Merge pull request borglab#1358 from borglab/hybrid/gaussian-mixture-…

…factor

gtsam/hybrid/GaussianMixture.cpp

@@ -22,6 +22,7 @@
 #include <gtsam/discrete/DiscreteValues.h>
 #include <gtsam/hybrid/GaussianMixture.h>
 #include <gtsam/hybrid/GaussianMixtureFactor.h>
+#include <gtsam/hybrid/HybridValues.h>
 #include <gtsam/inference/Conditional-inst.h>
 #include <gtsam/linear/GaussianFactorGraph.h>
 
@@ -149,17 +150,19 @@ boost::shared_ptr<GaussianMixtureFactor> GaussianMixture::likelihood(
   const DiscreteKeys discreteParentKeys = discreteKeys();
   const KeyVector continuousParentKeys = continuousParents();
   const GaussianMixtureFactor::Factors likelihoods(
-      conditionals(), [&](const GaussianConditional::shared_ptr &conditional) {
-        return conditional->likelihood(frontals);
+      conditionals_, [&](const GaussianConditional::shared_ptr &conditional) {
+        return GaussianMixtureFactor::FactorAndConstant{
+            conditional->likelihood(frontals),
+            conditional->logNormalizationConstant()};
       });
   return boost::make_shared<GaussianMixtureFactor>(
       continuousParentKeys, discreteParentKeys, likelihoods);
 }
 
 /* ************************************************************************* */
-std::set<DiscreteKey> DiscreteKeysAsSet(const DiscreteKeys &dkeys) {
+std::set<DiscreteKey> DiscreteKeysAsSet(const DiscreteKeys &discreteKeys) {
   std::set<DiscreteKey> s;
-  s.insert(dkeys.begin(), dkeys.end());
+  s.insert(discreteKeys.begin(), discreteKeys.end());
   return s;
 }
 
@@ -184,7 +187,7 @@ GaussianMixture::prunerFunc(const DecisionTreeFactor &decisionTree) {
                     const GaussianConditional::shared_ptr &conditional)
       -> GaussianConditional::shared_ptr {
     // typecast so we can use this to get probability value
-    DiscreteValues values(choices);
+    const DiscreteValues values(choices);
 
     // Case where the gaussian mixture has the same
     // discrete keys as the decision tree.
@@ -254,11 +257,10 @@ AlgebraicDecisionTree<Key> GaussianMixture::error(
 }
 
 /* *******************************************************************************/
-double GaussianMixture::error(const VectorValues &continuousValues,
-                              const DiscreteValues &discreteValues) const {
+double GaussianMixture::error(const HybridValues &values) const {
   // Directly index to get the conditional, no need to build the whole tree.
-  auto conditional = conditionals_(discreteValues);
-  return conditional->error(continuousValues);
+  auto conditional = conditionals_(values.discrete());
+  return conditional->error(values.continuous());
 }
 
 }  // namespace gtsam

gtsam/hybrid/GaussianMixture.h

@@ -30,6 +30,7 @@
 namespace gtsam {
 
 class GaussianMixtureFactor;
+class HybridValues;
 
 /**
  * @brief A conditional of gaussian mixtures indexed by discrete variables, as
@@ -87,7 +88,7 @@ class GTSAM_EXPORT GaussianMixture
   /// @name Constructors
   /// @{
 
-  /// Defaut constructor, mainly for serialization.
+  /// Default constructor, mainly for serialization.
   GaussianMixture() = default;
 
   /**
@@ -135,6 +136,7 @@ class GTSAM_EXPORT GaussianMixture
   /// @name Standard API
   /// @{
 
+  /// @brief Return the conditional Gaussian for the given discrete assignment.
   GaussianConditional::shared_ptr operator()(
       const DiscreteValues &discreteValues) const;
 
@@ -165,12 +167,10 @@ class GTSAM_EXPORT GaussianMixture
    * @brief Compute the error of this Gaussian Mixture given the continuous
    * values and a discrete assignment.
    *
-   * @param continuousValues Continuous values at which to compute the error.
-   * @param discreteValues The discrete assignment for a specific mode sequence.
+   * @param values Continuous values and discrete assignment.
    * @return double
    */
-  double error(const VectorValues &continuousValues,
-               const DiscreteValues &discreteValues) const;
+  double error(const HybridValues &values) const override;
 
