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add incremental pruning to HybridGaussianISAM

release/4.3a0
Varun Agrawal 2022-08-16 17:23:52 -04:00
parent 77bea319dd
commit ac20cff710
5 changed files with 449 additions and 363 deletions
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27
gtsam/hybrid/GaussianMixture.cpp
View File

@ -119,11 +119,36 @@ void GaussianMixture::print(const std::string &s,
"", [&](Key k) { return formatter(k); },
[&](const GaussianConditional::shared_ptr &gf) -> std::string {
RedirectCout rd;
if (!gf->empty())
if (gf && !gf->empty())
gf->print("", formatter);
else
return {"nullptr"};
return rd.str();
});
}
/* *******************************************************************************/
void GaussianMixture::prune(const DecisionTreeFactor &decisionTree) {
// Functional which loops over all assignments and create a set of
// GaussianConditionals
auto pruner = [&decisionTree](
const Assignment<Key> &choices,
const GaussianConditional::shared_ptr &conditional)
-> GaussianConditional::shared_ptr {
// typecast so we can use this to get probability value
DiscreteValues values(choices);
if (decisionTree(values) == 0.0) {
// empty aka null pointer
boost::shared_ptr<GaussianConditional> null;
return null;
} else {
return conditional;
}
};
auto pruned_conditionals = conditionals_.apply(pruner);
conditionals_.root_ = pruned_conditionals.root_;
}
} // namespace gtsam

12
gtsam/hybrid/GaussianMixture.h
View File

@ -21,6 +21,7 @@
#include <gtsam/discrete/DecisionTree-inl.h>
#include <gtsam/discrete/DecisionTree.h>
#include <gtsam/discrete/DecisionTreeFactor.h>
#include <gtsam/discrete/DiscreteKey.h>
#include <gtsam/hybrid/HybridFactor.h>
#include <gtsam/inference/Conditional.h>
@ -121,7 +122,7 @@ class GTSAM_EXPORT GaussianMixture
/// Test equality with base HybridFactor
bool equals(const HybridFactor &lf, double tol = 1e-9) const override;
/* print utility */
/// Print utility
void print(
const std::string &s = "GaussianMixture\n",
const KeyFormatter &formatter = DefaultKeyFormatter) const override;
@ -131,6 +132,15 @@ class GTSAM_EXPORT GaussianMixture
/// Getter for the underlying Conditionals DecisionTree
const Conditionals &conditionals();
/**
* @brief Prune the decision tree of Gaussian factors as per the discrete
* `decisionTree`.
*
* @param decisionTree A pruned decision tree of discrete keys where the
* leaves are probabilities.
*/
void prune(const DecisionTreeFactor &decisionTree);
/**
* @brief Merge the Gaussian Factor Graphs in `this` and `sum` while
* maintaining the decision tree structure.

52
gtsam/hybrid/HybridGaussianISAM.cpp
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@ -41,6 +41,7 @@ HybridGaussianISAM::HybridGaussianISAM(const HybridBayesTree& bayesTree)
void HybridGaussianISAM::updateInternal(
const HybridGaussianFactorGraph& newFactors,
HybridBayesTree::Cliques* orphans,
const boost::optional<Ordering>& ordering,
const HybridBayesTree::Eliminate& function) {
// Remove the contaminated part of the Bayes tree
BayesNetType bn;
@ -78,12 +79,19 @@ void HybridGaussianISAM::updateInternal(
// Get an ordering where the new keys are eliminated last
const VariableIndex index(factors);
const Ordering ordering = Ordering::ColamdConstrainedLast(
index, KeyVector(newKeysDiscreteLast.begin(), newKeysDiscreteLast.end()),
Ordering elimination_ordering;
if (ordering) {
elimination_ordering = *ordering;
} else {
elimination_ordering = Ordering::ColamdConstrainedLast(
index,
KeyVector(newKeysDiscreteLast.begin(), newKeysDiscreteLast.end()),
true);
}
// eliminate all factors (top, added, orphans) into a new Bayes tree
auto bayesTree = factors.eliminateMultifrontal(ordering, function, index);
HybridBayesTree::shared_ptr bayesTree =
factors.eliminateMultifrontal(elimination_ordering, function, index);
// Re-add into Bayes tree data structures
this->roots_.insert(this->roots_.end(), bayesTree->roots().begin(),
@ -93,9 +101,45 @@ void HybridGaussianISAM::updateInternal(
/* ************************************************************************* */
void HybridGaussianISAM::update(const HybridGaussianFactorGraph& newFactors,
const boost::optional<Ordering>& ordering,
const HybridBayesTree::Eliminate& function) {
Cliques orphans;
this->updateInternal(newFactors, &orphans, function);
this->updateInternal(newFactors, &orphans, ordering, function);
}
void HybridGaussianISAM::prune(const Key& root, const size_t maxNrLeaves) {
auto decisionTree = boost::dynamic_pointer_cast<DecisionTreeFactor>(
this->clique(root)->conditional()->inner());
DecisionTreeFactor prunedDiscreteFactor = decisionTree->prune(maxNrLeaves);
decisionTree->root_ = prunedDiscreteFactor.root_;
std::vector<gtsam::Key> prunedKeys;
for (auto&& clique : nodes()) {
// The cliques can be repeated for each frontal so we record it in
// prunedKeys and check if we have already pruned a particular clique.
