271 lines
8.4 KiB
C++
271 lines
8.4 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file Ordering.cpp
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* @author Richard Roberts
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* @author Andrew Melim
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* @date Sep 2, 2010
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*/
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#include <vector>
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#include <limits>
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#include <boost/format.hpp>
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#include <gtsam/inference/Ordering.h>
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#include <gtsam/3rdparty/CCOLAMD/Include/ccolamd.h>
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#ifdef GTSAM_SUPPORT_NESTED_DISSECTION
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#include <gtsam/3rdparty/metis/include/metis.h>
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#endif
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using namespace std;
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namespace gtsam {
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/* ************************************************************************* */
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FastMap<Key, size_t> Ordering::invert() const {
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FastMap<Key, size_t> inverted;
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for (size_t pos = 0; pos < this->size(); ++pos)
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inverted.insert(make_pair((*this)[pos], pos));
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return inverted;
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}
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/* ************************************************************************* */
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Ordering Ordering::Colamd(const VariableIndex& variableIndex) {
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// Call constrained version with all groups set to zero
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vector<int> dummy_groups(variableIndex.size(), 0);
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return Ordering::ColamdConstrained(variableIndex, dummy_groups);
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}
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/* ************************************************************************* */
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Ordering Ordering::ColamdConstrained(const VariableIndex& variableIndex,
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std::vector<int>& cmember) {
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gttic(Ordering_COLAMDConstrained);
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gttic(Prepare);
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const size_t nEntries = variableIndex.nEntries(), nFactors =
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variableIndex.nFactors(), nVars = variableIndex.size();
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// Convert to compressed column major format colamd wants it in (== MATLAB format!)
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const size_t Alen = ccolamd_recommended((int) nEntries, (int) nFactors,
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(int) nVars); /* colamd arg 3: size of the array A */
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vector<int> A = vector<int>(Alen); /* colamd arg 4: row indices of A, of size Alen */
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vector<int> p = vector<int>(nVars + 1); /* colamd arg 5: column pointers of A, of size n_col+1 */
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// Fill in input data for COLAMD
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p[0] = 0;
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int count = 0;
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vector<Key> keys(nVars); // Array to store the keys in the order we add them so we can retrieve them in permuted order
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size_t index = 0;
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for (auto key_factors: variableIndex) {
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// Arrange factor indices into COLAMD format
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const VariableIndex::Factors& column = key_factors.second;
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for(size_t factorIndex: column) {
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A[count++] = (int) factorIndex; // copy sparse column
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}
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p[index + 1] = count; // column j (base 1) goes from A[j-1] to A[j]-1
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// Store key in array and increment index
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keys[index] = key_factors.first;
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++index;
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}
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assert((size_t)count == variableIndex.nEntries());
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//double* knobs = NULL; /* colamd arg 6: parameters (uses defaults if NULL) */
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double knobs[CCOLAMD_KNOBS];
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ccolamd_set_defaults(knobs);
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knobs[CCOLAMD_DENSE_ROW] = -1;
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knobs[CCOLAMD_DENSE_COL] = -1;
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int stats[CCOLAMD_STATS]; /* colamd arg 7: colamd output statistics and error codes */
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gttoc(Prepare);
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// call colamd, result will be in p
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/* returns (1) if successful, (0) otherwise*/
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if (nVars > 0) {
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gttic(ccolamd);
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int rv = ccolamd((int) nFactors, (int) nVars, (int) Alen, &A[0], &p[0],
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knobs, stats, &cmember[0]);
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if (rv != 1)
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throw runtime_error(
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(boost::format("ccolamd failed with return value %1%") % rv).str());
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}
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// ccolamd_report(stats);
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// Convert elimination ordering in p to an ordering
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gttic(Fill_Ordering);
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Ordering result;
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result.resize(nVars);
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for (size_t j = 0; j < nVars; ++j)
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result[j] = keys[p[j]];
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gttoc(Fill_Ordering);
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return result;
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}
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/* ************************************************************************* */
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Ordering Ordering::ColamdConstrainedLast(const VariableIndex& variableIndex,
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const std::vector<Key>& constrainLast, bool forceOrder) {
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gttic(Ordering_COLAMDConstrainedLast);
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size_t n = variableIndex.size();
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std::vector<int> cmember(n, 0);
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// Build a mapping to look up sorted Key indices by Key
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// TODO(frank): think of a way to not build this
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FastMap<Key, size_t> keyIndices;
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size_t j = 0;
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for (auto key_factors: variableIndex)
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keyIndices.insert(keyIndices.end(), make_pair(key_factors.first, j++));
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// If at least some variables are not constrained to be last, constrain the
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// ones that should be constrained.
