From 1b5daf9a0e9fe8afd526ffd2d843acd4585658f0 Mon Sep 17 00:00:00 2001
From: Varun Agrawal <varagrawal@gmail.com>
Date: Fri, 26 Aug 2022 19:34:54 -0400
Subject: [PATCH 01/11] remove HybridLookupDAG
---
gtsam/hybrid/HybridLookupDAG.cpp | 76 ------
gtsam/hybrid/HybridLookupDAG.h | 119 ---------
gtsam/hybrid/tests/testHybridLookupDAG.cpp | 272 ---------------------
3 files changed, 467 deletions(-)
delete mode 100644 gtsam/hybrid/HybridLookupDAG.cpp
delete mode 100644 gtsam/hybrid/HybridLookupDAG.h
delete mode 100644 gtsam/hybrid/tests/testHybridLookupDAG.cpp
diff --git a/gtsam/hybrid/HybridLookupDAG.cpp b/gtsam/hybrid/HybridLookupDAG.cpp
deleted file mode 100644
index a322a81776..0000000000
--- a/gtsam/hybrid/HybridLookupDAG.cpp
+++ /dev/null
@@ -1,76 +0,0 @@
-/* ----------------------------------------------------------------------------
-
- * GTSAM Copyright 2010, Georgia Tech Research Corporation,
- * Atlanta, Georgia 30332-0415
- * All Rights Reserved
- * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
-
- * See LICENSE for the license information
-
- * -------------------------------------------------------------------------- */
-
-/**
- * @file DiscreteLookupDAG.cpp
- * @date Aug, 2022
- * @author Shangjie Xue
- */
-
-#include <gtsam/discrete/DiscreteBayesNet.h>
-#include <gtsam/discrete/DiscreteLookupDAG.h>
-#include <gtsam/discrete/DiscreteValues.h>
-#include <gtsam/hybrid/HybridBayesNet.h>
-#include <gtsam/hybrid/HybridConditional.h>
-#include <gtsam/hybrid/HybridLookupDAG.h>
-#include <gtsam/hybrid/HybridValues.h>
-#include <gtsam/linear/VectorValues.h>
-
-#include <string>
-#include <utility>
-
-using std::pair;
-using std::vector;
-
-namespace gtsam {
-
-/* ************************************************************************** */
-void HybridLookupTable::argmaxInPlace(HybridValues* values) const {
- // For discrete conditional, uses argmaxInPlace() method in
- // DiscreteLookupTable.
- if (isDiscrete()) {
- boost::static_pointer_cast<DiscreteLookupTable>(inner_)->argmaxInPlace(
- &(values->discrete));
- } else if (isContinuous()) {
- // For Gaussian conditional, uses solve() method in GaussianConditional.
- values->continuous.insert(
- boost::static_pointer_cast<GaussianConditional>(inner_)->solve(
- values->continuous));
- } else if (isHybrid()) {
- // For hybrid conditional, since children should not contain discrete
- // variable, we can condition on the discrete variable in the parents and
- // solve the resulting GaussianConditional.
- auto conditional =
- boost::static_pointer_cast<GaussianMixture>(inner_)->conditionals()(
- values->discrete);
- values->continuous.insert(conditional->solve(values->continuous));
- }
-}
-
-/* ************************************************************************** */
-HybridLookupDAG HybridLookupDAG::FromBayesNet(const HybridBayesNet& bayesNet) {
- HybridLookupDAG dag;
- for (auto&& conditional : bayesNet) {
- HybridLookupTable hlt(*conditional);
- dag.push_back(hlt);
- }
- return dag;
-}
-
-/* ************************************************************************** */
-HybridValues HybridLookupDAG::argmax(HybridValues result) const {
- // Argmax each node in turn in topological sort order (parents first).
- for (auto lookupTable : boost::adaptors::reverse(*this))
- lookupTable->argmaxInPlace(&result);
- return result;
-}
-
-} // namespace gtsam
diff --git a/gtsam/hybrid/HybridLookupDAG.h b/gtsam/hybrid/HybridLookupDAG.h
deleted file mode 100644
index cc1c58c58f..0000000000
--- a/gtsam/hybrid/HybridLookupDAG.h
+++ /dev/null
@@ -1,119 +0,0 @@
-/* ----------------------------------------------------------------------------
-
- * GTSAM Copyright 2010, Georgia Tech Research Corporation,
- * Atlanta, Georgia 30332-0415
- * All Rights Reserved
- * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
-
- * See LICENSE for the license information
-
- * -------------------------------------------------------------------------- */
-
-/**
- * @file HybridLookupDAG.h
- * @date Aug, 2022
- * @author Shangjie Xue
- */
-
-#pragma once
-
-#include <gtsam/discrete/DiscreteDistribution.h>
-#include <gtsam/discrete/DiscreteLookupDAG.h>
-#include <gtsam/hybrid/HybridConditional.h>
-#include <gtsam/hybrid/HybridValues.h>
-#include <gtsam/inference/BayesNet.h>
-#include <gtsam/inference/FactorGraph.h>
-
-#include <boost/shared_ptr.hpp>
-#include <string>
-#include <utility>
-#include <vector>
-
-namespace gtsam {
-
-/**
- * @brief HybridLookupTable table for max-product
- *
- * Similar to DiscreteLookupTable, inherits from hybrid conditional for
- * convenience. Is used in the max-product algorithm.
- */
-class GTSAM_EXPORT HybridLookupTable : public HybridConditional {
- public:
- using Base = HybridConditional;
- using This = HybridLookupTable;
- using shared_ptr = boost::shared_ptr<This>;
- using BaseConditional = Conditional<DecisionTreeFactor, This>;
-
- /**
- * @brief Construct a new Hybrid Lookup Table object form a HybridConditional.
- *
- * @param conditional input hybrid conditional
- */
- HybridLookupTable(HybridConditional& conditional) : Base(conditional){};
-
- /**
- * @brief Calculate assignment for frontal variables that maximizes value.
- * @param (in/out) parentsValues Known assignments for the parents.
- */
- void argmaxInPlace(HybridValues* parentsValues) const;
-};
-
-/** A DAG made from hybrid lookup tables, as defined above. Similar to
- * DiscreteLookupDAG */
-class GTSAM_EXPORT HybridLookupDAG : public BayesNet<HybridLookupTable> {
- public:
- using Base = BayesNet<HybridLookupTable>;
- using This = HybridLookupDAG;
- using shared_ptr = boost::shared_ptr<This>;
-
- /// @name Standard Constructors
- /// @{
-
- /// Construct empty DAG.
