252 lines
		
	
	
		
			12 KiB
		
	
	
	
		
			C
		
	
	
		
		
			
		
	
	
			252 lines
		
	
	
		
			12 KiB
		
	
	
	
		
			C
		
	
	
|  | /*
 | ||
|  |  * NestedDissection-inl.h | ||
|  |  * | ||
|  |  *   Created on: Nov 27, 2010 | ||
|  |  *       Author: nikai | ||
|  |  *  Description: | ||
|  |  */ | ||
|  | 
 | ||
|  | #pragma once
 | ||
|  | 
 | ||
|  | #include <boost/make_shared.hpp>
 | ||
|  | 
 | ||
|  | #include "partition/FindSeparator-inl.h"
 | ||
|  | #include "OrderedSymbols.h"
 | ||
|  | #include "NestedDissection.h"
 | ||
|  | 
 | ||
|  | namespace gtsam { namespace partition { | ||
|  | 
 | ||
|  | 	/* ************************************************************************* */ | ||
|  | 	template <class NLG, class SubNLG, class GenericGraph> | ||
|  | 	NestedDissection<NLG, SubNLG, GenericGraph>::NestedDissection( | ||
|  | 			const NLG& fg, const Ordering& ordering, const int numNodeStopPartition, const int minNodesPerMap, const bool verbose) : | ||
|  | 	fg_(fg), ordering_(ordering){ | ||
|  | 		GenericUnaryGraph unaryFactors; | ||
|  | 		GenericGraph gfg; | ||
|  | 		boost::tie(unaryFactors, gfg) = fg.createGenericGraph(ordering); | ||
|  | 
 | ||
|  | 		// build reverse mapping from integer to symbol
 | ||
|  | 		int numNodes = ordering.size(); | ||
|  | 		int2symbol_.resize(numNodes); | ||
|  | 		Ordering::const_iterator it = ordering.begin(), itLast = ordering.end(); | ||
|  | 		while(it != itLast) | ||
|  | 			int2symbol_[it->second] = (it++)->first; | ||
|  | 
 | ||
|  | 		vector<size_t> keys; | ||
|  | 		keys.reserve(numNodes); | ||
|  | 		for(int i=0; i<ordering.size(); ++i) | ||
|  | 			keys.push_back(i); | ||
|  | 
 | ||
|  | 		WorkSpace workspace(numNodes); | ||
|  | 		root_ = recursivePartition(gfg, unaryFactors, keys, vector<size_t>(), numNodeStopPartition, minNodesPerMap, boost::shared_ptr<SubNLG>(), workspace, verbose); | ||
|  | 	} | ||
|  | 
 | ||
|  | 	/* ************************************************************************* */ | ||
|  | 	template <class NLG, class SubNLG, class GenericGraph> | ||
|  | 	NestedDissection<NLG, SubNLG, GenericGraph>::NestedDissection( | ||
|  | 			const NLG& fg, const Ordering& ordering, const boost::shared_ptr<Cuts>& cuts, const bool verbose) : fg_(fg), ordering_(ordering){ | ||
|  | 		GenericUnaryGraph unaryFactors; | ||
|  | 		GenericGraph gfg; | ||
|  | 		boost::tie(unaryFactors, gfg) = fg.createGenericGraph(ordering); | ||
|  | 
 | ||
|  | 		// build reverse mapping from integer to symbol
 | ||
|  | 		int numNodes = ordering.size(); | ||
|  | 		int2symbol_.resize(numNodes); | ||
|  | 		Ordering::const_iterator it = ordering.begin(), itLast = ordering.end(); | ||
|  | 		while(it != itLast) | ||
|  | 			int2symbol_[it->second] = (it++)->first; | ||
|  | 
 | ||
|  | 		vector<size_t> keys; | ||
|  | 		keys.reserve(numNodes); | ||
|  | 		for(int i=0; i<ordering.size(); ++i) | ||
|  | 			keys.push_back(i); | ||
|  | 
 | ||
|  | 		WorkSpace workspace(numNodes); | ||
|  | 		root_ = recursivePartition(gfg, unaryFactors, keys, vector<size_t>(), cuts, boost::shared_ptr<SubNLG>(), workspace, verbose); | ||
|  | 	} | ||
|  | 
 | ||
|  | 	/* ************************************************************************* */ | ||
|  | 	template <class NLG, class SubNLG, class GenericGraph> | ||
|  | 	boost::shared_ptr<SubNLG> NestedDissection<NLG, SubNLG, GenericGraph>::makeSubNLG( | ||
|  | 			const NLG& fg, const vector<size_t>& frontals, const vector<size_t>& sep, const boost::shared_ptr<SubNLG>& parent) const { | ||
|  | 		OrderedSymbols frontalKeys; | ||
|  | 		BOOST_FOREACH(const size_t index, frontals) | ||
|  | 			frontalKeys.push_back(int2symbol_[index]); | ||
|  | 
 | ||
|  | 		UnorderedSymbols sepKeys; | ||
|  | 		BOOST_FOREACH(const size_t index, sep) | ||
