Made specific eliminate and eliminateOne methods for SymbolicFactorGraph and GaussianFactorGraph and made them accessible from MATLAB

gtsam.h

@@ -903,6 +903,7 @@ class SymbolicFactorGraph {
   void push_factor(size_t key1, size_t key2, size_t key3, size_t key4);
 
   pair<gtsam::IndexConditional*, gtsam::SymbolicFactorGraph> eliminateFrontals(size_t nFrontals) const;
+  pair<gtsam::IndexConditional*, gtsam::SymbolicFactorGraph> eliminateOne(size_t j) const;
 };
 
 #include <gtsam/inference/SymbolicSequentialSolver.h>
@@ -1234,6 +1235,7 @@ class GaussianFactorGraph {
 
   // Inference
   pair<gtsam::GaussianConditional*, gtsam::GaussianFactorGraph> eliminateFrontals(size_t nFrontals) const;
+  pair<gtsam::GaussianConditional*, gtsam::GaussianFactorGraph> eliminateOne(size_t j) const;
 
   // Building the graph
   void push_back(gtsam::GaussianFactor* factor);

gtsam/inference/FactorGraph.h

@@ -197,14 +197,11 @@ class VariableIndex;
      * BayesNet.  If the variables are not fully-connected, it is more efficient
      * to sequentially factorize multiple times.
      */
-    std::pair<typename FactorGraph<FACTOR>::sharedConditional, FactorGraph<FactorType> >
-      eliminate(
+    std::pair<sharedConditional, FactorGraph<FactorType> > eliminate(
       const std::vector<KeyType>& variables, const Eliminate& eliminateFcn,
       boost::optional<const VariableIndex&> variableIndex = boost::none);
 
-    /** Eliminate a single variable, by calling
-     * eliminate(const Graph&, const std::vector<typename Graph::KeyType>&, const typename Graph::Eliminate&, boost::optional<const VariableIndex&>)
-     */
+    /** Eliminate a single variable, by calling FactorGraph::eliminate. */
     std::pair<sharedConditional, FactorGraph<FactorType> > eliminateOne(
         KeyType variable, const Eliminate& eliminateFcn,
         boost::optional<const VariableIndex&> variableIndex = boost::none) {

gtsam/inference/SymbolicFactorGraph.cpp

@@ -70,6 +70,20 @@ namespace gtsam {
     return FactorGraph<IndexFactor>::eliminateFrontals(nFrontals, EliminateSymbolic);
   }
 
+  /* ************************************************************************* */
+  std::pair<SymbolicFactorGraph::sharedConditional, SymbolicFactorGraph>
+    SymbolicFactorGraph::eliminate(const std::vector<Index>& variables)
+  {
+    return FactorGraph<IndexFactor>::eliminate(variables, EliminateSymbolic);
+  }
+
+  /* ************************************************************************* */
+  std::pair<SymbolicFactorGraph::sharedConditional, SymbolicFactorGraph>
+    SymbolicFactorGraph::eliminateOne(Index variable)
+  {
+    return FactorGraph<IndexFactor>::eliminateOne(variable, EliminateSymbolic);
+  }
+
   /* ************************************************************************* */
   void SymbolicFactorGraph::permuteWithInverse(
     const Permutation& inversePermutation) {