- HybridLookupDAG() {}
-
- /// Create from BayesNet with LookupTables
- static HybridLookupDAG FromBayesNet(const HybridBayesNet& bayesNet);
-
- /// Destructor
- virtual ~HybridLookupDAG() {}
-
- /// @}
-
- /// @name Standard Interface
- /// @{
-
- /** Add a DiscreteLookupTable */
- template <typename... Args>
- void add(Args&&... args) {
- emplace_shared<HybridLookupTable>(std::forward<Args>(args)...);
- }
-
- /**
- * @brief argmax by back-substitution, optionally given certain variables.
- *
- * Assumes the DAG is reverse topologically sorted, i.e. last
- * conditional will be optimized first *and* that the
- * DAG does not contain any conditionals for the given variables. If the DAG
- * resulted from eliminating a factor graph, this is true for the elimination
- * ordering.
- *
- * @return given assignment extended w. optimal assignment for all variables.
- */
- HybridValues argmax(HybridValues given = HybridValues()) const;
- /// @}
-
- private:
- /** Serialization function */
- friend class boost::serialization::access;
- template <class ARCHIVE>
- void serialize(ARCHIVE& ar, const unsigned int /*version*/) {
- ar& BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
- }
-};
-
-// traits
-template <>
-struct traits<HybridLookupDAG> : public Testable<HybridLookupDAG> {};
-
-} // namespace gtsam
diff --git a/gtsam/hybrid/tests/testHybridLookupDAG.cpp b/gtsam/hybrid/tests/testHybridLookupDAG.cpp
deleted file mode 100644
index 0ab012d104..0000000000
--- a/gtsam/hybrid/tests/testHybridLookupDAG.cpp
+++ /dev/null
@@ -1,272 +0,0 @@
-/* ----------------------------------------------------------------------------
-
- * GTSAM Copyright 2010, Georgia Tech Research Corporation,
- * Atlanta, Georgia 30332-0415
- * All Rights Reserved
- * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
-
- * See LICENSE for the license information
-
- * -------------------------------------------------------------------------- */
-
-/**
- * @file testHybridLookupDAG.cpp
- * @date Aug, 2022
- * @author Shangjie Xue
- */
-
-#include <gtsam/base/Testable.h>
-#include <gtsam/base/TestableAssertions.h>
-#include <gtsam/discrete/Assignment.h>
-#include <gtsam/discrete/DecisionTreeFactor.h>
-#include <gtsam/discrete/DiscreteKey.h>
-#include <gtsam/discrete/DiscreteValues.h>
-#include <gtsam/hybrid/GaussianMixture.h>
-#include <gtsam/hybrid/HybridBayesNet.h>
-#include <gtsam/hybrid/HybridLookupDAG.h>
-#include <gtsam/hybrid/HybridValues.h>
-#include <gtsam/inference/Key.h>
-#include <gtsam/inference/Symbol.h>
-#include <gtsam/linear/GaussianConditional.h>
-#include <gtsam/linear/VectorValues.h>
-#include <gtsam/nonlinear/Values.h>
-
-// Include for test suite
-#include <CppUnitLite/TestHarness.h>
-
-#include <iostream>
-
-using namespace std;
-using namespace gtsam;
-using noiseModel::Isotropic;
-using symbol_shorthand::M;
-using symbol_shorthand::X;
-
-TEST(HybridLookupTable, basics) {
- // create a conditional gaussian node
- Matrix S1(2, 2);
- S1(0, 0) = 1;
- S1(1, 0) = 2;
- S1(0, 1) = 3;
- S1(1, 1) = 4;
-
- Matrix S2(2, 2);
- S2(0, 0) = 6;
- S2(1, 0) = 0.2;
- S2(0, 1) = 8;
- S2(1, 1) = 0.4;
-
- Matrix R1(2, 2);
- R1(0, 0) = 0.1;
- R1(1, 0) = 0.3;
- R1(0, 1) = 0.0;
- R1(1, 1) = 0.34;
-
- Matrix R2(2, 2);
- R2(0, 0) = 0.1;
- R2(1, 0) = 0.3;
- R2(0, 1) = 0.0;
- R2(1, 1) = 0.34;
-
- SharedDiagonal model = noiseModel::Diagonal::Sigmas(Vector2(1.0, 0.34));
-
- Vector2 d1(0.2, 0.5), d2(0.5, 0.2);
-
- auto conditional0 = boost::make_shared<GaussianConditional>(X(1), d1, R1,
- X(2), S1, model),
- conditional1 = boost::make_shared<GaussianConditional>(X(1), d2, R2,
- X(2), S2, model);
-
- // Create decision tree
- DiscreteKey m1(1, 2);
- GaussianMixture::Conditionals conditionals(
- {m1},
- vector<GaussianConditional::shared_ptr>{conditional0, conditional1});
- // GaussianMixture mixtureFactor2({X(1)}, {X(2)}, {m1}, conditionals);
-
- boost::shared_ptr<GaussianMixture> mixtureFactor(
- new GaussianMixture({X(1)}, {X(2)}, {m1}, conditionals));
-
- HybridConditional hc(mixtureFactor);
-
- GaussianMixture::Conditionals conditional2 =
- boost::static_pointer_cast<GaussianMixture>(hc.inner())->conditionals();
-
- DiscreteValues dv;
- dv[1] = 1;
-
- VectorValues cv;
- cv.insert(X(2), Vector2(0.0, 0.0));
-
- HybridValues hv(dv, cv);
-
- // std::cout << conditional2(values).markdown();
- EXPECT(assert_equal(*conditional2(dv), *conditionals(dv), 1e-6));
- EXPECT(conditional2(dv) == conditionals(dv));
- HybridLookupTable hlt(hc);
-
- // hlt.argmaxInPlace(&hv);
-
- HybridLookupDAG dag;
- dag.push_back(hlt);
- dag.argmax(hv);
-
- // HybridBayesNet hbn;
- // hbn.push_back(hc);
