Varun Agrawal
GitHub
commit
7977f77887
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@ -219,6 +219,16 @@ class DiscreteBayesTree {
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};
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#include <gtsam/discrete/DiscreteLookupDAG.h>
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class DiscreteLookupTable : gtsam::DiscreteConditional{
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DiscreteLookupTable(size_t nFrontals, const gtsam::DiscreteKeys& keys,
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const gtsam::DecisionTreeFactor::ADT& potentials);
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void print(
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const std::string& s = "Discrete Lookup Table: ",
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const gtsam::KeyFormatter& keyFormatter = gtsam::DefaultKeyFormatter) const;
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size_t argmax(const gtsam::DiscreteValues& parentsValues) const;
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};
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class DiscreteLookupDAG {
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DiscreteLookupDAG();
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void push_back(const gtsam::DiscreteLookupTable* table);
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@ -51,7 +51,7 @@ GaussianMixtureFactor GaussianMixtureFactor::FromFactors(
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void GaussianMixtureFactor::print(const std::string &s,
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const KeyFormatter &formatter) const {
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HybridFactor::print(s, formatter);
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std::cout << "]{\n";
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std::cout << "{\n";
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factors_.print(
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"", [&](Key k) { return formatter(k); },
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[&](const GaussianFactor::shared_ptr &gf) -> std::string {
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@ -22,7 +22,7 @@
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#include <gtsam/discrete/DecisionTree.h>
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#include <gtsam/discrete/DiscreteKey.h>
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#include <gtsam/hybrid/HybridFactor.h>
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#include <gtsam/hybrid/HybridGaussianFactor.h>
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#include <gtsam/linear/GaussianFactor.h>
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namespace gtsam {
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@ -76,7 +76,10 @@ class GTSAM_EXPORT HybridBayesTree : public BayesTree<HybridBayesTreeClique> {
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/**
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* @brief Class for Hybrid Bayes tree orphan subtrees.
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*
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* This does special stuff for the hybrid case
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* This object stores parent keys in our base type factor so that
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* eliminating those parent keys will pull this subtree into the
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* elimination.
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* This does special stuff for the hybrid case.
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*
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* @tparam CLIQUE
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*/
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@ -89,17 +89,23 @@ void HybridFactor::print(const std::string &s,
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if (isContinuous_) std::cout << "Continuous ";
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if (isDiscrete_) std::cout << "Discrete ";
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if (isHybrid_) std::cout << "Hybrid ";
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for (size_t c=0; c<continuousKeys_.size(); c++) {
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std::cout << "[";
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for (size_t c = 0; c < continuousKeys_.size(); c++) {
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std::cout << formatter(continuousKeys_.at(c));
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if (c < continuousKeys_.size() - 1) {
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std::cout << " ";
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} else {
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std::cout << "; ";
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std::cout << (discreteKeys_.size() > 0 ? "; " : "");
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}
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}
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for(auto && discreteKey: discreteKeys_) {
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std::cout << formatter(discreteKey.first) << " ";
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for (size_t d = 0; d < discreteKeys_.size(); d++) {
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std::cout << formatter(discreteKeys_.at(d).first);
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if (d < discreteKeys_.size() - 1) {
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std::cout << " ";
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}
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}
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std::cout << "]";
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}
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} // namespace gtsam
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@ -37,6 +37,8 @@ class GTSAM_EXPORT HybridGaussianFactor : public HybridFactor {
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using This = HybridGaussianFactor;
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using shared_ptr = boost::shared_ptr<This>;
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HybridGaussianFactor() = default;
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// Explicit conversion from a shared ptr of GF
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explicit HybridGaussianFactor(GaussianFactor::shared_ptr other);
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@ -52,7 +54,7 @@ class GTSAM_EXPORT HybridGaussianFactor : public HybridFactor {
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/// GTSAM print utility.
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void print(
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const std::string &s = "HybridFactor\n",
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const std::string &s = "HybridGaussianFactor\n",
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const KeyFormatter &formatter = DefaultKeyFormatter) const override;
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/// @}
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@ -98,6 +98,12 @@ GaussianMixtureFactor::Sum sumFrontals(
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} else if (f->isContinuous()) {
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deferredFactors.push_back(
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boost::dynamic_pointer_cast<HybridGaussianFactor>(f)->inner());
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} else if (f->isDiscrete()) {
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// Don't do anything for discrete-only factors
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// since we want to eliminate continuous values only.
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continue;
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} else {
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// We need to handle the case where the object is actually an
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// BayesTreeOrphanWrapper!
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@ -106,8 +112,8 @@ GaussianMixtureFactor::Sum sumFrontals(
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if (!orphan) {
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auto &fr = *f;
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throw std::invalid_argument(
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std::string("factor is discrete in continuous elimination") +
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typeid(fr).name());
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std::string("factor is discrete in continuous elimination ") +
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demangle(typeid(fr).name()));
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}
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}
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}
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@ -158,7 +164,7 @@ discreteElimination(const HybridGaussianFactorGraph &factors,
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}
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}
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auto result = EliminateDiscrete(dfg, frontalKeys);
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auto result = EliminateForMPE(dfg, frontalKeys);
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return {boost::make_shared<HybridConditional>(result.first),
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boost::make_shared<HybridDiscreteFactor>(result.second)};
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@ -24,6 +24,7 @@
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#include <gtsam/inference/EliminateableFactorGraph.h>
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#include <gtsam/inference/FactorGraph.h>
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#include <gtsam/inference/Ordering.h>
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#include <gtsam/linear/GaussianFactor.h>
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namespace gtsam {
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@ -0,0 +1,41 @@
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file HybridNonlinearFactor.cpp
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* @date May 28, 2022
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* @author Varun Agrawal
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*/
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#include <gtsam/hybrid/HybridNonlinearFactor.h>
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#include <boost/make_shared.hpp>
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namespace gtsam {
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/* ************************************************************************* */
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HybridNonlinearFactor::HybridNonlinearFactor(
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const NonlinearFactor::shared_ptr &other)
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: Base(other->keys()), inner_(other) {}
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/* ************************************************************************* */
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bool HybridNonlinearFactor::equals(const HybridFactor &lf, double tol) const {
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return Base::equals(lf, tol);
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}
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/* ************************************************************************* */
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void HybridNonlinearFactor::print(const std::string &s,
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const KeyFormatter &formatter) const {
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HybridFactor::print(s, formatter);
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inner_->print("\n", formatter);
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};
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} // namespace gtsam
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@ -0,0 +1,62 @@
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file HybridNonlinearFactor.h
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* @date May 28, 2022
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* @author Varun Agrawal
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*/
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#pragma once
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#include <gtsam/hybrid/HybridFactor.h>
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#include <gtsam/hybrid/HybridGaussianFactor.h>
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#include <gtsam/nonlinear/NonlinearFactor.h>
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namespace gtsam {
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/**
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* A HybridNonlinearFactor is a layer over NonlinearFactor so that we do not
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* have a diamond inheritance.
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*/
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class HybridNonlinearFactor : public HybridFactor {
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private:
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NonlinearFactor::shared_ptr inner_;
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public:
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using Base = HybridFactor;
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using This = HybridNonlinearFactor;
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using shared_ptr = boost::shared_ptr<This>;
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// Explicit conversion from a shared ptr of NonlinearFactor
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explicit HybridNonlinearFactor(const NonlinearFactor::shared_ptr &other);
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/// @name Testable
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/// @{
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/// Check equality.
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virtual bool equals(const HybridFactor &lf, double tol) const override;
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/// GTSAM print utility.
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void print(
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const std::string &s = "HybridNonlinearFactor\n",
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const KeyFormatter &formatter = DefaultKeyFormatter) const override;
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/// @}
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NonlinearFactor::shared_ptr inner() const { return inner_; }
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/// Linearize to a HybridGaussianFactor at the linearization point `c`.
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boost::shared_ptr<HybridGaussianFactor> linearize(const Values &c) const {
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return boost::make_shared<HybridGaussianFactor>(inner_->linearize(c));
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}
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};
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} // namespace gtsam
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@ -0,0 +1,94 @@
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file HybridNonlinearFactorGraph.cpp
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* @brief Nonlinear hybrid factor graph that uses type erasure
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* @author Varun Agrawal
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* @date May 28, 2022
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*/
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#include <gtsam/hybrid/HybridNonlinearFactorGraph.h>
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namespace gtsam {
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/* ************************************************************************* */
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void HybridNonlinearFactorGraph::add(
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boost::shared_ptr<NonlinearFactor> factor) {
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FactorGraph::add(boost::make_shared<HybridNonlinearFactor>(factor));
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}
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/* ************************************************************************* */
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void HybridNonlinearFactorGraph::print(const std::string& s,
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const KeyFormatter& keyFormatter) const {
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// Base::print(str, keyFormatter);
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std::cout << (s.empty() ? "" : s + " ") << std::endl;
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std::cout << "size: " << size() << std::endl;
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for (size_t i = 0; i < factors_.size(); i++) {
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std::stringstream ss;
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ss << "factor " << i << ": ";
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if (factors_[i]) {
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factors_[i]->print(ss.str(), keyFormatter);
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std::cout << std::endl;
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}
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}
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}
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/* ************************************************************************* */
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HybridGaussianFactorGraph HybridNonlinearFactorGraph::linearize(
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const Values& continuousValues) const {
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// create an empty linear FG
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HybridGaussianFactorGraph linearFG;
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linearFG.reserve(size());
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// linearize all hybrid factors
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for (auto&& factor : factors_) {
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// First check if it is a valid factor
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if (factor) {
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// Check if the factor is a hybrid factor.
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// It can be either a nonlinear MixtureFactor or a linear
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// GaussianMixtureFactor.
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if (factor->isHybrid()) {
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// Check if it is a nonlinear mixture factor
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if (auto nlmf = boost::dynamic_pointer_cast<MixtureFactor>(factor)) {
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linearFG.push_back(nlmf->linearize(continuousValues));
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} else {
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linearFG.push_back(factor);
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}
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// Now check if the factor is a continuous only factor.
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} else if (factor->isContinuous()) {
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// In this case, we check if factor->inner() is nonlinear since
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// HybridFactors wrap over continuous factors.
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auto nlhf = boost::dynamic_pointer_cast<HybridNonlinearFactor>(factor);
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if (auto nlf =
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boost::dynamic_pointer_cast<NonlinearFactor>(nlhf->inner())) {
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auto hgf = boost::make_shared<HybridGaussianFactor>(
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nlf->linearize(continuousValues));
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linearFG.push_back(hgf);
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} else {
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linearFG.push_back(factor);
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}
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// Finally if nothing else, we are discrete-only which doesn't need
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// lineariztion.
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} else {
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linearFG.push_back(factor);
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}
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} else {
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linearFG.push_back(GaussianFactor::shared_ptr());
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}
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}
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return linearFG;
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}
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} // namespace gtsam
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@ -0,0 +1,134 @@
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file HybridNonlinearFactorGraph.h
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* @brief Nonlinear hybrid factor graph that uses type erasure
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* @author Varun Agrawal
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* @date May 28, 2022
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*/
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#pragma once
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#include <gtsam/hybrid/HybridFactor.h>
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#include <gtsam/hybrid/HybridFactorGraph.h>
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#include <gtsam/hybrid/HybridGaussianFactorGraph.h>
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#include <gtsam/hybrid/HybridNonlinearFactor.h>
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#include <gtsam/hybrid/MixtureFactor.h>
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#include <gtsam/inference/Ordering.h>
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#include <gtsam/nonlinear/NonlinearFactor.h>
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#include <boost/format.hpp>
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namespace gtsam {
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/**
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* Nonlinear Hybrid Factor Graph
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* -----------------------
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* This is the non-linear version of a hybrid factor graph.
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* Everything inside needs to be hybrid factor or hybrid conditional.
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*/
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class GTSAM_EXPORT HybridNonlinearFactorGraph : public HybridFactorGraph {
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protected:
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/// Check if FACTOR type is derived from NonlinearFactor.
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template <typename FACTOR>
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using IsNonlinear = typename std::enable_if<
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std::is_base_of<NonlinearFactor, FACTOR>::value>::type;
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public:
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using Base = HybridFactorGraph;
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using This = HybridNonlinearFactorGraph; ///< this class
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using shared_ptr = boost::shared_ptr<This>; ///< shared_ptr to This
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using Values = gtsam::Values; ///< backwards compatibility
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using Indices = KeyVector; ///> map from keys to values
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/// @name Constructors
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/// @{
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HybridNonlinearFactorGraph() = default;
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/**
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* Implicit copy/downcast constructor to override explicit template container
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* constructor. In BayesTree this is used for:
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* `cachedSeparatorMarginal_.reset(*separatorMarginal)`
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* */
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template <class DERIVEDFACTOR>
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HybridNonlinearFactorGraph(const FactorGraph<DERIVEDFACTOR>& graph)
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: Base(graph) {}
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/// @}
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// Allow use of selected FactorGraph methods:
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using Base::empty;
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using Base::reserve;
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using Base::size;
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using Base::operator[];
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using Base::add;
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using Base::resize;
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/**
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* Add a nonlinear factor *pointer* to the internal nonlinear factor graph
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* @param nonlinearFactor - boost::shared_ptr to the factor to add
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*/
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template <typename FACTOR>
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IsNonlinear<FACTOR> push_nonlinear(
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const boost::shared_ptr<FACTOR>& nonlinearFactor) {
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Base::push_back(boost::make_shared<HybridNonlinearFactor>(nonlinearFactor));
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}
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/// Construct a factor and add (shared pointer to it) to factor graph.
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template <class FACTOR, class... Args>
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IsNonlinear<FACTOR> emplace_nonlinear(Args&&... args) {
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auto factor = boost::allocate_shared<FACTOR>(
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Eigen::aligned_allocator<FACTOR>(), std::forward<Args>(args)...);
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push_nonlinear(factor);
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}
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/**
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* @brief Add a single factor shared pointer to the hybrid factor graph.
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* Dynamically handles the factor type and assigns it to the correct
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* underlying container.
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*
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* @tparam FACTOR The factor type template
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* @param sharedFactor The factor to add to this factor graph.
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*/
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template <typename FACTOR>
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void push_back(const boost::shared_ptr<FACTOR>& sharedFactor) {
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if (auto p = boost::dynamic_pointer_cast<NonlinearFactor>(sharedFactor)) {
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push_nonlinear(p);
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} else {
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Base::push_back(sharedFactor);
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}
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}
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/// Add a nonlinear factor as a shared ptr.