gtsam/inference/SymbolicFactorGraph.h

@@ -66,6 +66,27 @@ namespace gtsam {
      * as the eliminate function argument.
      */
     std::pair<sharedConditional, SymbolicFactorGraph> eliminateFrontals(size_t nFrontals) const;
+            
+    /** Factor the factor graph into a conditional and a remaining factor graph.
+     * Given the factor graph \f$ f(X) \f$, and \c variables to factorize out
+     * \f$ V \f$, this function factorizes into \f$ f(X) = f(V;Y)f(Y) \f$, where
+     * \f$ Y := X\V \f$ are the remaining variables.  If \f$ f(X) = p(X) \f$ is
+     * a probability density or likelihood, the factorization produces a
+     * conditional probability density and a marginal \f$ p(X) = p(V|Y)p(Y) \f$.
+     *
+     * For efficiency, this function treats the variables to eliminate
+     * \c variables as fully-connected, so produces a dense (fully-connected)
+     * conditional on all of the variables in \c variables, instead of a sparse
+     * BayesNet.  If the variables are not fully-connected, it is more efficient
+     * to sequentially factorize multiple times.
+     * Note that this version simply calls
+     * FactorGraph<GaussianFactor>::eliminate with EliminateSymbolic as the eliminate
+     * function argument.
+     */
+    std::pair<sharedConditional, SymbolicFactorGraph> eliminate(const std::vector<Index>& variables);
+
+    /** Eliminate a single variable, by calling SymbolicFactorGraph::eliminate. */
+    std::pair<sharedConditional, SymbolicFactorGraph> eliminateOne(Index variable);
 
     /// @}
     /// @name Standard Interface

gtsam/linear/GaussianFactorGraph.cpp

@@ -54,6 +54,20 @@ namespace gtsam {
     return FactorGraph<GaussianFactor>::eliminateFrontals(nFrontals, EliminateQR);
   }
 
+  /* ************************************************************************* */
+  std::pair<GaussianFactorGraph::sharedConditional, GaussianFactorGraph>
+    GaussianFactorGraph::eliminate(const std::vector<Index>& variables)
+  {
+    return FactorGraph<GaussianFactor>::eliminate(variables, EliminateQR);
+  }
+
+  /* ************************************************************************* */
+  std::pair<GaussianFactorGraph::sharedConditional, GaussianFactorGraph>
+    GaussianFactorGraph::eliminateOne(Index variable)
+  {
+    return FactorGraph<GaussianFactor>::eliminateOne(variable, EliminateQR);
+  }
+
   /* ************************************************************************* */
   void GaussianFactorGraph::permuteWithInverse(
       const Permutation& inversePermutation) {
@@ -511,9 +525,6 @@ break;
   GaussianFactorGraph::EliminationResult EliminatePreferCholesky(
       const FactorGraph<GaussianFactor>& factors, size_t nrFrontals) {
 
-    typedef JacobianFactor J;
-    typedef HessianFactor H;
-
     // If any JacobianFactors have constrained noise models, we have to convert
     // all factors to JacobianFactors.  Otherwise, we can convert all factors
     // to HessianFactors.  This is because QR can handle constrained noise

gtsam/linear/GaussianFactorGraph.h

@@ -123,6 +123,27 @@ namespace gtsam {
      * eliminate function argument.
      */
     std::pair<sharedConditional, GaussianFactorGraph> eliminateFrontals(size_t nFrontals) const;
+        
+    /** Factor the factor graph into a conditional and a remaining factor graph.
+     * Given the factor graph \f$ f(X) \f$, and \c variables to factorize out
+     * \f$ V \f$, this function factorizes into \f$ f(X) = f(V;Y)f(Y) \f$, where
+     * \f$ Y := X\V \f$ are the remaining variables.  If \f$ f(X) = p(X) \f$ is
+     * a probability density or likelihood, the factorization produces a
+     * conditional probability density and a marginal \f$ p(X) = p(V|Y)p(Y) \f$.
+     *
+     * For efficiency, this function treats the variables to eliminate
+     * \c variables as fully-connected, so produces a dense (fully-connected)
+     * conditional on all of the variables in \c variables, instead of a sparse
+     * BayesNet.  If the variables are not fully-connected, it is more efficient
+     * to sequentially factorize multiple times.
+     * Note that this version simply calls
+     * FactorGraph<GaussianFactor>::eliminate with EliminateQR as the eliminate
+     * function argument.
+     */
+    std::pair<sharedConditional, GaussianFactorGraph> eliminate(const std::vector<Index>& variables);
+
+    /** Eliminate a single variable, by calling GaussianFactorGraph::eliminate. */
+    std::pair<sharedConditional, GaussianFactorGraph> eliminateOne(Index variable);
 
     /** Permute the variables in the factors */
     void permuteWithInverse(const Permutation& inversePermutation);