- // hbn.optimize();
-}
-
-TEST(HybridLookupTable, hybrid_argmax) {
- Matrix S1(2, 2);
- S1(0, 0) = 1;
- S1(1, 0) = 0;
- S1(0, 1) = 0;
- S1(1, 1) = 1;
-
- Vector2 d1(0.2, 0.5), d2(-0.5, 0.6);
-
- SharedDiagonal model = noiseModel::Diagonal::Sigmas(Vector2(1.0, 0.34));
-
- auto conditional0 =
- boost::make_shared<GaussianConditional>(X(1), d1, S1, model),
- conditional1 =
- boost::make_shared<GaussianConditional>(X(1), d2, S1, model);
-
- DiscreteKey m1(1, 2);
- GaussianMixture::Conditionals conditionals(
- {m1},
- vector<GaussianConditional::shared_ptr>{conditional0, conditional1});
- boost::shared_ptr<GaussianMixture> mixtureFactor(
- new GaussianMixture({X(1)}, {}, {m1}, conditionals));
-
- HybridConditional hc(mixtureFactor);
-
- DiscreteValues dv;
- dv[1] = 1;
- VectorValues cv;
- // cv.insert(X(2),Vector2(0.0, 0.0));
- HybridValues hv(dv, cv);
-
- HybridLookupTable hlt(hc);
-
- hlt.argmaxInPlace(&hv);
-
- EXPECT(assert_equal(hv.at(X(1)), d2));
-}
-
-TEST(HybridLookupTable, discrete_argmax) {
- DiscreteKey X(0, 2), Y(1, 2);
-
- auto conditional = boost::make_shared<DiscreteConditional>(X | Y = "0/1 3/2");
-
- HybridConditional hc(conditional);
-
- HybridLookupTable hlt(hc);
-
- DiscreteValues dv;
- dv[1] = 0;
- VectorValues cv;
- // cv.insert(X(2),Vector2(0.0, 0.0));
- HybridValues hv(dv, cv);
-
- hlt.argmaxInPlace(&hv);
-
- EXPECT(assert_equal(hv.atDiscrete(0), 1));
-
- DecisionTreeFactor f1(X, "2 3");
- auto conditional2 = boost::make_shared<DiscreteConditional>(1, f1);
-
- HybridConditional hc2(conditional2);
-
- HybridLookupTable hlt2(hc2);
-
- HybridValues hv2;
-
- hlt2.argmaxInPlace(&hv2);
-
- EXPECT(assert_equal(hv2.atDiscrete(0), 1));
-}
-
-TEST(HybridLookupTable, gaussian_argmax) {
- Matrix S1(2, 2);
- S1(0, 0) = 1;
- S1(1, 0) = 0;
- S1(0, 1) = 0;
- S1(1, 1) = 1;
-
- Vector2 d1(0.2, 0.5), d2(-0.5, 0.6);
-
- SharedDiagonal model = noiseModel::Diagonal::Sigmas(Vector2(1.0, 0.34));
-
- auto conditional =
- boost::make_shared<GaussianConditional>(X(1), d1, S1, X(2), -S1, model);
-
- HybridConditional hc(conditional);
-
- HybridLookupTable hlt(hc);
-
- DiscreteValues dv;
- // dv[1]=0;
- VectorValues cv;
- cv.insert(X(2), d2);
- HybridValues hv(dv, cv);
-
- hlt.argmaxInPlace(&hv);
-
- EXPECT(assert_equal(hv.at(X(1)), d1 + d2));
-}
-
-TEST(HybridLookupDAG, argmax) {
- Matrix S1(2, 2);
- S1(0, 0) = 1;
- S1(1, 0) = 0;
- S1(0, 1) = 0;
- S1(1, 1) = 1;
-
- Vector2 d1(0.2, 0.5), d2(-0.5, 0.6);
-
- SharedDiagonal model = noiseModel::Diagonal::Sigmas(Vector2(1.0, 0.34));
-
- auto conditional0 =
- boost::make_shared<GaussianConditional>(X(2), d1, S1, model),
- conditional1 =
- boost::make_shared<GaussianConditional>(X(2), d2, S1, model);
-
- DiscreteKey m1(1, 2);
- GaussianMixture::Conditionals conditionals(
- {m1},
- vector<GaussianConditional::shared_ptr>{conditional0, conditional1});
- boost::shared_ptr<GaussianMixture> mixtureFactor(
- new GaussianMixture({X(2)}, {}, {m1}, conditionals));
- HybridConditional hc2(mixtureFactor);
- HybridLookupTable hlt2(hc2);
-
- auto conditional2 =
- boost::make_shared<GaussianConditional>(X(1), d1, S1, X(2), -S1, model);
-
- HybridConditional hc1(conditional2);
- HybridLookupTable hlt1(hc1);
-
- DecisionTreeFactor f1(m1, "2 3");
- auto discrete_conditional = boost::make_shared<DiscreteConditional>(1, f1);
-
- HybridConditional hc3(discrete_conditional);
- HybridLookupTable hlt3(hc3);
-
- HybridLookupDAG dag;
- dag.push_back(hlt1);
- dag.push_back(hlt2);
- dag.push_back(hlt3);
- auto hv = dag.argmax();
-
- EXPECT(assert_equal(hv.atDiscrete(1), 1));
- EXPECT(assert_equal(hv.at(X(2)), d2));
- EXPECT(assert_equal(hv.at(X(1)), d2 + d1));
-}
-
-/* ************************************************************************* */
-int main() {
- TestResult tr;
- return TestRegistry::runAllTests(tr);
-}
-/* ************************************************************************* */
From 9c7bf36db6e511d2eb9dda39929f42af2a00d288 Mon Sep 17 00:00:00 2001
From: Varun Agrawal <varagrawal@gmail.com>
Date: Fri, 26 Aug 2022 19:35:45 -0400
Subject: [PATCH 02/11] refactor HybridValues
---
gtsam/hybrid/HybridValues.h | 54 ++++++++++++++++++++++++-------------
1 file changed, 36 insertions(+), 18 deletions(-)
diff --git a/gtsam/hybrid/HybridValues.h b/gtsam/hybrid/HybridValues.h
index 5e1bd41646..d5c78f9512 100644
--- a/gtsam/hybrid/HybridValues.h
+++ b/gtsam/hybrid/HybridValues.h
@@ -36,40 +36,58 @@ namespace gtsam {
* Optimizing a HybridGaussianBayesNet returns this class.
*/
class GTSAM_EXPORT HybridValues {
- public:
+ private:
// DiscreteValue stored the discrete components of the HybridValues.
