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eliminateable fg

release/4.3a0
kartik arcot 2023-01-13 12:31:12 -08:00
parent d7f60353c9
commit 5880471136
8 changed files with 30 additions and 30 deletions
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4
gtsam/discrete/DiscreteFactorGraph.h
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@ -70,8 +70,8 @@ template<> struct EliminationTraits<DiscreteFactorGraph>
/// The default ordering generation function
static Ordering DefaultOrderingFunc(
const FactorGraphType& graph,
boost::optional<const VariableIndex&> variableIndex) {
return Ordering::Colamd(*variableIndex);
const VariableIndex& variableIndex) {
return Ordering::Colamd(variableIndex);
}
};

2
gtsam/hybrid/HybridGaussianFactorGraph.h
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@ -87,7 +87,7 @@ struct EliminationTraits<HybridGaussianFactorGraph> {
/// The default ordering generation function
static Ordering DefaultOrderingFunc(
const FactorGraphType& graph,
boost::optional<const VariableIndex&> variableIndex) {
const VariableIndex&) {
return HybridOrdering(graph);
}
};

2
gtsam/hybrid/HybridGaussianISAM.cpp
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@ -102,7 +102,7 @@ void HybridGaussianISAM::updateInternal(
// eliminate all factors (top, added, orphans) into a new Bayes tree
HybridBayesTree::shared_ptr bayesTree =
factors.eliminateMultifrontal(elimination_ordering, function, index);
factors.eliminateMultifrontal(elimination_ordering, function, &index);
if (maxNrLeaves) {
bayesTree->prune(*maxNrLeaves);

