toFactorGraph method in HybridBayesNet

gtsam/hybrid/HybridBayesNet.cpp

@@ -377,4 +377,27 @@ AlgebraicDecisionTree<Key> HybridBayesNet::probPrime(
   return error_tree.apply([](double error) { return exp(-error); });
 }
 
+/* ************************************************************************* */
+HybridGaussianFactorGraph HybridBayesNet::toFactorGraph(
+    const VectorValues &measurements) const {
+  HybridGaussianFactorGraph fg;
+
+  // For all nodes in the Bayes net, if its frontal variable is in measurements,
+  // replace it by a likelihood factor:
+  for (auto &&conditional : *this) {
+    if (conditional->frontalsIn(measurements)) {
+      if (auto gc = conditional->asGaussian())
+        fg.push_back(gc->likelihood(measurements));
+      else if (auto gm = conditional->asMixture())
+        fg.push_back(gm->likelihood(measurements));
+      else {
+        throw std::runtime_error("Unknown conditional type");
+      }
+    } else {
+      fg.push_back(conditional);
+    }
+  }
+  return fg;
+}
+
 }  // namespace gtsam

gtsam/hybrid/HybridBayesNet.h

@@ -229,6 +229,12 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
   AlgebraicDecisionTree<Key> probPrime(
       const VectorValues &continuousValues) const;
 
+  /**
+   * Convert a hybrid Bayes net to a hybrid Gaussian factor graph by converting
+   * all conditionals with instantiated measurements into likelihood factors.
+   */
+  HybridGaussianFactorGraph toFactorGraph(
+      const VectorValues &measurements) const;
   /// @}
 
  private:

gtsam/hybrid/HybridConditional.h

@@ -178,6 +178,16 @@ class GTSAM_EXPORT HybridConditional
   /// Return the error of the underlying conditional.
   double error(const HybridValues& values) const override;
 
+  /// Check if VectorValues `measurements` contains all frontal keys.
+  bool frontalsIn(const VectorValues& measurements) const {
+    for (Key key : frontals()) {
+      if (!measurements.exists(key)) {
+        return false;
+      }
+    }
+    return true;
+  }
+
   /// @}
 
  private:

gtsam/hybrid/tests/TinyHybridExample.h

@@ -64,30 +64,6 @@ inline HybridBayesNet createHybridBayesNet(int numMeasurements = 1,
   return bayesNet;
 }
 
-/**
- * Convert a hybrid Bayes net to a hybrid Gaussian factor graph.
- */
-inline HybridGaussianFactorGraph convertBayesNet(
-    const HybridBayesNet& bayesNet, const VectorValues& measurements) {
-  HybridGaussianFactorGraph fg;
-  // For all nodes in the Bayes net, if its frontal variable is in measurements,
-  // replace it by a likelihood factor:
-  for (const HybridConditional::shared_ptr& conditional : bayesNet) {
-    if (measurements.exists(conditional->firstFrontalKey())) {
-      if (auto gc = conditional->asGaussian())
-        fg.push_back(gc->likelihood(measurements));
-      else if (auto gm = conditional->asMixture())
-        fg.push_back(gm->likelihood(measurements));
-      else {
-        throw std::runtime_error("Unknown conditional type");
-      }
-    } else {
-      fg.push_back(conditional);
-    }
-  }
-  return fg;
-}
-
 /**
  * Create a tiny two variable hybrid factor graph which represents a discrete
  * mode and a continuous variable x0, given a number of measurements of the
@@ -101,10 +77,10 @@ inline HybridGaussianFactorGraph createHybridGaussianFactorGraph(
   auto bayesNet = createHybridBayesNet(numMeasurements, manyModes);
   if (measurements) {
     // Use the measurements to create a hybrid factor graph.
-    return convertBayesNet(bayesNet, *measurements);
+    return bayesNet.toFactorGraph(*measurements);
   } else {
     // Sample from the generative model to create a hybrid factor graph.
-    return convertBayesNet(bayesNet, bayesNet.sample().continuous());
+    return bayesNet.toFactorGraph(bayesNet.sample().continuous());
   }
 }
 

gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp

@@ -735,7 +735,7 @@ TEST(HybridGaussianFactorGraph, EliminateTiny22) {
   // Create Bayes net and convert to factor graph.
   auto bn = tiny::createHybridBayesNet(numMeasurements, manyModes);
   const VectorValues measurements{{Z(0), Vector1(4.0)}, {Z(1), Vector1(6.0)}};
-  auto fg = tiny::convertBayesNet(bn, measurements);
+  auto fg = bn.toFactorGraph(measurements);
   EXPECT_LONGS_EQUAL(5, fg.size());
 
   // Test elimination

gtsam/linear/GaussianConditional.cpp

@@ -263,7 +263,7 @@ double GaussianConditional::evaluate(const VectorValues& x) const {
     Vector frontalVec = gy.vector(KeyVector(beginFrontals(), endFrontals()));
     frontalVec = R().transpose().triangularView<Eigen::Lower>().solve(frontalVec);
 
-    // Check for indeterminant solution
+    // Check for indeterminate solution
     if (frontalVec.hasNaN()) throw IndeterminantLinearSystemException(this->keys().front());
 
     for (const_iterator it = beginParents(); it!= endParents(); it++)