|  | 			sepKeys.insert(int2symbol_[index]); | ||
|  | 
 | ||
|  | 		return boost::make_shared<SubNLG>(fg, frontalKeys, sepKeys, parent); | ||
|  | 	} | ||
|  | 
 | ||
|  | 	/* ************************************************************************* */ | ||
|  | 	template <class NLG, class SubNLG, class GenericGraph> | ||
|  | 	void NestedDissection<NLG, SubNLG, GenericGraph>::processFactor( | ||
|  | 			const typename GenericGraph::value_type& factor, const std::vector<int>& partitionTable,  // input
 | ||
|  | 			vector<GenericGraph>& frontalFactors, NLG& sepFactors, vector<set<size_t> >& childSeps, // output factor graphs
 | ||
|  | 			typename SubNLG::Weeklinks& weeklinks) const {                                                              // the links between child cliques
 | ||
|  | 		list<size_t> sep_; // the separator variables involved in the current factor
 | ||
|  | 		int partition1 = partitionTable[factor->key1.index]; | ||
|  | 		int partition2 = partitionTable[factor->key2.index]; | ||
|  | 		if (partition1 <= 0 && partition2 <= 0) {                                // is a factor in the current clique
 | ||
|  | 			sepFactors.push_back(fg_[factor->index]); | ||
|  | 		} | ||
|  | 		else if (partition1 > 0 && partition2 > 0 && partition1 != partition2) {  // is a weeklink (factor between two child cliques)
 | ||
|  | 			weeklinks.push_back(fg_[factor->index]); | ||
|  | 		} | ||
|  | 		else if (partition1 > 0 && partition2 > 0 && partition1 == partition2) { // is a local factor in one of the child cliques
 | ||
|  | 			frontalFactors[partition1 - 1].push_back(factor); | ||
|  | 		} | ||
|  | 		else {                                                          // is a joint factor in the child clique (involving varaibles in the current clique)
 | ||
|  | 			if (partition1 > 0 && partition2 <= 0) { | ||
|  | 				frontalFactors[partition1 - 1].push_back(factor); | ||
|  | 				childSeps[partition1 - 1].insert(factor->key2.index); | ||
|  | 			} else if (partition1 <= 0 && partition2 > 0) { | ||
|  | 				frontalFactors[partition2 - 1].push_back(factor); | ||
|  | 				childSeps[partition2 - 1].insert(factor->key1.index); | ||
|  | 			} else | ||
|  | 				throw runtime_error("processFactor: unexpected entries in the partition table!"); | ||
|  | 		} | ||
|  | 	} | ||
|  | 
 | ||
|  | 	/* ************************************************************************* */ | ||
|  | 	/**
 | ||
|  | 	 * given a factor graph and its partition {nodeMap}, split the factors between the child cliques ({frontalFactors}) | ||
|  | 	 *  and the current clique ({sepFactors}). Also split the variables between the child cliques ({childFrontals}) | ||
|  | 	 *  and the current clique ({localFrontals}). Those separator variables involved in {frontalFactors} are put into | ||
|  | 	 *  the correspoding ordering in {childSeps}. | ||
|  | 	 */ | ||
|  | 	// TODO: frontalFactors and localFrontals should be generated in findSeparator
 | ||
|  | 	template <class NLG, class SubNLG, class GenericGraph> | ||
|  | 	void NestedDissection<NLG, SubNLG, GenericGraph>::partitionFactorsAndVariables( | ||
|  | 			const GenericGraph& fg, const GenericUnaryGraph& unaryFactors, const std::vector<size_t>& keys, //input
 | ||
|  | 			const std::vector<int>& partitionTable, const int numSubmaps,                                   // input
 | ||
|  | 			vector<GenericGraph>& frontalFactors, vector<GenericUnaryGraph>& frontalUnaryFactors,  NLG& sepFactors,     // output factor graphs
 | ||
|  | 			vector<vector<size_t> >& childFrontals, vector<vector<size_t> >& childSeps, vector<size_t>& localFrontals,  // output sub-orderings
 | ||
|  | 			typename SubNLG::Weeklinks& weeklinks) const {                                                             // the links between child cliques
