]->findConstantEquations(subst_table);
|
|
}
|
|
|
|
void
|
|
ModelTree::addEquation(expr_t eq, int lineno, const map<string, string> &eq_tags)
|
|
{
|
|
equation_tags.add(equations.size(), eq_tags);
|
|
addEquation(eq, lineno);
|
|
}
|
|
|
|
void
|
|
ModelTree::addAuxEquation(expr_t eq)
|
|
{
|
|
auto beq = dynamic_cast<BinaryOpNode *>(eq);
|
|
assert(beq && beq->op_code == BinaryOpcode::equal);
|
|
|
|
aux_equations.push_back(beq);
|
|
}
|
|
|
|
void
|
|
ModelTree::addTrendVariables(const vector<int> &trend_vars, expr_t growth_factor) noexcept(false)
|
|
{
|
|
for (int id : trend_vars)
|
|
if (trend_symbols_map.find(id) != trend_symbols_map.end())
|
|
throw TrendException(symbol_table.getName(id));
|
|
else
|
|
trend_symbols_map[id] = growth_factor;
|
|
}
|
|
|
|
void
|
|
ModelTree::addNonstationaryVariables(const vector<int> &nonstationary_vars, bool log_deflator, expr_t deflator) noexcept(false)
|
|
{
|
|
for (int id : nonstationary_vars)
|
|
if (nonstationary_symbols_map.find(id) != nonstationary_symbols_map.end())
|
|
throw TrendException(symbol_table.getName(id));
|
|
else
|
|
nonstationary_symbols_map[id] = { log_deflator, deflator };
|
|
}
|
|
|
|
void
|
|
ModelTree::initializeVariablesAndEquations()
|
|
{
|
|
for (size_t j = 0; j < equations.size(); j++)
|
|
eq_idx_block2orig.push_back(j);
|
|
|
|
for (int j = 0; j < symbol_table.endo_nbr(); j++)
|
|
endo_idx_block2orig.push_back(j);
|
|
}
|
|
|
|
void
|
|
ModelTree::set_cutoff_to_zero()
|
|
{
|
|
cutoff = 0;
|
|
}
|
|
|
|
void
|
|
ModelTree::jacobianHelper(ostream &output, int eq_nb, int col_nb, ExprNodeOutputType output_type) const
|
|
{
|
|
if (isJuliaOutput(output_type))
|
|
output << " @inbounds ";
|
|
output << "g1" << LEFT_ARRAY_SUBSCRIPT(output_type);
|
|
if (isMatlabOutput(output_type) || isJuliaOutput(output_type))
|
|
output << eq_nb + 1 << "," << col_nb + 1;
|
|
else
|
|
output << eq_nb + col_nb *equations.size();
|
|
output << RIGHT_ARRAY_SUBSCRIPT(output_type);
|
|
}
|
|
|
|
void
|
|
ModelTree::computeParamsDerivatives(int paramsDerivsOrder)
|
|
{
|
|
assert(paramsDerivsOrder >= 1);
|
|
|
|
set<int> deriv_id_set;
|
|
addAllParamDerivId(deriv_id_set);
|
|
|
|
// First-order derivatives w.r.t. params
|
|
for (int param : deriv_id_set)
|
|
{
|
|
for (int eq = 0; eq < static_cast<int>(equations.size()); eq++)
|
|
{
|
|
expr_t d = equations[eq]->getDerivative(param);
|
|
if (d == Zero)
|
|
continue;
|
|
params_derivatives[{ 0, 1 }][{ eq, param }] = d;
|
|
}
|
|
|
|
for (int endoOrd = 1; endoOrd < static_cast<int>(derivatives.size()); endoOrd++)
|
|
for (const auto &[indices, dprev] : derivatives[endoOrd])
|
|
{
|
|
expr_t d = dprev->getDerivative(param);
|
|
if (d == Zero)
|
|
continue;
|
|
vector<int> new_indices = indices;
|
|
new_indices.push_back(param);
|
|
params_derivatives[{ endoOrd, 1 }][new_indices] = d;
|
|
}
|
|
}
|
|
|
|
// Higher-order derivatives w.r.t. parameters
|
|
for (int endoOrd = 0; endoOrd < static_cast<int>(derivatives.size()); endoOrd++)
|
|
for (int paramOrd = 2; paramOrd <= paramsDerivsOrder; paramOrd++)
|
|
for (const auto &[indices, dprev] : params_derivatives[{ endoOrd, paramOrd-1 }])
|
|
for (int param : deriv_id_set)
|
|
{
|
|
if (indices.back() > param)
|
|
continue;
|
|
|
|
expr_t d = dprev->getDerivative(param);
|
|
if (d == Zero)
|
|
continue;
|
|
vector<int> new_indices = indices;
|
|
new_indices.push_back(param);
|
|
// At this point, indices of both endogenous and parameters are sorted in non-decreasing order
|
|
params_derivatives[{ endoOrd, paramOrd }][new_indices] = d;
