Linear HybridBayesNet optimization

gtsam/hybrid/HybridBayesNet.cpp

@@ -11,10 +11,13 @@
  * @brief  A bayes net of Gaussian Conditionals indexed by discrete keys.
  * @author Fan Jiang
  * @author Varun Agrawal
+ * @author Shangjie Xue
  * @date   January 2022
  */
 
 #include <gtsam/hybrid/HybridBayesNet.h>
+#include <gtsam/hybrid/HybridValues.h>
+#include <gtsam/hybrid/HybridLookupDAG.h>
 
 namespace gtsam {
 
@@ -40,4 +43,10 @@ GaussianBayesNet HybridBayesNet::choose(
   return gbn;
 }
 
+/* *******************************************************************************/
+HybridValues HybridBayesNet::optimize() const {
+  auto dag = HybridLookupDAG::FromBayesNet(*this);
+  return dag.argmax();
+}
+
 }  // namespace gtsam

gtsam/hybrid/HybridBayesNet.h

@@ -18,6 +18,7 @@
 #pragma once
 
 #include <gtsam/hybrid/HybridConditional.h>
+#include <gtsam/hybrid/HybridValues.h>
 #include <gtsam/inference/BayesNet.h>
 #include <gtsam/linear/GaussianBayesNet.h>
 
@@ -61,6 +62,11 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
    * @return GaussianBayesNet
    */
   GaussianBayesNet choose(const DiscreteValues &assignment) const;
+
+  /// Solve the HybridBayesNet by back-substitution.
+  /// TODO(Shangjie) do we need to create a HybridGaussianBayesNet class, and
+  /// put this method there?
+  HybridValues optimize() const;
 };
 
 }  // namespace gtsam

gtsam/hybrid/HybridLookupDAG.cpp

@@ -0,0 +1,76 @@
+/* ----------------------------------------------------------------------------
+
+ * GTSAM Copyright 2010, Georgia Tech Research Corporation,
+ * Atlanta, Georgia 30332-0415
+ * All Rights Reserved
+ * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
+
+ * See LICENSE for the license information
+
+ * -------------------------------------------------------------------------- */
+
+/**
+ *  @file DiscreteLookupDAG.cpp
+ *  @date Aug, 2022
+ *  @author Shangjie Xue
+ */
+
+#include <gtsam/discrete/DiscreteBayesNet.h>
+#include <gtsam/discrete/DiscreteLookupDAG.h>
+#include <gtsam/discrete/DiscreteValues.h>
+#include <gtsam/hybrid/HybridBayesNet.h>
+#include <gtsam/hybrid/HybridConditional.h>
+#include <gtsam/hybrid/HybridLookupDAG.h>
+#include <gtsam/hybrid/HybridValues.h>
+#include <gtsam/linear/VectorValues.h>
+
+#include <string>
+#include <utility>
+
+using std::pair;
+using std::vector;
+
+namespace gtsam {
+
+/* ************************************************************************** */
+void HybridLookupTable::argmaxInPlace(HybridValues* values) const {
+  // For discrete conditional, uses argmaxInPlace() method in
+  // DiscreteLookupTable.
+  if (isDiscrete()) {
+    boost::static_pointer_cast<DiscreteLookupTable>(inner_)->argmaxInPlace(
+        &(values->discrete));
+  } else if (isContinuous()) {
+    // For Gaussian conditional, uses solve() method in GaussianConditional.
+    values->continuous.insert(
+        boost::static_pointer_cast<GaussianConditional>(inner_)->solve(
+            values->continuous));
+  } else if (isHybrid()) {
+    // For hybrid conditional, since children should not contain discrete
+    // variable, we can condition on the discrete variable in the parents and
+    // solve the resulting GaussianConditional.
+    auto conditional =
+        boost::static_pointer_cast<GaussianMixture>(inner_)->conditionals()(
+            values->discrete);
+    values->continuous.insert(conditional->solve(values->continuous));
+  }
+}
+
+/* ************************************************************************** */
+HybridLookupDAG HybridLookupDAG::FromBayesNet(const HybridBayesNet& bayesNet) {
+  HybridLookupDAG dag;
+  for (auto&& conditional : bayesNet) {
+    HybridLookupTable hlt(*conditional);
+    dag.push_back(hlt);
+  }
+  return dag;
+}
+
+/* ************************************************************************** */
+HybridValues HybridLookupDAG::argmax(HybridValues result) const {
+  // Argmax each node in turn in topological sort order (parents first).
+  for (auto lookupTable : boost::adaptors::reverse(*this))
+    lookupTable->argmaxInPlace(&result);
+  return result;
+}
+
+}  // namespace gtsam