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void add(boost::shared_ptr<NonlinearFactor> factor);
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/// Print the factor graph.
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void print(
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const std::string& s = "HybridNonlinearFactorGraph",
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const KeyFormatter& keyFormatter = DefaultKeyFormatter) const override;
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/**
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* @brief Linearize all the continuous factors in the
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* HybridNonlinearFactorGraph.
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*
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* @param continuousValues: Dictionary of continuous values.
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* @return HybridGaussianFactorGraph::shared_ptr
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*/
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HybridGaussianFactorGraph linearize(const Values& continuousValues) const;
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};
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template <>
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struct traits<HybridNonlinearFactorGraph>
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: public Testable<HybridNonlinearFactorGraph> {};
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} // namespace gtsam
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@ -0,0 +1,244 @@
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
|
||||
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
|
||||
|
||||
* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file MixtureFactor.h
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* @brief Nonlinear Mixture factor of continuous and discrete.
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* @author Kevin Doherty, kdoherty@mit.edu
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||||
* @author Varun Agrawal
|
||||
* @date December 2021
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <gtsam/discrete/DiscreteValues.h>
|
||||
#include <gtsam/hybrid/GaussianMixtureFactor.h>
|
||||
#include <gtsam/hybrid/HybridNonlinearFactor.h>
|
||||
#include <gtsam/nonlinear/NonlinearFactor.h>
|
||||
#include <gtsam/nonlinear/Symbol.h>
|
||||
|
||||
#include <algorithm>
|
||||
#include <boost/format.hpp>
|
||||
#include <cmath>
|
||||
#include <limits>
|
||||
#include <vector>
|
||||
|
||||
namespace gtsam {
|
||||
|
||||
/**
|
||||
* @brief Implementation of a discrete conditional mixture factor.
|
||||
*
|
||||
* Implements a joint discrete-continuous factor where the discrete variable
|
||||
* serves to "select" a mixture component corresponding to a NonlinearFactor
|
||||
* type of measurement.
|
||||
*
|
||||
* This class stores all factors as HybridFactors which can then be typecast to
|
||||
* one of (NonlinearFactor, GaussianFactor) which can then be checked to perform
|
||||
* the correct operation.
|
||||
*/
|
||||
class MixtureFactor : public HybridFactor {
|
||||
public:
|
||||
using Base = HybridFactor;
|
||||
using This = MixtureFactor;
|
||||
using shared_ptr = boost::shared_ptr<MixtureFactor>;
|
||||
using sharedFactor = boost::shared_ptr<NonlinearFactor>;
|
||||
|
||||
/**
|
||||
* @brief typedef for DecisionTree which has Keys as node labels and
|
||||
* NonlinearFactor as leaf nodes.
|
||||
*/
|
||||
using Factors = DecisionTree<Key, sharedFactor>;
|
||||
|
||||
private:
|
||||
/// Decision tree of Gaussian factors indexed by discrete keys.
|
||||
Factors factors_;
|
||||
bool normalized_;
|
||||
|
||||
public:
|
||||
MixtureFactor() = default;
|
||||
|
||||
/**
|
||||
* @brief Construct from Decision tree.
|
||||
*
|
||||
* @param keys Vector of keys for continuous factors.
|
||||
* @param discreteKeys Vector of discrete keys.
|
||||
* @param factors Decision tree with of shared factors.
|
||||
* @param normalized Flag indicating if the factor error is already
|
||||
* normalized.
|
||||
*/
|
||||
MixtureFactor(const KeyVector& keys, const DiscreteKeys& discreteKeys,
|
||||
const Factors& factors, bool normalized = false)
|
||||
: Base(keys, discreteKeys), factors_(factors), normalized_(normalized) {}
|
||||
|
||||
/**
|
||||
* @brief Convenience constructor that generates the underlying factor
|
||||
* decision tree for us.
|
||||
*
|
||||
* Here it is important that the vector of factors has the correct number of
|
||||
* elements based on the number of discrete keys and the cardinality of the
|
||||
* keys, so that the decision tree is constructed appropriately.
|
||||
*
|
||||
* @tparam FACTOR The type of the factor shared pointers being passed in. Will
|
||||
* be typecast to NonlinearFactor shared pointers.
|
||||
* @param keys Vector of keys for continuous factors.
|
||||
* @param discreteKeys Vector of discrete keys.
|
||||
* @param factors Vector of shared pointers to factors.
|
||||
* @param normalized Flag indicating if the factor error is already
|
||||
* normalized.
|
||||
*/
|
||||
template <typename FACTOR>
|
||||
MixtureFactor(const KeyVector& keys, const DiscreteKeys& discreteKeys,
|
||||
const std::vector<boost::shared_ptr<FACTOR>>& factors,
|
||||
bool normalized = false)
|
||||
: Base(keys, discreteKeys), normalized_(normalized) {
|
||||
std::vector<NonlinearFactor::shared_ptr> nonlinear_factors;
|
||||
for (auto&& f : factors) {
|
||||
nonlinear_factors.push_back(
|
||||
boost::dynamic_pointer_cast<NonlinearFactor>(f));
|
||||
}
|
||||
factors_ = Factors(discreteKeys, nonlinear_factors);
|
||||
}
|
||||
|
||||
~MixtureFactor() = default;
|
||||
|
||||
/**
|
||||
* @brief Compute error of factor given both continuous and discrete values.
|
||||
*
|
||||
* @param continuousVals The continuous Values.
|
||||
* @param discreteVals The discrete Values.
|
||||
* @return double The error of this factor.
|
||||
*/
|
||||
double error(const Values& continuousVals,
|
||||
const DiscreteValues& discreteVals) const {
|
||||
// Retrieve the factor corresponding to the assignment in discreteVals.
|
||||
auto factor = factors_(discreteVals);
|
||||
// Compute the error for the selected factor
|
||||
const double factorError = factor->error(continuousVals);
|
||||
if (normalized_) return factorError;
|
||||
return factorError +
|
||||
this->nonlinearFactorLogNormalizingConstant(factor, continuousVals);
|
||||
}
|
||||
|
||||
size_t dim() const {
|
||||
// TODO(Varun)
|
||||
throw std::runtime_error("MixtureFactor::dim not implemented.");
|
||||
}
|
||||
|
||||
/// Testable
|
||||
/// @{
|
||||
|
||||
/// print to stdout
|
||||
void print(
|
||||
const std::string& s = "MixtureFactor",
|
||||
const KeyFormatter& keyFormatter = DefaultKeyFormatter) const override {
|
||||
std::cout << (s.empty() ? "" : s + " ");
|
||||
Base::print("", keyFormatter);
|
||||
std::cout << "\nMixtureFactor\n";
|
||||
auto valueFormatter = [](const sharedFactor& v) {
|
||||
if (v) {
|
||||
return (boost::format("Nonlinear factor on %d keys") % v->size()).str();
|
||||
} else {
|
||||
return std::string("nullptr");
|
||||
}
|
||||
};
|
||||
factors_.print("", keyFormatter, valueFormatter);
|
||||
}
|
||||
|
||||
/// Check equality
|
||||
bool equals(const HybridFactor& other, double tol = 1e-9) const override {
|
||||
// We attempt a dynamic cast from HybridFactor to MixtureFactor. If it
|
||||
// fails, return false.
|
||||
if (!dynamic_cast<const MixtureFactor*>(&other)) return false;
|
||||
|
||||
// If the cast is successful, we'll properly construct a MixtureFactor
|
||||
// object from `other`
|
||||
const MixtureFactor& f(static_cast<const MixtureFactor&>(other));
|
||||
|
||||
// Ensure that this MixtureFactor and `f` have the same `factors_`.
|
||||
auto compare = [tol](const sharedFactor& a, const sharedFactor& b) {
|
||||
return traits<NonlinearFactor>::Equals(*a, *b, tol);
|
||||
};
|
||||
if (!factors_.equals(f.factors_, compare)) return false;
|
||||
|
||||
// If everything above passes, and the keys_, discreteKeys_ and normalized_
|
||||
// member variables are identical, return true.
|
||||
return (std::equal(keys_.begin(), keys_.end(), f.keys().begin()) &&
|
||||
(discreteKeys_ == f.discreteKeys_) &&
|
||||
(normalized_ == f.normalized_));
|
||||
}
|
||||
|
||||
/// @}
|
||||
|
||||
/// Linearize specific nonlinear factors based on the assignment in
|
||||
/// discreteValues.
|
||||
GaussianFactor::shared_ptr linearize(
|
||||
const Values& continuousVals, const DiscreteValues& discreteVals) const {
|
||||
auto factor = factors_(discreteVals);
|
||||
return factor->linearize(continuousVals);
|
||||
}
|
||||
|
||||
/// Linearize all the continuous factors to get a GaussianMixtureFactor.
|
||||
boost::shared_ptr<GaussianMixtureFactor> linearize(
|
||||
const Values& continuousVals) const {
|
||||
// functional to linearize each factor in the decision tree
|
||||
auto linearizeDT = [continuousVals](const sharedFactor& factor) {
|
||||
return factor->linearize(continuousVals);
|
||||
};
|
||||
|
||||
DecisionTree<Key, GaussianFactor::shared_ptr> linearized_factors(
|
||||
factors_, linearizeDT);
|
||||
|
||||
return boost::make_shared<GaussianMixtureFactor>(
|
||||
continuousKeys_, discreteKeys_, linearized_factors);
|
||||
}
|
||||
|
||||
/**
|
||||
* If the component factors are not already normalized, we want to compute
|
||||
* their normalizing constants so that the resulting joint distribution is
|
||||
* appropriately computed. Remember, this is the _negative_ normalizing
|
||||
* constant for the measurement likelihood (since we are minimizing the
|
||||
* _negative_ log-likelihood).
|
||||
*/
|
||||
double nonlinearFactorLogNormalizingConstant(const sharedFactor& factor,
|
||||
const Values& values) const {
|
||||
// Information matrix (inverse covariance matrix) for the factor.
|
||||
Matrix infoMat;
|
||||
|
||||
// If this is a NoiseModelFactor, we'll use its noiseModel to
|
||||
// otherwise noiseModelFactor will be nullptr
|
||||
if (auto noiseModelFactor =
|
||||
boost::dynamic_pointer_cast<NoiseModelFactor>(factor)) {
|
||||
// If dynamic cast to NoiseModelFactor succeeded, see if the noise model
|
||||
// is Gaussian
|
||||
auto noiseModel = noiseModelFactor->noiseModel();
|
||||
|
||||
auto gaussianNoiseModel =
|
||||
boost::dynamic_pointer_cast<noiseModel::Gaussian>(noiseModel);
|
||||
if (gaussianNoiseModel) {
|
||||
// If the noise model is Gaussian, retrieve the information matrix
|
||||
infoMat = gaussianNoiseModel->information();
|
||||
} else {
|
||||
// If the factor is not a Gaussian factor, we'll linearize it to get
|
||||
// something with a normalized noise model
|
||||
// TODO(kevin): does this make sense to do? I think maybe not in
|
||||
// general? Should we just yell at the user?
|
||||
auto gaussianFactor = factor->linearize(values);
|
||||
infoMat = gaussianFactor->information();
|
||||
}
|
||||
}
|
||||
|
||||
// Compute the (negative) log of the normalizing constant
|
||||
return -(factor->dim() * log(2.0 * M_PI) / 2.0) -
|
||||
(log(infoMat.determinant()) / 2.0);
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace gtsam
|
||||
|
|
@ -21,10 +21,16 @@
|
|||
#include <gtsam/discrete/DiscreteDistribution.h>
|
||||
#include <gtsam/hybrid/GaussianMixtureFactor.h>
|
||||
#include <gtsam/hybrid/HybridGaussianFactorGraph.h>
|
||||
#include <gtsam/hybrid/HybridNonlinearFactorGraph.h>
|
||||
#include <gtsam/hybrid/MixtureFactor.h>
|
||||
#include <gtsam/inference/Symbol.h>
|
||||
#include <gtsam/linear/JacobianFactor.h>
|
||||
#include <gtsam/linear/NoiseModel.h>
|
||||
#include <gtsam/nonlinear/PriorFactor.h>
|
||||
#include <gtsam/slam/BetweenFactor.h>
|
||||
|
||||
#include <vector>
|
||||
|
||||
#pragma once
|
||||
|
||||
namespace gtsam {
|
||||
|
|
@ -33,111 +39,6 @@ using symbol_shorthand::C;
|
|||
using symbol_shorthand::M;
|
||||
using symbol_shorthand::X;
|
||||
|
||||
/* ****************************************************************************/
|
||||
// Test fixture with switching network.
|
||||
// TODO(Varun) Currently this is only linear. We need to add nonlinear support
|
||||
// and then update to
|
||||
// https://github.com/borglab/gtsam/pull/973/files#diff-58c02b3b197ebf731694946e87762d252e9eaa2f5c6c4ba22d618085b321ca23
|
||||
struct Switching {
|
||||
size_t K;
|
||||
DiscreteKeys modes;
|
||||
HybridGaussianFactorGraph linearizedFactorGraph;
|
||||
Values linearizationPoint;
|
||||
|
||||
using MotionModel = BetweenFactor<double>;
|
||||
// using MotionMixture = MixtureFactor<MotionModel>;
|
||||
|
||||
/// Create with given number of time steps.
|
||||
Switching(size_t K, double between_sigma = 1.0, double prior_sigma = 0.1)
|
||||
: K(K) {
|
||||
// Create DiscreteKeys for binary K modes, modes[0] will not be used.
|
||||
modes = addDiscreteModes(K);
|
||||
|
||||
// Create hybrid factor graph.
|
||||
// Add a prior on X(1).
|
||||
auto prior = boost::make_shared<JacobianFactor>(
|
||||
X(1), Matrix11::Ones() / prior_sigma, Vector1::Zero());
|
||||
linearizedFactorGraph.push_back(prior);
|
||||
|
||||
// Add "motion models".
|
||||
linearizedFactorGraph = addMotionModels(K);
|
||||
|
||||
// Add measurement factors
|
||||
for (size_t k = 1; k <= K; k++) {
|
||||
linearizedFactorGraph.emplace_gaussian<JacobianFactor>(
|
||||
X(k), Matrix11::Ones() / 0.1, Vector1::Zero());
|
||||
}
|
||||
|
||||
// Add "mode chain"
|
||||
linearizedFactorGraph = addModeChain(linearizedFactorGraph);
|
||||
|
||||
// Create the linearization point.