if (std::find(prunedKeys.begin(), prunedKeys.end(), clique.first) !=
prunedKeys.end()) {
continue;
}
// Add all the keys of the current clique to be pruned to prunedKeys
for (auto&& key : clique.second->conditional()->frontals()) {
prunedKeys.push_back(key);
}
// Convert parents() to a KeyVector for comparison
KeyVector parents;
for (auto&& parent : clique.second->conditional()->parents()) {
parents.push_back(parent);
}
if (parents == decisionTree->keys()) {
auto gaussianMixture = boost::dynamic_pointer_cast<GaussianMixture>(
clique.second->conditional()->inner());
gaussianMixture->prune(prunedDiscreteFactor);
}
}
}
} // namespace gtsam

10
gtsam/hybrid/HybridGaussianISAM.h
View File

@ -48,6 +48,7 @@ class GTSAM_EXPORT HybridGaussianISAM : public ISAM<HybridBayesTree> {
void updateInternal(
const HybridGaussianFactorGraph& newFactors,
HybridBayesTree::Cliques* orphans,
const boost::optional<Ordering>& ordering = boost::none,
const HybridBayesTree::Eliminate& function =
HybridBayesTree::EliminationTraitsType::DefaultEliminate);
@ -59,8 +60,17 @@ class GTSAM_EXPORT HybridGaussianISAM : public ISAM<HybridBayesTree> {
* @param function Elimination function.
*/
void update(const HybridGaussianFactorGraph& newFactors,
const boost::optional<Ordering>& ordering = boost::none,
const HybridBayesTree::Eliminate& function =
HybridBayesTree::EliminationTraitsType::DefaultEliminate);
/**
* @brief
*
* @param root The root key in the discrete conditional decision tree.
* @param maxNumberLeaves
*/
void prune(const Key& root, const size_t maxNumberLeaves);
};
/// traits

703
gtsam/hybrid/tests/testHybridIncremental.cpp
View File

@ -54,7 +54,7 @@ TEST(HybridGaussianElimination, IncrementalElimination) {
// Create initial factor graph
// * * *
// | | |
// *- X1 -*- X2 -*- X3
// X1 -*- X2 -*- X3
// \*-M1-*/
graph1.push_back(switching.linearizedFactorGraph.at(0)); // P(X1)
graph1.push_back(switching.linearizedFactorGraph.at(1)); // P(X1, X2 | M1)
@ -179,7 +179,8 @@ TEST(HybridGaussianElimination, IncrementalInference) {
DiscreteValues m00;
m00[M(1)] = 0, m00[M(2)] = 0;
DiscreteConditional decisionTree = *(*discreteBayesTree)[M(2)]->conditional()->asDiscreteConditional();
DiscreteConditional decisionTree =
*(*discreteBayesTree)[M(2)]->conditional()->asDiscreteConditional();
double m00_prob = decisionTree(m00);
auto discreteConditional = isam[M(2)]->conditional()->asDiscreteConditional();
@ -200,8 +201,8 @@ TEST(HybridGaussianElimination, IncrementalInference) {
assignment[M(2)] = 1;
EXPECT(assert_equal(0.2, (*discreteConditional)(assignment), 1e-5));
// Check if the clique conditional generated from incremental elimination matches
// that of batch elimination.
// Check if the clique conditional generated from incremental elimination
// matches that of batch elimination.
auto expectedChordal = expectedRemainingGraph->eliminateMultifrontal();
auto expectedConditional = dynamic_pointer_cast<DecisionTreeFactor>(
(*expectedChordal)[M(2)]->conditional()->inner());
@ -213,419 +214,415 @@ TEST(HybridGaussianElimination, IncrementalInference) {
/* ****************************************************************************/
// Test if we can approximately do the inference
TEST(HybridGaussianElimination, Approx_inference) {
// Switching switching(4);
// IncrementalHybrid incrementalHybrid;
// HybridGaussianFactorGraph graph1;
Switching switching(4);
HybridGaussianISAM incrementalHybrid;
HybridGaussianFactorGraph graph1;
// // Add the 3 DC factors, x1-x2, x2-x3, x3-x4
// for (size_t i = 0; i < 3; i++) {
// graph1.push_back(switching.linearizedFactorGraph.dcGraph().at(i));
// }
// Add the 3 hybrid factors, x1-x2, x2-x3, x3-x4
for (size_t i = 1; i < 4; i++) {
graph1.push_back(switching.linearizedFactorGraph.at(i));
}
// // Add the Gaussian factors, 1 prior on X(1), 4 measurements
// for (size_t i = 0; i <= 4; i++) {
// graph1.push_back(switching.linearizedFactorGraph.gaussianGraph().at(i));
// }
// Add the Gaussian factors, 1 prior on X(1),
// 3 measurements on X(2), X(3), X(4)
graph1.push_back(switching.linearizedFactorGraph.at(0));
for (size_t i = 4; i <= 7; i++) {
graph1.push_back(switching.linearizedFactorGraph.at(i));
}
// // Create ordering.
// Ordering ordering;
// for (size_t j = 1; j <= 4; j++) {
// ordering += X(j);
// }
// Create ordering.
