Started re-implementing the synchronization functions for the Concurrent Filter
parent
634a4c5ef9
commit
04d595dec1
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@ -105,10 +105,43 @@ void ConcurrentBatchFilter::presync() {
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}
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/* ************************************************************************* */
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void ConcurrentBatchFilter::synchronize(const NonlinearFactorGraph& summarizedFactors) {
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void ConcurrentBatchFilter::synchronize(const NonlinearFactorGraph& summarizedFactors, const Values& separatorValues) {
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gttic(synchronize);
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// Remove the previous smoother summarization
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removeFactors(smootherSummarizationSlots_);
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// Create a factor graph containing the new smoother summarization, the factors to be sent to the smoother,
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// and all of the filter factors. This is the set of factors on the filter side since the smoother started
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// its previous update cycle.
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NonlinearFactorGraph filterGraph;
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filterGraph.push_back(factors_);
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filterGraph.push_back(smootherFactors_);
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filterGraph.push_back(summarizedFactors);
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Values filterValues;
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filterValues.insert(theta_);
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filterValues.insert(smootherValues_);
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filterValues.update(separatorValues); // ensure the smoother summarized factors are linearized around the values in the smoother
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// Optimize this graph using a modified version of L-M
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// TODO:
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// Calculate the marginal on the new separator from the filter factors. This is performed by marginalizing out
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// all of the filter variables that are not part of the new separator. This filter summarization will then be
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// sent to the smoother.
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Ordering filterOrdering;
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std::vector<Key> filterKeys;
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filterSummarization_ = marginalize(filterGraph, filterValues, filterOrdering, filterKeys, parameters_.getEliminationFunction());
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// Calculate the marginal on the new separator from the smoother factors. This is performed by marginalizing out
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// all of the smoother variables that are not part of the new separator. This smoother summarization will be
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// stored locally for use in the filter
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Ordering smootherOrdering;
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std::vector<Key> smootherKeys;
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smootherSummarizationSlots_ = insertFactors( marginalize(filterGraph, filterValues, smootherOrdering, smootherKeys, parameters_.getEliminationFunction()) );
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gttoc(synchronize);
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}
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@ -173,7 +206,7 @@ std::vector<size_t> ConcurrentBatchFilter::insertFactors(const NonlinearFactorGr
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}
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/* ************************************************************************* */
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void ConcurrentBatchFilter::removeFactors(const std::set<size_t>& slots) {
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void ConcurrentBatchFilter::removeFactors(const std::vector<size_t>& slots) {
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gttic(remove_factors);
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@ -345,24 +378,24 @@ void ConcurrentBatchFilter::marginalize(const FastList<Key>& keysToMove) {
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// Note: It is assumed the ordering already has these keys first
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{
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// Use the variable Index to mark the factors that will be marginalized
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BOOST_FOREACH(gtsam::Key key, keysToMove) {
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const gtsam::FastList<size_t>& slots = variableIndex_[ordering_.at(key)];
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BOOST_FOREACH(Key key, keysToMove) {
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const FastList<size_t>& slots = variableIndex_[ordering_.at(key)];
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removedFactorSlots.insert(slots.begin(), slots.end());
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}
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// Create the linear factor graph
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gtsam::GaussianFactorGraph linearFactorGraph = *factors_.linearize(theta_, ordering_);
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GaussianFactorGraph linearFactorGraph = *factors_.linearize(theta_, ordering_);
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// Construct an elimination tree to perform sparse elimination
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std::vector<EliminationForest::shared_ptr> forest( EliminationForest::Create(linearFactorGraph, variableIndex_) );
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// This is a tree. Only the top-most nodes/indices need to be eliminated; all of the children will be eliminated automatically
