Fixed many warnings with Clang, reformatted using BORG template.
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/* ----------------------------------------------------------------------------
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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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* 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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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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* -------------------------------------------------------------------------- */
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/**
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* @file MetisIndex-inl.h
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* @author Andrew Melim
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* @date Oct. 10, 2014
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*/
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* @file MetisIndex-inl.h
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* @author Andrew Melim
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* @date Oct. 10, 2014
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*/
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#pragma once
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#include <boost/foreach.hpp>
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#include <map>
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#include <vector>
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namespace gtsam {
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/* ************************************************************************* */
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template<class FACTOR>
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void MetisIndex::augment(const FactorGraph<FACTOR>& factors)
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{
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void MetisIndex::augment(const FactorGraph<FACTOR>& factors) {
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std::map<idx_t, FastSet<idx_t> > iAdjMap; // Stores a set of keys that are adjacent to key x, with adjMap.first
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std::map<idx_t, FastSet<idx_t> >::iterator iAdjMapIt;
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std::set<Key> keySet;
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/* ********** Convert to CSR format ********** */
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// Assuming that vertex numbering starts from 0 (C style),
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// then the adjacency list of vertex i is stored in array adjncy
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// starting at index xadj[i] and ending at(but not including)
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// index xadj[i + 1](i.e., adjncy[xadj[i]] through
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// and including adjncy[xadj[i + 1] - 1]).
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std::set<Key> keySet;
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/* ********** Convert to CSR format ********** */
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// Assuming that vertex numbering starts from 0 (C style),
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// then the adjacency list of vertex i is stored in array adjncy
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// starting at index xadj[i] and ending at(but not including)
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// index xadj[i + 1](i.e., adjncy[xadj[i]] through
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// and including adjncy[xadj[i + 1] - 1]).
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idx_t keyCounter = 0;
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// First: Record a copy of each key inside the factorgraph and create a
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// key to integer mapping. This is referenced during the adjaceny step
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for (size_t i = 0; i < factors.size(); i++){
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for (size_t i = 0; i < factors.size(); i++) {
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if (factors[i]) {
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BOOST_FOREACH(const Key& key, *factors[i]){
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BOOST_FOREACH(const Key& key, *factors[i]) {
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keySet.insert(keySet.end(), key); // Keep a track of all unique keys
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if (intKeyBMap_.left.find(key) == intKeyBMap_.left.end()){
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if (intKeyBMap_.left.find(key) == intKeyBMap_.left.end()) {
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intKeyBMap_.insert(bm_type::value_type(key, keyCounter));
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keyCounter++;
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}
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}
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}
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}
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// Create an adjacency mapping that stores the set of all adjacent keys for every key
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for (size_t i = 0; i < factors.size(); i++){
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if (factors[i]){
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BOOST_FOREACH(const Key& k1, *factors[i])
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BOOST_FOREACH(const Key& k2, *factors[i])
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if (k1 != k2){
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for (size_t i = 0; i < factors.size(); i++) {
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if (factors[i]) {
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BOOST_FOREACH(const Key& k1, *factors[i])
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BOOST_FOREACH(const Key& k2, *factors[i])
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if (k1 != k2) {
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// Store both in Key and idx_t format
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int i = intKeyBMap_.left.at(k1);
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int j = intKeyBMap_.left.at(k2);
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iAdjMap[i].insert(iAdjMap[i].end(), j);
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}
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}
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}
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}
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}
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// Number of keys referenced in this factor graph
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nKeys_ = keySet.size();
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xadj_.push_back(0);// Always set the first index to zero
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// Number of keys referenced in this factor graph
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nKeys_ = keySet.size();
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xadj_.push_back(0); // Always set the first index to zero
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for (iAdjMapIt = iAdjMap.begin(); iAdjMapIt != iAdjMap.end(); ++iAdjMapIt) {
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std::vector<idx_t> temp;
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// Copy from the FastSet into a temporary vector
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std::copy(iAdjMapIt->second.begin(), iAdjMapIt->second.end(), std::back_inserter(temp));
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// Insert each index's set in order by appending them to the end of adj_
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adj_.insert(adj_.end(), temp.begin(), temp.end());
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//adj_.push_back(temp);
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xadj_.push_back((idx_t)adj_.size());
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}
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std::vector<idx_t> temp;
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// Copy from the FastSet into a temporary vector
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std::copy(iAdjMapIt->second.begin(), iAdjMapIt->second.end(),
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std::back_inserter(temp));
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// Insert each index's set in order by appending them to the end of adj_
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adj_.insert(adj_.end(), temp.begin(), temp.end());
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//adj_.push_back(temp);
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xadj_.push_back((idx_t) adj_.size());
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}
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}
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}
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} // \ gtsam
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@ -1,89 +1,106 @@
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/* ----------------------------------------------------------------------------
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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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* 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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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file MetisIndex.h
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* @author Andrew Melim
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* @date Oct. 10, 2014
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*/
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* @file MetisIndex.h
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* @author Andrew Melim
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* @date Oct. 10, 2014
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*/
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#pragma once
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#include <vector>
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#include <boost/foreach.hpp>
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#include <gtsam/base/FastList.h>
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#include <gtsam/base/types.h>
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#include <gtsam/base/timing.h>
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#include <gtsam/inference/Key.h>
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#include <gtsam/inference/FactorGraph.h>
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#include <gtsam/base/FastVector.h>
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#include <gtsam/base/types.h>
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#include <gtsam/base/timing.h>
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#include <gtsam/3rdparty/metis/metis.h>
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// Boost bimap generates many ugly warnings in CLANG
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#ifdef __clang__
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# pragma clang diagnostic push
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# pragma clang diagnostic ignored "-Wredeclared-class-member"
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#endif
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#include <boost/bimap.hpp>
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#ifdef __clang__
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# pragma clang diagnostic pop
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#endif
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#include <vector>
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namespace gtsam {
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/**
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* The MetisIndex class converts a factor graph into the Compressed Sparse Row format for use in
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* METIS algorithms. Specifically, two vectors store the adjacency structure of the graph. It is built
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* from a factor graph prior to elimination, and stores the list of factors
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* that involve each variable.