gtsam/hybrid/HybridLookupDAG.h

@@ -0,0 +1,119 @@
+/* ----------------------------------------------------------------------------
+
+ * GTSAM Copyright 2010, Georgia Tech Research Corporation,
+ * Atlanta, Georgia 30332-0415
+ * All Rights Reserved
+ * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
+
+ * See LICENSE for the license information
+
+ * -------------------------------------------------------------------------- */
+
+/**
+ * @file HybridLookupDAG.h
+ * @date Aug, 2022
+ * @author Shangjie Xue
+ */
+
+#pragma once
+
+#include <gtsam/discrete/DiscreteDistribution.h>
+#include <gtsam/discrete/DiscreteLookupDAG.h>
+#include <gtsam/hybrid/HybridConditional.h>
+#include <gtsam/hybrid/HybridValues.h>
+#include <gtsam/inference/BayesNet.h>
+#include <gtsam/inference/FactorGraph.h>
+
+#include <boost/shared_ptr.hpp>
+#include <string>
+#include <utility>
+#include <vector>
+
+namespace gtsam {
+
+/**
+ * @brief HybridLookupTable table for max-product
+ *
+ * Similar to DiscreteLookupTable, inherits from hybrid conditional for
+ * convenience. Is used in the max-product algorithm.
+ */
+class GTSAM_EXPORT HybridLookupTable : public HybridConditional {
+ public:
+  using Base = HybridConditional;
+  using This = HybridLookupTable;
+  using shared_ptr = boost::shared_ptr<This>;
+  using BaseConditional = Conditional<DecisionTreeFactor, This>;
+
+  /**
+   * @brief Construct a new Hybrid Lookup Table object form a HybridConditional.
+   *
+   * @param conditional input hybrid conditional
+   */
+  HybridLookupTable(HybridConditional& conditional) : Base(conditional){};
+
+  /**
+   * @brief Calculate assignment for frontal variables that maximizes value.
+   * @param (in/out) parentsValues Known assignments for the parents.
+   */
+  void argmaxInPlace(HybridValues* parentsValues) const;
+};
+
+/** A DAG made from hybrid lookup tables, as defined above. Similar to
+ * DiscreteLookupDAG */
+class GTSAM_EXPORT HybridLookupDAG : public BayesNet<HybridLookupTable> {
+ public:
+  using Base = BayesNet<HybridLookupTable>;
+  using This = HybridLookupDAG;
+  using shared_ptr = boost::shared_ptr<This>;
+
+  /// @name Standard Constructors
+  /// @{
+
+  /// Construct empty DAG.
+  HybridLookupDAG() {}
+
+  /// Create from BayesNet with LookupTables
+  static HybridLookupDAG FromBayesNet(const HybridBayesNet& bayesNet);
+
+  /// Destructor
+  virtual ~HybridLookupDAG() {}
+
+  /// @}
+
+  /// @name Standard Interface
+  /// @{
+
+  /** Add a DiscreteLookupTable */
+  template <typename... Args>
+  void add(Args&&... args) {
+    emplace_shared<HybridLookupTable>(std::forward<Args>(args)...);
+  }
+
+  /**
+   * @brief argmax by back-substitution, optionally given certain variables.
+   *
+   * Assumes the DAG is reverse topologically sorted, i.e. last
+   * conditional will be optimized first *and* that the
+   * DAG does not contain any conditionals for the given variables. If the DAG
+   * resulted from eliminating a factor graph, this is true for the elimination
+   * ordering.
+   *
+   * @return given assignment extended w. optimal assignment for all variables.
+   */
+  HybridValues argmax(HybridValues given = HybridValues()) const;
+  /// @}
+
+ private:
+  /** Serialization function */
+  friend class boost::serialization::access;
+  template <class ARCHIVE>
+  void serialize(ARCHIVE& ar, const unsigned int /*version*/) {
+    ar& BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
+  }
+};
+
+// traits
+template <>
+struct traits<HybridLookupDAG> : public Testable<HybridLookupDAG> {};
+
+}  // namespace gtsam