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// Find the subset of nodes/keys that must be eliminated
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std::set<gtsam::Index> indicesToEliminate;
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BOOST_FOREACH(gtsam::Key key, keysToMove) {
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std::set<Index> indicesToEliminate;
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BOOST_FOREACH(Key key, keysToMove) {
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indicesToEliminate.insert(ordering_.at(key));
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}
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BOOST_FOREACH(gtsam::Key key, keysToMove) {
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BOOST_FOREACH(Key key, keysToMove) {
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EliminationForest::removeChildrenIndices(indicesToEliminate, forest.at(ordering_.at(key)));
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}
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@ -370,12 +403,12 @@ void ConcurrentBatchFilter::marginalize(const FastList<Key>& keysToMove) {
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// Convert the marginal factors into Linear Container Factors
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// Add the marginal factor variables to the separator
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NonlinearFactorGraph marginalFactors;
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BOOST_FOREACH(gtsam::Index index, indicesToEliminate) {
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BOOST_FOREACH(Index index, indicesToEliminate) {
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GaussianFactor::shared_ptr gaussianFactor = forest.at(index)->eliminateRecursive(parameters_.getEliminationFunction());
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LinearContainerFactor::shared_ptr marginalFactor(new LinearContainerFactor(gaussianFactor, ordering_, theta_));
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marginalFactors.push_back(marginalFactor);
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// Add the keys associated with the marginal factor to the separator values
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BOOST_FOREACH(gtsam::Key key, *marginalFactor) {
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BOOST_FOREACH(Key key, *marginalFactor) {
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if(!separatorValues_.exists(key)) {
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separatorValues_.insert(key, theta_.at(key));
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}
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@ -403,7 +436,7 @@ void ConcurrentBatchFilter::marginalize(const FastList<Key>& keysToMove) {
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}
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// Add the linearization point of the moved variables to the smoother cache
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BOOST_FOREACH(gtsam::Key key, keysToMove) {
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BOOST_FOREACH(Key key, keysToMove) {
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smootherValues_.insert(key, theta_.at(key));
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}
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}
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@ -411,10 +444,11 @@ void ConcurrentBatchFilter::marginalize(const FastList<Key>& keysToMove) {
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// Remove the marginalized variables and factors from the filter
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{
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// Remove marginalized factors from the factor graph
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removeFactors(removedFactorSlots);
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std::vector<size_t> slots(removedFactorSlots.begin(), removedFactorSlots.end());
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removeFactors(slots);
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// Remove marginalized keys from values (and separator)
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BOOST_FOREACH(gtsam::Key key, keysToMove) {
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BOOST_FOREACH(Key key, keysToMove) {
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theta_.erase(key);
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if(separatorValues_.exists(key)) {
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separatorValues_.erase(key);
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@ -423,8 +457,8 @@ void ConcurrentBatchFilter::marginalize(const FastList<Key>& keysToMove) {
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// Permute the ordering such that the removed keys are at the end.
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// This is a prerequisite for removing them from several structures
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std::vector<gtsam::Index> toBack;
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BOOST_FOREACH(gtsam::Key key, keysToMove) {
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std::vector<Index> toBack;
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BOOST_FOREACH(Key key, keysToMove) {
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toBack.push_back(ordering_.at(key));
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}
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Permutation forwardPermutation = Permutation::PushToBack(toBack, ordering_.size());
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@ -439,6 +473,48 @@ void ConcurrentBatchFilter::marginalize(const FastList<Key>& keysToMove) {
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}
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}
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/* ************************************************************************* */
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NonlinearFactorGraph ConcurrentBatchFilter::marginalize(const NonlinearFactorGraph& graph, const Values& values,
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const Ordering& ordering, const std::vector<Key>& marginalizeKeys, const GaussianFactorGraph::Eliminate& function) {
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// This function returns marginal factors (in the form of LinearContainerFactors) that result from
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// marginalizing out the selected variables.