 | ||
|  | 
 | ||
|  | 		// make three lists of variables A, B, and C
 | ||
|  | 		int partition; | ||
|  | 		childFrontals.resize(numSubmaps); | ||
|  | 		BOOST_FOREACH(const size_t key, keys){ | ||
|  | 			partition = partitionTable[key]; | ||
|  | 			switch (partition) { | ||
|  | 			case -1: break;                                        // the separator of the separator variables
 | ||
|  | 			case 0:	 localFrontals.push_back(key); break;          // the separator variables
 | ||
|  | 			default: childFrontals[partition-1].push_back(key);    // the frontal variables
 | ||
|  | 			} | ||
|  | 		} | ||
|  | 
 | ||
|  | 		// group the factors to {frontalFactors} and {sepFactors},and find the joint variables
 | ||
|  | 		vector<set<size_t> > childSeps_; | ||
|  | 		childSeps_.resize(numSubmaps); | ||
|  | 		childSeps.reserve(numSubmaps); | ||
|  | 		frontalFactors.resize(numSubmaps); | ||
|  | 		frontalUnaryFactors.resize(numSubmaps); | ||
|  | 		BOOST_FOREACH(typename GenericGraph::value_type factor, fg) | ||
|  | 			processFactor(factor, partitionTable, frontalFactors, sepFactors, childSeps_, weeklinks); | ||
|  | 		BOOST_FOREACH(const set<size_t>& childSep, childSeps_) | ||
|  | 			childSeps.push_back(vector<size_t>(childSep.begin(), childSep.end())); | ||
|  | 
 | ||
|  | 		// add unary factor to the current cluster or pass it to one of the child clusters
 | ||
|  | 		BOOST_FOREACH(const sharedGenericUnaryFactor& unaryFactor_, unaryFactors) { | ||
|  | 			partition = partitionTable[unaryFactor_->key.index]; | ||
|  | 			if (!partition) sepFactors.push_back(fg_[unaryFactor_->index]); | ||
|  | 			else frontalUnaryFactors[partition-1].push_back(unaryFactor_); | ||
|  | 		} | ||
|  | 	} | ||
|  | 
 | ||
|  | 	/* ************************************************************************* */ | ||
|  | 	template <class NLG, class SubNLG, class GenericGraph> | ||
|  | 	NLG NestedDissection<NLG, SubNLG, GenericGraph>::collectOriginalFactors( | ||
|  | 			const GenericGraph& gfg, const GenericUnaryGraph& unaryFactors) const { | ||
|  | 		NLG sepFactors; | ||
|  | 		typename GenericGraph::const_iterator it = gfg.begin(), itLast = gfg.end(); | ||
|  | 		while(it!=itLast) sepFactors.push_back(fg_[(*it++)->index]); | ||
|  | 		BOOST_FOREACH(const sharedGenericUnaryFactor& unaryFactor_, unaryFactors) | ||
|  | 			sepFactors.push_back(fg_[unaryFactor_->index]); | ||
|  | 		return sepFactors; | ||
|  | 	} | ||
|  | 
 | ||
|  | 	/* ************************************************************************* */ | ||
|  | 	template <class NLG, class SubNLG, class GenericGraph> | ||
|  | 	boost::shared_ptr<SubNLG> NestedDissection<NLG, SubNLG, GenericGraph>::recursivePartition( | ||
|  | 			const GenericGraph& gfg, const GenericUnaryGraph& unaryFactors, const vector<size_t>& frontals, const vector<size_t>& sep, | ||
|  | 			const int numNodeStopPartition, const int minNodesPerMap, const boost::shared_ptr<SubNLG>& parent, WorkSpace& workspace, const bool verbose) const { | ||
|  | 
 | ||
|  | 		// if no split needed
 | ||
|  | 		NLG sepFactors; // factors that should remain in the current cluster
 | ||
|  | 		if (frontals.size() <= numNodeStopPartition || gfg.size() <= numNodeStopPartition) { | ||
|  | 			sepFactors = collectOriginalFactors(gfg, unaryFactors); | ||
|  | 			return makeSubNLG(sepFactors, frontals, sep, parent); | ||
|  | 		} | ||
|  | 
 | ||
|  | 		// find the nested dissection separator
 | ||
|  | 		int numSubmaps = findSeparator(gfg, frontals, minNodesPerMap, workspace, verbose, int2symbol_, NLG::reduceGraph(), | ||
|  | 				NLG::minNrConstraintsPerCamera(),NLG::minNrConstraintsPerLandmark()); | ||
|  | 		partition::PartitionTable& partitionTable = workspace.partitionTable; | ||