   /**
    * @brief Prune the decision tree of Gaussian factors as per the discrete
@@ -193,7 +193,7 @@ class GTSAM_EXPORT GaussianMixture
 };
 
 /// Return the DiscreteKey vector as a set.
-std::set<DiscreteKey> DiscreteKeysAsSet(const DiscreteKeys &dkeys);
+std::set<DiscreteKey> DiscreteKeysAsSet(const DiscreteKeys &discreteKeys);
 
 // traits
 template <>

gtsam/hybrid/GaussianMixtureFactor.cpp

@@ -22,15 +22,20 @@
 #include <gtsam/discrete/DecisionTree-inl.h>
 #include <gtsam/discrete/DecisionTree.h>
 #include <gtsam/hybrid/GaussianMixtureFactor.h>
+#include <gtsam/hybrid/HybridValues.h>
+#include <gtsam/linear/GaussianFactor.h>
 #include <gtsam/linear/GaussianFactorGraph.h>
 
 namespace gtsam {
 
 /* *******************************************************************************/
 GaussianMixtureFactor::GaussianMixtureFactor(const KeyVector &continuousKeys,
                                              const DiscreteKeys &discreteKeys,
-                                             const Factors &factors)
-    : Base(continuousKeys, discreteKeys), factors_(factors) {}
+                                             const Mixture &factors)
+    : Base(continuousKeys, discreteKeys),
+      factors_(factors, [](const GaussianFactor::shared_ptr &gf) {
+        return FactorAndConstant{gf, 0.0};
+      }) {}
 
 /* *******************************************************************************/
 bool GaussianMixtureFactor::equals(const HybridFactor &lf, double tol) const {
@@ -43,11 +48,11 @@ bool GaussianMixtureFactor::equals(const HybridFactor &lf, double tol) const {
 
   // Check the base and the factors:
   return Base::equals(*e, tol) &&
-         factors_.equals(e->factors_,
-                         [tol](const GaussianFactor::shared_ptr &f1,
-                               const GaussianFactor::shared_ptr &f2) {
-                           return f1->equals(*f2, tol);
-                         });
+         factors_.equals(e->factors_, [tol](const FactorAndConstant &f1,
+                                            const FactorAndConstant &f2) {
+           return f1.factor->equals(*(f2.factor), tol) &&
+                  std::abs(f1.constant - f2.constant) < tol;
+         });
 }
 
 /* *******************************************************************************/
@@ -60,7 +65,8 @@ void GaussianMixtureFactor::print(const std::string &s,
   } else {
     factors_.print(
         "", [&](Key k) { return formatter(k); },
-        [&](const GaussianFactor::shared_ptr &gf) -> std::string {
+        [&](const FactorAndConstant &gf_z) -> std::string {
+          auto gf = gf_z.factor;
           RedirectCout rd;
           std::cout << ":\n";
           if (gf && !gf->empty()) {
@@ -75,8 +81,10 @@ void GaussianMixtureFactor::print(const std::string &s,
 }
 
 /* *******************************************************************************/
-const GaussianMixtureFactor::Factors &GaussianMixtureFactor::factors() {
-  return factors_;
+const GaussianMixtureFactor::Mixture GaussianMixtureFactor::factors() const {
+  return Mixture(factors_, [](const FactorAndConstant &factor_z) {
+    return factor_z.factor;
+  });
 }
 
 /* *******************************************************************************/
@@ -95,9 +103,9 @@ GaussianMixtureFactor::Sum GaussianMixtureFactor::add(
 /* *******************************************************************************/
 GaussianMixtureFactor::Sum GaussianMixtureFactor::asGaussianFactorGraphTree()
     const {
-  auto wrap = [](const GaussianFactor::shared_ptr &factor) {
+  auto wrap = [](const FactorAndConstant &factor_z) {
     GaussianFactorGraph result;
-    result.push_back(factor);
+    result.push_back(factor_z.factor);
     return result;
   };
   return {factors_, wrap};
@@ -107,21 +115,18 @@ GaussianMixtureFactor::Sum GaussianMixtureFactor::asGaussianFactorGraphTree()
 AlgebraicDecisionTree<Key> GaussianMixtureFactor::error(
     const VectorValues &continuousValues) const {
   // functor to convert from sharedFactor to double error value.
-  auto errorFunc =
-      [continuousValues](const GaussianFactor::shared_ptr &factor) {
-        return factor->error(continuousValues);
-      };
+  auto errorFunc = [continuousValues](const FactorAndConstant &factor_z) {
+    return factor_z.error(continuousValues);
+  };
   DecisionTree<Key, double> errorTree(factors_, errorFunc);
   return errorTree;
 }
 