Ordering ordering;
for (size_t j = 1; j <= 4; j++) {
ordering += X(j);
}
// // Now we calculate the actual factors using full elimination
// HybridBayesNet::shared_ptr unprunedHybridBayesNet;
// HybridGaussianFactorGraph::shared_ptr unprunedRemainingGraph;
// std::tie(unprunedHybridBayesNet, unprunedRemainingGraph) =
// switching.linearizedFactorGraph.eliminatePartialSequential(ordering);
// Now we calculate the actual factors using full elimination
HybridBayesTree::shared_ptr unprunedHybridBayesTree;
HybridGaussianFactorGraph::shared_ptr unprunedRemainingGraph;
std::tie(unprunedHybridBayesTree, unprunedRemainingGraph) =
switching.linearizedFactorGraph.eliminatePartialMultifrontal(ordering);
// size_t maxComponents = 5;
// incrementalHybrid.update(graph1, ordering, maxComponents);
size_t maxNrLeaves = 5;
incrementalHybrid.update(graph1);
// /*
// unpruned factor is:
// Choice(m3)
// 0 Choice(m2)
// 0 0 Choice(m1)
// 0 0 0 Leaf 0.2248 -
// 0 0 1 Leaf 0.3715 -
// 0 1 Choice(m1)
// 0 1 0 Leaf 0.3742 *
// 0 1 1 Leaf 0.6125 *
// 1 Choice(m2)
// 1 0 Choice(m1)
// 1 0 0 Leaf 0.3706 -
// 1 0 1 Leaf 0.6124 *
// 1 1 Choice(m1)
// 1 1 0 Leaf 0.611 *
// 1 1 1 Leaf 1 *
incrementalHybrid.prune(M(3), maxNrLeaves);
// pruned factors is:
// Choice(m3)
// 0 Choice(m2)
// 0 0 Leaf 0
// 0 1 Choice(m1)
// 0 1 0 Leaf 0.3742
// 0 1 1 Leaf 0.6125
// 1 Choice(m2)
// 1 0 Choice(m1)
// 1 0 0 Leaf 0
// 1 0 1 Leaf 0.6124
// 1 1 Choice(m1)
// 1 1 0 Leaf 0.611
// 1 1 1 Leaf 1
// */
/*
unpruned factor is:
Choice(m3)
0 Choice(m2)
0 0 Choice(m1)
0 0 0 Leaf 0.11267528
0 0 1 Leaf 0.18576102
0 1 Choice(m1)
0 1 0 Leaf 0.18754662
0 1 1 Leaf 0.30623871
1 Choice(m2)
1 0 Choice(m1)
1 0 0 Leaf 0.18576102
1 0 1 Leaf 0.30622428
1 1 Choice(m1)
1 1 0 Leaf 0.30623871
1 1 1 Leaf 0.5
// // Test that the remaining factor graph has one
// // DecisionTreeFactor on {M3, M2, M1}.
// auto remainingFactorGraph = incrementalHybrid.remainingFactorGraph();
// EXPECT_LONGS_EQUAL(1, remainingFactorGraph.size());
pruned factors is:
Choice(m3)
0 Choice(m2)
0 0 Leaf 0
0 1 Choice(m1)
0 1 0 Leaf 0.18754662
0 1 1 Leaf 0.30623871
1 Choice(m2)
1 0 Choice(m1)
1 0 0 Leaf 0
1 0 1 Leaf 0.30622428
1 1 Choice(m1)
1 1 0 Leaf 0.30623871
1 1 1 Leaf 0.5
*/
// auto discreteFactor_m1 = *dynamic_pointer_cast<DecisionTreeFactor>(
// incrementalHybrid.remainingDiscreteGraph().at(0));
// EXPECT(discreteFactor_m1.keys() == KeyVector({M(3), M(2), M(1)}));
auto discreteConditional_m1 = *dynamic_pointer_cast<DiscreteConditional>(
incrementalHybrid[M(1)]->conditional()->inner());
EXPECT(discreteConditional_m1.keys() == KeyVector({M(1), M(2), M(3)}));
// // Get the number of elements which are equal to 0.
// auto count = [](const double &value, int count) {
// return value > 0 ? count + 1 : count;
// };
// // Check that the number of leaves after pruning is 5.
// EXPECT_LONGS_EQUAL(5, discreteFactor_m1.fold(count, 0));
// Get the number of elements which are greater than 0.
auto count = [](const double &value, int count) {
return value > 0 ? count + 1 : count;
};
// Check that the number of leaves after pruning is 5.
EXPECT_LONGS_EQUAL(5, discreteConditional_m1.fold(count, 0));
// /* Expected hybrid Bayes net
// * factor 0: [x1 | x2 m1 ], 2 components
// * factor 1: [x2 | x3 m2 m1 ], 4 components
// * factor 2: [x3 | x4 m3 m2 m1 ], 8 components
// * factor 3: [x4 | m3 m2 m1 ], 8 components
// */
// auto hybridBayesNet = incrementalHybrid.hybridBayesNet();
// Check that the hybrid nodes of the bayes net match those of the pre-pruning
// bayes net, at the same positions.
auto &unprunedLastDensity = *dynamic_pointer_cast<GaussianMixture>(
unprunedHybridBayesTree->clique(X(4))->conditional()->inner());
auto &lastDensity = *dynamic_pointer_cast<GaussianMixture>(
incrementalHybrid[X(4)]->conditional()->inner());
// // Check if we have a bayes net with 4 hybrid nodes,
// // each with 2, 4, 5 (pruned), and 5 (pruned) leaves respetively.
// EXPECT_LONGS_EQUAL(4, hybridBayesNet.size());
// EXPECT_LONGS_EQUAL(2, hybridBayesNet.atGaussian(0)->nrComponents());
// EXPECT_LONGS_EQUAL(4, hybridBayesNet.atGaussian(1)->nrComponents());
// EXPECT_LONGS_EQUAL(5, hybridBayesNet.atGaussian(2)->nrComponents());
// EXPECT_LONGS_EQUAL(5, hybridBayesNet.atGaussian(3)->nrComponents());
std::vector<std::pair<DiscreteValues, double>> assignments =
discreteConditional_m1.enumerate();
// Loop over all assignments and check the pruned components
for (auto &&av : assignments) {
const DiscreteValues &assignment = av.first;
const double value = av.second;
// // Check that the hybrid nodes of the bayes net match those of the bayes
// net
// // before pruning, at the same positions.