- DiscreteValues discrete;
+ DiscreteValues discrete_;
// VectorValue stored the continuous components of the HybridValues.
- VectorValues continuous;
+ VectorValues continuous_;
+
+ public:
+ /// @name Standard Constructors
+ /// @{
// Default constructor creates an empty HybridValues.
- HybridValues() : discrete(), continuous(){};
+ HybridValues() = default;
// Construct from DiscreteValues and VectorValues.
HybridValues(const DiscreteValues& dv, const VectorValues& cv)
- : discrete(dv), continuous(cv){};
+ : discrete_(dv), continuous_(cv){};
+
+ /// @}
+ /// @name Testable
+ /// @{
// print required by Testable for unit testing
void print(const std::string& s = "HybridValues",
const KeyFormatter& keyFormatter = DefaultKeyFormatter) const {
std::cout << s << ": \n";
- discrete.print(" Discrete", keyFormatter); // print discrete components
- continuous.print(" Continuous",
- keyFormatter); // print continuous components
+ discrete_.print(" Discrete", keyFormatter); // print discrete components
+ continuous_.print(" Continuous",
+ keyFormatter); // print continuous components
};
// equals required by Testable for unit testing
bool equals(const HybridValues& other, double tol = 1e-9) const {
- return discrete.equals(other.discrete, tol) &&
- continuous.equals(other.continuous, tol);
+ return discrete_.equals(other.discrete_, tol) &&
+ continuous_.equals(other.continuous_, tol);
}
+ /// @}
+ /// @name Interface
+ /// @{
+
+ /// Return the discrete MPE assignment
+ DiscreteValues discrete() const { return discrete_; }
+
+ /// Return the delta update for the continuous vectors
+ VectorValues continuous() const { return continuous_; }
+
// Check whether a variable with key \c j exists in DiscreteValue.
- bool existsDiscrete(Key j) { return (discrete.find(j) != discrete.end()); };
+ bool existsDiscrete(Key j) { return (discrete_.find(j) != discrete_.end()); };
// Check whether a variable with key \c j exists in VectorValue.
- bool existsVector(Key j) { return continuous.exists(j); };
+ bool existsVector(Key j) { return continuous_.exists(j); };
// Check whether a variable with key \c j exists.
bool exists(Key j) { return existsDiscrete(j) || existsVector(j); };
@@ -78,13 +96,13 @@ class GTSAM_EXPORT HybridValues {
* the key \c j is already used.
* @param value The vector to be inserted.
* @param j The index with which the value will be associated. */
- void insert(Key j, int value) { discrete[j] = value; };
+ void insert(Key j, int value) { discrete_[j] = value; };
/** Insert a vector \c value with key \c j. Throws an invalid_argument
* exception if the key \c j is already used.
* @param value The vector to be inserted.
* @param j The index with which the value will be associated. */
- void insert(Key j, const Vector& value) { continuous.insert(j, value); }
+ void insert(Key j, const Vector& value) { continuous_.insert(j, value); }
// TODO(Shangjie)- update() and insert_or_assign() , similar to Values.h
@@ -92,13 +110,13 @@ class GTSAM_EXPORT HybridValues {
* Read/write access to the discrete value with key \c j, throws
* std::out_of_range if \c j does not exist.
*/
- size_t& atDiscrete(Key j) { return discrete.at(j); };
+ size_t& atDiscrete(Key j) { return discrete_.at(j); };
/**
* Read/write access to the vector value with key \c j, throws
* std::out_of_range if \c j does not exist.
*/
- Vector& at(Key j) { return continuous.at(j); };
+ Vector& at(Key j) { return continuous_.at(j); };
/// @name Wrapper support
/// @{
@@ -112,8 +130,8 @@ class GTSAM_EXPORT HybridValues {
std::string html(
const KeyFormatter& keyFormatter = DefaultKeyFormatter) const {
std::stringstream ss;
- ss << this->discrete.html(keyFormatter);
- ss << this->continuous.html(keyFormatter);
+ ss << this->discrete_.html(keyFormatter);
+ ss << this->continuous_.html(keyFormatter);
return ss.str();
};
From 0edcfd4ff88303f8911622de89be4f70db434faf Mon Sep 17 00:00:00 2001
From: Varun Agrawal <varagrawal@gmail.com>
Date: Fri, 26 Aug 2022 19:36:11 -0400
Subject: [PATCH 03/11] new HybridBayesNet optimize implementation
---
gtsam/hybrid/HybridBayesNet.cpp | 18 ++++++++++---
gtsam/hybrid/tests/testHybridBayesNet.cpp | 31 ++++++++++-------------
2 files changed, 28 insertions(+), 21 deletions(-)
diff --git a/gtsam/hybrid/HybridBayesNet.cpp b/gtsam/hybrid/HybridBayesNet.cpp
index f898178c2b..2238b08ce3 100644
--- a/gtsam/hybrid/HybridBayesNet.cpp
+++ b/gtsam/hybrid/HybridBayesNet.cpp
@@ -15,8 +15,9 @@
* @date January 2022
*/
+#include <gtsam/discrete/DiscreteBayesNet.h>
+#include <gtsam/discrete/DiscreteFactorGraph.h>
#include <gtsam/hybrid/HybridBayesNet.h>
-#include <gtsam/hybrid/HybridLookupDAG.h>
#include <gtsam/hybrid/HybridValues.h>
namespace gtsam {
@@ -139,8 +140,19 @@ GaussianBayesNet HybridBayesNet::choose(
/* *******************************************************************************/
HybridValues HybridBayesNet::optimize() const {
- auto dag = HybridLookupDAG::FromBayesNet(*this);
- return dag.argmax();
+ // Solve for the MPE
+ DiscreteBayesNet discrete_bn;
+ for (auto &conditional : factors_) {
+ if (conditional->isDiscrete()) {
+ discrete_bn.push_back(conditional->asDiscreteConditional());
+ }
+ }
+
+ DiscreteValues mpe = DiscreteFactorGraph(discrete_bn).optimize();
+
+ // Given the MPE, compute the optimal continuous values.