|
|
}
|
|
}
|
|
|
|
void
|
|
ModelTree::computeParamsDerivativesTemporaryTerms()
|
|
{
|
|
map<expr_t, pair<int, pair<int, int>>> reference_count;
|
|
|
|
/* The temp terms should be constructed in the same order as the for loops in
|
|
{Static,Dynamic}Model::write{Json,}ParamsDerivativesFile() */
|
|
params_derivs_temporary_terms.clear();
|
|
for (const auto &[order, derivs] : params_derivatives)
|
|
for (const auto &[indices, d] : derivs)
|
|
d->computeTemporaryTerms(order, params_derivs_temporary_terms,
|
|
reference_count, true);
|
|
|
|
int idx = 0;
|
|
for (auto &[mlv, value] : temporary_terms_mlv)
|
|
params_derivs_temporary_terms_idxs[mlv] = idx++;
|
|
for (const auto &[order, tts] : params_derivs_temporary_terms)
|
|
for (const auto &tt : tts)
|
|
params_derivs_temporary_terms_idxs[tt] = idx++;
|
|
}
|
|
|
|
bool
|
|
ModelTree::isNonstationary(int symb_id) const
|
|
{
|
|
return nonstationary_symbols_map.find(symb_id) != nonstationary_symbols_map.end();
|
|
}
|
|
|
|
void
|
|
ModelTree::writeJsonModelEquations(ostream &output, bool residuals) const
|
|
{
|
|
if (residuals)
|
|
output << endl << R"("residuals":[)" << endl;
|
|
else
|
|
output << endl << R"("model":[)" << endl;
|
|
for (int eq = 0; eq < static_cast<int>(equations.size()); eq++)
|
|
{
|
|
if (eq > 0)
|
|
output << ", ";
|
|
|
|
BinaryOpNode *eq_node = equations[eq];
|
|
expr_t lhs = eq_node->arg1;
|
|
expr_t rhs = eq_node->arg2;
|
|
|
|
if (residuals)
|
|
{
|
|
output << R"({"residual": {)"
|
|
<< R"("lhs": ")";
|
|
lhs->writeJsonOutput(output, {}, {});
|
|
output << R"(")";
|
|
|
|
output << R"(, "rhs": ")";
|
|
rhs->writeJsonOutput(output, {}, {});
|
|
output << R"(")";
|
|
try
|
|
{
|
|
// Test if the right hand side of the equation is empty.
|
|
if (rhs->eval({}) != 0)
|
|
{
|
|
output << R"(, "rhs": ")";
|
|
rhs->writeJsonOutput(output, {}, {});
|
|
output << R"(")";
|
|
}
|
|
}
|
|
catch (ExprNode::EvalException &e)
|
|
{
|
|
}
|
|
output << "}";
|
|
}
|
|
else
|
|
{
|
|
output << R"({"lhs": ")";
|
|
lhs->writeJsonOutput(output, {}, {});
|
|
output << R"(", "rhs": ")";
|
|
rhs->writeJsonOutput(output, {}, {});
|
|
output << R"(")"
|
|
<< R"(, "line": )" << equations_lineno[eq];
|
|
|
|
if (auto eqtags = getEquationTags(eq);
|
|
!eqtags.empty())
|
|
{
|
|
output << R"(, "tags": {)";
|
|
int i = 0;
|
|
for (const auto &[name, value] : eqtags)
|
|
{
|
|
if (i != 0)
|
|
output << ", ";
|
|
output << R"(")" << name << R"(": ")" << value << R"(")";
|
|
i++;
|
|
}
|
|
output << "}";
|
|
eqtags.clear();
|
|
}
|
|
}
|
|
output << "}" << endl;
|
|
}
|
|
output << endl << "]" << endl;
|
|
}
|
|
|
|
string
|
|
ModelTree::matlab_arch(const string &mexext)
|
|
{
|
|
if (mexext == "mexglx")
|
|
return "glnx86";
|
|
else if (mexext == "mexa64")
|
|
return "glnxa64";
|
|
if (mexext == "mexw32")
|
|
return "win32";
|
|
else if (mexext == "mexw64")
|
|
return "win64";
|
|
else if (mexext == "mexmaci")
|
|
{
|
|
cerr << "32-bit MATLAB not supported on macOS" << endl;
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
else if (mexext == "mexmaci64")
|
|
return "maci64";
|
|
else
|
|
{
|
|
cerr << "ERROR: 'mexext' option to preprocessor incorrectly set, needed with 'use_dll'" << endl;
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
}
|
|
|
|
void
|
|
ModelTree::compileMEX(const string &basename, const string &funcname, const string &mexext, const vector<filesystem::path> &src_files, const filesystem::path &matlabroot, const filesystem::path &dynareroot) const
|
|
