add new build method to replace create, and let create call Unique

gtsam/discrete/DecisionTree-inl.h

@@ -632,16 +632,16 @@ namespace gtsam {
   }
 
   /****************************************************************************/
-  // "create" is a bit of a complicated thing, but very useful.
+  // "build" is a bit of a complicated thing, but very useful.
   // It takes a range of labels and a corresponding range of values,
-  // and creates a decision tree, as follows:
+  // and builds a decision tree, as follows:
   // - if there is only one label, creates a choice node with values in leaves
   // - otherwise, it evenly splits up the range of values and creates a tree for
   //   each sub-range, and assigns that tree to first label's choices
   // Example:
-  // create([B A],[1 2 3 4]) would call
-  //   create([A],[1 2])
-  //   create([A],[3 4])
+  // build([B A],[1 2 3 4]) would call
+  //   build([A],[1 2])
+  //   build([A],[3 4])
   // and produce
   // B=0
   //  A=0: 1
@@ -649,12 +649,12 @@ namespace gtsam {
   // B=1
   //  A=0: 3
   //  A=1: 4
-  // Note, through the magic of "compose", create([A B],[1 2 3 4]) will produce
+  // Note, through the magic of "compose", build([A B],[1 2 3 4]) will produce
   // exactly the same tree as above: the highest label is always the root.
   // However, it will be *way* faster if labels are given highest to lowest.
   template<typename L, typename Y>
   template<typename It, typename ValueIt>
-  typename DecisionTree<L, Y>::NodePtr DecisionTree<L, Y>::create(
+  typename DecisionTree<L, Y>::NodePtr DecisionTree<L, Y>::build(
       It begin, It end, ValueIt beginY, ValueIt endY) const {
     // get crucial counts
     size_t nrChoices = begin->second;
@@ -684,12 +684,27 @@ namespace gtsam {
     std::vector<DecisionTree> functions;
     size_t split = size / nrChoices;
     for (size_t i = 0; i < nrChoices; i++, beginY += split) {
-      NodePtr f = create<It, ValueIt>(labelC, end, beginY, beginY + split);
+      NodePtr f = build<It, ValueIt>(labelC, end, beginY, beginY + split);
       functions.emplace_back(f);
     }
     return compose(functions.begin(), functions.end(), begin->first);
   }
 
+  /****************************************************************************/
+  // Take a range of labels and a corresponding range of values,
+  // and creates a decision tree.
+  template<typename L, typename Y>
+  template<typename It, typename ValueIt>
+  typename DecisionTree<L, Y>::NodePtr DecisionTree<L, Y>::create(
+      It begin, It end, ValueIt beginY, ValueIt endY) const {
+    auto node = build(begin, end, beginY, endY);
+    if (auto choice = std::dynamic_pointer_cast<const Choice>(node)) {
+      return Choice::Unique(choice);
+    } else {
+      return node;
+    }
+  }
+
   /****************************************************************************/
   template <typename L, typename Y>
   template <typename M, typename X>

gtsam/discrete/DecisionTree.h

@@ -139,7 +139,13 @@ namespace gtsam {
     /** Internal recursive function to create from keys, cardinalities,
      * and Y values
      */
-    template<typename It, typename ValueIt>
+    template <typename It, typename ValueIt>
+    NodePtr build(It begin, It end, ValueIt beginY, ValueIt endY) const;
+
+    /** Internal helper function to create from keys, cardinalities,
+     * and Y values
+     */
+    template <typename It, typename ValueIt>
     NodePtr create(It begin, It end, ValueIt beginY, ValueIt endY) const;
 
     /**