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Fix return type

release/4.3a0
Frank Dellaert 2022-01-25 23:47:53 -05:00
parent 09fa002bd7
commit d6b977927e
4 changed files with 15 additions and 13 deletions
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15
gtsam/discrete/DiscreteFactorGraph.cpp
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@ -149,16 +149,15 @@ namespace gtsam {
DiscreteBayesNet DiscreteFactorGraph::sumProduct( DiscreteBayesNet DiscreteFactorGraph::sumProduct(
OptionalOrderingType orderingType) const { OptionalOrderingType orderingType) const {
gttic(DiscreteFactorGraph_sumProduct); gttic(DiscreteFactorGraph_sumProduct);
auto bayesNet = BaseEliminateable::eliminateSequential(orderingType); auto bayesNet = eliminateSequential(orderingType);
return *bayesNet; return *bayesNet;
} }
DiscreteLookupDAG DiscreteFactorGraph::sumProduct( DiscreteBayesNet DiscreteFactorGraph::sumProduct(
const Ordering& ordering) const { const Ordering& ordering) const {
gttic(DiscreteFactorGraph_sumProduct); gttic(DiscreteFactorGraph_sumProduct);
auto bayesNet = auto bayesNet = eliminateSequential(ordering);
BaseEliminateable::eliminateSequential(ordering, EliminateForMPE); return *bayesNet;
return DiscreteLookupDAG::FromBayesNet(*bayesNet);
} }
/* ************************************************************************ */ /* ************************************************************************ */
@ -170,16 +169,14 @@ namespace gtsam {
DiscreteLookupDAG DiscreteFactorGraph::maxProduct( DiscreteLookupDAG DiscreteFactorGraph::maxProduct(
OptionalOrderingType orderingType) const { OptionalOrderingType orderingType) const {
gttic(DiscreteFactorGraph_maxProduct); gttic(DiscreteFactorGraph_maxProduct);
auto bayesNet = auto bayesNet = eliminateSequential(orderingType, EliminateForMPE);
BaseEliminateable::eliminateSequential(orderingType, EliminateForMPE);
return DiscreteLookupDAG::FromBayesNet(*bayesNet); return DiscreteLookupDAG::FromBayesNet(*bayesNet);
} }
DiscreteLookupDAG DiscreteFactorGraph::maxProduct( DiscreteLookupDAG DiscreteFactorGraph::maxProduct(
const Ordering& ordering) const { const Ordering& ordering) const {
gttic(DiscreteFactorGraph_maxProduct); gttic(DiscreteFactorGraph_maxProduct);
auto bayesNet = auto bayesNet = eliminateSequential(ordering, EliminateForMPE);
BaseEliminateable::eliminateSequential(ordering, EliminateForMPE);
return DiscreteLookupDAG::FromBayesNet(*bayesNet); return DiscreteLookupDAG::FromBayesNet(*bayesNet);
} }

2
gtsam/discrete/DiscreteFactorGraph.h
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@ -147,7 +147,7 @@ class GTSAM_EXPORT DiscreteFactorGraph
* @param ordering * @param ordering
* @return DiscreteBayesNet encoding posterior P(X|Z) * @return DiscreteBayesNet encoding posterior P(X|Z)
*/ */
DiscreteLookupDAG sumProduct(const Ordering& ordering) const; DiscreteBayesNet sumProduct(const Ordering& ordering) const;
/** /**
* @brief Implement the max-product algorithm * @brief Implement the max-product algorithm

6
gtsam/discrete/discrete.i
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@ -277,9 +277,9 @@ class DiscreteFactorGraph {
double operator()(const gtsam::DiscreteValues& values) const; double operator()(const gtsam::DiscreteValues& values) const;
gtsam::DiscreteValues optimize() const; gtsam::DiscreteValues optimize() const;
gtsam::DiscreteLookupDAG sumProduct(); gtsam::DiscreteBayesNet sumProduct();
gtsam::DiscreteLookupDAG sumProduct(gtsam::Ordering::OrderingType type); gtsam::DiscreteBayesNet sumProduct(gtsam::Ordering::OrderingType type);
gtsam::DiscreteLookupDAG sumProduct(const gtsam::Ordering& ordering); gtsam::DiscreteBayesNet sumProduct(const gtsam::Ordering& ordering);
gtsam::DiscreteLookupDAG maxProduct(); gtsam::DiscreteLookupDAG maxProduct();
gtsam::DiscreteLookupDAG maxProduct(gtsam::Ordering::OrderingType type); gtsam::DiscreteLookupDAG maxProduct(gtsam::Ordering::OrderingType type);

5
gtsam/discrete/tests/testDiscreteFactorGraph.cpp
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@ -158,6 +158,11 @@ TEST(DiscreteFactorGraph, test) {
// Test sumProduct alias with all orderings: // Test sumProduct alias with all orderings:
auto mpeProbability = expectedBayesNet(mpe); auto mpeProbability = expectedBayesNet(mpe);
EXPECT_DOUBLES_EQUAL(0.28125, mpeProbability, 1e-5); // regression EXPECT_DOUBLES_EQUAL(0.28125, mpeProbability, 1e-5); // regression
// Using custom ordering
DiscreteBayesNet bayesNet = graph.sumProduct(ordering);
EXPECT_DOUBLES_EQUAL(mpeProbability, bayesNet(mpe), 1e-5);
for (Ordering::OrderingType orderingType : for (Ordering::OrderingType orderingType :
{Ordering::COLAMD, Ordering::METIS, Ordering::NATURAL, {Ordering::COLAMD, Ordering::METIS, Ordering::NATURAL,
Ordering::CUSTOM}) { Ordering::CUSTOM}) {