Rename Hybrid Elements

doc/Hybrid.lyx

@@ -191,17 +191,17 @@ E_{gc}(x,y)=\frac{1}{2}\|Rx+Sy-d\|_{\Sigma}^{2}.\label{eq:gc_error}
 \end_layout
 
 \begin_layout Subsubsection*
-GaussianMixture
+HybridGaussianConditional
 \end_layout
 
 \begin_layout Standard
 A 
 \emph on
-GaussianMixture
+HybridGaussianConditional
 \emph default
  (maybe to be renamed to 
 \emph on
-GaussianMixtureComponent
+HybridGaussianConditionalComponent
 \emph default
 ) just indexes into a number of 
 \emph on
@@ -233,7 +233,7 @@ GaussianConditional
  to a set of discrete variables.
  As 
 \emph on
-GaussianMixture
+HybridGaussianConditional
 \emph default
  is a 
 \emph on
@@ -324,7 +324,7 @@ The key point here is that
 \color inherit
  is the log-normalization constant for the complete 
 \emph on
-GaussianMixture
+HybridGaussianConditional
 \emph default
  across all values of 
 \begin_inset Formula $m$
@@ -548,15 +548,15 @@ with
 \end_layout
 
 \begin_layout Subsubsection*
-GaussianMixtureFactor
+HybridGaussianFactor
 \end_layout
 
 \begin_layout Standard
 Analogously, a 
 \emph on
-GaussianMixtureFactor
+HybridGaussianFactor
 \emph default
- typically results from a GaussianMixture by having known values 
+ typically results from a HybridGaussianConditional by having known values 
 \begin_inset Formula $\bar{x}$
 \end_inset
 
@@ -817,7 +817,7 @@ E_{mf}(y,m)=\frac{1}{2}\|A_{m}y-b_{m}\|_{\Sigma_{mfm}}^{2}=E_{gcm}(\bar{x},y)+K_
 
 \end_inset
 
-which is identical to the GaussianMixture error 
+which is identical to the HybridGaussianConditional error 
 \begin_inset CommandInset ref
 LatexCommand eqref
 reference "eq:gm_error"

gtsam/hybrid/HybridBayesNet.cpp

@@ -168,11 +168,11 @@ HybridBayesNet HybridBayesNet::prune(size_t maxNrLeaves) {
   DecisionTreeFactor prunedDiscreteProbs =
       this->pruneDiscreteConditionals(maxNrLeaves);
 
-  /* To prune, we visitWith every leaf in the GaussianMixture.
+  /* To prune, we visitWith every leaf in the HybridGaussianConditional.
    * For each leaf, using the assignment we can check the discrete decision tree
    * for 0.0 probability, then just set the leaf to a nullptr.
    *
-   * We can later check the GaussianMixture for just nullptrs.
+   * We can later check the HybridGaussianConditional for just nullptrs.
    */
 
   HybridBayesNet prunedBayesNetFragment;
@@ -182,14 +182,16 @@ HybridBayesNet HybridBayesNet::prune(size_t maxNrLeaves) {
   for (auto &&conditional : *this) {
     if (auto gm = conditional->asMixture()) {
       // Make a copy of the Gaussian mixture and prune it!
-      auto prunedGaussianMixture = std::make_shared<GaussianMixture>(*gm);
-      prunedGaussianMixture->prune(prunedDiscreteProbs);  // imperative :-(
+      auto prunedHybridGaussianConditional =
+          std::make_shared<HybridGaussianConditional>(*gm);
+      prunedHybridGaussianConditional->prune(
+          prunedDiscreteProbs);  // imperative :-(
 
       // Type-erase and add to the pruned Bayes Net fragment.
-      prunedBayesNetFragment.push_back(prunedGaussianMixture);
+      prunedBayesNetFragment.push_back(prunedHybridGaussianConditional);
 
     } else {
-      // Add the non-GaussianMixture conditional
+      // Add the non-HybridGaussianConditional conditional
       prunedBayesNetFragment.push_back(conditional);
     }
   }

gtsam/hybrid/HybridBayesNet.h

@@ -79,7 +79,7 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
    *
    * Example:
    *   auto shared_ptr_to_a_conditional =
-   *     std::make_shared<GaussianMixture>(...);
+   *     std::make_shared<HybridGaussianConditional>(...);
    *  hbn.push_back(shared_ptr_to_a_conditional);
    */
   void push_back(HybridConditional &&conditional) {
@@ -106,7 +106,7 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
    * Preferred: Emplace a conditional directly using arguments.
    *
    * Examples:
-   *   hbn.emplace_shared<GaussianMixture>(...)));
+   *   hbn.emplace_shared<HybridGaussianConditional>(...)));
    *   hbn.emplace_shared<GaussianConditional>(...)));
    *   hbn.emplace_shared<DiscreteConditional>(...)));
    */