|
||||
for (size_t k = 1; k <= K; k++) {
|
||||
linearizationPoint.insert<double>(X(k), static_cast<double>(k));
|
||||
}
|
||||
}
|
||||
|
||||
/// Create DiscreteKeys for K binary modes.
|
||||
DiscreteKeys addDiscreteModes(size_t K) {
|
||||
DiscreteKeys m;
|
||||
for (size_t k = 0; k <= K; k++) {
|
||||
m.emplace_back(M(k), 2);
|
||||
}
|
||||
return m;
|
||||
}
|
||||
|
||||
/// Helper function to add motion models for each [k, k+1] interval.
|
||||
HybridGaussianFactorGraph addMotionModels(size_t K) {
|
||||
HybridGaussianFactorGraph hgfg;
|
||||
for (size_t k = 1; k < K; k++) {
|
||||
auto keys = {X(k), X(k + 1)};
|
||||
auto components = motionModels(k);
|
||||
hgfg.emplace_hybrid<GaussianMixtureFactor>(keys, DiscreteKeys{modes[k]},
|
||||
components);
|
||||
}
|
||||
return hgfg;
|
||||
}
|
||||
|
||||
// Create motion models for a given time step
|
||||
static std::vector<GaussianFactor::shared_ptr> motionModels(
|
||||
size_t k, double sigma = 1.0) {
|
||||
auto noise_model = noiseModel::Isotropic::Sigma(1, sigma);
|
||||
auto still = boost::make_shared<JacobianFactor>(
|
||||
X(k), -Matrix11::Ones() / sigma, X(k + 1),
|
||||
Matrix11::Ones() / sigma, Vector1::Zero()),
|
||||
moving = boost::make_shared<JacobianFactor>(
|
||||
X(k), -Matrix11::Ones() / sigma, X(k + 1),
|
||||
Matrix11::Ones() / sigma, -Vector1::Ones() / sigma);
|
||||
return {boost::dynamic_pointer_cast<GaussianFactor>(still),
|
||||
boost::dynamic_pointer_cast<GaussianFactor>(moving)};
|
||||
}
|
||||
|
||||
// // Add "mode chain" to NonlinearHybridFactorGraph
|
||||
// void addModeChain(HybridNonlinearFactorGraph& fg) {
|
||||
// auto prior = boost::make_shared<DiscreteDistribution>(modes[1], "1/1");
|
||||
// fg.push_discrete(prior);
|
||||
// for (size_t k = 1; k < K - 1; k++) {
|
||||
// auto parents = {modes[k]};
|
||||
// auto conditional = boost::make_shared<DiscreteConditional>(
|
||||
// modes[k + 1], parents, "1/2 3/2");
|
||||
// fg.push_discrete(conditional);
|
||||
// }
|
||||
// }
|
||||
|
||||
// Add "mode chain" to GaussianHybridFactorGraph
|
||||
HybridGaussianFactorGraph addModeChain(HybridGaussianFactorGraph& fg) {
|
||||
auto prior = boost::make_shared<DiscreteDistribution>(modes[1], "1/1");
|
||||
fg.push_discrete(prior);
|
||||
for (size_t k = 1; k < K - 1; k++) {
|
||||
auto parents = {modes[k]};
|
||||
auto conditional = boost::make_shared<DiscreteConditional>(
|
||||
modes[k + 1], parents, "1/2 3/2");
|
||||
fg.push_discrete(conditional);
|
||||
}
|
||||
return fg;
|
||||
}
|
||||
};
|
||||
|
||||
/**
|
||||
* @brief Create a switching system chain. A switching system is a continuous
|
||||
* system which depends on a discrete mode at each time step of the chain.
|
||||
|
|
@ -149,7 +50,7 @@ struct Switching {
|
|||
*/
|
||||
inline HybridGaussianFactorGraph::shared_ptr makeSwitchingChain(
|
||||
size_t n, std::function<Key(int)> keyFunc = X,
|
||||
std::function<Key(int)> dKeyFunc = C) {
|
||||
std::function<Key(int)> dKeyFunc = M) {
|
||||
HybridGaussianFactorGraph hfg;
|
||||
|
||||
hfg.add(JacobianFactor(keyFunc(1), I_3x3, Z_3x1));
|
||||
|
|
@ -182,7 +83,7 @@ inline HybridGaussianFactorGraph::shared_ptr makeSwitchingChain(
|
|||
* @return std::pair<KeyVector, std::vector<int>>
|
||||
*/
|
||||
inline std::pair<KeyVector, std::vector<int>> makeBinaryOrdering(
|
||||
std::vector<Key>& input) {
|
||||
std::vector<Key> &input) {
|
||||
KeyVector new_order;
|
||||
|
||||
std::vector<int> levels(input.size());
|
||||
|
|
@ -211,4 +112,103 @@ inline std::pair<KeyVector, std::vector<int>> makeBinaryOrdering(
|
|||
return {new_order, levels};
|
||||
}
|
||||
|
||||
/* ***************************************************************************
|
||||
*/
|
||||
using MotionModel = BetweenFactor<double>;
|
||||
// using MotionMixture = MixtureFactor<MotionModel>;
|
||||
|
||||
// Test fixture with switching network.
|
||||
struct Switching {
|
||||
size_t K;
|
||||
DiscreteKeys modes;
|
||||
HybridNonlinearFactorGraph nonlinearFactorGraph;
|
||||
HybridGaussianFactorGraph linearizedFactorGraph;
|
||||
Values linearizationPoint;
|
||||
|
||||
/// Create with given number of time steps.
|
||||
Switching(size_t K, double between_sigma = 1.0, double prior_sigma = 0.1)
|
||||
: K(K) {
|
||||
using symbol_shorthand::M;
|
||||
using symbol_shorthand::X;
|
||||
|
||||
// Create DiscreteKeys for binary K modes, modes[0] will not be used.
|
||||
for (size_t k = 0; k <= K; k++) {
|
||||
modes.emplace_back(M(k), 2);
|
||||
}
|
||||
|
||||
// Create hybrid factor graph.
|
||||
// Add a prior on X(1).
|
||||
auto prior = boost::make_shared<PriorFactor<double>>(
|
||||
X(1), 0, noiseModel::Isotropic::Sigma(1, prior_sigma));
|
||||
nonlinearFactorGraph.push_nonlinear(prior);
|
||||
|
||||
// Add "motion models".
|
||||
for (size_t k = 1; k < K; k++) {
|
||||
KeyVector keys = {X(k), X(k + 1)};
|
||||
auto motion_models = motionModels(k);
|
||||
std::vector<NonlinearFactor::shared_ptr> components;
|
||||
for (auto &&f : motion_models) {
|
||||
components.push_back(boost::dynamic_pointer_cast<NonlinearFactor>(f));
|
||||
}
|
||||
nonlinearFactorGraph.emplace_hybrid<MixtureFactor>(
|
||||
keys, DiscreteKeys{modes[k]}, components);
|
||||
}
|
||||
|
||||
// Add measurement factors
|
||||
auto measurement_noise = noiseModel::Isotropic::Sigma(1, 0.1);
|
||||
for (size_t k = 2; k <= K; k++) {
|
||||
nonlinearFactorGraph.emplace_nonlinear<PriorFactor<double>>(
|
||||
X(k), 1.0 * (k - 1), measurement_noise);
|
||||
}
|
||||
|
||||
// Add "mode chain"
|
||||
addModeChain(&nonlinearFactorGraph);
|
||||
|
||||
// Create the linearization point.
|
||||
for (size_t k = 1; k <= K; k++) {
|
||||
linearizationPoint.insert<double>(X(k), static_cast<double>(k));
|
||||
}
|
||||
|
||||
linearizedFactorGraph = nonlinearFactorGraph.linearize(linearizationPoint);
|
||||
}
|
||||
|
||||
// Create motion models for a given time step
|
||||
static std::vector<MotionModel::shared_ptr> motionModels(size_t k,
|
||||
double sigma = 1.0) {
|
||||
using symbol_shorthand::M;
|
||||
using symbol_shorthand::X;
|
||||
|
||||
auto noise_model = noiseModel::Isotropic::Sigma(1, sigma);
|
||||
auto still =
|
||||
boost::make_shared<MotionModel>(X(k), X(k + 1), 0.0, noise_model),
|
||||
moving =
|
||||
boost::make_shared<MotionModel>(X(k), X(k + 1), 1.0, noise_model);
|
||||
return {still, moving};
|
||||
}
|
||||
|
||||
// Add "mode chain" to HybridNonlinearFactorGraph
|
||||
void addModeChain(HybridNonlinearFactorGraph *fg) {
|
||||
auto prior = boost::make_shared<DiscreteDistribution>(modes[1], "1/1");
|
||||
fg->push_discrete(prior);
|
||||
for (size_t k = 1; k < K - 1; k++) {
|
||||
auto parents = {modes[k]};
|
||||
auto conditional = boost::make_shared<DiscreteConditional>(
|
||||
modes[k + 1], parents, "1/2 3/2");
|
||||
fg->push_discrete(conditional);
|
||||
}
|
||||
}
|
||||
|
||||
// Add "mode chain" to HybridGaussianFactorGraph
|
||||
void addModeChain(HybridGaussianFactorGraph *fg) {
|
||||
auto prior = boost::make_shared<DiscreteDistribution>(modes[1], "1/1");
|
||||
fg->push_discrete(prior);
|
||||
for (size_t k = 1; k < K - 1; k++) {
|
||||
auto parents = {modes[k]};
|
||||
auto conditional = boost::make_shared<DiscreteConditional>(
|
||||
modes[k + 1], parents, "1/2 3/2");
|
||||
fg->push_discrete(conditional);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace gtsam
|
||||
|
|
|
|||
|
|
@ -52,8 +52,8 @@ using namespace boost::assign;
|
|||
using namespace std;
|
||||
using namespace gtsam;
|
||||
|
||||
using gtsam::symbol_shorthand::C;
|
||||
using gtsam::symbol_shorthand::D;
|
||||
using gtsam::symbol_shorthand::M;
|
||||
using gtsam::symbol_shorthand::X;
|
||||
using gtsam::symbol_shorthand::Y;
|
||||
|
||||
|
|
@ -67,9 +67,9 @@ TEST(HybridGaussianFactorGraph, Creation) {
|
|||
|
||||
// Define a gaussian mixture conditional P(x0|x1, c0) and add it to the factor
|
||||
// graph
|
||||
GaussianMixture gm({X(0)}, {X(1)}, DiscreteKeys(DiscreteKey{C(0), 2}),
|
||||
GaussianMixture gm({X(0)}, {X(1)}, DiscreteKeys(DiscreteKey{M(0), 2}),
|
||||
GaussianMixture::Conditionals(
|
||||
C(0),
|
||||
M(0),
|
||||
boost::make_shared<GaussianConditional>(
|
||||
X(0), Z_3x1, I_3x3, X(1), I_3x3),
|
||||
boost::make_shared<GaussianConditional>(
|
||||
|
|
@ -96,11 +96,11 @@ TEST(HybridGaussianFactorGraph, EliminateMultifrontal) {
|
|||
// Test multifrontal elimination
|
||||
HybridGaussianFactorGraph hfg;
|
||||
|
||||
DiscreteKey c(C(1), 2);
|
||||
DiscreteKey m(M(1), 2);
|
||||
|
||||
// Add priors on x0 and c1
|
||||
hfg.add(JacobianFactor(X(0), I_3x3, Z_3x1));
|
||||
hfg.add(HybridDiscreteFactor(DecisionTreeFactor(c, {2, 8})));
|
||||
hfg.add(HybridDiscreteFactor(DecisionTreeFactor(m, {2, 8})));
|
||||
|
||||
Ordering ordering;
|
||||
ordering.push_back(X(0));
|
||||
|
|
@ -114,7 +114,7 @@ TEST(HybridGaussianFactorGraph, EliminateMultifrontal) {
|
|||
TEST(HybridGaussianFactorGraph, eliminateFullSequentialEqualChance) {
|
||||
HybridGaussianFactorGraph hfg;
|
||||
|
||||
DiscreteKey c1(C(1), 2);
|
||||
DiscreteKey m1(M(1), 2);
|
||||
|
||||
// Add prior on x0
|
||||
hfg.add(JacobianFactor(X(0), I_3x3, Z_3x1));
|
||||
|
|
@ -123,45 +123,45 @@ TEST(HybridGaussianFactorGraph, eliminateFullSequentialEqualChance) {
|
|||
|
||||
// Add a gaussian mixture factor ϕ(x1, c1)
|
||||
DecisionTree<Key, GaussianFactor::shared_ptr> dt(
|
||||
C(1), boost::make_shared<JacobianFactor>(X(1), I_3x3, Z_3x1),
|
||||
M(1), boost::make_shared<JacobianFactor>(X(1), I_3x3, Z_3x1),
|
||||
boost::make_shared<JacobianFactor>(X(1), I_3x3, Vector3::Ones()));
|
||||
|
||||
hfg.add(GaussianMixtureFactor({X(1)}, {c1}, dt));
|
||||
hfg.add(GaussianMixtureFactor({X(1)}, {m1}, dt));
|
||||
|
||||
auto result =
|
||||
hfg.eliminateSequential(Ordering::ColamdConstrainedLast(hfg, {C(1)}));
|
||||
hfg.eliminateSequential(Ordering::ColamdConstrainedLast(hfg, {M(1)}));
|
||||
|
||||
auto dc = result->at(2)->asDiscreteConditional();
|
||||
DiscreteValues mode;
|
||||
mode[C(1)] = 0;
|
||||
EXPECT_DOUBLES_EQUAL(0.6225, (*dc)(mode), 1e-3);
|
||||
DiscreteValues dv;
|
||||
dv[M(1)] = 0;
|
||||
EXPECT_DOUBLES_EQUAL(1, dc->operator()(dv), 1e-3);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST(HybridGaussianFactorGraph, eliminateFullSequentialSimple) {
|
||||
HybridGaussianFactorGraph hfg;
|
||||
|
||||
DiscreteKey c1(C(1), 2);
|
||||
DiscreteKey m1(M(1), 2);
|
||||
|
||||
// Add prior on x0
|
||||
hfg.add(JacobianFactor(X(0), I_3x3, Z_3x1));
|
||||
// Add factor between x0 and x1
|
||||
hfg.add(JacobianFactor(X(0), I_3x3, X(1), -I_3x3, Z_3x1));
|
||||
|
||||
DecisionTree<Key, GaussianFactor::shared_ptr> dt(
|
||||
C(1), boost::make_shared<JacobianFactor>(X(1), I_3x3, Z_3x1),
|
||||
boost::make_shared<JacobianFactor>(X(1), I_3x3, Vector3::Ones()));
|
||||
hfg.add(GaussianMixtureFactor({X(1)}, {c1}, dt));
|
||||
std::vector<GaussianFactor::shared_ptr> factors = {
|
||||
boost::make_shared<JacobianFactor>(X(1), I_3x3, Z_3x1),
|
||||
boost::make_shared<JacobianFactor>(X(1), I_3x3, Vector3::Ones())};
|
||||
hfg.add(GaussianMixtureFactor({X(1)}, {m1}, factors));
|
||||
|
||||
// Discrete probability table for c1
|
||||
hfg.add(HybridDiscreteFactor(DecisionTreeFactor(c1, {2, 8})));
|
||||
hfg.add(DecisionTreeFactor(m1, {2, 8}));
|
||||
// Joint discrete probability table for c1, c2
|
||||
hfg.add(HybridDiscreteFactor(
|
||||
DecisionTreeFactor({{C(1), 2}, {C(2), 2}}, "1 2 3 4")));
|
||||
hfg.add(DecisionTreeFactor({{M(1), 2}, {M(2), 2}}, "1 2 3 4"));
|
||||
|
||||
auto result = hfg.eliminateSequential(
|
||||
Ordering::ColamdConstrainedLast(hfg, {C(1), C(2)}));
|
||||
HybridBayesNet::shared_ptr result = hfg.eliminateSequential(
|
||||
Ordering::ColamdConstrainedLast(hfg, {M(1), M(2)}));
|
||||
|
||||
// There are 4 variables (2 continuous + 2 discrete) in the bayes net.