// auto &lastDensity = *(hybridBayesNet.atGaussian(3));
// auto &unprunedLastDensity = *(unprunedHybridBayesNet->atGaussian(3));
// std::vector<std::pair<DiscreteValues, double>> assignments =
// discreteFactor_m1.enumerate();
// // Loop over all assignments and check the pruned components
// for (auto &&av : assignments) {
// const DiscreteValues &assignment = av.first;
// const double value = av.second;
// if (value == 0.0) {
// EXPECT(lastDensity(assignment) == nullptr);
// } else {
// CHECK(lastDensity(assignment));
// EXPECT(assert_equal(*unprunedLastDensity(assignment),
// *lastDensity(assignment)));
// }
// }
if (value == 0.0) {
EXPECT(lastDensity(assignment) == nullptr);
} else {
CHECK(lastDensity(assignment));
EXPECT(assert_equal(*unprunedLastDensity(assignment),
*lastDensity(assignment)));
}
}
}
/* ****************************************************************************/
// Test approximate inference with an additional pruning step.
TEST(HybridGaussianElimination, Incremental_approximate) {
// Switching switching(5);
// IncrementalHybrid incrementalHybrid;
// HybridGaussianFactorGraph graph1;
Switching switching(5);
HybridGaussianISAM incrementalHybrid;
HybridGaussianFactorGraph graph1;
// // Add the 3 DC factors, x1-x2, x2-x3, x3-x4
// for (size_t i = 0; i < 3; i++) {
// graph1.push_back(switching.linearizedFactorGraph.dcGraph().at(i));
// }
/***** Run Round 1 *****/
// Add the 3 hybrid factors, x1-x2, x2-x3, x3-x4
for (size_t i = 1; i < 4; i++) {
graph1.push_back(switching.linearizedFactorGraph.at(i));
}
// // Add the Gaussian factors, 1 prior on X(1), 4 measurements
// for (size_t i = 0; i <= 4; i++) {
// graph1.push_back(switching.linearizedFactorGraph.gaussianGraph().at(i));
// }
// Add the Gaussian factors, 1 prior on X(1),
// 3 measurements on X(2), X(3), X(4)
graph1.push_back(switching.linearizedFactorGraph.at(0));
for (size_t i = 5; i <= 7; i++) {
graph1.push_back(switching.linearizedFactorGraph.at(i));
}
// // Create ordering.
// Ordering ordering;
// for (size_t j = 1; j <= 4; j++) {
// ordering += X(j);
// }
// Create ordering.
Ordering ordering;
for (size_t j = 1; j <= 4; j++) {
ordering += X(j);
}
// // Run update with pruning
// size_t maxComponents = 5;
// incrementalHybrid.update(graph1, ordering, maxComponents);
// Run update with pruning
size_t maxComponents = 5;
incrementalHybrid.update(graph1);
incrementalHybrid.prune(M(3), maxComponents);
// // Check if we have a bayes net with 4 hybrid nodes,
// // each with 2, 4, 8, and 5 (pruned) leaves respetively.
// auto actualBayesNet1 = incrementalHybrid.hybridBayesNet();
// CHECK_EQUAL(4, actualBayesNet1.size());
// EXPECT_LONGS_EQUAL(2, actualBayesNet1.atGaussian(0)->nrComponents());
// EXPECT_LONGS_EQUAL(4, actualBayesNet1.atGaussian(1)->nrComponents());
// EXPECT_LONGS_EQUAL(8, actualBayesNet1.atGaussian(2)->nrComponents());
// EXPECT_LONGS_EQUAL(5, actualBayesNet1.atGaussian(3)->nrComponents());
// Check if we have a bayes tree with 4 hybrid nodes,
// each with 2, 4, 8, and 5 (pruned) leaves respetively.
EXPECT_LONGS_EQUAL(4, incrementalHybrid.size());
EXPECT_LONGS_EQUAL(
2, incrementalHybrid[X(1)]->conditional()->asMixture()->nrComponents());
EXPECT_LONGS_EQUAL(
4, incrementalHybrid[X(2)]->conditional()->asMixture()->nrComponents());
EXPECT_LONGS_EQUAL(
5, incrementalHybrid[X(3)]->conditional()->asMixture()->nrComponents());
EXPECT_LONGS_EQUAL(
5, incrementalHybrid[X(4)]->conditional()->asMixture()->nrComponents());
// /***** Run Round 2 *****/
// HybridGaussianFactorGraph graph2;
// graph2.push_back(switching.linearizedFactorGraph.dcGraph().at(3));
// graph2.push_back(switching.linearizedFactorGraph.gaussianGraph().at(5));
/***** Run Round 2 *****/
HybridGaussianFactorGraph graph2;
graph2.push_back(switching.linearizedFactorGraph.at(4));
graph2.push_back(switching.linearizedFactorGraph.at(8));
// Ordering ordering2;
// ordering2 += X(4);
// ordering2 += X(5);
Ordering ordering2;
ordering2 += X(4);
ordering2 += X(5);
// // Run update with pruning a second time.