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// Calculate marginal factors on the remaining variables (after marginalizing 'marginalizeKeys')
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// Note: It is assumed the ordering already has these keys first
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// Create the linear factor graph
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GaussianFactorGraph linearFactorGraph = *graph.linearize(values, ordering);
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// Construct a variable index
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VariableIndex variableIndex(linearFactorGraph);
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// Construct an elimination tree to perform sparse elimination
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std::vector<EliminationForest::shared_ptr> forest( EliminationForest::Create(linearFactorGraph, variableIndex) );
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// This is a forest. Only the top-most node/index of each tree needs to be eliminated; all of the children will be eliminated automatically
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// Find the subset of nodes/keys that must be eliminated
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std::set<Index> indicesToEliminate;
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BOOST_FOREACH(Key key, marginalizeKeys) {
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indicesToEliminate.insert(ordering.at(key));
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}
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BOOST_FOREACH(Key key, marginalizeKeys) {
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EliminationForest::removeChildrenIndices(indicesToEliminate, forest.at(ordering.at(key)));
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}
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// Eliminate each top-most key, returning a Gaussian Factor on some of the remaining variables
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// Convert the marginal factors into Linear Container Factors
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// Add the marginal factor variables to the separator
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NonlinearFactorGraph marginalFactors;
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BOOST_FOREACH(Index index, indicesToEliminate) {
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GaussianFactor::shared_ptr gaussianFactor = forest.at(index)->eliminateRecursive(function);
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LinearContainerFactor::shared_ptr marginalFactor(new LinearContainerFactor(gaussianFactor, ordering, values));
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marginalFactors.push_back(marginalFactor);
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}
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return marginalFactors;
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}
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/* ************************************************************************* */
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void ConcurrentBatchFilter::PrintNonlinearFactor(const NonlinearFactor::shared_ptr& factor,
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const std::string& indent, const KeyFormatter& keyFormatter) {
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@ -479,20 +555,20 @@ std::vector<Index> ConcurrentBatchFilter::EliminationForest::ComputeParents(cons
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const size_t m = structure.nFactors();
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const size_t n = structure.size();
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static const gtsam::Index none = std::numeric_limits<gtsam::Index>::max();
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static const Index none = std::numeric_limits<Index>::max();
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// Allocate result parent vector and vector of last factor columns
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std::vector<gtsam::Index> parents(n, none);
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std::vector<gtsam::Index> prevCol(m, none);
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std::vector<Index> parents(n, none);
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std::vector<Index> prevCol(m, none);
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// for column j \in 1 to n do
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for (gtsam::Index j = 0; j < n; j++) {
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for (Index j = 0; j < n; j++) {
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// for row i \in Struct[A*j] do
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BOOST_FOREACH(const size_t i, structure[j]) {
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if (prevCol[i] != none) {
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gtsam::Index k = prevCol[i];
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Index k = prevCol[i];
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// find root r of the current tree that contains k
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gtsam::Index r = k;
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Index r = k;
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while (parents[r] != none)
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r = parents[r];
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if (r != j) parents[r] = j;
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@ -505,28 +581,28 @@ std::vector<Index> ConcurrentBatchFilter::EliminationForest::ComputeParents(cons
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}
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/* ************************************************************************* */
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std::vector<ConcurrentBatchFilter::EliminationForest::shared_ptr> ConcurrentBatchFilter::EliminationForest::Create(const gtsam::GaussianFactorGraph& factorGraph, const gtsam::VariableIndex& structure) {
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std::vector<ConcurrentBatchFilter::EliminationForest::shared_ptr> ConcurrentBatchFilter::EliminationForest::Create(const GaussianFactorGraph& factorGraph, const VariableIndex& structure) {