+ GaussianBayesNet gbn = this->choose(mpe);
+ return HybridValues(mpe, gbn.optimize());
}
/* *******************************************************************************/
diff --git a/gtsam/hybrid/tests/testHybridBayesNet.cpp b/gtsam/hybrid/tests/testHybridBayesNet.cpp
index 4602e8bac1..bcaf7e5993 100644
--- a/gtsam/hybrid/tests/testHybridBayesNet.cpp
+++ b/gtsam/hybrid/tests/testHybridBayesNet.cpp
@@ -136,24 +136,19 @@ TEST(HybridBayesNet, Optimize) {
HybridValues delta = hybridBayesNet->optimize();
- delta.print();
- VectorValues correct;
- correct.insert(X(1), 0 * Vector1::Ones());
- correct.insert(X(2), 1 * Vector1::Ones());
- correct.insert(X(3), 2 * Vector1::Ones());
- correct.insert(X(4), 3 * Vector1::Ones());
-
- DiscreteValues assignment111;
- assignment111[M(1)] = 1;
- assignment111[M(2)] = 1;
- assignment111[M(3)] = 1;
- std::cout << hybridBayesNet->choose(assignment111).error(correct) << std::endl;
-
- DiscreteValues assignment101;
- assignment101[M(1)] = 1;
- assignment101[M(2)] = 0;
- assignment101[M(3)] = 1;
- std::cout << hybridBayesNet->choose(assignment101).error(correct) << std::endl;
+ DiscreteValues expectedAssignment;
+ expectedAssignment[M(1)] = 1;
+ expectedAssignment[M(2)] = 0;
+ expectedAssignment[M(3)] = 1;
+ EXPECT(assert_equal(expectedAssignment, delta.discrete()));
+
+ VectorValues expectedValues;
+ expectedValues.insert(X(1), -0.999904 * Vector1::Ones());
+ expectedValues.insert(X(2), -0.99029 * Vector1::Ones());
+ expectedValues.insert(X(3), -1.00971 * Vector1::Ones());
+ expectedValues.insert(X(4), -1.0001 * Vector1::Ones());
+
+ EXPECT(assert_equal(expectedValues, delta.continuous(), 1e-5));
}
/* ************************************************************************* */
From 5169b2ec30577a9bb367a6125310251c04b056e2 Mon Sep 17 00:00:00 2001
From: Varun Agrawal <varagrawal@gmail.com>
Date: Fri, 26 Aug 2022 22:13:44 -0400
Subject: [PATCH 04/11] add HybridBayesTree optimize method
---
gtsam/hybrid/HybridBayesTree.cpp | 44 +++++++++++++++--
gtsam/hybrid/HybridBayesTree.h | 9 ++++
gtsam/hybrid/tests/testHybridBayesNet.cpp | 5 --
gtsam/hybrid/tests/testHybridBayesTree.cpp | 57 +++++++++++++++++++++-
4 files changed, 104 insertions(+), 11 deletions(-)
diff --git a/gtsam/hybrid/HybridBayesTree.cpp b/gtsam/hybrid/HybridBayesTree.cpp
index 30fe6f1683..e9c15b0edc 100644
--- a/gtsam/hybrid/HybridBayesTree.cpp
+++ b/gtsam/hybrid/HybridBayesTree.cpp
@@ -18,6 +18,8 @@
*/
#include <gtsam/base/treeTraversal-inst.h>
+#include <gtsam/discrete/DiscreteBayesNet.h>
+#include <gtsam/discrete/DiscreteFactorGraph.h>
#include <gtsam/hybrid/HybridBayesNet.h>
#include <gtsam/hybrid/HybridBayesTree.h>
#include <gtsam/inference/BayesTree-inst.h>
@@ -35,6 +37,42 @@ bool HybridBayesTree::equals(const This& other, double tol) const {
return Base::equals(other, tol);
}
+/* ************************************************************************* */
+HybridValues HybridBayesTree::optimize() const {
+ HybridBayesNet hbn;
+ DiscreteBayesNet dbn;
+
+ KeyVector added_keys;
+
+ // Iterate over all the nodes in the BayesTree
+ for (auto&& node : nodes()) {
+ // Check if conditional being added is already in the Bayes net.
+ if (std::find(added_keys.begin(), added_keys.end(), node.first) ==
+ added_keys.end()) {
+ // Access the clique and get the underlying hybrid conditional
+ HybridBayesTreeClique::shared_ptr clique = node.second;
+ HybridConditional::shared_ptr conditional = clique->conditional();
+
+ // Record the key being added
+ added_keys.insert(added_keys.end(), conditional->frontals().begin(),
+ conditional->frontals().end());
+
+ if (conditional->isHybrid()) {
+ // If conditional is hybrid, add it to a Hybrid Bayes net.
+ hbn.push_back(conditional);
+ } else if (conditional->isDiscrete()) {
+ // Else if discrete, we use it to compute the MPE
+ dbn.push_back(conditional->asDiscreteConditional());
+ }
+ }
+ }
+ // Get the MPE
+ DiscreteValues mpe = DiscreteFactorGraph(dbn).optimize();
+ // Given the MPE, compute the optimal continuous values.
+ GaussianBayesNet gbn = hbn.choose(mpe);
+ return HybridValues(mpe, gbn.optimize());
+}
+
/* ************************************************************************* */
VectorValues HybridBayesTree::optimize(const DiscreteValues& assignment) const {
GaussianBayesNet gbn;
@@ -50,11 +88,9 @@ VectorValues HybridBayesTree::optimize(const DiscreteValues& assignment) const {
HybridBayesTreeClique::shared_ptr clique = node.second;
HybridConditional::shared_ptr conditional = clique->conditional();
- KeyVector frontals(conditional->frontals().begin(),
- conditional->frontals().end());
-
// Record the key being added
- added_keys.insert(added_keys.end(), frontals.begin(), frontals.end());
+ added_keys.insert(added_keys.end(), conditional->frontals().begin(),
+ conditional->frontals().end());
// If conditional is hybrid (and not discrete-only), we get the Gaussian
// Conditional corresponding to the assignment and add it to the Gaussian
diff --git a/gtsam/hybrid/HybridBayesTree.h b/gtsam/hybrid/HybridBayesTree.h
index b7950a4838..361fbe86fe 100644
--- a/gtsam/hybrid/HybridBayesTree.h
+++ b/gtsam/hybrid/HybridBayesTree.h
@@ -70,6 +70,15 @@ class GTSAM_EXPORT HybridBayesTree : public BayesTree<HybridBayesTreeClique> {
/** Check equality */
bool equals(const This& other, double tol = 1e-9) const;
+ /**
+ * @brief Optimize the hybrid Bayes tree by computing the MPE for the current
+ * set of discrete variables and using it to compute the best continuous
+ * update delta.
