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release/4.3a0
Frank Dellaert 2024-10-22 13:50:39 -07:00
parent 25f90701c7
commit 5145b8c47a
4 changed files with 14 additions and 22 deletions
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2
gtsam/hybrid/HybridGaussianISAM.cpp
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@ -10,7 +10,7 @@
* -------------------------------------------------------------------------- */ * -------------------------------------------------------------------------- */
/** /**
* @file HybridGaussianISAM.h * @file HybridGaussianISAM.cpp
* @date March 31, 2022 * @date March 31, 2022
* @author Fan Jiang * @author Fan Jiang
* @author Frank Dellaert * @author Frank Dellaert

8
gtsam/hybrid/HybridNonlinearISAM.cpp
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@ -39,8 +39,8 @@ void HybridNonlinearISAM::update(const HybridNonlinearFactorGraph& newFactors,
if (newFactors.size() > 0) { if (newFactors.size() > 0) {
// Reorder and relinearize every reorderInterval updates // Reorder and relinearize every reorderInterval updates
if (reorderInterval_ > 0 && ++reorderCounter_ >= reorderInterval_) { if (reorderInterval_ > 0 && ++reorderCounter_ >= reorderInterval_) {
// TODO(Varun) Relinearization doesn't take into account pruning // TODO(Varun) Re-linearization doesn't take into account pruning
reorder_relinearize(); reorderRelinearize();
reorderCounter_ = 0; reorderCounter_ = 0;
} }
@ -60,7 +60,7 @@ void HybridNonlinearISAM::update(const HybridNonlinearFactorGraph& newFactors,
} }
/* ************************************************************************* */ /* ************************************************************************* */
void HybridNonlinearISAM::reorder_relinearize() { void HybridNonlinearISAM::reorderRelinearize() {
if (factors_.size() > 0) { if (factors_.size() > 0) {
// Obtain the new linearization point // Obtain the new linearization point
const Values newLinPoint = estimate(); const Values newLinPoint = estimate();
@ -69,7 +69,7 @@ void HybridNonlinearISAM::reorder_relinearize() {
// Just recreate the whole BayesTree // Just recreate the whole BayesTree
// TODO: allow for constrained ordering here // TODO: allow for constrained ordering here
// TODO: decouple relinearization and reordering to avoid // TODO: decouple re-linearization and reordering to avoid
isam_.update(*factors_.linearize(newLinPoint), {}, {}, isam_.update(*factors_.linearize(newLinPoint), {}, {},
eliminationFunction_); eliminationFunction_);

10
gtsam/hybrid/HybridNonlinearISAM.h
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@ -37,7 +37,7 @@ class GTSAM_EXPORT HybridNonlinearISAM {
/// The discrete assignment /// The discrete assignment
DiscreteValues assignment_; DiscreteValues assignment_;
/** The original factors, used when relinearizing */ /** The original factors, used when re-linearizing */
HybridNonlinearFactorGraph factors_; HybridNonlinearFactorGraph factors_;
/** The reordering interval and counter */ /** The reordering interval and counter */
@ -52,8 +52,8 @@ class GTSAM_EXPORT HybridNonlinearISAM {
/// @{ /// @{
/** /**
* Periodically reorder and relinearize * Periodically reorder and re-linearize
* @param reorderInterval is the number of updates between reorderings, * @param reorderInterval is the number of updates between re-orderings,
* 0 never reorders (and is dangerous for memory consumption) * 0 never reorders (and is dangerous for memory consumption)
* 1 (default) reorders every time, in worse case is batch every update * 1 (default) reorders every time, in worse case is batch every update
* typical values are 50 or 100 * typical values are 50 or 100
@ -124,8 +124,8 @@ class GTSAM_EXPORT HybridNonlinearISAM {
const std::optional<size_t>& maxNrLeaves = {}, const std::optional<size_t>& maxNrLeaves = {},
const std::optional<Ordering>& ordering = {}); const std::optional<Ordering>& ordering = {});
/** Relinearization and reordering of variables */ /** Re-linearization and reordering of variables */
void reorder_relinearize(); void reorderRelinearize();
/// @} /// @}
}; };

16
gtsam/hybrid/tests/testHybridEstimation.cpp
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@ -339,12 +339,8 @@ TEST(HybridEstimation, Probability) {
HybridValues hybrid_values = bayesNet->optimize(); HybridValues hybrid_values = bayesNet->optimize();
// This is the correct sequence as designed // This is the correct sequence as designed
DiscreteValues discrete_seq; DiscreteValues expectedSequence{{M(0), 1}, {M(1), 1}, {M(2), 0}};
discrete_seq[M(0)] = 1; EXPECT(assert_equal(expectedSequence, hybrid_values.discrete()));
discrete_seq[M(1)] = 1;
discrete_seq[M(2)] = 0;
EXPECT(assert_equal(discrete_seq, hybrid_values.discrete()));
} }
/****************************************************************************/ /****************************************************************************/
@ -411,12 +407,8 @@ TEST(HybridEstimation, ProbabilityMultifrontal) {
HybridValues hybrid_values = discreteBayesTree->optimize(); HybridValues hybrid_values = discreteBayesTree->optimize();
// This is the correct sequence as designed // This is the correct sequence as designed
DiscreteValues discrete_seq; DiscreteValues expectedSequence{{M(0), 1}, {M(1), 1}, {M(2), 0}};
discrete_seq[M(0)] = 1; EXPECT(assert_equal(expectedSequence, hybrid_values.discrete()));
discrete_seq[M(1)] = 1;
discrete_seq[M(2)] = 0;
EXPECT(assert_equal(discrete_seq, hybrid_values.discrete()));
} }
/********************************************************************************* /*********************************************************************************