{
|
|
const string opt_flags = "-O3 -g0 --param ira-max-conflict-table-size=1 -fno-forward-propagate -fno-gcse -fno-dce -fno-dse -fno-tree-fre -fno-tree-pre -fno-tree-cselim -fno-tree-dse -fno-tree-dce -fno-tree-pta -fno-gcse-after-reload";
|
|
|
|
filesystem::path compiler;
|
|
ostringstream flags;
|
|
string libs;
|
|
|
|
if (matlabroot.empty())
|
|
{
|
|
cerr << "ERROR: 'matlabroot' option to preprocessor is not set, needed with 'use_dll'" << endl;
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
|
|
if (mexext == "mex")
|
|
{
|
|
// Octave
|
|
compiler = matlabroot / "bin" / "mkoctfile";
|
|
flags << "--mex";
|
|
}
|
|
else
|
|
{
|
|
// MATLAB
|
|
compiler = "gcc";
|
|
string arch = matlab_arch(mexext);
|
|
auto include_dir = matlabroot / "extern" / "include";
|
|
flags << "-I " << include_dir;
|
|
auto bin_dir = matlabroot / "bin" / arch;
|
|
flags << " -L " << bin_dir;
|
|
flags << " -fexceptions -DNDEBUG";
|
|
libs = "-lmex -lmx";
|
|
if (mexext == "mexa64")
|
|
{
|
|
// GNU/Linux
|
|
flags << " -D_GNU_SOURCE -fPIC -pthread"
|
|
<< " -shared -Wl,--no-undefined -Wl,-rpath-link," << bin_dir;
|
|
libs += " -lm";
|
|
}
|
|
else if (mexext == "mexw64")
|
|
{
|
|
// Windows
|
|
flags << " -static-libgcc -shared";
|
|
// Put the MinGW environment shipped with Dynare in the path
|
|
auto mingwpath = dynareroot / "mingw64" / "bin";
|
|
string newpath = "PATH=" + mingwpath.string() + ';' + string{getenv("PATH")};
|
|
if (putenv(const_cast<char *>(newpath.c_str())) != 0)
|
|
{
|
|
cerr << "Can't set PATH" << endl;
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
}
|
|
#ifdef __APPLE__
|
|
else if (mexext == "mexmaci64")
|
|
{
|
|
// macOS
|
|
char dynare_m_path[PATH_MAX];
|
|
uint32_t size = PATH_MAX;
|
|
string gcc_relative_path;
|
|
if (_NSGetExecutablePath(dynare_m_path, &size) == 0)
|
|
{
|
|
string str = dynare_m_path;
|
|
gcc_relative_path = str.substr(0, str.find_last_of("/")) + "/../../.brew/bin/gcc-10";
|
|
}
|
|
|
|
if (filesystem::exists(gcc_relative_path))
|
|
compiler = gcc_relative_path;
|
|
else if (filesystem::exists("/usr/local/bin/gcc-10"))
|
|
compiler = "/usr/local/bin/gcc-10";
|
|
else
|
|
{
|
|
cerr << "ERROR: You must install gcc-10 on your system before using the `use_dll` option of Dynare. "
|
|
<< "You can do this via the Dynare installation package." << endl;
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
flags << " -fno-common -Wl,-twolevel_namespace -undefined error -bundle";
|
|
libs += " -lm";
|
|
}
|
|
#endif
|
|
else
|
|
{
|
|
cerr << "ERROR: unsupported value '" << mexext << "' for 'mexext' option" << endl;
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
}
|
|
|
|
filesystem::path mex_dir{"+" + basename};
|
|
filesystem::path binary{mex_dir / (funcname + "." + mexext)};
|
|
|
|
ostringstream cmd;
|
|
|
|
#ifdef _WIN32
|
|
/* On Windows, system() hands the command over to "cmd.exe /C". We need to
|
|
enclose the whole command line within double quotes if we want the inner
|
|
quotes to be correctly handled. See "cmd /?" for more details. */
|
|
cmd << '"';
|
|
#endif
|
|
|
|
if (user_set_compiler.empty())
|
|
cmd << compiler << " ";
|
|
else
|
|
if (!filesystem::exists(user_set_compiler))
|
|
{
|
|
cerr << "Error: The specified compiler '" << user_set_compiler << "' cannot be found on your system" << endl;
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
else
|
|
cmd << user_set_compiler << " ";
|
|
|
|
if (user_set_subst_flags.empty())
|
|
cmd << opt_flags << " " << flags.str() << " ";
|
|
else
|
|
