GraphAndConstant struct for hybrid elimination

gtsam/hybrid/GaussianMixture.cpp

@@ -53,11 +53,11 @@ GaussianMixture::GaussianMixture(
 /* *******************************************************************************/
 GaussianMixture::Sum GaussianMixture::add(
     const GaussianMixture::Sum &sum) const {
-  using Y = GaussianFactorGraph;
+  using Y = GaussianMixtureFactor::GraphAndConstant;
   auto add = [](const Y &graph1, const Y &graph2) {
-    auto result = graph1;
+    auto result = graph1.graph;
-    result.push_back(graph2);
+    result.push_back(graph2.graph);
-    return result;
+    return Y(result, graph1.constant + graph2.constant);
   };
   const Sum tree = asGaussianFactorGraphTree();
   return sum.empty() ? tree : sum.apply(tree, add);
@@ -65,10 +65,15 @@ GaussianMixture::Sum GaussianMixture::add(
 
 /* *******************************************************************************/
 GaussianMixture::Sum GaussianMixture::asGaussianFactorGraphTree() const {
-  auto lambda = [](const GaussianFactor::shared_ptr &factor) {
+  auto lambda = [](const GaussianConditional::shared_ptr &conditional) {
     GaussianFactorGraph result;
-    result.push_back(factor);
+    result.push_back(conditional);
-    return result;
+    if (conditional) {
+      return GaussianMixtureFactor::GraphAndConstant(
+          result, conditional->logNormalizationConstant());
+    } else {
+      return GaussianMixtureFactor::GraphAndConstant(result, 0.0);
+    }
   };
   return {conditionals_, lambda};
 }

gtsam/hybrid/GaussianMixture.h

@@ -23,13 +23,13 @@
 #include <gtsam/discrete/DecisionTree.h>
 #include <gtsam/discrete/DecisionTreeFactor.h>
 #include <gtsam/discrete/DiscreteKey.h>
+#include <gtsam/hybrid/GaussianMixtureFactor.h>
 #include <gtsam/hybrid/HybridFactor.h>
 #include <gtsam/inference/Conditional.h>
 #include <gtsam/linear/GaussianConditional.h>
 
 namespace gtsam {
 
-class GaussianMixtureFactor;
 class HybridValues;
 
 /**
@@ -60,7 +60,7 @@ class GTSAM_EXPORT GaussianMixture
   using BaseConditional = Conditional<HybridFactor, GaussianMixture>;
 
   /// Alias for DecisionTree of GaussianFactorGraphs
-  using Sum = DecisionTree<Key, GaussianFactorGraph>;
+  using Sum = DecisionTree<Key, GaussianMixtureFactor::GraphAndConstant>;
 
   /// typedef for Decision Tree of Gaussian Conditionals
   using Conditionals = DecisionTree<Key, GaussianConditional::shared_ptr>;

gtsam/hybrid/GaussianMixtureFactor.cpp

@@ -90,11 +90,11 @@ const GaussianMixtureFactor::Mixture GaussianMixtureFactor::factors() const {
 /* *******************************************************************************/
 GaussianMixtureFactor::Sum GaussianMixtureFactor::add(
     const GaussianMixtureFactor::Sum &sum) const {
-  using Y = GaussianFactorGraph;
+  using Y = GaussianMixtureFactor::GraphAndConstant;
   auto add = [](const Y &graph1, const Y &graph2) {
-    auto result = graph1;
+    auto result = graph1.graph;
-    result.push_back(graph2);
+    result.push_back(graph2.graph);
-    return result;
+    return Y(result, graph1.constant + graph2.constant);
   };
   const Sum tree = asGaussianFactorGraphTree();
   return sum.empty() ? tree : sum.apply(tree, add);
@@ -106,7 +106,7 @@ GaussianMixtureFactor::Sum GaussianMixtureFactor::asGaussianFactorGraphTree()
   auto wrap = [](const FactorAndConstant &factor_z) {
     GaussianFactorGraph result;
     result.push_back(factor_z.factor);
-    return result;
+    return GaussianMixtureFactor::GraphAndConstant(result, factor_z.constant);
   };
   return {factors_, wrap};
 }