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int group = (constrainLast.size() != n ? 1 : 0);
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for (Key key: constrainLast) {
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cmember[keyIndices.at(key)] = group;
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if (forceOrder)
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++group;
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}
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return Ordering::ColamdConstrained(variableIndex, cmember);
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}
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/* ************************************************************************* */
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Ordering Ordering::ColamdConstrainedFirst(const VariableIndex& variableIndex,
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const std::vector<Key>& constrainFirst, bool forceOrder) {
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gttic(Ordering_COLAMDConstrainedFirst);
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const int none = -1;
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size_t n = variableIndex.size();
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std::vector<int> cmember(n, none);
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// Build a mapping to look up sorted Key indices by Key
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FastMap<Key, size_t> keyIndices;
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size_t j = 0;
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for (auto key_factors: variableIndex)
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keyIndices.insert(keyIndices.end(), make_pair(key_factors.first, j++));
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// If at least some variables are not constrained to be last, constrain the
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// ones that should be constrained.
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int group = 0;
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for (Key key: constrainFirst) {
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cmember[keyIndices.at(key)] = group;
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if (forceOrder)
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++group;
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}
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if (!forceOrder && !constrainFirst.empty())
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++group;
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for(int& c: cmember)
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if (c == none)
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c = group;
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return Ordering::ColamdConstrained(variableIndex, cmember);
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}
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/* ************************************************************************* */
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Ordering Ordering::ColamdConstrained(const VariableIndex& variableIndex,
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const FastMap<Key, int>& groups) {
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gttic(Ordering_COLAMDConstrained);
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size_t n = variableIndex.size();
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std::vector<int> cmember(n, 0);
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// Build a mapping to look up sorted Key indices by Key
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FastMap<Key, size_t> keyIndices;
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size_t j = 0;
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for (auto key_factors: variableIndex)
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keyIndices.insert(keyIndices.end(), make_pair(key_factors.first, j++));
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// Assign groups
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typedef FastMap<Key, int>::value_type key_group;
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for(const key_group& p: groups) {
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// FIXME: check that no groups are skipped
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cmember[keyIndices.at(p.first)] = p.second;
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}
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return Ordering::ColamdConstrained(variableIndex, cmember);
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}
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/* ************************************************************************* */
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Ordering Ordering::Metis(const MetisIndex& met) {
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#ifdef GTSAM_SUPPORT_NESTED_DISSECTION
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gttic(Ordering_METIS);
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vector<idx_t> xadj = met.xadj();
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vector<idx_t> adj = met.adj();
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vector<idx_t> perm, iperm;
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idx_t size = met.nValues();
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for (idx_t i = 0; i < size; i++) {
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perm.push_back(0);
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iperm.push_back(0);
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}
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int outputError;
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outputError = METIS_NodeND(&size, &xadj[0], &adj[0], NULL, NULL, &perm[0],
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&iperm[0]);
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Ordering result;
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if (outputError != METIS_OK) {
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std::cout << "METIS failed during Nested Dissection ordering!\n";
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return result;
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}
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result.resize(size);
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for (size_t j = 0; j < (size_t) size; ++j) {
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// We have to add the minKey value back to obtain the original key in the Values
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result[j] = met.intToKey(perm[j]);
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}
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return result;
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#else
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throw runtime_error("GTSAM was built without support for Metis-based "
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"nested dissection");
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#endif
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}
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/* ************************************************************************* */
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void Ordering::print(const std::string& str,
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const KeyFormatter& keyFormatter) const {
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cout << str;
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// Print ordering in index order
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// Print the ordering with varsPerLine ordering entries printed on each line,
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// for compactness.
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static const size_t varsPerLine = 10;
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bool endedOnNewline = false;
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for (size_t i = 0; i < size(); ++i) {
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if (i % varsPerLine == 0)
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cout << "Position " << i << ": ";
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if (i % varsPerLine != 0)
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cout << ", ";
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cout << keyFormatter(at(i));
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if (i % varsPerLine == varsPerLine - 1) {
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cout << "\n";
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endedOnNewline = true;
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} else {
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endedOnNewline = false;
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}
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}
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if (!endedOnNewline)
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cout << "\n";
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cout.flush();
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}
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/* ************************************************************************* */
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bool Ordering::equals(const Ordering& other, double tol) const {
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return (*this) == other;
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}
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}
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