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// Compute the tree structure
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std::vector<gtsam::Index> parents(ComputeParents(structure));
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std::vector<Index> parents(ComputeParents(structure));
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// Number of variables
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const size_t n = structure.size();
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static const gtsam::Index none = std::numeric_limits<gtsam::Index>::max();
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static const Index none = std::numeric_limits<Index>::max();
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// Create tree structure
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std::vector<shared_ptr> trees(n);
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for (gtsam::Index k = 1; k <= n; k++) {
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gtsam::Index j = n - k; // Start at the last variable and loop down to 0
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for (Index k = 1; k <= n; k++) {
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Index j = n - k; // Start at the last variable and loop down to 0
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trees[j].reset(new EliminationForest(j)); // Create a new node on this variable
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if (parents[j] != none) // If this node has a parent, add it to the parent's children
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trees[parents[j]]->add(trees[j]);
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}
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// Hang factors in right places
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BOOST_FOREACH(const sharedFactor& factor, factorGraph) {
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BOOST_FOREACH(const GaussianFactor::shared_ptr& factor, factorGraph) {
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if(factor && factor->size() > 0) {
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gtsam::Index j = *std::min_element(factor->begin(), factor->end());
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Index j = *std::min_element(factor->begin(), factor->end());
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if(j < structure.size())
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trees[j]->add(factor);
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}
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}
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/* ************************************************************************* */
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ConcurrentBatchFilter::EliminationForest::sharedFactor ConcurrentBatchFilter::EliminationForest::eliminateRecursive(Eliminate function) {
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GaussianFactor::shared_ptr ConcurrentBatchFilter::EliminationForest::eliminateRecursive(GaussianFactorGraph::Eliminate function) {
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// Create the list of factors to be eliminated, initially empty, and reserve space
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gtsam::GaussianFactorGraph factors;
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GaussianFactorGraph factors;
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factors.reserve(this->factors_.size() + this->subTrees_.size());
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// Add all factors associated with the current node
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@ -550,7 +626,7 @@ ConcurrentBatchFilter::EliminationForest::sharedFactor ConcurrentBatchFilter::El
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factors.push_back(child->eliminateRecursive(function));
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// Combine all factors (from this node and from subtrees) into a joint factor
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gtsam::GaussianFactorGraph::EliminationResult eliminated(function(factors, 1));
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GaussianFactorGraph::EliminationResult eliminated(function(factors, 1));
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return eliminated.second;
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}
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@ -157,7 +157,7 @@ protected:
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*
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* @param summarizedFactors An updated version of the smoother branch summarized factors
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*/
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virtual void synchronize(const NonlinearFactorGraph& summarizedFactors);
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virtual void synchronize(const NonlinearFactorGraph& summarizedFactors, const Values& separatorValues);
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/**
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* Perform any required operations after the synchronization process finishes.
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@ -179,7 +179,7 @@ private:
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*
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* @param slots The slots in the factor graph that should be deleted
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*/
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void removeFactors(const std::set<size_t>& slots);
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void removeFactors(const std::vector<size_t>& slots);
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/** Use colamd to update into an efficient ordering */
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void reorder(const boost::optional<FastList<Key> >& keysToMove = boost::none);
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@ -192,6 +192,12 @@ private:
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*/
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void marginalize(const FastList<Key>& keysToMove);
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/** Marginalize out the set of requested variables from the filter, caching them for the smoother
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* This effectively moves the separator.