40
gtsam/inference/EliminateableFactorGraph-inst.h
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@ -36,7 +36,7 @@ namespace gtsam {
// no Ordering is provided. When removing optional from VariableIndex, create VariableIndex
// before creating ordering.
VariableIndex computedVariableIndex(asDerived());
return eliminateSequential(orderingType, function, computedVariableIndex);
return eliminateSequential(orderingType, function, &computedVariableIndex);
}
else {
// Compute an ordering and call this function again. We are guaranteed to have a
@ -52,7 +52,7 @@ namespace gtsam {
return eliminateSequential(computedOrdering, function, variableIndex);
} else {
Ordering computedOrdering = EliminationTraitsType::DefaultOrderingFunc(
asDerived(), variableIndex);
asDerived(), *variableIndex);
return eliminateSequential(computedOrdering, function, variableIndex);
}
}
@ -68,7 +68,7 @@ namespace gtsam {
if(!variableIndex) {
// If no VariableIndex provided, compute one and call this function again
VariableIndex computedVariableIndex(asDerived());
return eliminateSequential(ordering, function, computedVariableIndex);
return eliminateSequential(ordering, function, &computedVariableIndex);
} else {
gttic(eliminateSequential);
// Do elimination
@ -99,7 +99,7 @@ namespace gtsam {
// creating ordering.
VariableIndex computedVariableIndex(asDerived());
return eliminateMultifrontal(orderingType, function,
computedVariableIndex);
&computedVariableIndex);
} else {
// Compute an ordering and call this function again. We are guaranteed to
// have a VariableIndex already here because we computed one if needed in
@ -115,7 +115,7 @@ namespace gtsam {
return eliminateMultifrontal(computedOrdering, function, variableIndex);
} else {
Ordering computedOrdering = EliminationTraitsType::DefaultOrderingFunc(
asDerived(), variableIndex);
asDerived(), *variableIndex);
return eliminateMultifrontal(computedOrdering, function, variableIndex);
}
}
@ -131,7 +131,7 @@ namespace gtsam {
if(!variableIndex) {
// If no VariableIndex provided, compute one and call this function again
VariableIndex computedVariableIndex(asDerived());
return eliminateMultifrontal(ordering, function, computedVariableIndex);
return eliminateMultifrontal(ordering, function, &computedVariableIndex);
} else {
gttic(eliminateMultifrontal);
// Do elimination with given ordering
@ -162,7 +162,7 @@ namespace gtsam {
} else {
// If no variable index is provided, compute one and call this function again
VariableIndex computedVariableIndex(asDerived());
return eliminatePartialSequential(ordering, function, computedVariableIndex);
return eliminatePartialSequential(ordering, function, &computedVariableIndex);
}
}
@ -183,7 +183,7 @@ namespace gtsam {
} else {
// If no variable index is provided, compute one and call this function again
VariableIndex computedVariableIndex(asDerived());
return eliminatePartialSequential(variables, function, computedVariableIndex);
return eliminatePartialSequential(variables, function, &computedVariableIndex);
}
}
@ -202,7 +202,7 @@ namespace gtsam {
} else {
// If no variable index is provided, compute one and call this function again
VariableIndex computedVariableIndex(asDerived());
return eliminatePartialMultifrontal(ordering, function, computedVariableIndex);
return eliminatePartialMultifrontal(ordering, function, &computedVariableIndex);
}
}
@ -223,7 +223,7 @@ namespace gtsam {
} else {
// If no variable index is provided, compute one and call this function again
VariableIndex computedVariableIndex(asDerived());
return eliminatePartialMultifrontal(variables, function, computedVariableIndex);
return eliminatePartialMultifrontal(variables, function, &computedVariableIndex);
}
}
@ -237,7 +237,7 @@ namespace gtsam {
if(!variableIndex) {
// If no variable index is provided, compute one and call this function again
VariableIndex index(asDerived());
return marginalMultifrontalBayesNet(variables, function, index);
return marginalMultifrontalBayesNet(variables, function, &index);
} else {
// No ordering was provided for the marginalized variables, so order them using constrained
// COLAMD.
@ -255,7 +255,7 @@ namespace gtsam {
Ordering marginalVarsOrdering(totalOrdering.end() - nVars, totalOrdering.end());
// Call this function again with the computed orderings
return marginalMultifrontalBayesNet(marginalVarsOrdering, marginalizationOrdering, function, *variableIndex);
return marginalMultifrontalBayesNet(marginalVarsOrdering, marginalizationOrdering, function, variableIndex);
}
}
@ -270,7 +270,7 @@ namespace gtsam {
if(!variableIndex) {
// If no variable index is provided, compute one and call this function again
VariableIndex index(asDerived());
return marginalMultifrontalBayesNet(variables, marginalizedVariableOrdering, function, index);
return marginalMultifrontalBayesNet(variables, marginalizedVariableOrdering, function, &index);
} else {
gttic(marginalMultifrontalBayesNet);
// An ordering was provided for the marginalized variables, so we can first eliminate them
@ -278,7 +278,7 @@ namespace gtsam {
boost::shared_ptr<BayesTreeType> bayesTree;
boost::shared_ptr<FactorGraphType> factorGraph;
boost::tie(bayesTree,factorGraph) =
eliminatePartialMultifrontal(marginalizedVariableOrdering, function, *variableIndex);
eliminatePartialMultifrontal(marginalizedVariableOrdering, function, variableIndex);
if(const Ordering* varsAsOrdering = boost::get<const Ordering&>(&variables))
{
@ -304,7 +304,7 @@ namespace gtsam {
if(!variableIndex) {
// If no variable index is provided, compute one and call this function again
VariableIndex computedVariableIndex(asDerived());
return marginalMultifrontalBayesTree(variables, function, computedVariableIndex);
return marginalMultifrontalBayesTree(variables, function, &computedVariableIndex);
} else {
// No ordering was provided for the marginalized variables, so order them using constrained
// COLAMD.
@ -322,7 +322,7 @@ namespace gtsam {
Ordering marginalVarsOrdering(totalOrdering.end() - nVars, totalOrdering.end());
// Call this function again with the computed orderings
return marginalMultifrontalBayesTree(marginalVarsOrdering, marginalizationOrdering, function, *variableIndex);
return marginalMultifrontalBayesTree(marginalVarsOrdering, marginalizationOrdering, function, variableIndex);
}
}
@ -337,7 +337,7 @@ namespace gtsam {
if(!variableIndex) {
// If no variable index is provided, compute one and call this function again
VariableIndex computedVariableIndex(asDerived());
return marginalMultifrontalBayesTree(variables, marginalizedVariableOrdering, function, computedVariableIndex);
return marginalMultifrontalBayesTree(variables, marginalizedVariableOrdering, function, &computedVariableIndex);
} else {
gttic(marginalMultifrontalBayesTree);
// An ordering was provided for the marginalized variables, so we can first eliminate them
@ -345,7 +345,7 @@ namespace gtsam {
boost::shared_ptr<BayesTreeType> bayesTree;
boost::shared_ptr<FactorGraphType> factorGraph;
boost::tie(bayesTree,factorGraph) =
eliminatePartialMultifrontal(marginalizedVariableOrdering, function, *variableIndex);
eliminatePartialMultifrontal(marginalizedVariableOrdering, function, variableIndex);
if(const Ordering* varsAsOrdering = boost::get<const Ordering&>(&variables))
{
@ -377,13 +377,13 @@ namespace gtsam {
Ordering marginalizationOrdering(totalOrdering.begin(), totalOrdering.end() - variables.size());
// Eliminate and return the remaining factor graph
return eliminatePartialMultifrontal(marginalizationOrdering, function, *variableIndex).second;
return eliminatePartialMultifrontal(marginalizationOrdering, function, variableIndex).second;
}
else
{
// If no variable index is provided, compute one and call this function again
VariableIndex computedVariableIndex(asDerived());
return marginal(variables, function, computedVariableIndex);
return marginal(variables, function, &computedVariableIndex);
}
}