+ *
+ * @return HybridValues
+ */
+ HybridValues optimize() const;
+
/**
* @brief Recursively optimize the BayesTree to produce a vector solution.
*
diff --git a/gtsam/hybrid/tests/testHybridBayesNet.cpp b/gtsam/hybrid/tests/testHybridBayesNet.cpp
index bcaf7e5993..56f3680aed 100644
--- a/gtsam/hybrid/tests/testHybridBayesNet.cpp
+++ b/gtsam/hybrid/tests/testHybridBayesNet.cpp
@@ -125,11 +125,6 @@ TEST(HybridBayesNet, OptimizeAssignment) {
TEST(HybridBayesNet, Optimize) {
Switching s(4);
- Ordering ordering;
- for (auto&& kvp : s.linearizationPoint) {
- ordering += kvp.key;
- }
-
Ordering hybridOrdering = s.linearizedFactorGraph.getHybridOrdering();
HybridBayesNet::shared_ptr hybridBayesNet =
s.linearizedFactorGraph.eliminateSequential(hybridOrdering);
diff --git a/gtsam/hybrid/tests/testHybridBayesTree.cpp b/gtsam/hybrid/tests/testHybridBayesTree.cpp
index a693af1b2d..3584d7db98 100644
--- a/gtsam/hybrid/tests/testHybridBayesTree.cpp
+++ b/gtsam/hybrid/tests/testHybridBayesTree.cpp
@@ -16,6 +16,7 @@
* @date August 2022
*/
+#include <gtsam/discrete/DiscreteFactorGraph.h>
#include <gtsam/hybrid/HybridBayesTree.h>
#include <gtsam/hybrid/HybridGaussianISAM.h>
@@ -31,8 +32,8 @@ using symbol_shorthand::M;
using symbol_shorthand::X;
/* ****************************************************************************/
-// Test for optimizing a HybridBayesTree.
-TEST(HybridBayesTree, Optimize) {
+// Test for optimizing a HybridBayesTree with a given assignment.
+TEST(HybridBayesTree, OptimizeAssignment) {
Switching s(4);
HybridGaussianISAM isam;
@@ -85,6 +86,58 @@ TEST(HybridBayesTree, Optimize) {
EXPECT(assert_equal(expected, delta));
}
+/* ****************************************************************************/
+// Test for optimizing a HybridBayesTree.
+TEST(HybridBayesTree, Optimize) {
+ Switching s(4);
+
+ HybridGaussianISAM isam;
+ HybridGaussianFactorGraph graph1;
+
+ // Add the 3 hybrid factors, x1-x2, x2-x3, x3-x4
+ for (size_t i = 1; i < 4; i++) {
+ graph1.push_back(s.linearizedFactorGraph.at(i));
+ }
+
+ // Add the Gaussian factors, 1 prior on X(1),
+ // 3 measurements on X(2), X(3), X(4)
+ graph1.push_back(s.linearizedFactorGraph.at(0));
+ for (size_t i = 4; i <= 6; i++) {
+ graph1.push_back(s.linearizedFactorGraph.at(i));
+ }
+
+ // Add the discrete factors
+ for (size_t i = 7; i <= 9; i++) {
+ graph1.push_back(s.linearizedFactorGraph.at(i));
+ }
+
+ isam.update(graph1);
+
+ HybridValues delta = isam.optimize();
+
+ // Create ordering.
+ Ordering ordering;
+ for (size_t k = 1; k <= s.K; k++) ordering += X(k);
+
+ HybridBayesNet::shared_ptr hybridBayesNet;
+ HybridGaussianFactorGraph::shared_ptr remainingFactorGraph;
+ std::tie(hybridBayesNet, remainingFactorGraph) =
+ s.linearizedFactorGraph.eliminatePartialSequential(ordering);
+
+ DiscreteFactorGraph dfg;
+ for (auto&& f : *remainingFactorGraph) {
+ auto factor = dynamic_pointer_cast<HybridDiscreteFactor>(f);
+ dfg.push_back(
+ boost::dynamic_pointer_cast<DecisionTreeFactor>(factor->inner()));
+ }
+
+ DiscreteValues expectedMPE = dfg.optimize();
+ VectorValues expectedValues = hybridBayesNet->optimize(expectedMPE);
+
+ EXPECT(assert_equal(expectedMPE, delta.discrete()));
+ EXPECT(assert_equal(expectedValues, delta.continuous()));
+}
+
/* ************************************************************************* */
int main() {
TestResult tr;
From 8645ec0f259240c5ad3f21f5abd9a8c6af11edaa Mon Sep 17 00:00:00 2001
From: Varun Agrawal <varagrawal@gmail.com>
Date: Sat, 27 Aug 2022 15:43:13 -0400
Subject: [PATCH 05/11] update wrapper for HybridValues
---
gtsam/hybrid/hybrid.i | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/gtsam/hybrid/hybrid.i b/gtsam/hybrid/hybrid.i
index aa63259d9d..dfbf3919db 100644
--- a/gtsam/hybrid/hybrid.i
+++ b/gtsam/hybrid/hybrid.i
@@ -6,8 +6,8 @@ namespace gtsam {
#include <gtsam/hybrid/HybridValues.h>
class HybridValues {
- gtsam::DiscreteValues discrete;
- gtsam::VectorValues continuous;
+ gtsam::DiscreteValues discrete() const;
+ gtsam::VectorValues continuous() const;
HybridValues();
HybridValues(const gtsam::DiscreteValues &dv, const gtsam::VectorValues &cv);
void print(string s = "HybridValues",
From 31bbf513f46cbf0909f5991dbbe5a246e195f6d5 Mon Sep 17 00:00:00 2001
From: Varun Agrawal <varagrawal@gmail.com>
Date: Sat, 27 Aug 2022 19:23:27 -0400
Subject: [PATCH 06/11] add discrete factors to HybridBayesTree test
---
gtsam/hybrid/tests/testHybridBayesTree.cpp | 5 +++++
1 file changed, 5 insertions(+)
diff --git a/gtsam/hybrid/tests/testHybridBayesTree.cpp b/gtsam/hybrid/tests/testHybridBayesTree.cpp
index 3584d7db98..d457e6b746 100644
--- a/gtsam/hybrid/tests/testHybridBayesTree.cpp
+++ b/gtsam/hybrid/tests/testHybridBayesTree.cpp
@@ -51,6 +51,11 @@ TEST(HybridBayesTree, OptimizeAssignment) {
graph1.push_back(s.linearizedFactorGraph.at(i));
}
+ // Add the discrete factors
+ for (size_t i = 7; i <= 9; i++) {
+ graph1.push_back(s.linearizedFactorGraph.at(i));
+ }
+
isam.update(graph1);
DiscreteValues assignment;
From 496ddf886fcde5dac0053ff5c22daee428c92d2d Mon Sep 17 00:00:00 2001
From: Varun Agrawal <varagrawal@gmail.com>
Date: Sun, 28 Aug 2022 09:55:44 -0400
Subject: [PATCH 07/11] fix gbn optimize everywhere for HybridBayesTree
---
gtsam/hybrid/HybridBayesTree.cpp | 9 +++++++++
1 file changed, 9 insertions(+)
diff --git a/gtsam/hybrid/HybridBayesTree.cpp b/gtsam/hybrid/HybridBayesTree.cpp
index 678b4ca34d..d0f6f2fe26 100644
--- a/gtsam/hybrid/HybridBayesTree.cpp
+++ b/gtsam/hybrid/HybridBayesTree.cpp
@@ -70,6 +70,14 @@ HybridValues HybridBayesTree::optimize() const {
DiscreteValues mpe = DiscreteFactorGraph(dbn).optimize();
// Given the MPE, compute the optimal continuous values.