gtsam/hybrid/GaussianMixtureFactor.h

@@ -25,10 +25,10 @@
 #include <gtsam/discrete/DiscreteKey.h>
 #include <gtsam/hybrid/HybridFactor.h>
 #include <gtsam/linear/GaussianFactor.h>
+#include <gtsam/linear/GaussianFactorGraph.h>
 
 namespace gtsam {
 
-class GaussianFactorGraph;
 class HybridValues;
 class DiscreteValues;
 class VectorValues;
@@ -50,7 +50,6 @@ class GTSAM_EXPORT GaussianMixtureFactor : public HybridFactor {
   using This = GaussianMixtureFactor;
   using shared_ptr = boost::shared_ptr<This>;
 
-  using Sum = DecisionTree<Key, GaussianFactorGraph>;
   using sharedFactor = boost::shared_ptr<GaussianFactor>;
 
   /// Gaussian factor and log of normalizing constant.
@@ -60,8 +59,9 @@ class GTSAM_EXPORT GaussianMixtureFactor : public HybridFactor {
 
     // Return error with constant correction.
     double error(const VectorValues &values) const {
-      // Note minus sign: constant is log of normalization constant for probabilities.
+      // Note: constant is log of normalization constant for probabilities.
-      // Errors is the negative log-likelihood, hence we subtract the constant here.
+      // Errors is the negative log-likelihood,
+      // hence we subtract the constant here.
       return factor->error(values) - constant;
     }
 
@@ -71,6 +71,22 @@ class GTSAM_EXPORT GaussianMixtureFactor : public HybridFactor {
     }
   };
 
+  /// Gaussian factor graph and log of normalizing constant.
+  struct GraphAndConstant {
+    GaussianFactorGraph graph;
+    double constant;
+
+    GraphAndConstant(const GaussianFactorGraph &graph, double constant)
+        : graph(graph), constant(constant) {}
+
+    // Check pointer equality.
+    bool operator==(const GraphAndConstant &other) const {
+      return graph == other.graph && constant == other.constant;
+    }
+  };
+
+  using Sum = DecisionTree<Key, GraphAndConstant>;
+
   /// typedef for Decision Tree of Gaussian factors and log-constant.
   using Factors = DecisionTree<Key, FactorAndConstant>;
   using Mixture = DecisionTree<Key, sharedFactor>;

gtsam/hybrid/HybridGaussianFactorGraph.cpp

@@ -61,18 +61,19 @@ template class EliminateableFactorGraph<HybridGaussianFactorGraph>;
 /* ************************************************************************ */
 static GaussianMixtureFactor::Sum &addGaussian(
     GaussianMixtureFactor::Sum &sum, const GaussianFactor::shared_ptr &factor) {
-  using Y = GaussianFactorGraph;
   // If the decision tree is not initialized, then initialize it.
   if (sum.empty()) {
     GaussianFactorGraph result;
     result.push_back(factor);
-    sum = GaussianMixtureFactor::Sum(result);
+    sum = GaussianMixtureFactor::Sum(
+        GaussianMixtureFactor::GraphAndConstant(result, 0.0));
 
   } else {
-    auto add = [&factor](const Y &graph) {
+    auto add = [&factor](
-      auto result = graph;
+                   const GaussianMixtureFactor::GraphAndConstant &graph_z) {
+      auto result = graph_z.graph;
       result.push_back(factor);
-      return result;
+      return GaussianMixtureFactor::GraphAndConstant(result, graph_z.constant);
     };
     sum = sum.apply(add);
   }
@@ -190,19 +191,23 @@ hybridElimination(const HybridGaussianFactorGraph &factors,
   DiscreteKeys discreteSeparator(discreteSeparatorSet.begin(),
                                  discreteSeparatorSet.end());
 