 /* *******************************************************************************/
-double GaussianMixtureFactor::error(
-    const VectorValues &continuousValues,
-    const DiscreteValues &discreteValues) const {
-  // Directly index to get the conditional, no need to build the whole tree.
-  auto factor = factors_(discreteValues);
-  return factor->error(continuousValues);
+double GaussianMixtureFactor::error(const HybridValues &values) const {
+  const FactorAndConstant factor_z = factors_(values.discrete());
+  return factor_z.error(values.continuous());
 }
+/* *******************************************************************************/
 
 }  // namespace gtsam

gtsam/hybrid/GaussianMixtureFactor.h

@@ -23,17 +23,15 @@
 #include <gtsam/discrete/AlgebraicDecisionTree.h>
 #include <gtsam/discrete/DecisionTree.h>
 #include <gtsam/discrete/DiscreteKey.h>
-#include <gtsam/discrete/DiscreteValues.h>
-#include <gtsam/hybrid/HybridGaussianFactor.h>
+#include <gtsam/hybrid/HybridFactor.h>
 #include <gtsam/linear/GaussianFactor.h>
-#include <gtsam/linear/VectorValues.h>
 
 namespace gtsam {
 
 class GaussianFactorGraph;
-
-// Needed for wrapper.
-using GaussianFactorVector = std::vector<gtsam::GaussianFactor::shared_ptr>;
+class HybridValues;
+class DiscreteValues;
+class VectorValues;
 
 /**
  * @brief Implementation of a discrete conditional mixture factor.
@@ -53,9 +51,29 @@ class GTSAM_EXPORT GaussianMixtureFactor : public HybridFactor {
   using shared_ptr = boost::shared_ptr<This>;
 
   using Sum = DecisionTree<Key, GaussianFactorGraph>;
-
-  /// typedef for Decision Tree of Gaussian Factors
-  using Factors = DecisionTree<Key, GaussianFactor::shared_ptr>;
+  using sharedFactor = boost::shared_ptr<GaussianFactor>;
+
+  /// Gaussian factor and log of normalizing constant.
+  struct FactorAndConstant {
+    sharedFactor factor;
+    double constant;
+
+    // Return error with constant correction.
+    double error(const VectorValues &values) const {
+      // Note minus sign: constant is log of normalization constant for probabilities.
+      // Errors is the negative log-likelihood, hence we subtract the constant here.
+      return factor->error(values) - constant;
+    }
+
+    // Check pointer equality.
+    bool operator==(const FactorAndConstant &other) const {
+      return factor == other.factor && constant == other.constant;
+    }
+  };
+
+  /// typedef for Decision Tree of Gaussian factors and log-constant.
+  using Factors = DecisionTree<Key, FactorAndConstant>;
+  using Mixture = DecisionTree<Key, sharedFactor>;
 
  private:
   /// Decision tree of Gaussian factors indexed by discrete keys.
@@ -82,12 +100,17 @@ class GTSAM_EXPORT GaussianMixtureFactor : public HybridFactor {
    * @param continuousKeys A vector of keys representing continuous variables.
    * @param discreteKeys A vector of keys representing discrete variables and
    * their cardinalities.
-   * @param factors The decision tree of Gaussian Factors stored as the mixture
+   * @param factors The decision tree of Gaussian factors stored as the mixture
    * density.
    */
   GaussianMixtureFactor(const KeyVector &continuousKeys,
                         const DiscreteKeys &discreteKeys,
-                        const Factors &factors);
+                        const Mixture &factors);
+
+  GaussianMixtureFactor(const KeyVector &continuousKeys,
+                        const DiscreteKeys &discreteKeys,
+                        const Factors &factors_and_z)
+      : Base(continuousKeys, discreteKeys), factors_(factors_and_z) {}
 
