remove HybridLookupDAG
Varun Agrawal
parent
129fa68627
commit
1b5daf9a0e
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@ -1,76 +0,0 @@
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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 DiscreteLookupDAG.cpp
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* @date Aug, 2022
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* @author Shangjie Xue
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*/
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#include <gtsam/discrete/DiscreteBayesNet.h>
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#include <gtsam/discrete/DiscreteLookupDAG.h>
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#include <gtsam/discrete/DiscreteValues.h>
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#include <gtsam/hybrid/HybridBayesNet.h>
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#include <gtsam/hybrid/HybridConditional.h>
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#include <gtsam/hybrid/HybridLookupDAG.h>
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#include <gtsam/hybrid/HybridValues.h>
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#include <gtsam/linear/VectorValues.h>
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#include <string>
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#include <utility>
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using std::pair;
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using std::vector;
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namespace gtsam {
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/* ************************************************************************** */
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void HybridLookupTable::argmaxInPlace(HybridValues* values) const {
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// For discrete conditional, uses argmaxInPlace() method in
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// DiscreteLookupTable.
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if (isDiscrete()) {
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boost::static_pointer_cast<DiscreteLookupTable>(inner_)->argmaxInPlace(
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&(values->discrete));
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} else if (isContinuous()) {
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// For Gaussian conditional, uses solve() method in GaussianConditional.
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values->continuous.insert(
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boost::static_pointer_cast<GaussianConditional>(inner_)->solve(
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values->continuous));
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} else if (isHybrid()) {
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// For hybrid conditional, since children should not contain discrete
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// variable, we can condition on the discrete variable in the parents and
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// solve the resulting GaussianConditional.
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auto conditional =
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boost::static_pointer_cast<GaussianMixture>(inner_)->conditionals()(
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values->discrete);
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values->continuous.insert(conditional->solve(values->continuous));
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}
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}
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/* ************************************************************************** */
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HybridLookupDAG HybridLookupDAG::FromBayesNet(const HybridBayesNet& bayesNet) {
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HybridLookupDAG dag;
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for (auto&& conditional : bayesNet) {
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HybridLookupTable hlt(*conditional);
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dag.push_back(hlt);
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}
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return dag;
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}
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/* ************************************************************************** */
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HybridValues HybridLookupDAG::argmax(HybridValues result) const {
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// Argmax each node in turn in topological sort order (parents first).
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for (auto lookupTable : boost::adaptors::reverse(*this))
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lookupTable->argmaxInPlace(&result);
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return result;
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}
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} // namespace gtsam
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@ -1,119 +0,0 @@
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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 HybridLookupDAG.h
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* @date Aug, 2022
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* @author Shangjie Xue
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*/
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#pragma once
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#include <gtsam/discrete/DiscreteDistribution.h>
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#include <gtsam/discrete/DiscreteLookupDAG.h>
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#include <gtsam/hybrid/HybridConditional.h>
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#include <gtsam/hybrid/HybridValues.h>
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#include <gtsam/inference/BayesNet.h>
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#include <gtsam/inference/FactorGraph.h>
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#include <boost/shared_ptr.hpp>
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#include <string>
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#include <utility>
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#include <vector>
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namespace gtsam {
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/**
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* @brief HybridLookupTable table for max-product
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*
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* Similar to DiscreteLookupTable, inherits from hybrid conditional for
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* convenience. Is used in the max-product algorithm.
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*/
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class GTSAM_EXPORT HybridLookupTable : public HybridConditional {
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public:
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using Base = HybridConditional;
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using This = HybridLookupTable;
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using shared_ptr = boost::shared_ptr<This>;
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using BaseConditional = Conditional<DecisionTreeFactor, This>;
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/**
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* @brief Construct a new Hybrid Lookup Table object form a HybridConditional.
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*
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* @param conditional input hybrid conditional
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*/
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HybridLookupTable(HybridConditional& conditional) : Base(conditional){};
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/**
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* @brief Calculate assignment for frontal variables that maximizes value.
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* @param (in/out) parentsValues Known assignments for the parents.
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*/
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void argmaxInPlace(HybridValues* parentsValues) const;
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};
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/** A DAG made from hybrid lookup tables, as defined above. Similar to
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* DiscreteLookupDAG */
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class GTSAM_EXPORT HybridLookupDAG : public BayesNet<HybridLookupTable> {
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public:
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using Base = BayesNet<HybridLookupTable>;
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using This = HybridLookupDAG;
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using shared_ptr = boost::shared_ptr<This>;
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/// @name Standard Constructors
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/// @{
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/// Construct empty DAG.
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HybridLookupDAG() {}
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/// Create from BayesNet with LookupTables
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static HybridLookupDAG FromBayesNet(const HybridBayesNet& bayesNet);
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/// Destructor
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virtual ~HybridLookupDAG() {}
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/// @}
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/// @name Standard Interface
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/// @{
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/** Add a DiscreteLookupTable */
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template <typename... Args>
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void add(Args&&... args) {
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emplace_shared<HybridLookupTable>(std::forward<Args>(args)...);
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}
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/**
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* @brief argmax by back-substitution, optionally given certain variables.