// incrementalHybrid.update(graph2, ordering2, maxComponents);
// Run update with pruning a second time.
incrementalHybrid.update(graph2);
incrementalHybrid.prune(M(4), maxComponents);
// // Check if we have a bayes net with 2 hybrid nodes,
// // each with 10 (pruned), and 5 (pruned) leaves respetively.
// auto actualBayesNet = incrementalHybrid.hybridBayesNet();
// CHECK_EQUAL(2, actualBayesNet.size());
// EXPECT_LONGS_EQUAL(10, actualBayesNet.atGaussian(0)->nrComponents());
// EXPECT_LONGS_EQUAL(5, actualBayesNet.atGaussian(1)->nrComponents());
// Check if we have a bayes tree with pruned hybrid nodes,
// with 5 (pruned) leaves.
CHECK_EQUAL(5, incrementalHybrid.size());
EXPECT_LONGS_EQUAL(
5, incrementalHybrid[X(4)]->conditional()->asMixture()->nrComponents());
EXPECT_LONGS_EQUAL(
5, incrementalHybrid[X(5)]->conditional()->asMixture()->nrComponents());
}
/* ************************************************************************/
// Test for figuring out the optimal ordering to ensure we get
// a discrete graph after elimination.
TEST(IncrementalHybrid, NonTrivial) {
// // This is a GTSAM-only test for running inference on a single legged
// robot.
// // The leg links are represented by the chain X-Y-Z-W, where X is the base
// and
// // W is the foot.
// // We use BetweenFactor<Pose2> as constraints between each of the poses.
TEST(HybridGaussianISAM, NonTrivial) {
// This is a GTSAM-only test for running inference on a single legged robot.
// The leg links are represented by the chain X-Y-Z-W, where X is the base and
// W is the foot.
// We use BetweenFactor<Pose2> as constraints between each of the poses.
// /*************** Run Round 1 ***************/
// NonlinearHybridFactorGraph fg;
/*************** Run Round 1 ***************/
HybridNonlinearFactorGraph fg;
// // Add a prior on pose x1 at the origin.
// // A prior factor consists of a mean and
// // a noise model (covariance matrix)
// Pose2 prior(0.0, 0.0, 0.0); // prior mean is at origin
// auto priorNoise = noiseModel::Diagonal::Sigmas(
// Vector3(0.3, 0.3, 0.1)); // 30cm std on x,y, 0.1 rad on theta
// fg.emplace_nonlinear<PriorFactor<Pose2>>(X(0), prior, priorNoise);
// // Add a prior on pose x1 at the origin.
// // A prior factor consists of a mean and
// // a noise model (covariance matrix)
// Pose2 prior(0.0, 0.0, 0.0); // prior mean is at origin
// auto priorNoise = noiseModel::Diagonal::Sigmas(
// Vector3(0.3, 0.3, 0.1)); // 30cm std on x,y, 0.1 rad on theta
// fg.emplace_nonlinear<PriorFactor<Pose2>>(X(0), prior, priorNoise);
// // create a noise model for the landmark measurements
// auto poseNoise = noiseModel::Isotropic::Sigma(3, 0.1);
// // create a noise model for the landmark measurements
// auto poseNoise = noiseModel::Isotropic::Sigma(3, 0.1);
// // We model a robot's single leg as X - Y - Z - W
// // where X is the base link and W is the foot link.
// // We model a robot's single leg as X - Y - Z - W
// // where X is the base link and W is the foot link.
// // Add connecting poses similar to PoseFactors in GTD
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(X(0), Y(0), Pose2(0, 1.0, 0),
// poseNoise);
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(Y(0), Z(0), Pose2(0, 1.0, 0),
// poseNoise);
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(Z(0), W(0), Pose2(0, 1.0, 0),
// poseNoise);
// // Add connecting poses similar to PoseFactors in GTD
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(X(0), Y(0), Pose2(0, 1.0, 0),
// poseNoise);
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(Y(0), Z(0), Pose2(0, 1.0, 0),
// poseNoise);
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(Z(0), W(0), Pose2(0, 1.0, 0),
// poseNoise);
// // Create initial estimate
// Values initial;
// initial.insert(X(0), Pose2(0.0, 0.0, 0.0));
// initial.insert(Y(0), Pose2(0.0, 1.0, 0.0));
// initial.insert(Z(0), Pose2(0.0, 2.0, 0.0));
// initial.insert(W(0), Pose2(0.0, 3.0, 0.0));
// // Create initial estimate
// Values initial;
// initial.insert(X(0), Pose2(0.0, 0.0, 0.0));
// initial.insert(Y(0), Pose2(0.0, 1.0, 0.0));
// initial.insert(Z(0), Pose2(0.0, 2.0, 0.0));
// initial.insert(W(0), Pose2(0.0, 3.0, 0.0));
// HybridGaussianFactorGraph gfg = fg.linearize(initial);
// fg = NonlinearHybridFactorGraph();
// HybridGaussianFactorGraph gfg = fg.linearize(initial);
// fg = HybridNonlinearFactorGraph();
// IncrementalHybrid inc;
// HybridGaussianISAM inc;
// // Regular ordering going up the chain.
// Ordering ordering;
// ordering += W(0);
// ordering += Z(0);
// ordering += Y(0);
// ordering += X(0);
// // Regular ordering going up the chain.
// Ordering ordering;
// ordering += W(0);
// ordering += Z(0);
// ordering += Y(0);
// ordering += X(0);
// // Update without pruning
// // The result is a HybridBayesNet with no discrete variables
// // (equivalent to a GaussianBayesNet).