|
||||
EXPECT_LONGS_EQUAL(4, result->size());
|
||||
}
|
||||
|
||||
|
|
@ -169,31 +169,33 @@ TEST(HybridGaussianFactorGraph, eliminateFullSequentialSimple) {
|
|||
TEST(HybridGaussianFactorGraph, eliminateFullMultifrontalSimple) {
|
||||
HybridGaussianFactorGraph hfg;
|
||||
|
||||
DiscreteKey c1(C(1), 2);
|
||||
DiscreteKey m1(M(1), 2);
|
||||
|
||||
hfg.add(JacobianFactor(X(0), I_3x3, Z_3x1));
|
||||
hfg.add(JacobianFactor(X(0), I_3x3, X(1), -I_3x3, Z_3x1));
|
||||
|
||||
hfg.add(GaussianMixtureFactor::FromFactors(
|
||||
{X(1)}, {{C(1), 2}},
|
||||
{X(1)}, {{M(1), 2}},
|
||||
{boost::make_shared<JacobianFactor>(X(1), I_3x3, Z_3x1),
|
||||
boost::make_shared<JacobianFactor>(X(1), I_3x3, Vector3::Ones())}));
|
||||
|
||||
hfg.add(DecisionTreeFactor(c1, {2, 8}));
|
||||
hfg.add(DecisionTreeFactor({{C(1), 2}, {C(2), 2}}, "1 2 3 4"));
|
||||
hfg.add(DecisionTreeFactor(m1, {2, 8}));
|
||||
hfg.add(DecisionTreeFactor({{M(1), 2}, {M(2), 2}}, "1 2 3 4"));
|
||||
|
||||
auto result = hfg.eliminateMultifrontal(
|
||||
Ordering::ColamdConstrainedLast(hfg, {C(1), C(2)}));
|
||||
HybridBayesTree::shared_ptr result = hfg.eliminateMultifrontal(
|
||||
Ordering::ColamdConstrainedLast(hfg, {M(1), M(2)}));
|
||||
|
||||
// The bayes tree should have 3 cliques
|
||||
EXPECT_LONGS_EQUAL(3, result->size());
|
||||
// GTSAM_PRINT(*result);
|
||||
// GTSAM_PRINT(*result->marginalFactor(C(2)));
|
||||
// GTSAM_PRINT(*result->marginalFactor(M(2)));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST(HybridGaussianFactorGraph, eliminateFullMultifrontalCLG) {
|
||||
HybridGaussianFactorGraph hfg;
|
||||
|
||||
DiscreteKey c(C(1), 2);
|
||||
DiscreteKey m(M(1), 2);
|
||||
|
||||
// Prior on x0
|
||||
hfg.add(JacobianFactor(X(0), I_3x3, Z_3x1));
|
||||
|
|
@ -202,16 +204,16 @@ TEST(HybridGaussianFactorGraph, eliminateFullMultifrontalCLG) {
|
|||
|
||||
// Decision tree with different modes on x1
|
||||
DecisionTree<Key, GaussianFactor::shared_ptr> dt(
|
||||
C(1), boost::make_shared<JacobianFactor>(X(1), I_3x3, Z_3x1),
|
||||
M(1), boost::make_shared<JacobianFactor>(X(1), I_3x3, Z_3x1),
|
||||
boost::make_shared<JacobianFactor>(X(1), I_3x3, Vector3::Ones()));
|
||||
|
||||
// Hybrid factor P(x1|c1)
|
||||
hfg.add(GaussianMixtureFactor({X(1)}, {c}, dt));
|
||||
hfg.add(GaussianMixtureFactor({X(1)}, {m}, dt));
|
||||
// Prior factor on c1
|
||||
hfg.add(HybridDiscreteFactor(DecisionTreeFactor(c, {2, 8})));
|
||||
hfg.add(HybridDiscreteFactor(DecisionTreeFactor(m, {2, 8})));
|
||||
|
||||
// Get a constrained ordering keeping c1 last
|
||||
auto ordering_full = Ordering::ColamdConstrainedLast(hfg, {C(1)});
|
||||
auto ordering_full = Ordering::ColamdConstrainedLast(hfg, {M(1)});
|
||||
|
||||
// Returns a Hybrid Bayes Tree with distribution P(x0|x1)P(x1|c1)P(c1)
|
||||
HybridBayesTree::shared_ptr hbt = hfg.eliminateMultifrontal(ordering_full);
|
||||
|
|
@ -232,48 +234,48 @@ TEST(HybridGaussianFactorGraph, eliminateFullMultifrontalTwoClique) {
|
|||
|
||||
{
|
||||
hfg.add(GaussianMixtureFactor::FromFactors(
|
||||
{X(0)}, {{C(0), 2}},
|
||||
{X(0)}, {{M(0), 2}},
|
||||
{boost::make_shared<JacobianFactor>(X(0), I_3x3, Z_3x1),
|
||||
boost::make_shared<JacobianFactor>(X(0), I_3x3, Vector3::Ones())}));
|
||||
|
||||
DecisionTree<Key, GaussianFactor::shared_ptr> dt1(
|
||||
C(1), boost::make_shared<JacobianFactor>(X(2), I_3x3, Z_3x1),
|
||||
M(1), boost::make_shared<JacobianFactor>(X(2), I_3x3, Z_3x1),
|
||||
boost::make_shared<JacobianFactor>(X(2), I_3x3, Vector3::Ones()));
|
||||
|
||||
hfg.add(GaussianMixtureFactor({X(2)}, {{C(1), 2}}, dt1));
|
||||
hfg.add(GaussianMixtureFactor({X(2)}, {{M(1), 2}}, dt1));
|
||||
}
|
||||
|
||||
hfg.add(HybridDiscreteFactor(
|
||||
DecisionTreeFactor({{C(1), 2}, {C(2), 2}}, "1 2 3 4")));
|
||||
DecisionTreeFactor({{M(1), 2}, {M(2), 2}}, "1 2 3 4")));
|
||||
|
||||
hfg.add(JacobianFactor(X(3), I_3x3, X(4), -I_3x3, Z_3x1));
|
||||
hfg.add(JacobianFactor(X(4), I_3x3, X(5), -I_3x3, Z_3x1));
|
||||
|
||||
{
|
||||
DecisionTree<Key, GaussianFactor::shared_ptr> dt(
|
||||
C(3), boost::make_shared<JacobianFactor>(X(3), I_3x3, Z_3x1),
|
||||
M(3), boost::make_shared<JacobianFactor>(X(3), I_3x3, Z_3x1),
|
||||
boost::make_shared<JacobianFactor>(X(3), I_3x3, Vector3::Ones()));
|
||||
|
||||
hfg.add(GaussianMixtureFactor({X(3)}, {{C(3), 2}}, dt));
|
||||
hfg.add(GaussianMixtureFactor({X(3)}, {{M(3), 2}}, dt));
|
||||
|
||||
DecisionTree<Key, GaussianFactor::shared_ptr> dt1(
|
||||
C(2), boost::make_shared<JacobianFactor>(X(5), I_3x3, Z_3x1),
|
||||
M(2), boost::make_shared<JacobianFactor>(X(5), I_3x3, Z_3x1),
|
||||
boost::make_shared<JacobianFactor>(X(5), I_3x3, Vector3::Ones()));
|
||||
|
||||
hfg.add(GaussianMixtureFactor({X(5)}, {{C(2), 2}}, dt1));
|
||||
hfg.add(GaussianMixtureFactor({X(5)}, {{M(2), 2}}, dt1));
|
||||
}
|
||||
|
||||
auto ordering_full =
|
||||
Ordering::ColamdConstrainedLast(hfg, {C(0), C(1), C(2), C(3)});
|
||||
Ordering::ColamdConstrainedLast(hfg, {M(0), M(1), M(2), M(3)});
|
||||
|
||||
HybridBayesTree::shared_ptr hbt;
|
||||
HybridGaussianFactorGraph::shared_ptr remaining;
|
||||
std::tie(hbt, remaining) = hfg.eliminatePartialMultifrontal(ordering_full);
|
||||
|
||||
// GTSAM_PRINT(*hbt);
|
||||
// GTSAM_PRINT(*remaining);
|
||||
// 9 cliques in the bayes tree and 0 remaining variables to eliminate.
|
||||
EXPECT_LONGS_EQUAL(9, hbt->size());
|
||||
EXPECT_LONGS_EQUAL(0, remaining->size());
|
||||
|
||||
// hbt->marginalFactor(X(1))->print("HBT: ");
|
||||
/*
|
||||
(Fan) Explanation: the Junction tree will need to reeliminate to get to the
|
||||
marginal on X(1), which is not possible because it involves eliminating
|
||||
|
|
@ -307,13 +309,13 @@ TEST(HybridGaussianFactorGraph, Switching) {
|
|||
// X(3), X(7)
|
||||
// X(2), X(8)
|
||||
// X(1), X(4), X(6), X(9)
|
||||
// C(5) will be the center, C(1-4), C(6-8)
|
||||
// C(3), C(7)
|
||||
// C(1), C(4), C(2), C(6), C(8)
|
||||
// M(5) will be the center, M(1-4), M(6-8)
|
||||
// M(3), M(7)
|
||||
// M(1), M(4), M(2), M(6), M(8)
|
||||
// auto ordering_full =
|
||||
// Ordering(KeyVector{X(1), X(4), X(2), X(6), X(9), X(8), X(3), X(7),
|
||||
// X(5),
|
||||
// C(1), C(4), C(2), C(6), C(8), C(3), C(7), C(5)});
|
||||
// M(1), M(4), M(2), M(6), M(8), M(3), M(7), M(5)});
|
||||
KeyVector ordering;
|
||||
|
||||
{
|
||||
|
|
@ -336,7 +338,7 @@ TEST(HybridGaussianFactorGraph, Switching) {
|
|||
std::iota(naturalC.begin(), naturalC.end(), 1);
|
||||
std::vector<Key> ordC;
|
||||
std::transform(naturalC.begin(), naturalC.end(), std::back_inserter(ordC),
|
||||
[](int x) { return C(x); });
|
||||
[](int x) { return M(x); });
|
||||
KeyVector ndC;
|
||||
std::vector<int> lvls;
|
||||
|
||||
|
|
@ -353,9 +355,9 @@ TEST(HybridGaussianFactorGraph, Switching) {
|
|||
HybridGaussianFactorGraph::shared_ptr remaining;
|
||||
std::tie(hbt, remaining) = hfg->eliminatePartialMultifrontal(ordering_full);
|
||||
|
||||
// GTSAM_PRINT(*hbt);
|
||||
// GTSAM_PRINT(*remaining);
|
||||
// hbt->marginalFactor(C(11))->print("HBT: ");
|
||||
// 12 cliques in the bayes tree and 0 remaining variables to eliminate.
|
||||
EXPECT_LONGS_EQUAL(12, hbt->size());
|
||||
EXPECT_LONGS_EQUAL(0, remaining->size());
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
|
@ -368,13 +370,13 @@ TEST(HybridGaussianFactorGraph, SwitchingISAM) {
|
|||
// X(3), X(7)
|
||||
// X(2), X(8)
|
||||
// X(1), X(4), X(6), X(9)
|
||||
// C(5) will be the center, C(1-4), C(6-8)
|
||||
// C(3), C(7)
|
||||
// C(1), C(4), C(2), C(6), C(8)
|
||||
// M(5) will be the center, M(1-4), M(6-8)
|
||||
// M(3), M(7)
|
||||
// M(1), M(4), M(2), M(6), M(8)
|
||||
// auto ordering_full =
|
||||
// Ordering(KeyVector{X(1), X(4), X(2), X(6), X(9), X(8), X(3), X(7),
|
||||
// X(5),
|
||||
// C(1), C(4), C(2), C(6), C(8), C(3), C(7), C(5)});
|
||||
// M(1), M(4), M(2), M(6), M(8), M(3), M(7), M(5)});
|
||||
KeyVector ordering;
|
||||
|
||||
{
|
||||
|
|
@ -397,7 +399,7 @@ TEST(HybridGaussianFactorGraph, SwitchingISAM) {
|
|||
std::iota(naturalC.begin(), naturalC.end(), 1);
|
||||
std::vector<Key> ordC;
|
||||
std::transform(naturalC.begin(), naturalC.end(), std::back_inserter(ordC),
|
||||
[](int x) { return C(x); });
|
||||
[](int x) { return M(x); });
|
||||
KeyVector ndC;
|
||||
std::vector<int> lvls;
|
||||
|
||||
|
|
@ -407,9 +409,6 @@ TEST(HybridGaussianFactorGraph, SwitchingISAM) {
|
|||
}
|
||||
auto ordering_full = Ordering(ordering);
|
||||
|
||||
// GTSAM_PRINT(*hfg);
|
||||
// GTSAM_PRINT(ordering_full);
|
||||
|
||||
HybridBayesTree::shared_ptr hbt;
|
||||
HybridGaussianFactorGraph::shared_ptr remaining;
|
||||
std::tie(hbt, remaining) = hfg->eliminatePartialMultifrontal(ordering_full);
|
||||
|
|
@ -417,19 +416,18 @@ TEST(HybridGaussianFactorGraph, SwitchingISAM) {
|
|||
auto new_fg = makeSwitchingChain(12);
|
||||
auto isam = HybridGaussianISAM(*hbt);
|
||||
|
||||
{
|
||||
// Run an ISAM update.