   /**
    * @brief Construct a new GaussianMixtureFactor object using a vector of
@@ -99,9 +122,9 @@ class GTSAM_EXPORT GaussianMixtureFactor : public HybridFactor {
    */
   GaussianMixtureFactor(const KeyVector &continuousKeys,
                         const DiscreteKeys &discreteKeys,
-                        const std::vector<GaussianFactor::shared_ptr> &factors)
+                        const std::vector<sharedFactor> &factors)
       : GaussianMixtureFactor(continuousKeys, discreteKeys,
-                              Factors(discreteKeys, factors)) {}
+                              Mixture(discreteKeys, factors)) {}
 
   /// @}
   /// @name Testable
@@ -113,9 +136,11 @@ class GTSAM_EXPORT GaussianMixtureFactor : public HybridFactor {
       const std::string &s = "GaussianMixtureFactor\n",
       const KeyFormatter &formatter = DefaultKeyFormatter) const override;
   /// @}
+  /// @name Standard API
+  /// @{
 
   /// Getter for the underlying Gaussian Factor Decision Tree.
-  const Factors &factors();
+  const Mixture factors() const;
 
   /**
    * @brief Combine the Gaussian Factor Graphs in `sum` and `this` while
@@ -137,21 +162,17 @@ class GTSAM_EXPORT GaussianMixtureFactor : public HybridFactor {
   AlgebraicDecisionTree<Key> error(const VectorValues &continuousValues) const;
 
   /**
-   * @brief Compute the error of this Gaussian Mixture given the continuous
-   * values and a discrete assignment.
-   *
-   * @param continuousValues Continuous values at which to compute the error.
-   * @param discreteValues The discrete assignment for a specific mode sequence.
+   * @brief Compute the log-likelihood, including the log-normalizing constant.
    * @return double
    */
-  double error(const VectorValues &continuousValues,
-               const DiscreteValues &discreteValues) const;
+  double error(const HybridValues &values) const override;
 
   /// Add MixtureFactor to a Sum, syntactic sugar.
   friend Sum &operator+=(Sum &sum, const GaussianMixtureFactor &factor) {
     sum = factor.add(sum);
     return sum;
   }
+  /// @}
 };
 
 // traits

gtsam/hybrid/HybridBayesNet.cpp

@@ -1,5 +1,5 @@
 /* ----------------------------------------------------------------------------
- * GTSAM Copyright 2010, Georgia Tech Research Corporation,
+ * GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,
  * Atlanta, Georgia 30332-0415
  * All Rights Reserved
  * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
@@ -12,6 +12,7 @@
  * @author Fan Jiang
  * @author Varun Agrawal
  * @author Shangjie Xue
+ * @author Frank Dellaert
  * @date   January 2022
  */
 
@@ -321,10 +322,9 @@ HybridValues HybridBayesNet::sample() const {
 }
 
 /* ************************************************************************* */
-double HybridBayesNet::error(const VectorValues &continuousValues,
-                             const DiscreteValues &discreteValues) const {
-  GaussianBayesNet gbn = choose(discreteValues);
-  return gbn.error(continuousValues);
+double HybridBayesNet::error(const HybridValues &values) const {
+  GaussianBayesNet gbn = choose(values.discrete());
+  return gbn.error(values.continuous());
 }
 
 /* ************************************************************************* */

gtsam/hybrid/HybridBayesNet.h

@@ -206,12 +206,10 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
    * @brief 0.5 * sum of squared Mahalanobis distances
    * for a specific discrete assignment.
    *
-   * @param continuousValues Continuous values at which to compute the error.
-   * @param discreteValues Discrete assignment for a specific mode sequence.
+   * @param values Continuous values and discrete assignment.
    * @return double
    */
-  double error(const VectorValues &continuousValues,
-               const DiscreteValues &discreteValues) const;
+  double error(const HybridValues &values) const;
 
   /**
    * @brief Compute conditional error for each discrete assignment,