// // Factorization is:
// // `P(X | measurements) = P(W0|Z0) P(Z0|Y0) P(Y0|X0) P(X0)`
// inc.update(gfg, ordering);
// // Update without pruning
// // The result is a HybridBayesNet with no discrete variables
// // (equivalent to a GaussianBayesNet).
// // Factorization is:
// // `P(X | measurements) = P(W0|Z0) P(Z0|Y0) P(Y0|X0) P(X0)`
// inc.update(gfg, ordering);
// /*************** Run Round 2 ***************/
// using PlanarMotionModel = BetweenFactor<Pose2>;
// /*************** Run Round 2 ***************/
// using PlanarMotionModel = BetweenFactor<Pose2>;
// // Add odometry factor with discrete modes.
// Pose2 odometry(1.0, 0.0, 0.0);
// KeyVector contKeys = {W(0), W(1)};
// auto noise_model = noiseModel::Isotropic::Sigma(3, 1.0);
// auto still = boost::make_shared<PlanarMotionModel>(W(0), W(1), Pose2(0, 0,
// 0),
// noise_model),
// moving = boost::make_shared<PlanarMotionModel>(W(0), W(1), odometry,
// noise_model);
// std::vector<PlanarMotionModel::shared_ptr> components = {moving, still};
// auto dcFactor = boost::make_shared<DCMixtureFactor<PlanarMotionModel>>(
// contKeys, DiscreteKeys{gtsam::DiscreteKey(M(1), 2)}, components);
// fg.push_back(dcFactor);
// // Add odometry factor with discrete modes.
// Pose2 odometry(1.0, 0.0, 0.0);
// KeyVector contKeys = {W(0), W(1)};
// auto noise_model = noiseModel::Isotropic::Sigma(3, 1.0);
// auto still = boost::make_shared<PlanarMotionModel>(W(0), W(1), Pose2(0,
// 0, 0),
// noise_model),
// moving = boost::make_shared<PlanarMotionModel>(W(0), W(1), odometry,
// noise_model);
// std::vector<PlanarMotionModel::shared_ptr> components = {moving, still};
// auto dcFactor = boost::make_shared<DCMixtureFactor<PlanarMotionModel>>(
// contKeys, DiscreteKeys{gtsam::DiscreteKey(M(1), 2)}, components);
// fg.push_back(dcFactor);
// // Add equivalent of ImuFactor
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(X(0), X(1), Pose2(1.0, 0.0, 0),
// poseNoise);
// // PoseFactors-like at k=1
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(X(1), Y(1), Pose2(0, 1, 0),
// poseNoise);
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(Y(1), Z(1), Pose2(0, 1, 0),
// poseNoise);
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(Z(1), W(1), Pose2(-1, 1, 0),
// poseNoise);
// // Add equivalent of ImuFactor
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(X(0), X(1), Pose2(1.0, 0.0,
// 0),
// poseNoise);
// // PoseFactors-like at k=1
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(X(1), Y(1), Pose2(0, 1, 0),
// poseNoise);
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(Y(1), Z(1), Pose2(0, 1, 0),
// poseNoise);
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(Z(1), W(1), Pose2(-1, 1, 0),
// poseNoise);
// initial.insert(X(1), Pose2(1.0, 0.0, 0.0));
// initial.insert(Y(1), Pose2(1.0, 1.0, 0.0));
// initial.insert(Z(1), Pose2(1.0, 2.0, 0.0));
// // The leg link did not move so we set the expected pose accordingly.
// initial.insert(W(1), Pose2(0.0, 3.0, 0.0));
// initial.insert(X(1), Pose2(1.0, 0.0, 0.0));
// initial.insert(Y(1), Pose2(1.0, 1.0, 0.0));
// initial.insert(Z(1), Pose2(1.0, 2.0, 0.0));
// // The leg link did not move so we set the expected pose accordingly.
// initial.insert(W(1), Pose2(0.0, 3.0, 0.0));
// // Ordering for k=1.
// // This ordering follows the intuition that we eliminate the previous
// // timestep, and then the current timestep.
// ordering = Ordering();
// ordering += W(0);
// ordering += Z(0);
// ordering += Y(0);
// ordering += X(0);
// ordering += W(1);
// ordering += Z(1);
// ordering += Y(1);
// ordering += X(1);
// // Ordering for k=1.
// // This ordering follows the intuition that we eliminate the previous
// // timestep, and then the current timestep.
// ordering = Ordering();
// ordering += W(0);
// ordering += Z(0);
// ordering += Y(0);
// ordering += X(0);
// ordering += W(1);
// ordering += Z(1);
// ordering += Y(1);
// ordering += X(1);
// gfg = fg.linearize(initial);
// fg = NonlinearHybridFactorGraph();
// gfg = fg.linearize(initial);
// fg = HybridNonlinearFactorGraph();
// // Update without pruning
// // The result is a HybridBayesNet with 1 discrete variable M(1).
// // P(X | measurements) = P(W0|Z0, W1, M1) P(Z0|Y0, W1, M1) P(Y0|X0, W1, M1)
// // P(X0 | X1, W1, M1) P(W1|Z1, X1, M1) P(Z1|Y1, X1,
// M1)
// // P(Y1 | X1, M1)P(X1 | M1)P(M1)
// // The MHS tree is a 2 level tree for time indices (0, 1) with 2 leaves.
// inc.update(gfg, ordering);
// // Update without pruning
// // The result is a HybridBayesNet with 1 discrete variable M(1).