GaussianBayesNet gbn = hbn.choose(mpe);
+
+ // If TBB is enabled, the bayes net order gets reversed,
+ // so we pre-reverse it
+#ifdef GTSAM_USE_TBB
+ auto reversed = boost::adaptors::reverse(gbn);
+ gbn = GaussianBayesNet(reversed.begin(), reversed.end());
+#endif
+
return HybridValues(mpe, gbn.optimize());
}
@@ -104,6 +112,7 @@ VectorValues HybridBayesTree::optimize(const DiscreteValues& assignment) const {
}
}
}
+
// If TBB is enabled, the bayes net order gets reversed,
// so we pre-reverse it
#ifdef GTSAM_USE_TBB
From 6912d015ef898de8f17eefa834d0d72b7c8933a4 Mon Sep 17 00:00:00 2001
From: Varun Agrawal <varagrawal@gmail.com>
Date: Mon, 29 Aug 2022 09:26:18 -0400
Subject: [PATCH 08/11] assert for discrete only conditional
---
gtsam/hybrid/HybridBayesNet.cpp | 4 +++-
1 file changed, 3 insertions(+), 1 deletion(-)
diff --git a/gtsam/hybrid/HybridBayesNet.cpp b/gtsam/hybrid/HybridBayesNet.cpp
index 2238b08ce3..c108a47c27 100644
--- a/gtsam/hybrid/HybridBayesNet.cpp
+++ b/gtsam/hybrid/HybridBayesNet.cpp
@@ -131,7 +131,9 @@ GaussianBayesNet HybridBayesNet::choose(
gbn.push_back(gm(assignment));
} catch (std::exception &exc) {
- // if factor at `idx` is discrete-only, just continue.
+ // factor at `idx` is discrete-only, so we simply continue.
+ assert(factors_.at(idx)->discreteKeys().size() ==
+ factors_.at(idx)->keys().size());
continue;
}
}
From 4e451d5c0b9642279cb47d3814f2fbb8982a025e Mon Sep 17 00:00:00 2001
From: Varun Agrawal <varagrawal@gmail.com>
Date: Mon, 29 Aug 2022 16:22:55 -0400
Subject: [PATCH 09/11] helper to return HybridConditional as
GaussianConditional
---
gtsam/hybrid/HybridConditional.h | 11 +++++++++++
1 file changed, 11 insertions(+)
diff --git a/gtsam/hybrid/HybridConditional.h b/gtsam/hybrid/HybridConditional.h
index 91c9f84950..96ea6d9699 100644
--- a/gtsam/hybrid/HybridConditional.h
+++ b/gtsam/hybrid/HybridConditional.h
@@ -139,6 +139,17 @@ class GTSAM_EXPORT HybridConditional
return boost::static_pointer_cast<GaussianMixture>(inner_);
}
+ /**
+ * @brief Return HybridConditional as a GaussianConditional
+ *
+ * @return GaussianConditional::shared_ptr
+ */
+ GaussianConditional::shared_ptr asGaussian() {
+ if (!isContinuous())
+ throw std::invalid_argument("Not a continuous conditional");
+ return boost::static_pointer_cast<GaussianConditional>(inner_);
+ }
+
/**
* @brief Return conditional as a DiscreteConditional
*
From 8c41f63167ade58f73131b922b7fd533e605e020 Mon Sep 17 00:00:00 2001
From: Varun Agrawal <varagrawal@gmail.com>
Date: Mon, 29 Aug 2022 16:24:26 -0400
Subject: [PATCH 10/11] rename atGaussian to atMixture and add new atGaussian
for continuous conditionals, fix choose method for all types
---
gtsam/hybrid/HybridBayesNet.cpp | 24 ++++++++++++++++-------
gtsam/hybrid/HybridBayesNet.h | 5 ++++-
gtsam/hybrid/tests/testHybridBayesNet.cpp | 8 ++++----
3 files changed, 25 insertions(+), 12 deletions(-)
diff --git a/gtsam/hybrid/HybridBayesNet.cpp b/gtsam/hybrid/HybridBayesNet.cpp
index c108a47c27..787c91a0d7 100644
--- a/gtsam/hybrid/HybridBayesNet.cpp
+++ b/gtsam/hybrid/HybridBayesNet.cpp
@@ -112,10 +112,15 @@ HybridBayesNet HybridBayesNet::prune(
}
/* ************************************************************************* */
-GaussianMixture::shared_ptr HybridBayesNet::atGaussian(size_t i) const {
+GaussianMixture::shared_ptr HybridBayesNet::atMixture(size_t i) const {
return factors_.at(i)->asMixture();
}
+/* ************************************************************************* */
+GaussianConditional::shared_ptr HybridBayesNet::atGaussian(size_t i) const {
+ return factors_.at(i)->asGaussian();
+}
+
/* ************************************************************************* */
DiscreteConditional::shared_ptr HybridBayesNet::atDiscrete(size_t i) const {
return factors_.at(i)->asDiscreteConditional();
@@ -126,17 +131,22 @@ GaussianBayesNet HybridBayesNet::choose(
const DiscreteValues &assignment) const {
GaussianBayesNet gbn;
for (size_t idx = 0; idx < size(); idx++) {
- try {
- GaussianMixture gm = *this->atGaussian(idx);
+ if (factors_.at(idx)->isHybrid()) {
+ // If factor is hybrid, select based on assignment.