|
||||
HybridGaussianFactorGraph factorGraph;
|
||||
factorGraph.push_back(new_fg->at(new_fg->size() - 2));
|
||||
factorGraph.push_back(new_fg->at(new_fg->size() - 1));
|
||||
isam.update(factorGraph);
|
||||
// std::cout << isam.dot();
|
||||
// isam.marginalFactor(C(11))->print();
|
||||
}
|
||||
|
||||
// ISAM should have 12 factors after the last update
|
||||
EXPECT_LONGS_EQUAL(12, isam.size());
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// TODO(Varun) Actually test something!
|
||||
TEST_DISABLED(HybridGaussianFactorGraph, SwitchingTwoVar) {
|
||||
TEST(HybridGaussianFactorGraph, SwitchingTwoVar) {
|
||||
const int N = 7;
|
||||
auto hfg = makeSwitchingChain(N, X);
|
||||
hfg->push_back(*makeSwitchingChain(N, Y, D));
|
||||
|
|
@ -449,7 +447,7 @@ TEST_DISABLED(HybridGaussianFactorGraph, SwitchingTwoVar) {
|
|||
}
|
||||
|
||||
for (size_t i = 1; i <= N - 1; i++) {
|
||||
ordX.emplace_back(C(i));
|
||||
ordX.emplace_back(M(i));
|
||||
}
|
||||
for (size_t i = 1; i <= N - 1; i++) {
|
||||
ordX.emplace_back(D(i));
|
||||
|
|
@ -461,8 +459,8 @@ TEST_DISABLED(HybridGaussianFactorGraph, SwitchingTwoVar) {
|
|||
dw.positionHints['c'] = 0;
|
||||
dw.positionHints['d'] = 3;
|
||||
dw.positionHints['y'] = 2;
|
||||
std::cout << hfg->dot(DefaultKeyFormatter, dw);
|
||||
std::cout << "\n";
|
||||
// std::cout << hfg->dot(DefaultKeyFormatter, dw);
|
||||
// std::cout << "\n";
|
||||
}
|
||||
|
||||
{
|
||||
|
|
@ -471,10 +469,10 @@ TEST_DISABLED(HybridGaussianFactorGraph, SwitchingTwoVar) {
|
|||
// dw.positionHints['c'] = 0;
|
||||
// dw.positionHints['d'] = 3;
|
||||
dw.positionHints['x'] = 1;
|
||||
std::cout << "\n";
|
||||
// std::cout << "\n";
|
||||
// std::cout << hfg->eliminateSequential(Ordering(ordX))
|
||||
// ->dot(DefaultKeyFormatter, dw);
|
||||
hfg->eliminateMultifrontal(Ordering(ordX))->dot(std::cout);
|
||||
// hfg->eliminateMultifrontal(Ordering(ordX))->dot(std::cout);
|
||||
}
|
||||
|
||||
Ordering ordering_partial;
|
||||
|
|
@ -482,22 +480,22 @@ TEST_DISABLED(HybridGaussianFactorGraph, SwitchingTwoVar) {
|
|||
ordering_partial.emplace_back(X(i));
|
||||
ordering_partial.emplace_back(Y(i));
|
||||
}
|
||||
{
|
||||
HybridBayesNet::shared_ptr hbn;
|
||||
HybridGaussianFactorGraph::shared_ptr remaining;
|
||||
std::tie(hbn, remaining) =
|
||||
hfg->eliminatePartialSequential(ordering_partial);
|
||||
|
||||
// remaining->print();
|
||||
EXPECT_LONGS_EQUAL(14, hbn->size());
|
||||
EXPECT_LONGS_EQUAL(11, remaining->size());
|
||||
|
||||
{
|
||||
DotWriter dw;
|
||||
dw.positionHints['x'] = 1;
|
||||
dw.positionHints['c'] = 0;
|
||||
dw.positionHints['d'] = 3;
|
||||
dw.positionHints['y'] = 2;
|
||||
std::cout << remaining->dot(DefaultKeyFormatter, dw);
|
||||
std::cout << "\n";
|
||||
}
|
||||
// std::cout << remaining->dot(DefaultKeyFormatter, dw);
|
||||
// std::cout << "\n";
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -74,7 +74,7 @@ TEST(GaussianMixtureFactor, Sum) {
|
|||
// Check that number of keys is 3
|
||||
EXPECT_LONGS_EQUAL(3, mixtureFactorA.keys().size());
|
||||
|
||||
// Check that number of discrete keys is 1 // TODO(Frank): should not exist?
|
||||
// Check that number of discrete keys is 1
|
||||
EXPECT_LONGS_EQUAL(1, mixtureFactorA.discreteKeys().size());
|
||||
|
||||
// Create sum of two mixture factors: it will be a decision tree now on both
|
||||
|
|
@ -104,7 +104,7 @@ TEST(GaussianMixtureFactor, Printing) {
|
|||
GaussianMixtureFactor mixtureFactor({X(1), X(2)}, {m1}, factors);
|
||||
|
||||
std::string expected =
|
||||
R"(Hybrid x1 x2; 1 ]{
|
||||
R"(Hybrid [x1 x2; 1]{
|
||||
Choice(1)
|
||||
0 Leaf :
|
||||
A[x1] = [
|
||||
|
|
|
|||
|
|
@ -0,0 +1,45 @@
|
|||
/* ----------------------------------------------------------------------------
|
||||
* 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 testHybridFactorGraph.cpp
|
||||
* @brief Unit tests for HybridFactorGraph
|
||||
* @author Varun Agrawal
|
||||
* @date May 2021
|
||||
*/
|
||||
|
||||
#include <CppUnitLite/Test.h>
|
||||
#include <CppUnitLite/TestHarness.h>
|
||||
#include <gtsam/base/TestableAssertions.h>
|
||||
#include <gtsam/base/utilities.h>
|
||||
#include <gtsam/hybrid/HybridFactorGraph.h>
|
||||
#include <gtsam/inference/Symbol.h>
|
||||
#include <gtsam/nonlinear/PriorFactor.h>
|
||||
|
||||
using namespace std;
|
||||
using namespace gtsam;
|
||||
using noiseModel::Isotropic;
|
||||
using symbol_shorthand::L;
|
||||
using symbol_shorthand::M;
|
||||
using symbol_shorthand::X;
|
||||
|
||||
/* ****************************************************************************
|
||||
* Test that any linearizedFactorGraph gaussian factors are appended to the
|
||||
* existing gaussian factor graph in the hybrid factor graph.
|
||||
*/
|
||||
TEST(HybridFactorGraph, Constructor) {
|
||||
// Initialize the hybrid factor graph
|
||||
HybridFactorGraph fg;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
int main() {
|
||||
TestResult tr;
|
||||
return TestRegistry::runAllTests(tr);
|
||||
}
|
||||
/* ************************************************************************* */
|
||||
|
|
@ -0,0 +1,649 @@
|
|||
/* ----------------------------------------------------------------------------
|
||||
|
||||
* 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 testHybridIncremental.cpp
|
||||
* @brief Unit tests for incremental inference
|
||||
* @author Fan Jiang, Varun Agrawal, Frank Dellaert
|
||||
* @date Jan 2021
|
||||
*/
|
||||
|
||||
#include <gtsam/discrete/DiscreteBayesNet.h>
|
||||
#include <gtsam/discrete/DiscreteDistribution.h>
|
||||
#include <gtsam/geometry/Pose2.h>
|
||||
#include <gtsam/hybrid/HybridGaussianISAM.h>
|
||||
#include <gtsam/linear/GaussianBayesNet.h>
|
||||
#include <gtsam/linear/GaussianFactorGraph.h>
|
||||
#include <gtsam/nonlinear/PriorFactor.h>
|
||||
#include <gtsam/sam/BearingRangeFactor.h>
|
||||
|
||||
#include <numeric>
|
||||
|
||||
#include "Switching.h"
|
||||
|
||||
// Include for test suite
|
||||
#include <CppUnitLite/TestHarness.h>
|
||||
|
||||
using namespace std;
|
||||
using namespace gtsam;
|
||||
using noiseModel::Isotropic;
|
||||
using symbol_shorthand::L;
|
||||
using symbol_shorthand::M;
|
||||
using symbol_shorthand::W;
|
||||
using symbol_shorthand::X;
|
||||
using symbol_shorthand::Y;
|
||||
using symbol_shorthand::Z;
|
||||
|
||||
/* ****************************************************************************/
|
||||
// Test if we can perform elimination incrementally.
|
||||
TEST(HybridGaussianElimination, IncrementalElimination) {
|
||||
Switching switching(3);
|
||||
HybridGaussianISAM isam;
|
||||
HybridGaussianFactorGraph graph1;
|
||||
|
||||
// Create initial factor graph
|
||||
// * * *
|
||||
// | | |
|
||||
// *- X1 -*- X2 -*- X3
|
||||
// \*-M1-*/
|
||||
graph1.push_back(switching.linearizedFactorGraph.at(0)); // P(X1)
|
||||
graph1.push_back(switching.linearizedFactorGraph.at(1)); // P(X1, X2 | M1)
|
||||
graph1.push_back(switching.linearizedFactorGraph.at(2)); // P(X2, X3 | M2)
|
||||
graph1.push_back(switching.linearizedFactorGraph.at(5)); // P(M1)
|
||||
|
||||
// Create ordering.
|
||||
Ordering ordering;
|
||||
ordering += X(1);
|
||||
ordering += X(2);
|
||||
|
||||
// Run update step
|
||||
isam.update(graph1);
|
||||
|
||||
// Check that after update we have 3 hybrid Bayes net nodes:
|
||||
// P(X1 | X2, M1) and P(X2, X3 | M1, M2), P(M1, M2)
|
||||
EXPECT_LONGS_EQUAL(3, isam.size());
|
||||
EXPECT(isam[X(1)]->conditional()->frontals() == KeyVector{X(1)});
|
||||
EXPECT(isam[X(1)]->conditional()->parents() == KeyVector({X(2), M(1)}));
|
||||
EXPECT(isam[X(2)]->conditional()->frontals() == KeyVector({X(2), X(3)}));
|
||||
EXPECT(isam[X(2)]->conditional()->parents() == KeyVector({M(1), M(2)}));
|
||||
|
||||
/********************************************************/
|
||||
// New factor graph for incremental update.
|
||||
HybridGaussianFactorGraph graph2;
|
||||
|
||||
graph1.push_back(switching.linearizedFactorGraph.at(3)); // P(X2)
|
||||
graph2.push_back(switching.linearizedFactorGraph.at(4)); // P(X3)
|
||||
graph2.push_back(switching.linearizedFactorGraph.at(6)); // P(M1, M2)
|
||||
|
||||
// Create ordering.
|
||||
Ordering ordering2;
|
||||
ordering2 += X(2);
|
||||
ordering2 += X(3);
|
||||
|
||||
isam.update(graph2);
|
||||
|
||||
// Check that after the second update we have
|
||||
// 1 additional hybrid Bayes net node:
|
||||
// P(X2, X3 | M1, M2)
|
||||
EXPECT_LONGS_EQUAL(3, isam.size());
|
||||
EXPECT(isam[X(3)]->conditional()->frontals() == KeyVector({X(2), X(3)}));
|
||||
EXPECT(isam[X(3)]->conditional()->parents() == KeyVector({M(1), M(2)}));
|
||||
}
|
||||
|
||||
/* ****************************************************************************/
|
||||
// Test if we can incrementally do the inference
|
||||
TEST(HybridGaussianElimination, IncrementalInference) {
|
||||
Switching switching(3);
|
||||
HybridGaussianISAM isam;
|
||||
HybridGaussianFactorGraph graph1;
|
||||
|
||||
// Create initial factor graph
|
||||
// * * *
|
||||
// | | |
|
||||
// *- X1 -*- X2 -*- X3
|
||||
// | |
|
||||
// *-M1 - * - M2
|
||||
graph1.push_back(switching.linearizedFactorGraph.at(0)); // P(X1)
|
||||
graph1.push_back(switching.linearizedFactorGraph.at(1)); // P(X1, X2 | M1)
|
||||
graph1.push_back(switching.linearizedFactorGraph.at(3)); // P(X2)
|
||||
graph1.push_back(switching.linearizedFactorGraph.at(5)); // P(M1)
|
||||
|
||||
//TODO(Varun) we cannot enforce ordering
|
||||
// // Create ordering.
|
||||
// Ordering ordering1;
|
||||
// ordering1 += X(1);
|
||||
// ordering1 += X(2);
|
||||
|
||||
// Run update step
|
||||
isam.update(graph1);
|
||||
|
||||
/********************************************************/
|
||||
// New factor graph for incremental update.
|
||||
HybridGaussianFactorGraph graph2;
|
||||
|
||||
graph2.push_back(switching.linearizedFactorGraph.at(2)); // P(X2, X3 | M2)
|
||||
graph2.push_back(switching.linearizedFactorGraph.at(4)); // P(X3)
|
||||
graph2.push_back(switching.linearizedFactorGraph.at(6)); // P(M1, M2)
|
||||
|
||||
//TODO(Varun) we cannot enforce ordering
|
||||
// // Create ordering.
|
||||
// Ordering ordering2;
|
||||
// ordering2 += X(2);
|
||||
// ordering2 += X(3);
|
||||
|
||||
isam.update(graph2);
|
||||
GTSAM_PRINT(isam);
|
||||
|
||||
/********************************************************/
|
||||
// Run batch elimination so we can compare results.