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*
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* Assumes the DAG is reverse topologically sorted, i.e. last
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* conditional will be optimized first *and* that the
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* DAG does not contain any conditionals for the given variables. If the DAG
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* resulted from eliminating a factor graph, this is true for the elimination
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* ordering.
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*
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* @return given assignment extended w. optimal assignment for all variables.
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*/
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HybridValues argmax(HybridValues given = HybridValues()) const;
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/// @}
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private:
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/** Serialization function */
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friend class boost::serialization::access;
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template <class ARCHIVE>
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void serialize(ARCHIVE& ar, const unsigned int /*version*/) {
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ar& BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
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}
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};
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// traits
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template <>
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struct traits<HybridLookupDAG> : public Testable<HybridLookupDAG> {};
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} // namespace gtsam
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@ -1,272 +0,0 @@
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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 testHybridLookupDAG.cpp
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* @date Aug, 2022
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* @author Shangjie Xue
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*/
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#include <gtsam/base/Testable.h>
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#include <gtsam/base/TestableAssertions.h>
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#include <gtsam/discrete/Assignment.h>
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#include <gtsam/discrete/DecisionTreeFactor.h>
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#include <gtsam/discrete/DiscreteKey.h>
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#include <gtsam/discrete/DiscreteValues.h>
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#include <gtsam/hybrid/GaussianMixture.h>
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#include <gtsam/hybrid/HybridBayesNet.h>
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#include <gtsam/hybrid/HybridLookupDAG.h>
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#include <gtsam/hybrid/HybridValues.h>
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#include <gtsam/inference/Key.h>
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/linear/GaussianConditional.h>
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#include <gtsam/linear/VectorValues.h>
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#include <gtsam/nonlinear/Values.h>
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// Include for test suite
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#include <CppUnitLite/TestHarness.h>
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#include <iostream>
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using namespace std;
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using namespace gtsam;
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using noiseModel::Isotropic;
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using symbol_shorthand::M;
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using symbol_shorthand::X;
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TEST(HybridLookupTable, basics) {
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// create a conditional gaussian node
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Matrix S1(2, 2);
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S1(0, 0) = 1;
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S1(1, 0) = 2;
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S1(0, 1) = 3;
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S1(1, 1) = 4;
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Matrix S2(2, 2);
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S2(0, 0) = 6;
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S2(1, 0) = 0.2;
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S2(0, 1) = 8;
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S2(1, 1) = 0.4;
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Matrix R1(2, 2);
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R1(0, 0) = 0.1;
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R1(1, 0) = 0.3;
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R1(0, 1) = 0.0;
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R1(1, 1) = 0.34;
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Matrix R2(2, 2);
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R2(0, 0) = 0.1;
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R2(1, 0) = 0.3;
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R2(0, 1) = 0.0;
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R2(1, 1) = 0.34;
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SharedDiagonal model = noiseModel::Diagonal::Sigmas(Vector2(1.0, 0.34));
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Vector2 d1(0.2, 0.5), d2(0.5, 0.2);
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auto conditional0 = boost::make_shared<GaussianConditional>(X(1), d1, R1,
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X(2), S1, model),
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conditional1 = boost::make_shared<GaussianConditional>(X(1), d2, R2,
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X(2), S2, model);
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// Create decision tree
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DiscreteKey m1(1, 2);
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GaussianMixture::Conditionals conditionals(
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{m1},
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vector<GaussianConditional::shared_ptr>{conditional0, conditional1});
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// GaussianMixture mixtureFactor2({X(1)}, {X(2)}, {m1}, conditionals);
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boost::shared_ptr<GaussianMixture> mixtureFactor(
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new GaussianMixture({X(1)}, {X(2)}, {m1}, conditionals));
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HybridConditional hc(mixtureFactor);
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GaussianMixture::Conditionals conditional2 =
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boost::static_pointer_cast<GaussianMixture>(hc.inner())->conditionals();
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DiscreteValues dv;
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dv[1] = 1;
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VectorValues cv;
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cv.insert(X(2), Vector2(0.0, 0.0));
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HybridValues hv(dv, cv);
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// std::cout << conditional2(values).markdown();
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EXPECT(assert_equal(*conditional2(dv), *conditionals(dv), 1e-6));
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EXPECT(conditional2(dv) == conditionals(dv));
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HybridLookupTable hlt(hc);
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// hlt.argmaxInPlace(&hv);
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HybridLookupDAG dag;
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dag.push_back(hlt);
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dag.argmax(hv);
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// HybridBayesNet hbn;
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// hbn.push_back(hc);
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// hbn.optimize();
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}
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TEST(HybridLookupTable, hybrid_argmax) {
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Matrix S1(2, 2);
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S1(0, 0) = 1;
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S1(1, 0) = 0;