+ GaussianMixture gm = *this->atMixture(idx);
gbn.push_back(gm(assignment));
- } catch (std::exception &exc) {
- // factor at `idx` is discrete-only, so we simply continue.
- assert(factors_.at(idx)->discreteKeys().size() ==
- factors_.at(idx)->keys().size());
+ } else if (factors_.at(idx)->isContinuous()) {
+ // If continuous only, add gaussian conditional.
+ factors_.at(idx)->print();
+ gbn.push_back((this->atGaussian(idx)));
+
+ } else if (factors_.at(idx)->isDiscrete()) {
+ // If factor at `idx` is discrete-only, we simply continue.
continue;
}
}
+
return gbn;
}
diff --git a/gtsam/hybrid/HybridBayesNet.h b/gtsam/hybrid/HybridBayesNet.h
index a16a4f42c5..616ea06989 100644
--- a/gtsam/hybrid/HybridBayesNet.h
+++ b/gtsam/hybrid/HybridBayesNet.h
@@ -54,7 +54,10 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
}
/// Get a specific Gaussian mixture by index `i`.
- GaussianMixture::shared_ptr atGaussian(size_t i) const;
+ GaussianMixture::shared_ptr atMixture(size_t i) const;
+
+ /// Get a specific Gaussian conditional by index `i`.
+ GaussianConditional::shared_ptr atGaussian(size_t i) const;
/// Get a specific discrete conditional by index `i`.
DiscreteConditional::shared_ptr atDiscrete(size_t i) const;
diff --git a/gtsam/hybrid/tests/testHybridBayesNet.cpp b/gtsam/hybrid/tests/testHybridBayesNet.cpp
index 56f3680aed..c7516c0f63 100644
--- a/gtsam/hybrid/tests/testHybridBayesNet.cpp
+++ b/gtsam/hybrid/tests/testHybridBayesNet.cpp
@@ -73,16 +73,16 @@ TEST(HybridBayesNet, Choose) {
EXPECT_LONGS_EQUAL(4, gbn.size());
EXPECT(assert_equal(*(*boost::dynamic_pointer_cast<GaussianMixture>(
- hybridBayesNet->atGaussian(0)))(assignment),
+ hybridBayesNet->atMixture(0)))(assignment),
*gbn.at(0)));
EXPECT(assert_equal(*(*boost::dynamic_pointer_cast<GaussianMixture>(
- hybridBayesNet->atGaussian(1)))(assignment),
+ hybridBayesNet->atMixture(1)))(assignment),
*gbn.at(1)));
EXPECT(assert_equal(*(*boost::dynamic_pointer_cast<GaussianMixture>(
- hybridBayesNet->atGaussian(2)))(assignment),
+ hybridBayesNet->atMixture(2)))(assignment),
*gbn.at(2)));
EXPECT(assert_equal(*(*boost::dynamic_pointer_cast<GaussianMixture>(
- hybridBayesNet->atGaussian(3)))(assignment),
+ hybridBayesNet->atMixture(3)))(assignment),
*gbn.at(3)));
}
From ca4293b70ddcdadb2de7c1107856519243a128d6 Mon Sep 17 00:00:00 2001
From: Varun Agrawal <varagrawal@gmail.com>
Date: Wed, 31 Aug 2022 10:36:07 -0400
Subject: [PATCH 11/11] handle Gaussian conditionals in the BayesTree
---
gtsam/hybrid/HybridBayesNet.cpp | 1 -
gtsam/hybrid/HybridBayesTree.cpp | 16 +++++++++++-----
2 files changed, 11 insertions(+), 6 deletions(-)
diff --git a/gtsam/hybrid/HybridBayesNet.cpp b/gtsam/hybrid/HybridBayesNet.cpp
index 787c91a0d7..bca50a902e 100644
--- a/gtsam/hybrid/HybridBayesNet.cpp
+++ b/gtsam/hybrid/HybridBayesNet.cpp
@@ -138,7 +138,6 @@ GaussianBayesNet HybridBayesNet::choose(
} else if (factors_.at(idx)->isContinuous()) {
// If continuous only, add gaussian conditional.
- factors_.at(idx)->print();
gbn.push_back((this->atGaussian(idx)));
} else if (factors_.at(idx)->isDiscrete()) {
diff --git a/gtsam/hybrid/HybridBayesTree.cpp b/gtsam/hybrid/HybridBayesTree.cpp
index d0f6f2fe26..49ae0bd877 100644
--- a/gtsam/hybrid/HybridBayesTree.cpp
+++ b/gtsam/hybrid/HybridBayesTree.cpp
@@ -57,12 +57,14 @@ HybridValues HybridBayesTree::optimize() const {
added_keys.insert(added_keys.end(), conditional->frontals().begin(),
conditional->frontals().end());
- if (conditional->isHybrid()) {
- // If conditional is hybrid, add it to a Hybrid Bayes net.
- hbn.push_back(conditional);
- } else if (conditional->isDiscrete()) {
- // Else if discrete, we use it to compute the MPE
+ if (conditional->isDiscrete()) {
+ // If discrete, we use it to compute the MPE
dbn.push_back(conditional->asDiscreteConditional());
+
+ } else {
+ // Else conditional is hybrid or continuous-only,
+ // so we directly add it to the Hybrid Bayes net.
+ hbn.push_back(conditional);
}
}
}
@@ -109,6 +111,10 @@ VectorValues HybridBayesTree::optimize(const DiscreteValues& assignment) const {
(*gm)(assignment);
gbn.push_back(gaussian_conditional);
+
+ } else if (conditional->isContinuous()) {
+ // If conditional is Gaussian, we simply add it to the Bayes net.
+ gbn.push_back(conditional->asGaussian());
}
}
}