|
||||
Ordering ordering;
|
||||
ordering += X(1);
|
||||
ordering += X(2);
|
||||
ordering += X(3);
|
||||
|
||||
// Now we calculate the actual factors using full elimination
|
||||
HybridBayesNet::shared_ptr expectedHybridBayesNet;
|
||||
HybridGaussianFactorGraph::shared_ptr expectedRemainingGraph;
|
||||
std::tie(expectedHybridBayesNet, expectedRemainingGraph) =
|
||||
switching.linearizedFactorGraph.eliminatePartialSequential(ordering);
|
||||
|
||||
// The densities on X(1) should be the same
|
||||
auto x1_conditional =
|
||||
dynamic_pointer_cast<GaussianMixture>(isam[X(1)]->conditional()->inner());
|
||||
EXPECT(
|
||||
assert_equal(*x1_conditional, *(expectedHybridBayesNet->atGaussian(0))));
|
||||
|
||||
// The densities on X(2) should be the same
|
||||
auto x2_conditional =
|
||||
dynamic_pointer_cast<GaussianMixture>(isam[X(2)]->conditional()->inner());
|
||||
EXPECT(
|
||||
assert_equal(*x2_conditional, *(expectedHybridBayesNet->atGaussian(1))));
|
||||
|
||||
// // The densities on X(3) should be the same
|
||||
// auto x3_conditional =
|
||||
// dynamic_pointer_cast<GaussianMixture>(isam[X(3)]->conditional()->inner());
|
||||
// EXPECT(
|
||||
// assert_equal(*x3_conditional, *(expectedHybridBayesNet->atGaussian(2))));
|
||||
|
||||
GTSAM_PRINT(*expectedHybridBayesNet);
|
||||
|
||||
// we only do the manual continuous elimination for 0,0
|
||||
// the other discrete probabilities on M(2) are calculated the same way
|
||||
auto m00_prob = [&]() {
|
||||
GaussianFactorGraph gf;
|
||||
// gf.add(switching.linearizedFactorGraph.gaussianGraph().at(3));
|
||||
|
||||
DiscreteValues m00;
|
||||
m00[M(1)] = 0, m00[M(2)] = 0;
|
||||
// auto dcMixture =
|
||||
// dynamic_pointer_cast<DCGaussianMixtureFactor>(graph2.dcGraph().at(0));
|
||||
// gf.add(dcMixture->factors()(m00));
|
||||
// auto x2_mixed =
|
||||
// boost::dynamic_pointer_cast<GaussianMixture>(hybridBayesNet.at(1));
|
||||
// gf.add(x2_mixed->factors()(m00));
|
||||
auto result_gf = gf.eliminateSequential();
|
||||
return gf.probPrime(result_gf->optimize());
|
||||
}();
|
||||
|
||||
/// Test if the probability values are as expected with regression tests.
|
||||
// DiscreteValues assignment;
|
||||
// EXPECT(assert_equal(m00_prob, 0.60656, 1e-5));
|
||||
// assignment[M(1)] = 0;
|
||||
// assignment[M(2)] = 0;
|
||||
// EXPECT(assert_equal(m00_prob, (*discreteFactor)(assignment), 1e-5));
|
||||
// assignment[M(1)] = 1;
|
||||
// assignment[M(2)] = 0;
|
||||
// EXPECT(assert_equal(0.612477, (*discreteFactor)(assignment), 1e-5));
|
||||
// assignment[M(1)] = 0;
|
||||
// assignment[M(2)] = 1;
|
||||
// EXPECT(assert_equal(0.999952, (*discreteFactor)(assignment), 1e-5));
|
||||
// assignment[M(1)] = 1;
|
||||
// assignment[M(2)] = 1;
|
||||
// EXPECT(assert_equal(1.0, (*discreteFactor)(assignment), 1e-5));
|
||||
|
||||
// DiscreteFactorGraph dfg;
|
||||
// dfg.add(*discreteFactor);
|
||||
// dfg.add(discreteFactor_m1);
|
||||
// dfg.add_factors(switching.linearizedFactorGraph.discreteGraph());
|
||||
|
||||
// // Check if the chordal graph generated from incremental elimination
|
||||
// matches
|
||||
// // that of batch elimination.
|
||||
// auto chordal = dfg.eliminateSequential();
|
||||
// auto expectedChordal =
|
||||
// expectedRemainingGraph->discreteGraph().eliminateSequential();
|
||||
|
||||
// EXPECT(assert_equal(*expectedChordal, *chordal, 1e-6));
|
||||
}
|
||||
|
||||
/* ****************************************************************************/
|
||||
// // Test if we can approximately do the inference
|
||||
// TEST(DCGaussianElimination, Approx_inference) {
|
||||
// Switching switching(4);
|
||||
// IncrementalHybrid 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 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));
|
||||
// }
|
||||
|
||||
// // 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);
|
||||
|
||||
// size_t maxComponents = 5;
|
||||
// incrementalHybrid.update(graph1, ordering, maxComponents);
|
||||
|
||||
// /*
|
||||
// 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 *
|
||||
|
||||
// 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
|
||||
// */
|
||||
|
||||
// // Test that the remaining factor graph has one
|
||||
// // DecisionTreeFactor on {M3, M2, M1}.
|
||||
// auto remainingFactorGraph = incrementalHybrid.remainingFactorGraph();
|
||||
// EXPECT_LONGS_EQUAL(1, remainingFactorGraph.size());
|
||||
|
||||
// auto discreteFactor_m1 = *dynamic_pointer_cast<DecisionTreeFactor>(
|
||||
// incrementalHybrid.remainingDiscreteGraph().at(0));
|
||||
// EXPECT(discreteFactor_m1.keys() == KeyVector({M(3), M(2), M(1)}));
|
||||
|
||||
// // 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));
|
||||
|
||||
// /* 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 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());
|
||||
|
||||
// // 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)));
|
||||
// }
|
||||
// }
|
||||
// }
|
||||
|
||||
/* ****************************************************************************/
|
||||
// // Test approximate inference with an additional pruning step.
|
||||
// TEST(DCGaussianElimination, Incremental_approximate) {
|
||||
// Switching switching(5);
|
||||
// IncrementalHybrid 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 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));
|
||||
// }
|
||||
|
||||
// // 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);
|
||||
|
||||
// // 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());
|
||||
|
||||
// /***** Run Round 2 *****/
|
||||
// HybridGaussianFactorGraph graph2;
|
||||
// graph2.push_back(switching.linearizedFactorGraph.dcGraph().at(3));
|
||||
// graph2.push_back(switching.linearizedFactorGraph.gaussianGraph().at(5));
|
||||
|
||||
// Ordering ordering2;
|
||||
// ordering2 += X(4);
|
||||
// ordering2 += X(5);
|
||||
|
||||
// // Run update with pruning a second time.
|
||||
// incrementalHybrid.update(graph2, ordering2, 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());
|
||||
// }
|
||||
|
||||
/* ************************************************************************/
|
||||
// // 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.
|
||||
|
||||
// /*************** Run Round 1 ***************/
|
||||
// NonlinearHybridFactorGraph 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);
|
||||
|
||||
// // 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.
|
||||
|
||||
// // 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));
|
||||
|
||||
// HybridGaussianFactorGraph gfg = fg.linearize(initial);
|
||||
// fg = NonlinearHybridFactorGraph();
|
||||
|
||||
// IncrementalHybrid inc;
|
||||
|
||||
// // 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);
|
||||
|
||||
// /*************** 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 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));
|
||||
|
||||
// // 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();
|
||||
|
||||
// // 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);
|
||||
|
||||
// // 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));
|
||||
|
||||
// // 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();
|
||||
|
||||
// // 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);
|
||||
|
||||
// // 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));
|
||||
|
||||
// // 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();
|
||||
|
||||
// // 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());
|
||||
|
||||
// // 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());
|
||||
// }
|
||||
|
||||
/* ************************************************************************* */
|
||||
int main() {
|
||||
TestResult tr;
|
||||
return TestRegistry::runAllTests(tr);
|
||||
}
|
||||
|
|
@ -0,0 +1,761 @@
|
|||
/* ----------------------------------------------------------------------------
|
||||
* 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 testHybridNonlinearFactorGraph.cpp
|
||||
* @brief Unit tests for HybridNonlinearFactorGraph
|
||||
* @author Varun Agrawal
|
||||
* @author Fan Jiang
|
||||
* @author Frank Dellaert
|
||||
* @date December 2021
|
||||
*/
|
||||
|
||||
#include <gtsam/base/TestableAssertions.h>
|
||||
#include <gtsam/base/utilities.h>
|
||||
#include <gtsam/discrete/DiscreteBayesNet.h>
|
||||
#include <gtsam/discrete/DiscreteDistribution.h>
|
||||
#include <gtsam/discrete/DiscreteFactorGraph.h>
|
||||
#include <gtsam/geometry/Pose2.h>
|
||||
#include <gtsam/hybrid/HybridEliminationTree.h>
|
||||
#include <gtsam/hybrid/HybridFactor.h>
|
||||
#include <gtsam/hybrid/HybridNonlinearFactorGraph.h>
|
||||
#include <gtsam/hybrid/MixtureFactor.h>
|
||||
#include <gtsam/linear/GaussianBayesNet.h>
|
||||
#include <gtsam/linear/GaussianFactorGraph.h>
|
||||
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
|
||||
#include <gtsam/nonlinear/PriorFactor.h>
|
||||
#include <gtsam/sam/BearingRangeFactor.h>
|
||||
#include <gtsam/slam/BetweenFactor.h>
|
||||
|
||||
#include <numeric>
|
||||
|
||||
#include "Switching.h"
|
||||
|
||||
// Include for test suite
|
||||
#include <CppUnitLite/TestHarness.h>
|
||||
|
||||
using namespace std;
|
||||
using namespace gtsam;
|
||||
using noiseModel::Isotropic;
|
||||
using symbol_shorthand::L;
|
||||
using symbol_shorthand::M;
|
||||
using symbol_shorthand::X;
|
||||
|
||||
/* ****************************************************************************
|
||||
* Test that any linearizedFactorGraph gaussian factors are appended to the
|
||||
* existing gaussian factor graph in the hybrid factor graph.
|
||||
*/
|
||||
TEST(HybridFactorGraph, GaussianFactorGraph) {
|
||||
HybridNonlinearFactorGraph fg;
|
||||
|
||||
// Add a simple prior factor to the nonlinear factor graph
|
||||
fg.emplace_nonlinear<PriorFactor<double>>(X(0), 0, Isotropic::Sigma(1, 0.1));
|
||||
|
||||
// Linearization point
|
||||
Values linearizationPoint;
|
||||
linearizationPoint.insert<double>(X(0), 0);
|
||||
|
||||
HybridGaussianFactorGraph ghfg = fg.linearize(linearizationPoint);
|
||||
|
||||
// Add a factor to the GaussianFactorGraph
|
||||
ghfg.add(JacobianFactor(X(0), I_1x1, Vector1(5)));
|
||||
|
||||
EXPECT_LONGS_EQUAL(2, ghfg.size());
|
||||
}
|
||||
|
||||
/***************************************************************************
|
||||
* Test equality for Hybrid Nonlinear Factor Graph.
|
||||
*/
|
||||
TEST(HybridNonlinearFactorGraph, Equals) {
|
||||
HybridNonlinearFactorGraph graph1;
|
||||
HybridNonlinearFactorGraph graph2;
|
||||
|
||||
// Test empty factor graphs
|
||||
EXPECT(assert_equal(graph1, graph2));
|
||||
|
||||
auto f0 = boost::make_shared<PriorFactor<Pose2>>(
|
||||
1, Pose2(), noiseModel::Isotropic::Sigma(3, 0.001));
|
||||
graph1.push_back(f0);
|
||||
graph2.push_back(f0);
|
||||
|
||||
auto f1 = boost::make_shared<BetweenFactor<Pose2>>(
|
||||
1, 2, Pose2(), noiseModel::Isotropic::Sigma(3, 0.1));
|
||||
graph1.push_back(f1);
|
||||
graph2.push_back(f1);
|
||||
|
||||
// Test non-empty graphs
|
||||
EXPECT(assert_equal(graph1, graph2));
|
||||
}
|
||||
|
||||
/***************************************************************************
|
||||
* Test that the resize method works correctly for a HybridNonlinearFactorGraph.
|
||||
*/
|
||||
TEST(HybridNonlinearFactorGraph, Resize) {
|
||||
HybridNonlinearFactorGraph fg;
|
||||
auto nonlinearFactor = boost::make_shared<BetweenFactor<double>>();
|
||||
fg.push_back(nonlinearFactor);
|
||||
|
||||
auto discreteFactor = boost::make_shared<DecisionTreeFactor>();
|
||||
fg.push_back(discreteFactor);
|
||||
|
||||
auto dcFactor = boost::make_shared<MixtureFactor>();
|
||||
fg.push_back(dcFactor);
|
||||
|
||||
EXPECT_LONGS_EQUAL(fg.size(), 3);
|
||||
|
||||
fg.resize(0);
|
||||
EXPECT_LONGS_EQUAL(fg.size(), 0);
|
||||
}
|
||||
|
||||
/***************************************************************************
|
||||
* Test that the resize method works correctly for a
|
||||
* HybridGaussianFactorGraph.
|
||||
*/
|
||||
TEST(HybridGaussianFactorGraph, Resize) {
|
||||
HybridNonlinearFactorGraph nhfg;
|
||||
auto nonlinearFactor = boost::make_shared<BetweenFactor<double>>(
|
||||
X(0), X(1), 0.0, Isotropic::Sigma(1, 0.1));
|
||||
nhfg.push_back(nonlinearFactor);
|
||||
auto discreteFactor = boost::make_shared<DecisionTreeFactor>();
|
||||
nhfg.push_back(discreteFactor);
|
||||
|
||||
KeyVector contKeys = {X(0), X(1)};
|
||||
auto noise_model = noiseModel::Isotropic::Sigma(1, 1.0);
|
||||
auto still = boost::make_shared<MotionModel>(X(0), X(1), 0.0, noise_model),
|
||||
moving = boost::make_shared<MotionModel>(X(0), X(1), 1.0, noise_model);
|
||||
|
||||
std::vector<MotionModel::shared_ptr> components = {still, moving};
|
||||
auto dcFactor = boost::make_shared<MixtureFactor>(
|
||||
contKeys, DiscreteKeys{gtsam::DiscreteKey(M(1), 2)}, components);
|
||||
nhfg.push_back(dcFactor);
|
||||
|
||||
Values linearizationPoint;
|
||||
linearizationPoint.insert<double>(X(0), 0);
|
||||
linearizationPoint.insert<double>(X(1), 1);
|
||||
|
||||
// Generate `HybridGaussianFactorGraph` by linearizing
|
||||
HybridGaussianFactorGraph gfg = nhfg.linearize(linearizationPoint);
|
||||
|
||||
EXPECT_LONGS_EQUAL(gfg.size(), 3);
|
||||
|
||||
gfg.resize(0);
|
||||
EXPECT_LONGS_EQUAL(gfg.size(), 0);
|
||||
}
|
||||
|
||||
/*****************************************************************************
|
||||
* Test push_back on HFG makes the correct distinction.