gtsam/hybrid/HybridValues.h

@@ -0,0 +1,127 @@
+/* ----------------------------------------------------------------------------
+
+ * 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 HybridValues.h
+ *  @date Jul 28, 2022
+ *  @author Shangjie Xue
+ */
+
+#pragma once
+
+#include <gtsam/discrete/Assignment.h>
+#include <gtsam/discrete/DiscreteKey.h>
+#include <gtsam/discrete/DiscreteValues.h>
+#include <gtsam/inference/Key.h>
+#include <gtsam/linear/VectorValues.h>
+#include <gtsam/nonlinear/Values.h>
+
+#include <map>
+#include <string>
+#include <vector>
+
+namespace gtsam {
+
+/**
+ * HybridValues represents a collection of DiscreteValues and VectorValues.  It
+ * is typically used to store the variables of a HybridGaussianFactorGraph.
+ * Optimizing a HybridGaussianBayesNet returns this class.
+ */
+class GTSAM_EXPORT HybridValues {
+ public:
+  // DiscreteValue stored the discrete components of the HybridValues.
+  DiscreteValues discrete;
+
+  // VectorValue stored the continuous components of the HybridValues.
+  VectorValues continuous;
+
+  // Default constructor creates an empty HybridValues.
+  HybridValues() : discrete(), continuous(){};
+
+  // Construct from DiscreteValues and VectorValues.
+  HybridValues(const DiscreteValues& dv, const VectorValues& cv)
+      : discrete(dv), continuous(cv){};
+
+  // print required by Testable for unit testing
+  void print(const std::string& s = "HybridValues",
+             const KeyFormatter& keyFormatter = DefaultKeyFormatter) const {
+    std::cout << s << ": \n";
+    discrete.print("  Discrete", keyFormatter);  // print discrete components
+    continuous.print("  Continuous",
+                     keyFormatter);  // print continuous components
+  };
+
+  // equals required by Testable for unit testing
+  bool equals(const HybridValues& other, double tol = 1e-9) const {
+    return discrete.equals(other.discrete, tol) &&
+           continuous.equals(other.continuous, tol);
+  }
+
+  // Check whether a variable with key \c j exists in DiscreteValue.
+  bool existsDiscrete(Key j) { return (discrete.find(j) != discrete.end()); };
+
+  // Check whether a variable with key \c j exists in VectorValue.
+  bool existsVector(Key j) { return continuous.exists(j); };
+
+  // Check whether a variable with key \c j exists.
+  bool exists(Key j) { return existsDiscrete(j) || existsVector(j); };
+
+  /** Insert a discrete \c value with key \c j.  Replaces the existing value if
+   * the key \c j is already used.
+   * @param value The vector to be inserted.
+   * @param j The index with which the value will be associated. */
+  void insert(Key j, int value) { discrete[j] = value; };
+
+  /** Insert a vector \c value with key \c j.  Throws an invalid_argument
+   * exception if the key \c j is already used.
+   * @param value The vector to be inserted.
+   * @param j The index with which the value will be associated. */
+  void insert(Key j, const Vector& value) { continuous.insert(j, value); }
+
+  // TODO(Shangjie)- update() and insert_or_assign() , similar to Values.h
+
+  /**
+   * Read/write access to the discrete value with key \c j, throws
+   * std::out_of_range if \c j does not exist.
+   */
+  size_t& atDiscrete(Key j) { return discrete.at(j); };
+
+  /**
+   * Read/write access to the vector value with key \c j, throws
+   * std::out_of_range if \c j does not exist.
+   */
+  Vector& at(Key j) { return continuous.at(j); };
+
+  /// @name Wrapper support
+  /// @{
+
+  /**
+   * @brief Output as a html table.
+   *
+   * @param keyFormatter function that formats keys.
+   * @return string html output.
+   */
+  std::string html(
+      const KeyFormatter& keyFormatter = DefaultKeyFormatter) const {
+    std::stringstream ss;
+    ss << this->discrete.html(keyFormatter);
+    ss << this->continuous.html(keyFormatter);
+    return ss.str();
+  };
+
+  /// @}
+};
+
+// traits
+template <>
+struct traits<HybridValues> : public Testable<HybridValues> {};
+
+}  // namespace gtsam