-  // sum out frontals, this is the factor 𝜏 on the separator
+  // Collect all the frontal factors to create Gaussian factor graphs
+  // indexed on the discrete keys.
   GaussianMixtureFactor::Sum sum = sumFrontals(factors);
 
   // If a tree leaf contains nullptr,
   // convert that leaf to an empty GaussianFactorGraph.
   // Needed since the DecisionTree will otherwise create
   // a GFG with a single (null) factor.
-  auto emptyGaussian = [](const GaussianFactorGraph &gfg) {
+  auto emptyGaussian =
-    bool hasNull =
+      [](const GaussianMixtureFactor::GraphAndConstant &graph_z) {
-        std::any_of(gfg.begin(), gfg.end(),
+        bool hasNull = std::any_of(
+            graph_z.graph.begin(), graph_z.graph.end(),
             [](const GaussianFactor::shared_ptr &ptr) { return !ptr; });
 
-    return hasNull ? GaussianFactorGraph() : gfg;
+        return hasNull ? GaussianMixtureFactor::GraphAndConstant(
+                             GaussianFactorGraph(), 0.0)
+                       : graph_z;
       };
   sum = GaussianMixtureFactor::Sum(sum, emptyGaussian);
 
@@ -210,11 +215,12 @@ hybridElimination(const HybridGaussianFactorGraph &factors,
                                     GaussianMixtureFactor::FactorAndConstant>;
 
   KeyVector keysOfEliminated;  // Not the ordering
-  KeyVector keysOfSeparator;   // TODO(frank): Is this just (keys - ordering)?
+  KeyVector keysOfSeparator;
 
   // This is the elimination method on the leaf nodes
-  auto eliminate = [&](const GaussianFactorGraph &graph) -> EliminationPair {
+  auto eliminate = [&](const GaussianMixtureFactor::GraphAndConstant &graph_z)
-    if (graph.empty()) {
+      -> EliminationPair {
+    if (graph_z.graph.empty()) {
       return {nullptr, {nullptr, 0.0}};
     }
 
@@ -224,7 +230,8 @@ hybridElimination(const HybridGaussianFactorGraph &factors,
 
     std::pair<boost::shared_ptr<GaussianConditional>,
               boost::shared_ptr<GaussianFactor>>
-        conditional_factor = EliminatePreferCholesky(graph, frontalKeys);
+        conditional_factor =
+            EliminatePreferCholesky(graph_z.graph, frontalKeys);
 
     // Initialize the keysOfEliminated to be the keys of the
     // eliminated GaussianConditional
@@ -235,8 +242,10 @@ hybridElimination(const HybridGaussianFactorGraph &factors,
     gttoc_(hybrid_eliminate);
 #endif
 
-    // TODO(Varun) The normalizing constant has to be computed correctly
+    GaussianConditional::shared_ptr conditional = conditional_factor.first;
-    return {conditional_factor.first, {conditional_factor.second, 0.0}};
+    // Get the log of the log normalization constant inverse.
+    double logZ = -conditional->logNormalizationConstant() + graph_z.constant;
+    return {conditional, {conditional_factor.second, logZ}};
   };
 
   // Perform elimination!
@@ -270,6 +279,13 @@ hybridElimination(const HybridGaussianFactorGraph &factors,
           }
         };
     DecisionTree<Key, double> fdt(separatorFactors, factorProb);
+    // Normalize the values of decision tree to be valid probabilities
+    double sum = 0.0;
+    auto visitor = [&](double y) { sum += y; };
+    fdt.visit(visitor);
+    // fdt = DecisionTree<Key, double>(fdt,
+    //                                 [sum](const double &x) { return x / sum;
+    //                                 });
 
     auto discreteFactor =
         boost::make_shared<DecisionTreeFactor>(discreteSeparator, fdt);