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S1(0, 1) = 0;
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S1(1, 1) = 1;
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Vector2 d1(0.2, 0.5), d2(-0.5, 0.6);
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SharedDiagonal model = noiseModel::Diagonal::Sigmas(Vector2(1.0, 0.34));
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auto conditional0 =
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boost::make_shared<GaussianConditional>(X(1), d1, S1, model),
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conditional1 =
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boost::make_shared<GaussianConditional>(X(1), d2, S1, model);
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DiscreteKey m1(1, 2);
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GaussianMixture::Conditionals conditionals(
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{m1},
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vector<GaussianConditional::shared_ptr>{conditional0, conditional1});
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boost::shared_ptr<GaussianMixture> mixtureFactor(
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new GaussianMixture({X(1)}, {}, {m1}, conditionals));
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HybridConditional hc(mixtureFactor);
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DiscreteValues dv;
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dv[1] = 1;
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VectorValues cv;
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// cv.insert(X(2),Vector2(0.0, 0.0));
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HybridValues hv(dv, cv);
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HybridLookupTable hlt(hc);
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hlt.argmaxInPlace(&hv);
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EXPECT(assert_equal(hv.at(X(1)), d2));
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}
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TEST(HybridLookupTable, discrete_argmax) {
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DiscreteKey X(0, 2), Y(1, 2);
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auto conditional = boost::make_shared<DiscreteConditional>(X | Y = "0/1 3/2");
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HybridConditional hc(conditional);
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HybridLookupTable hlt(hc);
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DiscreteValues dv;
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dv[1] = 0;
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VectorValues cv;
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// cv.insert(X(2),Vector2(0.0, 0.0));
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HybridValues hv(dv, cv);
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hlt.argmaxInPlace(&hv);
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EXPECT(assert_equal(hv.atDiscrete(0), 1));
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DecisionTreeFactor f1(X, "2 3");
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auto conditional2 = boost::make_shared<DiscreteConditional>(1, f1);
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HybridConditional hc2(conditional2);
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HybridLookupTable hlt2(hc2);
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HybridValues hv2;
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hlt2.argmaxInPlace(&hv2);
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EXPECT(assert_equal(hv2.atDiscrete(0), 1));
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}
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TEST(HybridLookupTable, gaussian_argmax) {
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Matrix S1(2, 2);
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S1(0, 0) = 1;
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S1(1, 0) = 0;
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S1(0, 1) = 0;
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S1(1, 1) = 1;
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Vector2 d1(0.2, 0.5), d2(-0.5, 0.6);
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SharedDiagonal model = noiseModel::Diagonal::Sigmas(Vector2(1.0, 0.34));
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auto conditional =
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boost::make_shared<GaussianConditional>(X(1), d1, S1, X(2), -S1, model);
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HybridConditional hc(conditional);
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HybridLookupTable hlt(hc);
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DiscreteValues dv;
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// dv[1]=0;
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VectorValues cv;
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cv.insert(X(2), d2);
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HybridValues hv(dv, cv);
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hlt.argmaxInPlace(&hv);
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EXPECT(assert_equal(hv.at(X(1)), d1 + d2));
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}
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TEST(HybridLookupDAG, argmax) {
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Matrix S1(2, 2);
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S1(0, 0) = 1;
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S1(1, 0) = 0;
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S1(0, 1) = 0;
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S1(1, 1) = 1;
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Vector2 d1(0.2, 0.5), d2(-0.5, 0.6);
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SharedDiagonal model = noiseModel::Diagonal::Sigmas(Vector2(1.0, 0.34));
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auto conditional0 =
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boost::make_shared<GaussianConditional>(X(2), d1, S1, model),
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conditional1 =
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boost::make_shared<GaussianConditional>(X(2), d2, S1, model);
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DiscreteKey m1(1, 2);
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GaussianMixture::Conditionals conditionals(
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{m1},
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vector<GaussianConditional::shared_ptr>{conditional0, conditional1});
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boost::shared_ptr<GaussianMixture> mixtureFactor(
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new GaussianMixture({X(2)}, {}, {m1}, conditionals));
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HybridConditional hc2(mixtureFactor);
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HybridLookupTable hlt2(hc2);
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auto conditional2 =
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boost::make_shared<GaussianConditional>(X(1), d1, S1, X(2), -S1, model);
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HybridConditional hc1(conditional2);
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HybridLookupTable hlt1(hc1);
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DecisionTreeFactor f1(m1, "2 3");
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auto discrete_conditional = boost::make_shared<DiscreteConditional>(1, f1);
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HybridConditional hc3(discrete_conditional);
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HybridLookupTable hlt3(hc3);
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HybridLookupDAG dag;
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dag.push_back(hlt1);
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dag.push_back(hlt2);
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dag.push_back(hlt3);
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auto hv = dag.argmax();
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EXPECT(assert_equal(hv.atDiscrete(1), 1));
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EXPECT(assert_equal(hv.at(X(2)), d2));
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EXPECT(assert_equal(hv.at(X(1)), d2 + d1));
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}
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/* ************************************************************************* */
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int main() {
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TestResult tr;
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return TestRegistry::runAllTests(tr);
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}
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/* ************************************************************************* */
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