|
||||
*/
|
||||
TEST(HybridFactorGraph, PushBack) {
|
||||
HybridNonlinearFactorGraph fg;
|
||||
|
||||
auto nonlinearFactor = boost::make_shared<BetweenFactor<double>>();
|
||||
fg.push_back(nonlinearFactor);
|
||||
|
||||
EXPECT_LONGS_EQUAL(fg.size(), 1);
|
||||
|
||||
fg = HybridNonlinearFactorGraph();
|
||||
|
||||
auto discreteFactor = boost::make_shared<DecisionTreeFactor>();
|
||||
fg.push_back(discreteFactor);
|
||||
|
||||
EXPECT_LONGS_EQUAL(fg.size(), 1);
|
||||
|
||||
fg = HybridNonlinearFactorGraph();
|
||||
|
||||
auto dcFactor = boost::make_shared<MixtureFactor>();
|
||||
fg.push_back(dcFactor);
|
||||
|
||||
EXPECT_LONGS_EQUAL(fg.size(), 1);
|
||||
|
||||
// Now do the same with HybridGaussianFactorGraph
|
||||
HybridGaussianFactorGraph ghfg;
|
||||
|
||||
auto gaussianFactor = boost::make_shared<JacobianFactor>();
|
||||
ghfg.push_back(gaussianFactor);
|
||||
|
||||
EXPECT_LONGS_EQUAL(ghfg.size(), 1);
|
||||
|
||||
ghfg = HybridGaussianFactorGraph();
|
||||
ghfg.push_back(discreteFactor);
|
||||
|
||||
EXPECT_LONGS_EQUAL(ghfg.size(), 1);
|
||||
|
||||
ghfg = HybridGaussianFactorGraph();
|
||||
ghfg.push_back(dcFactor);
|
||||
|
||||
EXPECT_LONGS_EQUAL(ghfg.size(), 1);
|
||||
}
|
||||
|
||||
/****************************************************************************
|
||||
* Test construction of switching-like hybrid factor graph.
|
||||
*/
|
||||
TEST(HybridFactorGraph, Switching) {
|
||||
Switching self(3);
|
||||
|
||||
EXPECT_LONGS_EQUAL(7, self.nonlinearFactorGraph.size());
|
||||
EXPECT_LONGS_EQUAL(7, self.linearizedFactorGraph.size());
|
||||
}
|
||||
|
||||
/****************************************************************************
|
||||
* Test linearization on a switching-like hybrid factor graph.
|
||||
*/
|
||||
TEST(HybridFactorGraph, Linearization) {
|
||||
Switching self(3);
|
||||
|
||||
// Linearize here:
|
||||
HybridGaussianFactorGraph actualLinearized =
|
||||
self.nonlinearFactorGraph.linearize(self.linearizationPoint);
|
||||
|
||||
EXPECT_LONGS_EQUAL(7, actualLinearized.size());
|
||||
}
|
||||
|
||||
/****************************************************************************
|
||||
* Test elimination tree construction
|
||||
*/
|
||||
TEST(HybridFactorGraph, EliminationTree) {
|
||||
Switching self(3);
|
||||
|
||||
// Create ordering.
|
||||
Ordering ordering;
|
||||
for (size_t k = 1; k <= self.K; k++) ordering += X(k);
|
||||
|
||||
// Create elimination tree.
|
||||
HybridEliminationTree etree(self.linearizedFactorGraph, ordering);
|
||||
EXPECT_LONGS_EQUAL(1, etree.roots().size())
|
||||
}
|
||||
|
||||
/****************************************************************************
|
||||
*Test elimination function by eliminating x1 in *-x1-*-x2 graph.
|
||||
*/
|
||||
TEST(GaussianElimination, Eliminate_x1) {
|
||||
Switching self(3);
|
||||
|
||||
// Gather factors on x1, has a simple Gaussian and a mixture factor.
|
||||
HybridGaussianFactorGraph factors;
|
||||
// Add gaussian prior
|
||||
factors.push_back(self.linearizedFactorGraph[0]);
|
||||
// Add first hybrid factor
|
||||
factors.push_back(self.linearizedFactorGraph[1]);
|
||||
|
||||
// TODO(Varun) remove this block since sum is no longer exposed.
|
||||
// // Check that sum works:
|
||||
// auto sum = factors.sum();
|
||||
// Assignment<Key> mode;
|
||||
// mode[M(1)] = 1;
|
||||
// auto actual = sum(mode); // Selects one of 2 modes.
|
||||
// EXPECT_LONGS_EQUAL(2, actual.size()); // Prior and motion model.
|
||||
|
||||
// Eliminate x1
|
||||
Ordering ordering;
|
||||
ordering += X(1);
|
||||
|
||||
auto result = EliminateHybrid(factors, ordering);
|
||||
CHECK(result.first);
|
||||
EXPECT_LONGS_EQUAL(1, result.first->nrFrontals());
|
||||
CHECK(result.second);
|
||||
// Has two keys, x2 and m1
|
||||
EXPECT_LONGS_EQUAL(2, result.second->size());
|
||||
}
|
||||
|
||||
/****************************************************************************
|
||||
* Test elimination function by eliminating x2 in x1-*-x2-*-x3 chain.
|
||||
* m1/ \m2
|
||||
*/
|
||||
TEST(HybridsGaussianElimination, Eliminate_x2) {
|
||||
Switching self(3);
|
||||
|
||||
// Gather factors on x2, will be two mixture factors (with x1 and x3, resp.).
|
||||
HybridGaussianFactorGraph factors;
|
||||
factors.push_back(self.linearizedFactorGraph[1]); // involves m1
|
||||
factors.push_back(self.linearizedFactorGraph[2]); // involves m2
|
||||
|
||||
// TODO(Varun) remove this block since sum is no longer exposed.
|
||||
// // Check that sum works:
|
||||
// auto sum = factors.sum();
|
||||
// Assignment<Key> mode;
|
||||
// mode[M(1)] = 0;
|
||||
// mode[M(2)] = 1;
|
||||
// auto actual = sum(mode); // Selects one of 4 mode
|
||||
// combinations. EXPECT_LONGS_EQUAL(2, actual.size()); // 2 motion models.
|
||||
|
||||
// Eliminate x2
|
||||
Ordering ordering;
|
||||
ordering += X(2);
|
||||
|
||||
std::pair<HybridConditional::shared_ptr, HybridFactor::shared_ptr> result =
|
||||
EliminateHybrid(factors, ordering);
|
||||
CHECK(result.first);
|
||||
EXPECT_LONGS_EQUAL(1, result.first->nrFrontals());
|
||||
CHECK(result.second);
|
||||
// Note: separator keys should include m1, m2.
|
||||
EXPECT_LONGS_EQUAL(4, result.second->size());
|
||||
}
|
||||
|
||||
/****************************************************************************
|
||||
* Helper method to generate gaussian factor graphs with a specific mode.
|
||||
*/
|
||||
GaussianFactorGraph::shared_ptr batchGFG(double between,
|
||||
Values linearizationPoint) {
|
||||
NonlinearFactorGraph graph;
|
||||
graph.addPrior<double>(X(1), 0, Isotropic::Sigma(1, 0.1));
|
||||
|
||||
auto between_x1_x2 = boost::make_shared<MotionModel>(
|
||||
X(1), X(2), between, Isotropic::Sigma(1, 1.0));
|
||||
|
||||
graph.push_back(between_x1_x2);
|
||||
|
||||
return graph.linearize(linearizationPoint);
|
||||
}
|
||||
|
||||
/****************************************************************************
|
||||
* Test elimination function by eliminating x1 and x2 in graph.
|
||||
*/
|
||||
TEST(HybridGaussianElimination, EliminateHybrid_2_Variable) {
|
||||
Switching self(2, 1.0, 0.1);
|
||||
|
||||
auto factors = self.linearizedFactorGraph;
|
||||
|
||||
// Eliminate x1
|
||||
Ordering ordering;
|
||||
ordering += X(1);
|
||||
ordering += X(2);
|
||||
|
||||
HybridConditional::shared_ptr hybridConditionalMixture;
|
||||
HybridFactor::shared_ptr factorOnModes;
|
||||
|
||||
std::tie(hybridConditionalMixture, factorOnModes) =
|
||||
EliminateHybrid(factors, ordering);
|
||||
|
||||
auto gaussianConditionalMixture =
|
||||
dynamic_pointer_cast<GaussianMixture>(hybridConditionalMixture->inner());
|
||||
|
||||
CHECK(gaussianConditionalMixture);
|
||||
// Frontals = [x1, x2]
|
||||
EXPECT_LONGS_EQUAL(2, gaussianConditionalMixture->nrFrontals());
|
||||
// 1 parent, which is the mode
|
||||
EXPECT_LONGS_EQUAL(1, gaussianConditionalMixture->nrParents());
|
||||
|
||||
// This is now a HybridDiscreteFactor
|
||||
auto hybridDiscreteFactor =
|
||||
dynamic_pointer_cast<HybridDiscreteFactor>(factorOnModes);
|
||||
// Access the type-erased inner object and convert to DecisionTreeFactor
|
||||
auto discreteFactor =
|
||||
dynamic_pointer_cast<DecisionTreeFactor>(hybridDiscreteFactor->inner());
|
||||
CHECK(discreteFactor);
|
||||
EXPECT_LONGS_EQUAL(1, discreteFactor->discreteKeys().size());
|
||||
EXPECT(discreteFactor->root_->isLeaf() == false);
|
||||
}
|
||||
|
||||
// /*
|
||||
// ****************************************************************************/
|
||||
// /// Test the toDecisionTreeFactor method
|
||||
// TEST(HybridFactorGraph, ToDecisionTreeFactor) {
|
||||
// size_t K = 3;
|
||||
|
||||
// // Provide tight sigma values so that the errors are visibly different.
|
||||
// double between_sigma = 5e-8, prior_sigma = 1e-7;
|
||||
|
||||
// Switching self(K, between_sigma, prior_sigma);
|
||||
|
||||
// // Clear out discrete factors since sum() cannot hanldle those
|
||||
// HybridGaussianFactorGraph linearizedFactorGraph(
|
||||
// self.linearizedFactorGraph.gaussianGraph(), DiscreteFactorGraph(),
|
||||
// self.linearizedFactorGraph.dcGraph());
|
||||
|
||||
// auto decisionTreeFactor = linearizedFactorGraph.toDecisionTreeFactor();
|
||||
|
||||
// auto allAssignments =
|
||||
// DiscreteValues::CartesianProduct(linearizedFactorGraph.discreteKeys());
|
||||
|
||||
// // Get the error of the discrete assignment m1=0, m2=1.
|
||||
// double actual = (*decisionTreeFactor)(allAssignments[1]);
|
||||
|
||||
// /********************************************/
|
||||
// // Create equivalent factor graph for m1=0, m2=1
|
||||
// GaussianFactorGraph graph = linearizedFactorGraph.gaussianGraph();
|
||||
|
||||
// for (auto &p : linearizedFactorGraph.dcGraph()) {
|
||||
// if (auto mixture =
|
||||
// boost::dynamic_pointer_cast<DCGaussianMixtureFactor>(p)) {
|
||||
// graph.add((*mixture)(allAssignments[1]));
|
||||
// }
|
||||
// }
|
||||
|
||||
// VectorValues values = graph.optimize();
|
||||
// double expected = graph.probPrime(values);
|
||||
// /********************************************/
|
||||
// EXPECT_DOUBLES_EQUAL(expected, actual, 1e-12);
|
||||
// // REGRESSION:
|
||||
// EXPECT_DOUBLES_EQUAL(0.6125, actual, 1e-4);
|
||||
// }
|
||||
|
||||
/****************************************************************************
|
||||
* Test partial elimination
|
||||
*/
|
||||
TEST(HybridFactorGraph, Partial_Elimination) {
|
||||
Switching self(3);
|
||||
|
||||
auto linearizedFactorGraph = self.linearizedFactorGraph;
|
||||
|
||||
// Create ordering.
|
||||
Ordering ordering;
|
||||
for (size_t k = 1; k <= self.K; k++) ordering += X(k);
|
||||
|
||||
// Eliminate partially.
|
||||
HybridBayesNet::shared_ptr hybridBayesNet;
|
||||
HybridGaussianFactorGraph::shared_ptr remainingFactorGraph;
|
||||
std::tie(hybridBayesNet, remainingFactorGraph) =
|
||||
linearizedFactorGraph.eliminatePartialSequential(ordering);
|
||||
|
||||
CHECK(hybridBayesNet);
|
||||
// GTSAM_PRINT(*hybridBayesNet); // HybridBayesNet
|
||||
EXPECT_LONGS_EQUAL(3, hybridBayesNet->size());
|
||||
EXPECT(hybridBayesNet->at(0)->frontals() == KeyVector{X(1)});
|
||||
EXPECT(hybridBayesNet->at(0)->parents() == KeyVector({X(2), M(1)}));
|
||||
EXPECT(hybridBayesNet->at(1)->frontals() == KeyVector{X(2)});
|
||||
EXPECT(hybridBayesNet->at(1)->parents() == KeyVector({X(3), M(1), M(2)}));
|
||||
EXPECT(hybridBayesNet->at(2)->frontals() == KeyVector{X(3)});
|
||||
EXPECT(hybridBayesNet->at(2)->parents() == KeyVector({M(1), M(2)}));
|
||||
|
||||
CHECK(remainingFactorGraph);
|
||||
// GTSAM_PRINT(*remainingFactorGraph); // HybridFactorGraph
|
||||
EXPECT_LONGS_EQUAL(3, remainingFactorGraph->size());
|
||||
EXPECT(remainingFactorGraph->at(0)->keys() == KeyVector({M(1)}));
|
||||
EXPECT(remainingFactorGraph->at(1)->keys() == KeyVector({M(2), M(1)}));
|
||||
EXPECT(remainingFactorGraph->at(2)->keys() == KeyVector({M(1), M(2)}));
|
||||
}
|
||||
|
||||
/****************************************************************************
|
||||
* Test full elimination
|
||||
*/
|
||||
TEST(HybridFactorGraph, Full_Elimination) {
|
||||
Switching self(3);
|
||||
|
||||
auto linearizedFactorGraph = self.linearizedFactorGraph;
|
||||
|
||||
// We first do a partial elimination
|
||||
HybridBayesNet::shared_ptr hybridBayesNet_partial;
|
||||
HybridGaussianFactorGraph::shared_ptr remainingFactorGraph_partial;
|
||||
DiscreteBayesNet discreteBayesNet;
|
||||
|
||||
{
|
||||
// Create ordering.
