Created derived classes for SymbolicSequentialSolver and SymbolicMultifrontalSolver. This simplifies calling eliminate, mimics the Gaussian versions, and makes matlab wrapping possible.

gtsam/inference/SymbolicMultifrontalSolver.h

@@ -22,7 +22,44 @@
 
 namespace gtsam {
 
-// The base class provides all of the needed functionality
-typedef GenericMultifrontalSolver<IndexFactor, JunctionTree<FactorGraph<IndexFactor> > > SymbolicMultifrontalSolver;
+  class SymbolicMultifrontalSolver : GenericMultifrontalSolver<IndexFactor, JunctionTree<FactorGraph<IndexFactor> > > {
+
+  protected:
+    typedef GenericMultifrontalSolver<IndexFactor, JunctionTree<FactorGraph<IndexFactor> > > Base;
+
+  public:
+    /**
+     * Construct the solver for a factor graph.  This builds the junction
+     * tree, which does the symbolic elimination, identifies the cliques,
+     * and distributes all the factors to the right cliques.
+     */
+    SymbolicMultifrontalSolver(const SymbolicFactorGraph& factorGraph) : Base(factorGraph) {};
+
+    /**
+     * Construct the solver with a shared pointer to a factor graph and to a
+     * VariableIndex.  The solver will store these pointers, so this constructor
+     * is the fastest.
+     */
+    SymbolicMultifrontalSolver(const SymbolicFactorGraph::shared_ptr& factorGraph,
+        const VariableIndex::shared_ptr& variableIndex) : Base(factorGraph, variableIndex) {};
+
+    /**
+     * Eliminate the factor graph sequentially.  Uses a column elimination tree
+     * to recursively eliminate.
+     */
+    SymbolicBayesTree::shared_ptr eliminate() const { return Base::eliminate(&EliminateSymbolic); };
+
+    /**
+     * Compute the marginal joint over a set of variables, by integrating out
+     * all of the other variables.  Returns the result as a factor graph.
+     */
+    SymbolicFactorGraph::shared_ptr jointFactorGraph(const std::vector<Index>& js) const { return Base::jointFactorGraph(js, &EliminateSymbolic); };
+
+    /**
+     * Compute the marginal Gaussian density over a variable, by integrating out
+     * all of the other variables.  This function returns the result as a factor.
+     */
+    IndexFactor::shared_ptr marginalFactor(Index j) const { return Base::marginalFactor(j, &EliminateSymbolic); };
+  };
 
 }

gtsam/inference/SymbolicSequentialSolver.h

@@ -21,8 +21,51 @@
 
 namespace gtsam {
 
-// The base class provides all of the needed functionality
-typedef GenericSequentialSolver<IndexFactor> SymbolicSequentialSolver;
+  class SymbolicSequentialSolver : GenericSequentialSolver<IndexFactor> {
+
+  protected:
+    typedef GenericSequentialSolver<IndexFactor> Base;
+
+  public:
+    /**
+     * Construct the solver for a factor graph.  This builds the junction
+     * tree, which does the symbolic elimination, identifies the cliques,
+     * and distributes all the factors to the right cliques.
+     */
+    SymbolicSequentialSolver(const SymbolicFactorGraph& factorGraph) : Base(factorGraph) {};
+
+    /**
+     * Construct the solver with a shared pointer to a factor graph and to a
+     * VariableIndex.  The solver will store these pointers, so this constructor
+     * is the fastest.
+     */
+    SymbolicSequentialSolver(const SymbolicFactorGraph::shared_ptr& factorGraph,
+        const VariableIndex::shared_ptr& variableIndex) : Base(factorGraph, variableIndex) {};
+
+    /** Print to cout */
+    void print(const std::string& name = "SymbolicSequentialSolver: ") const { Base::print(name); };
+
+    /** Test whether is equal to another */
+    bool equals(const SymbolicSequentialSolver& other, double tol = 1e-9) const { return Base::equals(other, tol); };
+
+    /**
+     * Eliminate the factor graph sequentially.  Uses a column elimination tree
+     * to recursively eliminate.
+     */
+    SymbolicBayesNet::shared_ptr eliminate() const { return Base::eliminate(&EliminateSymbolic); };
+
+    /**
+     * Compute the marginal joint over a set of variables, by integrating out
+     * all of the other variables.  Returns the result as a factor graph.
+     */
+    SymbolicFactorGraph::shared_ptr jointFactorGraph(const std::vector<Index>& js) const { return Base::jointFactorGraph(js, &EliminateSymbolic); };
+
+    /**
+     * Compute the marginal Gaussian density over a variable, by integrating out
+     * all of the other variables.  This function returns the result as a factor.
+     */
+    IndexFactor::shared_ptr marginalFactor(Index j) const { return Base::marginalFactor(j, &EliminateSymbolic); };
+  };
 
 }
 

gtsam/inference/tests/testEliminationTree.cpp

@@ -101,8 +101,7 @@ TEST(EliminationTree, eliminate )
   fg.push_factor(3, 4);
 
   // eliminate
-  SymbolicBayesNet actual = *SymbolicSequentialSolver(fg).eliminate(
-			&EliminateSymbolic);
+  SymbolicBayesNet actual = *SymbolicSequentialSolver(fg).eliminate();
 
   CHECK(assert_equal(expected,actual));
 }

gtsam/inference/tests/testJunctionTree.cpp

@@ -83,8 +83,7 @@ TEST( JunctionTree, eliminate)
   SymbolicJunctionTree jt(fg);
   SymbolicBayesTree::sharedClique actual = jt.eliminate(&EliminateSymbolic);
 
-  BayesNet<IndexConditional> bn(*SymbolicSequentialSolver(fg).eliminate(
-			&EliminateSymbolic));
+  BayesNet<IndexConditional> bn(*SymbolicSequentialSolver(fg).eliminate());
   SymbolicBayesTree expected(bn);
 
 //  cout << "BT from JT:\n";

tests/testSymbolicBayesNetB.cpp

@@ -54,8 +54,7 @@ TEST( SymbolicBayesNet, constructor )
 	SymbolicFactorGraph fg(factorGraph);
 
 	// eliminate it
-  SymbolicBayesNet actual = *SymbolicSequentialSolver(fg).eliminate(
-			&EliminateSymbolic);
+  SymbolicBayesNet actual = *SymbolicSequentialSolver(fg).eliminate();
 
   CHECK(assert_equal(expected, actual));
 }

tests/testSymbolicFactorGraphB.cpp

@@ -143,7 +143,7 @@ TEST( SymbolicFactorGraph, eliminate )
 	SymbolicFactorGraph fg(factorGraph);
 
 	// eliminate it
-  SymbolicBayesNet actual = *SymbolicSequentialSolver(fg).eliminate(&EliminateSymbolic);
+  SymbolicBayesNet actual = *SymbolicSequentialSolver(fg).eliminate();
 
   CHECK(assert_equal(expected,actual));
 }