gtsam/hybrid/HybridConditional.cpp

@@ -55,7 +55,7 @@ HybridConditional::HybridConditional(
 
 /* ************************************************************************ */
 HybridConditional::HybridConditional(
-    const std::shared_ptr<GaussianMixture> &gaussianMixture)
+    const std::shared_ptr<HybridGaussianConditional> &gaussianMixture)
     : BaseFactor(KeyVector(gaussianMixture->keys().begin(),
                            gaussianMixture->keys().begin() +
                                gaussianMixture->nrContinuous()),
@@ -157,10 +157,10 @@ double HybridConditional::logNormalizationConstant() const {
     return gc->logNormalizationConstant();
   }
   if (auto gm = asMixture()) {
-    return gm->logNormalizationConstant(); // 0.0!
+    return gm->logNormalizationConstant();  // 0.0!
   }
   if (auto dc = asDiscrete()) {
-    return dc->logNormalizationConstant(); // 0.0!
+    return dc->logNormalizationConstant();  // 0.0!
   }
   throw std::runtime_error(
       "HybridConditional::logProbability: conditional type not handled");

gtsam/hybrid/HybridConditional.h

@@ -18,8 +18,8 @@
 #pragma once
 
 #include <gtsam/discrete/DiscreteConditional.h>
-#include <gtsam/hybrid/GaussianMixture.h>
 #include <gtsam/hybrid/HybridFactor.h>
+#include <gtsam/hybrid/HybridGaussianConditional.h>
 #include <gtsam/hybrid/HybridGaussianFactorGraph.h>
 #include <gtsam/inference/Conditional.h>
 #include <gtsam/inference/Key.h>
@@ -39,7 +39,7 @@ namespace gtsam {
  * As a type-erased variant of:
  * - DiscreteConditional
  * - GaussianConditional
- * - GaussianMixture
+ * - HybridGaussianConditional
  *
  * The reason why this is important is that `Conditional<T>` is a CRTP class.
  * CRTP is static polymorphism such that all CRTP classes, while bearing the
@@ -127,7 +127,8 @@ class GTSAM_EXPORT HybridConditional
    * @param gaussianMixture Gaussian Mixture Conditional used to create the
    * HybridConditional.
    */
-  HybridConditional(const std::shared_ptr<GaussianMixture>& gaussianMixture);
+  HybridConditional(
+      const std::shared_ptr<HybridGaussianConditional>& gaussianMixture);
 
   /// @}
   /// @name Testable
@@ -146,12 +147,12 @@ class GTSAM_EXPORT HybridConditional
   /// @{
 
   /**
-   * @brief Return HybridConditional as a GaussianMixture
+   * @brief Return HybridConditional as a HybridGaussianConditional
    * @return nullptr if not a mixture
-   * @return GaussianMixture::shared_ptr otherwise
+   * @return HybridGaussianConditional::shared_ptr otherwise
    */
-  GaussianMixture::shared_ptr asMixture() const {
-    return std::dynamic_pointer_cast<GaussianMixture>(inner_);
+  HybridGaussianConditional::shared_ptr asMixture() const {
+    return std::dynamic_pointer_cast<HybridGaussianConditional>(inner_);
   }
 
   /**
@@ -222,8 +223,10 @@ class GTSAM_EXPORT HybridConditional
       boost::serialization::void_cast_register<GaussianConditional, Factor>(
           static_cast<GaussianConditional*>(NULL), static_cast<Factor*>(NULL));
     } else {
-      boost::serialization::void_cast_register<GaussianMixture, Factor>(
-          static_cast<GaussianMixture*>(NULL), static_cast<Factor*>(NULL));
+      boost::serialization::void_cast_register<HybridGaussianConditional,
+                                               Factor>(
+          static_cast<HybridGaussianConditional*>(NULL),
+          static_cast<Factor*>(NULL));
     }
   }
 #endif

gtsam/hybrid/HybridEliminationTree.h

@@ -35,8 +35,8 @@ class GTSAM_EXPORT HybridEliminationTree
 
  public:
   typedef EliminationTree<HybridBayesNet, HybridGaussianFactorGraph>
-      Base;                                    ///< Base class
-  typedef HybridEliminationTree This;          ///< This class
+      Base;                                  ///< Base class
+  typedef HybridEliminationTree This;        ///< This class
   typedef std::shared_ptr<This> shared_ptr;  ///< Shared pointer to this class
 
   /// @name Constructors

gtsam/hybrid/HybridFactor.h

@@ -45,9 +45,9 @@ DiscreteKeys CollectDiscreteKeys(const DiscreteKeys &key1,
  * Base class for *truly* hybrid probabilistic factors
  *
  * Examples:
- *  - MixtureFactor
- *  - GaussianMixtureFactor
- *  - GaussianMixture
+ *  - HybridNonlinearFactor
+ *  - HybridGaussianFactor
+ *  - HybridGaussianConditional
  *
  * @ingroup hybrid
  */