|  | 		if (numSubmaps == 0) throw runtime_error("recursivePartition: get zero submap after ND!"); | ||
|  | 
 | ||
|  | 		// split the factors between child cliques and the current clique
 | ||
|  | 		vector<GenericGraph> frontalFactors; vector<GenericUnaryGraph> frontalUnaryFactors; typename SubNLG::Weeklinks weeklinks; | ||
|  | 		vector<size_t> localFrontals; vector<vector<size_t> > childFrontals, childSeps; | ||
|  | 		partitionFactorsAndVariables(gfg, unaryFactors, frontals, partitionTable, numSubmaps, | ||
|  | 				frontalFactors, frontalUnaryFactors, sepFactors, childFrontals, childSeps, localFrontals, weeklinks); | ||
|  | 
 | ||
|  | 		// make a new cluster
 | ||
|  | 		boost::shared_ptr<SubNLG> current = makeSubNLG(sepFactors, localFrontals, sep, parent); | ||
|  | 		current->setWeeklinks(weeklinks); | ||
|  | 
 | ||
|  | 		// check whether all the submaps are fully constrained
 | ||
|  | 		for (int i=0; i<numSubmaps; i++) { | ||
|  | 			checkSingularity(frontalFactors[i], childFrontals[i], workspace, NLG::minNrConstraintsPerCamera(),NLG::minNrConstraintsPerLandmark()); | ||
|  | 		} | ||
|  | 
 | ||
|  | 		// create child clusters
 | ||
|  | 		for (int i=0; i<numSubmaps; i++) { | ||
|  | 			boost::shared_ptr<SubNLG> child = recursivePartition(frontalFactors[i], frontalUnaryFactors[i], childFrontals[i], childSeps[i], | ||
|  | 					numNodeStopPartition, minNodesPerMap, current, workspace, verbose); | ||
|  | 			current->addChild(child); | ||
|  | 		} | ||
|  | 
 | ||
|  | 		return current; | ||
|  | 	} | ||
|  | 
 | ||
|  | 	/* ************************************************************************* */ | ||
|  | 	template <class NLG, class SubNLG, class GenericGraph> | ||
|  | 	boost::shared_ptr<SubNLG> NestedDissection<NLG, SubNLG, GenericGraph>::recursivePartition( | ||
|  | 			const GenericGraph& gfg, const GenericUnaryGraph& unaryFactors, const vector<size_t>& frontals, const vector<size_t>& sep, | ||
|  | 			const boost::shared_ptr<Cuts>& cuts, const boost::shared_ptr<SubNLG>& parent, WorkSpace& workspace, const bool verbose) const { | ||
|  | 
 | ||
|  | 		// if there is no need to cut any more
 | ||
|  | 		NLG sepFactors; // factors that should remain in the current cluster
 | ||
|  | 		if (!cuts.get()) { | ||
|  | 			sepFactors = collectOriginalFactors(gfg, unaryFactors); | ||
|  | 			return makeSubNLG(sepFactors, frontals, sep, parent); | ||
|  | 		} | ||
|  | 
 | ||
|  | 		// retrieve the current partitioning info
 | ||
|  | 		int numSubmaps = 2; | ||
|  | 		partition::PartitionTable& partitionTable = cuts->partitionTable; | ||
|  | 
 | ||
|  | 		// split the factors between child cliques and the current clique
 | ||
|  | 		vector<GenericGraph> frontalFactors; vector<GenericUnaryGraph> frontalUnaryFactors; typename SubNLG::Weeklinks weeklinks; | ||
|  | 		vector<size_t> localFrontals; vector<vector<size_t> > childFrontals, childSeps; | ||
|  | 		partitionFactorsAndVariables(gfg, unaryFactors, frontals, partitionTable, numSubmaps, | ||
|  | 				frontalFactors, frontalUnaryFactors, sepFactors, childFrontals, childSeps, localFrontals, weeklinks); | ||
|  | 
 | ||
|  | 		// make a new cluster
 | ||
|  | 		boost::shared_ptr<SubNLG> current = makeSubNLG(sepFactors, localFrontals, sep, parent); | ||
|  | 		current->setWeeklinks(weeklinks); | ||
|  | 
 | ||
|  | 		// create child clusters
 | ||
|  | 		for (int i=0; i<2; i++) { | ||
|  | 			boost::shared_ptr<SubNLG> child = recursivePartition(frontalFactors[i], frontalUnaryFactors[i], childFrontals[i], childSeps[i], | ||
|  | 					cuts->children.empty() ? boost::shared_ptr<Cuts>() : cuts->children[i], current, workspace, verbose); | ||
|  | 			current->addChild(child); | ||
|  | 		} | ||
|  | 		return current; | ||
|  | 	} | ||
|  | }} //namespace
 |