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*/
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static NonlinearFactorGraph marginalize(const NonlinearFactorGraph& graph, const Values& values,
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const Ordering& ordering, const std::vector<Key>& marginalizeKeys, const GaussianFactorGraph::Eliminate& function = EliminateQR);
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/** Print just the nonlinear keys in a nonlinear factor */
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static void PrintNonlinearFactor(const NonlinearFactor::shared_ptr& factor,
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const std::string& indent = "", const KeyFormatter& keyFormatter = DefaultKeyFormatter);
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class EliminationForest {
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public:
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typedef boost::shared_ptr<EliminationForest> shared_ptr; ///< Shared pointer to this class
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typedef gtsam::GaussianFactor Factor; ///< The factor Type
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typedef Factor::shared_ptr sharedFactor; ///< Shared pointer to a factor
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typedef gtsam::BayesNet<Factor::ConditionalType> BayesNet; ///< The BayesNet
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typedef gtsam::GaussianFactorGraph::Eliminate Eliminate; ///< The eliminate subroutine
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// typedef GaussianFactor Factor; ///< The factor Type
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// typedef Factor::shared_ptr sharedFactor; ///< Shared pointer to a factor
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// typedef BayesNet<Factor::ConditionalType> BayesNet; ///< The BayesNet
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// typedef GaussianFactorGraph::Eliminate Eliminate; ///< The eliminate subroutine
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private:
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typedef gtsam::FastList<sharedFactor> Factors;
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typedef gtsam::FastList<shared_ptr> SubTrees;
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typedef std::vector<Factor::ConditionalType::shared_ptr> Conditionals;
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typedef FastList<GaussianFactor::shared_ptr> Factors;
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typedef FastList<shared_ptr> SubTrees;
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typedef std::vector<GaussianConditional::shared_ptr> Conditionals;
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gtsam::Index key_; ///< index associated with root
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Index key_; ///< index associated with root
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Factors factors_; ///< factors associated with root
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SubTrees subTrees_; ///< sub-trees
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/** default constructor, private, as you should use Create below */
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EliminationForest(gtsam::Index key = 0) : key_(key) {}
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EliminationForest(Index key = 0) : key_(key) {}
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/**
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* Static internal function to build a vector of parent pointers using the
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* algorithm of Gilbert et al., 2001, BIT.
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*/
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static std::vector<gtsam::Index> ComputeParents(const gtsam::VariableIndex& structure);
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static std::vector<Index> ComputeParents(const VariableIndex& structure);
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/** add a factor, for Create use only */
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void add(const sharedFactor& factor) { factors_.push_back(factor); }
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void add(const GaussianFactor::shared_ptr& factor) { factors_.push_back(factor); }
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/** add a subtree, for Create use only */
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void add(const shared_ptr& child) { subTrees_.push_back(child); }
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@ -236,7 +242,7 @@ private:
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public:
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/** return the key associated with this tree node */
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gtsam::Index key() const { return key_; }
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Index key() const { return key_; }
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/** return the const reference of children */
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const SubTrees& children() const { return subTrees_; }
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const Factors& factors() const { return factors_; }
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/** Create an elimination tree from a factor graph */
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static std::vector<shared_ptr> Create(const gtsam::GaussianFactorGraph& factorGraph, const gtsam::VariableIndex& structure);
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static std::vector<shared_ptr> Create(const GaussianFactorGraph& factorGraph, const VariableIndex& structure);
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/** Recursive routine that eliminates the factors arranged in an elimination tree */
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sharedFactor eliminateRecursive(Eliminate function);
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GaussianFactor::shared_ptr eliminateRecursive(GaussianFactorGraph::Eliminate function);
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/** Recursive function that helps find the top of each tree */
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static void removeChildrenIndices(std::set<Index>& indices, const EliminationForest::shared_ptr& tree);
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@ -52,7 +52,7 @@ public:
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void presync() { ConcurrentBatchFilter::presync(); };
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void getSummarizedFactors(NonlinearFactorGraph& summarizedFactors, Values& rootValues) { ConcurrentBatchFilter::getSummarizedFactors(summarizedFactors, rootValues); };
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void getSmootherFactors(NonlinearFactorGraph& smootherFactors, Values& smootherValues) { ConcurrentBatchFilter::getSmootherFactors(smootherFactors, smootherValues); };
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void synchronize(const NonlinearFactorGraph& summarizedFactors) { ConcurrentBatchFilter::synchronize(summarizedFactors); };
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void synchronize(const NonlinearFactorGraph& summarizedFactors, const Values& separatorValues) { ConcurrentBatchFilter::synchronize(summarizedFactors, separatorValues); };
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void postsync() { ConcurrentBatchFilter::postsync(); };
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};
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