cmd << user_set_subst_flags << " ";
|
|
|
|
if (!user_set_add_flags.empty())
|
|
cmd << user_set_add_flags << " ";
|
|
|
|
for (auto &src : src_files)
|
|
cmd << src << " ";
|
|
cmd << "-o " << binary << " ";
|
|
|
|
if (user_set_subst_libs.empty())
|
|
cmd << libs;
|
|
else
|
|
cmd << user_set_subst_libs;
|
|
|
|
if (!user_set_add_libs.empty())
|
|
cmd << " " << user_set_add_libs;
|
|
|
|
#ifdef _WIN32
|
|
cmd << '"';
|
|
#endif
|
|
|
|
cout << "Compiling " << funcname << " MEX..." << endl << cmd.str() << endl;
|
|
|
|
if (system(cmd.str().c_str()))
|
|
{
|
|
cerr << "Compilation failed" << endl;
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
}
|
|
|
|
void
|
|
ModelTree::reorderAuxiliaryEquations()
|
|
{
|
|
using namespace boost;
|
|
|
|
// Create the mapping between auxiliary variables and auxiliary equations
|
|
int n = static_cast<int>(aux_equations.size());
|
|
map<int, int> auxEndoToEq;
|
|
for (int i = 0; i < n; i++)
|
|
{
|
|
auto varexpr = dynamic_cast<VariableNode *>(aux_equations[i]->arg1);
|
|
assert(varexpr && symbol_table.getType(varexpr->symb_id) == SymbolType::endogenous);
|
|
auxEndoToEq[varexpr->symb_id] = i;
|
|
}
|
|
assert(static_cast<int>(auxEndoToEq.size()) == n);
|
|
|
|
/* Construct the directed acyclic graph where auxiliary equations are
|
|
vertices and edges represent dependency relationships. */
|
|
using Graph = adjacency_list<vecS, vecS, directedS>;
|
|
Graph g(n);
|
|
for (int i = 0; i < n; i++)
|
|
{
|
|
set<int> endos;
|
|
aux_equations[i]->collectVariables(SymbolType::endogenous, endos);
|
|
for (int endo : endos)
|
|
if (auto it = auxEndoToEq.find(endo);
|
|
it != auxEndoToEq.end() && it->second != i)
|
|
add_edge(i, it->second, g);
|
|
}
|
|
|
|
// Topological sort of the graph
|
|
using Vertex = graph_traits<Graph>::vertex_descriptor;
|
|
vector<Vertex> ordered;
|
|
topological_sort(g, back_inserter(ordered));
|
|
|
|
// Reorder auxiliary equations accordingly
|
|
auto aux_equations_old = aux_equations;
|
|
auto index = get(vertex_index, g); // Maps vertex descriptors to their index
|
|
for (int i = 0; i < n; i++)
|
|
aux_equations[i] = aux_equations_old[index[ordered[i]]];
|
|
}
|
|
|
|
map<tuple<int, int, int>, expr_t>
|
|
ModelTree::collectFirstOrderDerivativesEndogenous()
|
|
{
|
|
map<tuple<int, int, int>, expr_t> endo_derivatives;
|
|
for (auto &[indices, d1] : derivatives[1])
|
|
if (getTypeByDerivID(indices[1]) == SymbolType::endogenous)
|
|
{
|
|
int eq = indices[0];
|
|
int var = symbol_table.getTypeSpecificID(getSymbIDByDerivID(indices[1]));
|
|
int lag = getLagByDerivID(indices[1]);
|
|
endo_derivatives[{ eq, var, lag }] = d1;
|
|
}
|
|
return endo_derivatives;
|
|
}
|
|
|
|
ModelTree::jacob_map_t
|
|
ModelTree::computeSymbolicJacobian() const
|
|
{
|
|
jacob_map_t symbolic_jacobian;
|
|
for (int i = 0; i < static_cast<int>(equations.size()); i++)
|
|
{
|
|
set<pair<int, int>> endos_and_lags;
|
|
equations[i]->collectEndogenous(endos_and_lags);
|
|
for (const auto &[endo, lag] : endos_and_lags)
|
|
symbolic_jacobian[{ i, endo }] = 1;
|
|
}
|
|
return symbolic_jacobian;
|
|
}
|
|
|
|
void
|
|
ModelTree::updateReverseVariableEquationOrderings()
|
|
{
|
|
int n = equations.size();
|
|
eq_idx_orig2block.resize(n);
|
|
endo_idx_orig2block.resize(n);
|
|
for (int i = 0; i < n; i++)
|
|
{
|
|
endo_idx_orig2block[endo_idx_block2orig[i]] = i;
|
|
eq_idx_orig2block[eq_idx_block2orig[i]] = i;
|
|
}
|
|
}
|
|
|
|
bool
|
|
ModelTree::hasOccbinTags() const
|
|
{
|
|
return equation_tags.hasOccbinTags();
|
|
}
|