|
||||
Ordering ordering;
|
||||
for (size_t k = 1; k <= self.K; k++) ordering += X(k);
|
||||
|
||||
// Eliminate partially.
|
||||
std::tie(hybridBayesNet_partial, remainingFactorGraph_partial) =
|
||||
linearizedFactorGraph.eliminatePartialSequential(ordering);
|
||||
|
||||
DiscreteFactorGraph discrete_fg;
|
||||
// TODO(Varun) Make this a function of HybridGaussianFactorGraph?
|
||||
for (HybridFactor::shared_ptr& factor : (*remainingFactorGraph_partial)) {
|
||||
auto df = dynamic_pointer_cast<HybridDiscreteFactor>(factor);
|
||||
discrete_fg.push_back(df->inner());
|
||||
}
|
||||
ordering.clear();
|
||||
for (size_t k = 1; k < self.K; k++) ordering += M(k);
|
||||
discreteBayesNet =
|
||||
*discrete_fg.eliminateSequential(ordering, EliminateForMPE);
|
||||
}
|
||||
|
||||
// Create ordering.
|
||||
Ordering ordering;
|
||||
for (size_t k = 1; k <= self.K; k++) ordering += X(k);
|
||||
for (size_t k = 1; k < self.K; k++) ordering += M(k);
|
||||
|
||||
// Eliminate partially.
|
||||
HybridBayesNet::shared_ptr hybridBayesNet =
|
||||
linearizedFactorGraph.eliminateSequential(ordering);
|
||||
|
||||
CHECK(hybridBayesNet);
|
||||
EXPECT_LONGS_EQUAL(5, hybridBayesNet->size());
|
||||
// p(x1 | x2, m1)
|
||||
EXPECT(hybridBayesNet->at(0)->frontals() == KeyVector{X(1)});
|
||||
EXPECT(hybridBayesNet->at(0)->parents() == KeyVector({X(2), M(1)}));
|
||||
// p(x2 | x3, m1, m2)
|
||||
EXPECT(hybridBayesNet->at(1)->frontals() == KeyVector{X(2)});
|
||||
EXPECT(hybridBayesNet->at(1)->parents() == KeyVector({X(3), M(1), M(2)}));
|
||||
// p(x3 | m1, m2)
|
||||
EXPECT(hybridBayesNet->at(2)->frontals() == KeyVector{X(3)});
|
||||
EXPECT(hybridBayesNet->at(2)->parents() == KeyVector({M(1), M(2)}));
|
||||
// P(m1 | m2)
|
||||
EXPECT(hybridBayesNet->at(3)->frontals() == KeyVector{M(1)});
|
||||
EXPECT(hybridBayesNet->at(3)->parents() == KeyVector({M(2)}));
|
||||
EXPECT(
|
||||
dynamic_pointer_cast<DiscreteConditional>(hybridBayesNet->at(3)->inner())
|
||||
->equals(*discreteBayesNet.at(0)));
|
||||
// P(m2)
|
||||
EXPECT(hybridBayesNet->at(4)->frontals() == KeyVector{M(2)});
|
||||
EXPECT_LONGS_EQUAL(0, hybridBayesNet->at(4)->nrParents());
|
||||
EXPECT(
|
||||
dynamic_pointer_cast<DiscreteConditional>(hybridBayesNet->at(4)->inner())
|
||||
->equals(*discreteBayesNet.at(1)));
|
||||
}
|
||||
|
||||
/****************************************************************************
|
||||
* Test printing
|
||||
*/
|
||||
TEST(HybridFactorGraph, Printing) {
|
||||
Switching self(3);
|
||||
|
||||
auto linearizedFactorGraph = self.linearizedFactorGraph;
|
||||
|
||||
// Create ordering.
|
||||
Ordering ordering;
|
||||
for (size_t k = 1; k <= self.K; k++) ordering += X(k);
|
||||
|
||||
// Eliminate partially.
|
||||
HybridBayesNet::shared_ptr hybridBayesNet;
|
||||
HybridGaussianFactorGraph::shared_ptr remainingFactorGraph;
|
||||
std::tie(hybridBayesNet, remainingFactorGraph) =
|
||||
linearizedFactorGraph.eliminatePartialSequential(ordering);
|
||||
|
||||
string expected_hybridFactorGraph = R"(
|
||||
size: 7
|
||||
factor 0: Continuous [x1]
|
||||
|
||||
A[x1] = [
|
||||
10
|
||||
]
|
||||
b = [ -10 ]
|
||||
No noise model
|
||||
factor 1: Hybrid [x1 x2; m1]{
|
||||
Choice(m1)
|
||||
0 Leaf :
|
||||
A[x1] = [
|
||||
-1
|
||||
]
|
||||
A[x2] = [
|
||||
1
|
||||
]
|
||||
b = [ -1 ]
|
||||
No noise model
|
||||
|
||||
1 Leaf :
|
||||
A[x1] = [
|
||||
-1
|
||||
]
|
||||
A[x2] = [
|
||||
1
|
||||
]
|
||||
b = [ -0 ]
|
||||
No noise model
|
||||
|
||||
}
|
||||
factor 2: Hybrid [x2 x3; m2]{
|
||||
Choice(m2)
|
||||
0 Leaf :
|
||||
A[x2] = [
|
||||
-1
|
||||
]
|
||||
A[x3] = [
|
||||
1
|
||||
]
|
||||
b = [ -1 ]
|
||||
No noise model
|
||||
|
||||
1 Leaf :
|
||||
A[x2] = [
|
||||
-1
|
||||
]
|
||||
A[x3] = [
|
||||
1
|
||||
]
|
||||
b = [ -0 ]
|
||||
No noise model
|
||||
|
||||
}
|
||||
factor 3: Continuous [x2]
|
||||
|
||||
A[x2] = [
|
||||
10
|
||||
]
|
||||
b = [ -10 ]
|
||||
No noise model
|
||||
factor 4: Continuous [x3]
|
||||
|
||||
A[x3] = [
|
||||
10
|
||||
]
|
||||
b = [ -10 ]
|
||||
No noise model
|
||||
factor 5: Discrete [m1]
|
||||
P( m1 ):
|
||||
Leaf 0.5
|
||||
|
||||
factor 6: Discrete [m2 m1]
|
||||
P( m2 | m1 ):
|
||||
Choice(m2)
|
||||
0 Choice(m1)
|
||||
0 0 Leaf 0.33333333
|
||||
0 1 Leaf 0.6
|
||||
1 Choice(m1)
|
||||
1 0 Leaf 0.66666667
|
||||
1 1 Leaf 0.4
|
||||
|
||||
)";
|
||||
EXPECT(assert_print_equal(expected_hybridFactorGraph, linearizedFactorGraph));
|
||||
|
||||
// Expected output for hybridBayesNet.
|
||||
string expected_hybridBayesNet = R"(
|
||||
size: 3
|
||||
factor 0: Hybrid P( x1 | x2 m1)
|
||||
Discrete Keys = (m1, 2),
|
||||
Choice(m1)
|
||||
0 Leaf p(x1 | x2)
|
||||
R = [ 10.0499 ]
|
||||
S[x2] = [ -0.0995037 ]
|
||||
d = [ -9.85087 ]
|
||||
No noise model
|
||||
|
||||
1 Leaf p(x1 | x2)
|
||||
R = [ 10.0499 ]
|
||||
S[x2] = [ -0.0995037 ]
|
||||
d = [ -9.95037 ]
|
||||
No noise model
|
||||
|
||||
factor 1: Hybrid P( x2 | x3 m1 m2)
|
||||
Discrete Keys = (m1, 2), (m2, 2),
|
||||
Choice(m2)
|
||||
0 Choice(m1)
|
||||
0 0 Leaf p(x2 | x3)
|
||||
R = [ 10.099 ]
|
||||
S[x3] = [ -0.0990196 ]
|
||||
d = [ -9.99901 ]
|
||||
No noise model
|
||||
|
||||
0 1 Leaf p(x2 | x3)
|
||||
R = [ 10.099 ]
|
||||
S[x3] = [ -0.0990196 ]
|
||||
d = [ -9.90098 ]
|
||||
No noise model
|
||||
|
||||
1 Choice(m1)
|
||||
1 0 Leaf p(x2 | x3)
|
||||
R = [ 10.099 ]
|
||||
S[x3] = [ -0.0990196 ]
|
||||
d = [ -10.098 ]
|
||||
No noise model
|
||||
|
||||
1 1 Leaf p(x2 | x3)
|
||||
R = [ 10.099 ]
|
||||
S[x3] = [ -0.0990196 ]
|
||||
d = [ -10 ]
|
||||
No noise model
|
||||
|
||||
factor 2: Hybrid P( x3 | m1 m2)
|
||||
Discrete Keys = (m1, 2), (m2, 2),
|
||||
Choice(m2)
|
||||
0 Choice(m1)
|
||||
0 0 Leaf p(x3)
|
||||
R = [ 10.0494 ]
|
||||
d = [ -10.1489 ]
|
||||
No noise model
|
||||
|
||||
0 1 Leaf p(x3)
|
||||
R = [ 10.0494 ]
|
||||
d = [ -10.1479 ]
|
||||
No noise model
|
||||
|
||||
1 Choice(m1)
|
||||
1 0 Leaf p(x3)
|
||||
R = [ 10.0494 ]
|
||||
d = [ -10.0504 ]
|
||||
No noise model
|
||||
|
||||
1 1 Leaf p(x3)
|
||||
R = [ 10.0494 ]
|
||||
d = [ -10.0494 ]
|
||||
No noise model
|
||||
|
||||
)";
|
||||
EXPECT(assert_print_equal(expected_hybridBayesNet, *hybridBayesNet));
|
||||
}
|
||||
|
||||
/****************************************************************************
|
||||
* Simple PlanarSLAM example test with 2 poses and 2 landmarks (each pose
|
||||
* connects to 1 landmark) to expose issue with default decision tree creation
|
||||
* in hybrid elimination. The hybrid factor is between the poses X0 and X1.
|
||||
* The issue arises if we eliminate a landmark variable first since it is not
|
||||
* connected to a HybridFactor.
|
||||
*/
|
||||
TEST(HybridFactorGraph, DefaultDecisionTree) {
|
||||
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);
|
||||
|
||||
using PlanarMotionModel = BetweenFactor<Pose2>;
|
||||
|
||||
// Add odometry factor
|
||||
Pose2 odometry(2.0, 0.0, 0.0);
|
||||
KeyVector contKeys = {X(0), X(1)};
|
||||
auto noise_model = noiseModel::Isotropic::Sigma(3, 1.0);
|
||||
auto still = boost::make_shared<PlanarMotionModel>(X(0), X(1), Pose2(0, 0, 0),
|
||||
noise_model),
|
||||
moving = boost::make_shared<PlanarMotionModel>(X(0), X(1), odometry,
|
||||
noise_model);
|
||||
std::vector<PlanarMotionModel::shared_ptr> motion_models = {still, moving};
|
||||
// TODO(Varun) Make a templated constructor for MixtureFactor which does this?
|
||||
std::vector<NonlinearFactor::shared_ptr> components;
|
||||
for (auto&& f : motion_models) {
|
||||
components.push_back(boost::dynamic_pointer_cast<NonlinearFactor>(f));
|
||||
}
|
||||
fg.emplace_hybrid<MixtureFactor>(
|
||||
contKeys, DiscreteKeys{gtsam::DiscreteKey(M(1), 2)}, components);
|
||||
|
||||
// Add Range-Bearing measurements to from X0 to L0 and X1 to L1.
|
||||
// create a noise model for the landmark measurements
|
||||
auto measurementNoise = noiseModel::Diagonal::Sigmas(
|
||||
Vector2(0.1, 0.2)); // 0.1 rad std on bearing, 20cm on range
|
||||
// create the measurement values - indices are (pose id, landmark id)
|
||||
Rot2 bearing11 = Rot2::fromDegrees(45), bearing22 = Rot2::fromDegrees(90);
|
||||
double range11 = std::sqrt(4.0 + 4.0), range22 = 2.0;
|
||||
|
||||
// Add Bearing-Range factors
|
||||
fg.emplace_nonlinear<BearingRangeFactor<Pose2, Point2>>(
|
||||
X(0), L(0), bearing11, range11, measurementNoise);
|
||||
fg.emplace_nonlinear<BearingRangeFactor<Pose2, Point2>>(
|
||||
X(1), L(1), bearing22, range22, measurementNoise);
|
||||
|
||||
// Create (deliberately inaccurate) initial estimate
|
||||
Values initialEstimate;
|
||||
initialEstimate.insert(X(0), Pose2(0.5, 0.0, 0.2));
|
||||
initialEstimate.insert(X(1), Pose2(2.3, 0.1, -0.2));
|
||||
initialEstimate.insert(L(0), Point2(1.8, 2.1));
|
||||
initialEstimate.insert(L(1), Point2(4.1, 1.8));
|
||||
|
||||
// We want to eliminate variables not connected to DCFactors first.
|
||||
Ordering ordering;
|
||||
ordering += L(0);
|
||||
ordering += L(1);
|
||||
ordering += X(0);
|
||||
ordering += X(1);
|
||||
|
||||
HybridGaussianFactorGraph linearized = fg.linearize(initialEstimate);
|
||||
gtsam::HybridBayesNet::shared_ptr hybridBayesNet;
|
||||
gtsam::HybridGaussianFactorGraph::shared_ptr remainingFactorGraph;
|
||||
|
||||
// This should NOT fail
|
||||
std::tie(hybridBayesNet, remainingFactorGraph) =
|
||||
linearized.eliminatePartialSequential(ordering);
|
||||
EXPECT_LONGS_EQUAL(4, hybridBayesNet->size());
|
||||
EXPECT_LONGS_EQUAL(1, remainingFactorGraph->size());
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
int main() {
|
||||
TestResult tr;
|
||||
return TestRegistry::runAllTests(tr);
|
||||
}
|
||||
/* ************************************************************************* */
|
||||
Loading…
Reference in New Issue