Formatting
Andrew Melim
parent
88a11329c0
commit
ea19fae155
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@ -23,55 +23,53 @@
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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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std::map<int, FastSet<int> > adjMap;
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std::map<int, FastSet<int> >::iterator adjMapIt;
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std::set<Key> keySet;
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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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std::map<int, FastSet<int> > adjMap;
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std::map<int, FastSet<int> >::iterator adjMapIt;
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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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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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adjMap[k1].insert(adjMap[k1].end(), k2); // Insert at the end
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}
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keySet.insert(keySet.end(), k1); // Keep a track of all unique keySet
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}
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}
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}
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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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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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adjMap[k1].insert(adjMap[k1].end(), k2); // Insert at the end
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}
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keySet.insert(keySet.end(), k1); // Keep a track of all unique keySet
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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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// Number of keys referenced in this factor graph
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nKeys_ = keySet.size();
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// Starting with a nonzero key crashes METIS
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// Find the smallest key in the graph
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size_t minKey = *keySet.begin(); // set is ordered
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// Starting with a nonzero key crashes METIS
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// Find the smallest key in the graph
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size_t minKey = *keySet.begin(); // set is ordered
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xadj_.push_back(0);// Always set the first index to zero
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for (adjMapIt = adjMap.begin(); adjMapIt != adjMap.end(); ++adjMapIt) {
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std::vector<Key> temp;
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// Copy from the FastSet into a temporary vector
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std::copy(adjMapIt->second.begin(), adjMapIt->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(adj_.size());
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}
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xadj_.push_back(0);// Always set the first index to zero
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for (adjMapIt = adjMap.begin(); adjMapIt != adjMap.end(); ++adjMapIt) {
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std::vector<Key> temp;
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// Copy from the FastSet into a temporary vector
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std::copy(adjMapIt->second.begin(), adjMapIt->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(adj_.size());
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}
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// Normalize, subtract the smallest key
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std::transform(adj_.begin(), adj_.end(), adj_.begin(), std::bind2nd(std::minus<size_t>(), minKey));
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}
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// Normalize, subtract the smallest key
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std::transform(adj_.begin(), adj_.end(), adj_.begin(), std::bind2nd(std::minus<size_t>(), minKey));
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}
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}
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@ -28,53 +28,53 @@
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#include <gtsam/inference/FactorGraph.h>
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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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* fromt 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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public:
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typedef boost::shared_ptr<MetisIndex> shared_ptr;
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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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* fromt 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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public:
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typedef boost::shared_ptr<MetisIndex> shared_ptr;
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private:
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FastVector<int> xadj_; // Index of node's adjacency list in adj
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FastVector<int> adj_; // Stores ajacency lists of all nodes, appended into a single vector
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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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private:
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FastVector<int> xadj_; // Index of node's adjacency list in adj
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FastVector<int> adj_; // Stores ajacency lists of all nodes, appended into a single vector
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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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public:
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/// @name Standard Constructors
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/// @{
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public:
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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() : nFactors_(0), nKeys_(0) {}
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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) : nFactors_(0), nKeys_(0) {
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augment(factorGraph); }
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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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/// @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<int> xadj() const { return xadj_; }
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std::vector<int> adj() const { return adj_; }
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size_t nValues() const { return nKeys_; }
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std::vector<int> xadj() const { return xadj_; }
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std::vector<int> adj() const { return adj_; }
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size_t nValues() const { return nKeys_; }
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/// @}
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};
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/// @}
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};
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}
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