remove augment method in favor of conversion

gtsam/hybrid/GaussianMixture.cpp

@@ -200,27 +200,24 @@ std::shared_ptr<GaussianMixtureFactor> GaussianMixture::likelihood(
   const GaussianMixtureFactor::Factors likelihoods(
       conditionals_, [&](const GaussianConditional::shared_ptr &conditional) {
         const auto likelihood_m = conditional->likelihood(given);
-        return likelihood_m;
+        const double Cgm_Kgcm =
+            logConstant_ - conditional->logNormalizationConstant();
+        if (Cgm_Kgcm == 0.0) {
+          return likelihood_m;
+        } else {
+          // Add a constant factor to the likelihood in case the noise models
+          // are not all equal.
+          GaussianFactorGraph gfg;
+          gfg.push_back(likelihood_m);
+          Vector c(1);
+          c << std::sqrt(2.0 * Cgm_Kgcm);
+          auto constantFactor = std::make_shared<JacobianFactor>(c);
+          gfg.push_back(constantFactor);
+          return std::make_shared<JacobianFactor>(gfg);
+        }
       });
-
-  // First compute all the sqrt(|2 pi Sigma|) terms
-  auto computeLogNormalizers = [](const GaussianFactor::shared_ptr &gf) {
-    auto jf = std::dynamic_pointer_cast<JacobianFactor>(gf);
-    // If we have, say, a Hessian factor, then no need to do anything
-    if (!jf) return 0.0;
-
-    auto model = jf->get_model();
-    // If there is no noise model, there is nothing to do.
-    if (!model) {
-      return 0.0;
-    }
-    return ComputeLogNormalizer(model);
-  };
-
-  AlgebraicDecisionTree<Key> log_normalizers =
-      DecisionTree<Key, double>(likelihoods, computeLogNormalizers);
   return std::make_shared<GaussianMixtureFactor>(
-      continuousParentKeys, discreteParentKeys, likelihoods, log_normalizers);
+      continuousParentKeys, discreteParentKeys, likelihoods);
 }
 
 /* ************************************************************************* */

gtsam/hybrid/GaussianMixtureFactor.cpp

@@ -28,55 +28,11 @@
 
 namespace gtsam {
 
-/**
- * @brief Helper function to augment the [A|b] matrices in the factor components
- * with the normalizer values.
- * This is done by storing the normalizer value in
- * the `b` vector as an additional row.
- *
- * @param factors DecisionTree of GaussianFactor shared pointers.
- * @param logNormalizers Tree of log-normalizers corresponding to each
- * Gaussian factor in factors.
- * @return GaussianMixtureFactor::Factors
- */
-GaussianMixtureFactor::Factors augment(
-    const GaussianMixtureFactor::Factors &factors,
-    const AlgebraicDecisionTree<Key> &logNormalizers) {
-  // Find the minimum value so we can "proselytize" to positive values.
-  // Done because we can't have sqrt of negative numbers.
-  double min_log_normalizer = logNormalizers.min();
-  AlgebraicDecisionTree<Key> log_normalizers = logNormalizers.apply(
-      [&min_log_normalizer](double n) { return n - min_log_normalizer; });
-
-  // Finally, update the [A|b] matrices.
-  auto update = [&log_normalizers](
-                    const Assignment<Key> &assignment,
-                    const GaussianMixtureFactor::sharedFactor &gf) {
-    auto jf = std::dynamic_pointer_cast<JacobianFactor>(gf);
-    if (!jf) return gf;
-    // If the log_normalizer is 0, do nothing
-    if (log_normalizers(assignment) == 0.0) return gf;
-
-    GaussianFactorGraph gfg;
-    gfg.push_back(jf);
-
-    Vector c(1);
-    c << std::sqrt(log_normalizers(assignment));
-    auto constantFactor = std::make_shared<JacobianFactor>(c);
-
-    gfg.push_back(constantFactor);
-    return std::dynamic_pointer_cast<GaussianFactor>(
-        std::make_shared<JacobianFactor>(gfg));
-  };
-  return factors.apply(update);
-}
-
 /* *******************************************************************************/
-GaussianMixtureFactor::GaussianMixtureFactor(
+GaussianMixtureFactor::GaussianMixtureFactor(const KeyVector &continuousKeys,
-    const KeyVector &continuousKeys, const DiscreteKeys &discreteKeys,
+                                             const DiscreteKeys &discreteKeys,
-    const Factors &factors, const AlgebraicDecisionTree<Key> &logNormalizers)
+                                             const Factors &factors)
-    : Base(continuousKeys, discreteKeys),
+    : Base(continuousKeys, discreteKeys), factors_(factors) {}
-      factors_(augment(factors, logNormalizers)) {}
 
 /* *******************************************************************************/
 bool GaussianMixtureFactor::equals(const HybridFactor &lf, double tol) const {

gtsam/hybrid/GaussianMixtureFactor.h

@@ -82,14 +82,10 @@ class GTSAM_EXPORT GaussianMixtureFactor : public HybridFactor {
    * their cardinalities.
    * @param factors The decision tree of Gaussian factors stored as the mixture
    * density.
-   * @param logNormalizers Tree of log-normalizers corresponding to each
-   * Gaussian factor in factors.
    */
   GaussianMixtureFactor(const KeyVector &continuousKeys,
                         const DiscreteKeys &discreteKeys,
-                        const Factors &factors,
+                        const Factors &factors);
-                        const AlgebraicDecisionTree<Key> &logNormalizers =
-                            AlgebraicDecisionTree<Key>(0.0));
 
   /**
    * @brief Construct a new GaussianMixtureFactor object using a vector of
@@ -98,16 +94,12 @@ class GTSAM_EXPORT GaussianMixtureFactor : public HybridFactor {
    * @param continuousKeys Vector of keys for continuous factors.
    * @param discreteKeys Vector of discrete keys.
    * @param factors Vector of gaussian factor shared pointers.
-   * @param logNormalizers Tree of log-normalizers corresponding to each
-   * Gaussian factor in factors.
    */
   GaussianMixtureFactor(const KeyVector &continuousKeys,
                         const DiscreteKeys &discreteKeys,
-                        const std::vector<sharedFactor> &factors,
+                        const std::vector<sharedFactor> &factors)
-                        const AlgebraicDecisionTree<Key> &logNormalizers =
-                            AlgebraicDecisionTree<Key>(0.0))
       : GaussianMixtureFactor(continuousKeys, discreteKeys,
-                              Factors(discreteKeys, factors), logNormalizers) {}
+                              Factors(discreteKeys, factors)) {}
 
   /// @}
   /// @name Testable