2
gtsam/inference/EliminateableFactorGraph.h
View File

@ -89,7 +89,7 @@ namespace gtsam {
typedef std::function<EliminationResult(const FactorGraphType&, const Ordering&)> Eliminate;
/// Typedef for an optional variable index as an argument to elimination functions
typedef boost::optional<const VariableIndex&> OptionalVariableIndex;
typedef const VariableIndex* OptionalVariableIndex;
/// Typedef for an optional ordering type
typedef std::optional<Ordering::OrderingType> OptionalOrderingType;

2
gtsam/inference/ISAM-inst.h
View File

@ -49,7 +49,7 @@ void ISAM<BAYESTREE>::updateInternal(const FactorGraphType& newFactors,
KeyVector(newFactorKeys.begin(), newFactorKeys.end()));
// eliminate all factors (top, added, orphans) into a new Bayes tree
auto bayesTree = factors.eliminateMultifrontal(ordering, function, index);
auto bayesTree = factors.eliminateMultifrontal(ordering, function, &index);
// Re-add into Bayes tree data structures
this->roots_.insert(this->roots_.end(), bayesTree->roots().begin(),

4
gtsam/linear/GaussianFactorGraph.h
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@ -57,8 +57,8 @@ namespace gtsam {
/// The default ordering generation function
static Ordering DefaultOrderingFunc(
const FactorGraphType& graph,
boost::optional<const VariableIndex&> variableIndex) {
return Ordering::Colamd(*variableIndex);
const VariableIndex& variableIndex) {
return Ordering::Colamd(variableIndex);
}
};

4
gtsam/symbolic/SymbolicFactorGraph.h
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@ -49,8 +49,8 @@ namespace gtsam {
/// The default ordering generation function
static Ordering DefaultOrderingFunc(
const FactorGraphType& graph,
boost::optional<const VariableIndex&> variableIndex) {
return Ordering::Colamd(*variableIndex);
const VariableIndex& variableIndex) {
return Ordering::Colamd(variableIndex);
}
};