HybridBayesNet::optimize

gtsam/hybrid/HybridBayesNet.cpp

@@ -16,8 +16,8 @@
  */
 
 #include <gtsam/hybrid/HybridBayesNet.h>
-#include <gtsam/hybrid/HybridValues.h>
 #include <gtsam/hybrid/HybridLookupDAG.h>
+#include <gtsam/hybrid/HybridValues.h>
 
 namespace gtsam {
 
@@ -112,13 +112,12 @@ HybridBayesNet HybridBayesNet::prune(
 
 /* ************************************************************************* */
 GaussianMixture::shared_ptr HybridBayesNet::atGaussian(size_t i) const {
-  return boost::dynamic_pointer_cast<GaussianMixture>(factors_.at(i)->inner());
+  return factors_.at(i)->asMixture();
 }
 
 /* ************************************************************************* */
 DiscreteConditional::shared_ptr HybridBayesNet::atDiscrete(size_t i) const {
-  return boost::dynamic_pointer_cast<DiscreteConditional>(
+  return factors_.at(i)->asDiscreteConditional();
-      factors_.at(i)->inner());
 }
 
 /* ************************************************************************* */
@@ -138,4 +137,10 @@ HybridValues HybridBayesNet::optimize() const {
   return dag.argmax();
 }
 
+/* *******************************************************************************/
+VectorValues HybridBayesNet::optimize(const DiscreteValues &assignment) const {
+  GaussianBayesNet gbn = this->choose(assignment);
+  return gbn.optimize();
+}
+
 }  // namespace gtsam

gtsam/hybrid/HybridBayesNet.h

@@ -72,6 +72,15 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
   /// TODO(Shangjie) do we need to create a HybridGaussianBayesNet class, and
   /// put this method there?
   HybridValues optimize() const;
+
+  /**
+   * @brief Given the discrete assignment, return the optimized estimate for the
+   * selected Gaussian BayesNet.
+   *
+   * @param assignment An assignment of discrete values.
+   * @return Values
+   */
+  VectorValues optimize(const DiscreteValues &assignment) const;
 };
 
 }  // namespace gtsam

gtsam/hybrid/HybridConditional.h

@@ -69,7 +69,7 @@ class GTSAM_EXPORT HybridConditional
       BaseConditional;  ///< Typedef to our conditional base class
 
  protected:
-  // Type-erased pointer to the inner type
+  /// Type-erased pointer to the inner type
   boost::shared_ptr<Factor> inner_;
 
  public:
@@ -127,8 +127,7 @@ class GTSAM_EXPORT HybridConditional
    * @param gaussianMixture Gaussian Mixture Conditional used to create the
    * HybridConditional.
    */
-  HybridConditional(
+  HybridConditional(boost::shared_ptr<GaussianMixture> gaussianMixture);
-      boost::shared_ptr<GaussianMixture> gaussianMixture);
 
   /**
    * @brief Return HybridConditional as a GaussianMixture
@@ -168,10 +167,10 @@ class GTSAM_EXPORT HybridConditional
   /// Get the type-erased pointer to the inner type
   boost::shared_ptr<Factor> inner() { return inner_; }
 
-};  // DiscreteConditional
+};  // HybridConditional
 
 // traits
 template <>
-struct traits<HybridConditional> : public Testable<DiscreteConditional> {};
+struct traits<HybridConditional> : public Testable<HybridConditional> {};
 
 }  // namespace gtsam

gtsam/hybrid/tests/testHybridBayesNet.cpp

@@ -85,6 +85,40 @@ TEST(HybridBayesNet, Choose) {
                       *gbn.at(3)));
 }
 
+/* ****************************************************************************/
+// Test bayes net optimize
+TEST(HybridBayesNet, Optimize) {
+  Switching s(4);
+
+  Ordering ordering;
+  for (auto&& kvp : s.linearizationPoint) {
+    ordering += kvp.key;
+  }
+
+  HybridBayesNet::shared_ptr hybridBayesNet;
+  HybridGaussianFactorGraph::shared_ptr remainingFactorGraph;
+  std::tie(hybridBayesNet, remainingFactorGraph) =
+      s.linearizedFactorGraph.eliminatePartialSequential(ordering);
+
+  DiscreteValues assignment;
+  assignment[M(1)] = 1;
+  assignment[M(2)] = 1;
+  assignment[M(3)] = 1;
+
+  VectorValues delta = hybridBayesNet->optimize(assignment);
+
+  // The linearization point has the same value as the key index,
+  // e.g. X(1) = 1, X(2) = 2,
+  // but the factors specify X(k) = k-1, so delta should be -1.
+  VectorValues expected_delta;
+  expected_delta.insert(make_pair(X(1), -Vector1::Ones()));
+  expected_delta.insert(make_pair(X(2), -Vector1::Ones()));
+  expected_delta.insert(make_pair(X(3), -Vector1::Ones()));
+  expected_delta.insert(make_pair(X(4), -Vector1::Ones()));
+
+  EXPECT(assert_equal(expected_delta, delta));
+}
+
 /* ************************************************************************* */
 int main() {
   TestResult tr;