// // P(X | measurements) = P(W0|Z0, W1, M1) P(Z0|Y0, W1, M1) P(Y0|X0, W1,
// M1)
// // P(X0 | X1, W1, M1) P(W1|Z1, X1, M1) P(Z1|Y1, X1,
// M1)
// // P(Y1 | X1, M1)P(X1 | M1)P(M1)
// // The MHS tree is a 2 level tree for time indices (0, 1) with 2 leaves.
// inc.update(gfg, ordering);
// /*************** Run Round 3 ***************/
// // Add odometry factor with discrete modes.
// contKeys = {W(1), W(2)};
// still = boost::make_shared<PlanarMotionModel>(W(1), W(2), Pose2(0, 0, 0),
// noise_model);
// moving =
// boost::make_shared<PlanarMotionModel>(W(1), W(2), odometry,
// noise_model);
// components = {moving, still};
// dcFactor = boost::make_shared<DCMixtureFactor<PlanarMotionModel>>(
// contKeys, DiscreteKeys{gtsam::DiscreteKey(M(2), 2)}, components);
// fg.push_back(dcFactor);
// /*************** Run Round 3 ***************/
// // Add odometry factor with discrete modes.
// contKeys = {W(1), W(2)};
// still = boost::make_shared<PlanarMotionModel>(W(1), W(2), Pose2(0, 0, 0),
// noise_model);
// moving =
// boost::make_shared<PlanarMotionModel>(W(1), W(2), odometry,
// noise_model);
// components = {moving, still};
// dcFactor = boost::make_shared<DCMixtureFactor<PlanarMotionModel>>(
// contKeys, DiscreteKeys{gtsam::DiscreteKey(M(2), 2)}, components);
// fg.push_back(dcFactor);
// // Add equivalent of ImuFactor
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(X(1), X(2), Pose2(1.0, 0.0, 0),
// poseNoise);
// // PoseFactors-like at k=1
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(X(2), Y(2), Pose2(0, 1, 0),
// poseNoise);
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(Y(2), Z(2), Pose2(0, 1, 0),
// poseNoise);
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(Z(2), W(2), Pose2(-2, 1, 0),
// poseNoise);
// // Add equivalent of ImuFactor
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(X(1), X(2), Pose2(1.0, 0.0,
// 0),
// poseNoise);
// // PoseFactors-like at k=1
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(X(2), Y(2), Pose2(0, 1, 0),
// poseNoise);
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(Y(2), Z(2), Pose2(0, 1, 0),
// poseNoise);
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(Z(2), W(2), Pose2(-2, 1, 0),
// poseNoise);
// initial.insert(X(2), Pose2(2.0, 0.0, 0.0));
// initial.insert(Y(2), Pose2(2.0, 1.0, 0.0));
// initial.insert(Z(2), Pose2(2.0, 2.0, 0.0));
// initial.insert(W(2), Pose2(0.0, 3.0, 0.0));
// initial.insert(X(2), Pose2(2.0, 0.0, 0.0));
// initial.insert(Y(2), Pose2(2.0, 1.0, 0.0));
// initial.insert(Z(2), Pose2(2.0, 2.0, 0.0));
// initial.insert(W(2), Pose2(0.0, 3.0, 0.0));
// // Ordering at k=2. Same intuition as before.
// ordering = Ordering();
// ordering += W(1);
// ordering += Z(1);
// ordering += Y(1);
// ordering += X(1);
// ordering += W(2);
// ordering += Z(2);
// ordering += Y(2);
// ordering += X(2);
// // Ordering at k=2. Same intuition as before.
// ordering = Ordering();
// ordering += W(1);
// ordering += Z(1);
// ordering += Y(1);
// ordering += X(1);
// ordering += W(2);
// ordering += Z(2);
// ordering += Y(2);
// ordering += X(2);
// gfg = fg.linearize(initial);
// fg = NonlinearHybridFactorGraph();
// gfg = fg.linearize(initial);
// fg = HybridNonlinearFactorGraph();
// // Now we prune!
// // P(X | measurements) = P(W0|Z0, W1, M1) P(Z0|Y0, W1, M1) P(Y0|X0, W1, M1)
// P(X0 | X1, W1, M1)
// // P(W1|W2, Z1, X1, M1, M2) P(Z1| W2, Y1, X1, M1, M2)
// P(Y1 | W2, X1, M1, M2)
// // P(X1 | W2, X2, M1, M2) P(W2|Z2, X2, M1, M2)
// P(Z2|Y2, X2, M1, M2)
// // P(Y2 | X2, M1, M2)P(X2 | M1, M2) P(M1, M2)
// // The MHS at this point should be a 3 level tree on (0, 1, 2).
// // 0 has 2 choices, 1 has 4 choices and 2 has 4 choices.
// inc.update(gfg, ordering, 2);
// // Now we prune!
// // P(X | measurements) = P(W0|Z0, W1, M1) P(Z0|Y0, W1, M1) P(Y0|X0, W1,
// M1) P(X0 | X1, W1, M1)
// // P(W1|W2, Z1, X1, M1, M2) P(Z1| W2, Y1, X1, M1,
// M2) P(Y1 | W2, X1, M1, M2)
// // P(X1 | W2, X2, M1, M2) P(W2|Z2, X2, M1, M2)
// P(Z2|Y2, X2, M1, M2)
// // P(Y2 | X2, M1, M2)P(X2 | M1, M2) P(M1, M2)
// // The MHS at this point should be a 3 level tree on (0, 1, 2).
// // 0 has 2 choices, 1 has 4 choices and 2 has 4 choices.