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* \nosubgrouping
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*/
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class GTSAM_EXPORT MetisIndex
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{
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* The MetisIndex class converts a factor graph into the Compressed Sparse Row format for use in
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* METIS algorithms. Specifically, two vectors store the adjacency structure of the graph. It is built
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* from a factor graph prior to elimination, and stores the list of factors
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* that involve each variable.
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* \nosubgrouping
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*/
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class GTSAM_EXPORT MetisIndex {
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public:
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typedef boost::shared_ptr<MetisIndex> shared_ptr;
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typedef boost::bimap<Key, idx_t> bm_type;
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typedef boost::shared_ptr<MetisIndex> shared_ptr;
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typedef boost::bimap<Key, idx_t> bm_type;
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private:
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FastVector<idx_t> xadj_; // Index of node's adjacency list in adj
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FastVector<idx_t> adj_; // Stores ajacency lists of all nodes, appended into a single vector
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FastVector<idx_t> iadj_; // Integer keys for passing into metis. One to one mapping with adj_;
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FastVector<idx_t> xadj_; // Index of node's adjacency list in adj
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FastVector<idx_t> adj_; // Stores ajacency lists of all nodes, appended into a single vector
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FastVector<idx_t> iadj_; // Integer keys for passing into metis. One to one mapping with adj_;
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boost::bimap<Key, idx_t> intKeyBMap_; // Stores Key <-> integer value relationship
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size_t nFactors_; // Number of factors in the original factor graph
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size_t nKeys_;
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size_t nKeys_;
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public:
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/// @name Standard Constructors
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/// @{
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/// @name Standard Constructors
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/// @{
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/** Default constructor, creates empty MetisIndex */
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MetisIndex() : nFactors_(0), nKeys_(0) {}
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/** Default constructor, creates empty MetisIndex */
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MetisIndex() :
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nKeys_(0) {
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}
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template<class FG>
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MetisIndex(const FG& factorGraph) : nFactors_(0), nKeys_(0) {
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augment(factorGraph); }
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template<class FG>
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MetisIndex(const FG& factorGraph) :
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nKeys_(0) {
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augment(factorGraph);
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}
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~MetisIndex(){}
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/// @}
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/// @name Advanced Interface
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/// @{
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~MetisIndex() {
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}
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/// @}
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/// @name Advanced Interface
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/// @{
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/**
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* Augment the variable index with new factors. This can be used when
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* solving problems incrementally.
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*/
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template<class FACTOR>
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void augment(const FactorGraph<FACTOR>& factors);
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/**
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* Augment the variable index with new factors. This can be used when
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* solving problems incrementally.
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*/
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template<class FACTOR>
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void augment(const FactorGraph<FACTOR>& factors);
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std::vector<idx_t> xadj() const { return xadj_; }
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std::vector<idx_t> adj() const { return adj_; }
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size_t nValues() const { return nKeys_; }
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std::vector<idx_t> xadj() const {
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return xadj_;
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}
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std::vector<idx_t> adj() const {
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return adj_;
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}
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size_t nValues() const {
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return nKeys_;
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}
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Key intToKey(idx_t value) const {
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assert(value >= 0);
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return intKeyBMap_.right.find(value)->second;
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}
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/// @}
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/// @}
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};
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}
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} // \ namesace gtsam
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#include <gtsam/inference/MetisIndex-inl.h>
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