// inc.update(gfg, ordering, 2);
// /*************** Run Round 4 ***************/
// // Add odometry factor with discrete modes.
// contKeys = {W(2), W(3)};
// still = boost::make_shared<PlanarMotionModel>(W(2), W(3), Pose2(0, 0, 0),
// noise_model);
// moving =
// boost::make_shared<PlanarMotionModel>(W(2), W(3), odometry,
// noise_model);
// components = {moving, still};
// dcFactor = boost::make_shared<DCMixtureFactor<PlanarMotionModel>>(
// contKeys, DiscreteKeys{gtsam::DiscreteKey(M(3), 2)}, components);
// fg.push_back(dcFactor);
// /*************** Run Round 4 ***************/
// // Add odometry factor with discrete modes.
// contKeys = {W(2), W(3)};
// still = boost::make_shared<PlanarMotionModel>(W(2), W(3), Pose2(0, 0, 0),
// noise_model);
// moving =
// boost::make_shared<PlanarMotionModel>(W(2), W(3), odometry,
// noise_model);
// components = {moving, still};
// dcFactor = boost::make_shared<DCMixtureFactor<PlanarMotionModel>>(
// contKeys, DiscreteKeys{gtsam::DiscreteKey(M(3), 2)}, components);
// fg.push_back(dcFactor);
// // Add equivalent of ImuFactor
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(X(2), X(3), Pose2(1.0, 0.0, 0),
// poseNoise);
// // PoseFactors-like at k=3
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(X(3), Y(3), Pose2(0, 1, 0),
// poseNoise);
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(Y(3), Z(3), Pose2(0, 1, 0),
// poseNoise);
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(Z(3), W(3), Pose2(-3, 1, 0),
// poseNoise);
// // Add equivalent of ImuFactor
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(X(2), X(3), Pose2(1.0, 0.0,
// 0),
// poseNoise);
// // PoseFactors-like at k=3
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(X(3), Y(3), Pose2(0, 1, 0),
// poseNoise);
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(Y(3), Z(3), Pose2(0, 1, 0),
// poseNoise);
// fg.emplace_nonlinear<BetweenFactor<Pose2>>(Z(3), W(3), Pose2(-3, 1, 0),
// poseNoise);
// initial.insert(X(3), Pose2(3.0, 0.0, 0.0));
// initial.insert(Y(3), Pose2(3.0, 1.0, 0.0));
// initial.insert(Z(3), Pose2(3.0, 2.0, 0.0));
// initial.insert(W(3), Pose2(0.0, 3.0, 0.0));
// initial.insert(X(3), Pose2(3.0, 0.0, 0.0));
// initial.insert(Y(3), Pose2(3.0, 1.0, 0.0));
// initial.insert(Z(3), Pose2(3.0, 2.0, 0.0));
// initial.insert(W(3), Pose2(0.0, 3.0, 0.0));
// // Ordering at k=3. Same intuition as before.
// ordering = Ordering();
// ordering += W(2);
// ordering += Z(2);
// ordering += Y(2);
// ordering += X(2);
// ordering += W(3);
// ordering += Z(3);
// ordering += Y(3);
// ordering += X(3);
// // Ordering at k=3. Same intuition as before.
// ordering = Ordering();
// ordering += W(2);
// ordering += Z(2);
// ordering += Y(2);
// ordering += X(2);
// ordering += W(3);
// ordering += Z(3);
// ordering += Y(3);
// ordering += X(3);
// gfg = fg.linearize(initial);
// fg = NonlinearHybridFactorGraph();
// gfg = fg.linearize(initial);
// fg = HybridNonlinearFactorGraph();
// // Keep pruning!
// inc.update(gfg, ordering, 3);
// // Keep pruning!
// inc.update(gfg, ordering, 3);
// // The final discrete graph should not be empty since we have eliminated
// // all continuous variables.
// EXPECT(!inc.remainingDiscreteGraph().empty());
// // The final discrete graph should not be empty since we have eliminated
// // all continuous variables.
// EXPECT(!inc.remainingDiscreteGraph().empty());
// // Test if the optimal discrete mode assignment is (1, 1, 1).
// DiscreteValues optimal_assignment =
// inc.remainingDiscreteGraph().optimize(); DiscreteValues
// expected_assignment; expected_assignment[M(1)] = 1;
// expected_assignment[M(2)] = 1;
// expected_assignment[M(3)] = 1;
// EXPECT(assert_equal(expected_assignment, optimal_assignment));
// // Test if the optimal discrete mode assignment is (1, 1, 1).
// DiscreteValues optimal_assignment =
// inc.remainingDiscreteGraph().optimize(); DiscreteValues
// expected_assignment; expected_assignment[M(1)] = 1;
// expected_assignment[M(2)] = 1;
// expected_assignment[M(3)] = 1;
// EXPECT(assert_equal(expected_assignment, optimal_assignment));
// // Test if pruning worked correctly by checking that we only have 3 leaves
// in
// // the last node.
// auto lastConditional = boost::dynamic_pointer_cast<GaussianMixture>(
// inc.hybridBayesNet().at(inc.hybridBayesNet().size() - 1));
// EXPECT_LONGS_EQUAL(3, lastConditional->nrComponents());
// // Test if pruning worked correctly by checking that we only have 3
// leaves in
// // the last node.
// auto lastConditional = boost::dynamic_pointer_cast<GaussianMixture>(
// inc.hybridBayesNet().at(inc.hybridBayesNet().size() - 1));
// EXPECT_LONGS_EQUAL(3, lastConditional->nrComponents());
}
/* ************************************************************************* */