fix odd behavior in nrAssignments

gtsam/discrete/DecisionTree-inl.h

@@ -137,8 +137,8 @@ namespace gtsam {
     // Applying binary operator to two leaves results in a leaf
     NodePtr apply_g_op_fL(const Leaf& fL, const Binary& op) const override {
       // fL op gL
-      // TODO(Varun) nrAssignments setting is not correct.
-      // Depending on f and g, the nrAssignments can be different. This is a bug!
+      // The nrAssignments is always set to fL since we consider g operating on
+      // (or modifying) f.
       NodePtr h(new Leaf(op(fL.constant_, constant_), fL.nrAssignments()));
       return h;
     }
@@ -149,8 +149,9 @@ namespace gtsam {
     }
 
     /** choose a branch, create new memory ! */
-    NodePtr choose(const L& label, size_t index) const override {
-      return NodePtr(new Leaf(constant(), nrAssignments()));
+    NodePtr choose(const L& label, size_t index,
+                   bool make_unique = true) const override {
+      return NodePtr(new Leaf(constant(), 1));
     }
 
     bool isLeaf() const override { return true; }
@@ -468,14 +469,22 @@ namespace gtsam {
     }
 
     /** choose a branch, recursively */
-    NodePtr choose(const L& label, size_t index) const override {
+    NodePtr choose(const L& label, size_t index,
+                   bool make_unique = true) const override {
       if (label_ == label) return branches_[index];  // choose branch
 
       // second case, not label of interest, just recurse
       auto r = std::make_shared<Choice>(label_, branches_.size());
-      for (auto&& branch : branches_)
-        r->push_back(branch->choose(label, index));
-      return Unique(r);
+      for (auto&& branch : branches_) {
+        r->push_back(branch->choose(label, index, make_unique));
+      }
+
+      if (make_unique) {
+        return Unique(r);
+      } else {
+        return r;
+      }
+      // return Unique(r);
     }
 
    private:
@@ -997,9 +1006,9 @@ namespace gtsam {
   template<typename L, typename Y>
   DecisionTree<L, Y> DecisionTree<L, Y>::combine(const L& label,
       size_t cardinality, const Binary& op) const {
-    DecisionTree result = choose(label, 0);
+    DecisionTree result = choose(label, 0, false);
     for (size_t index = 1; index < cardinality; index++) {
-      DecisionTree chosen = choose(label, index);
+      DecisionTree chosen = choose(label, index, false);
       result = result.apply(chosen, op);
     }
     return result;

gtsam/discrete/DecisionTree.h

@@ -129,7 +129,8 @@ namespace gtsam {
       virtual Ptr apply_f_op_g(const Node&, const Binary&) const = 0;
       virtual Ptr apply_g_op_fL(const Leaf&, const Binary&) const = 0;
       virtual Ptr apply_g_op_fC(const Choice&, const Binary&) const = 0;
-      virtual Ptr choose(const L& label, size_t index) const = 0;
+      virtual Ptr choose(const L& label, size_t index,
+                         bool make_unique = true) const = 0;
       virtual bool isLeaf() const = 0;
 
      private:
@@ -403,8 +404,9 @@ namespace gtsam {
 
     /** create a new function where value(label)==index
      * It's like "restrict" in Darwiche09book pg329, 330? */
-    DecisionTree choose(const L& label, size_t index) const {
-      NodePtr newRoot = root_->choose(label, index);
+    DecisionTree choose(const L& label, size_t index,
+                        bool make_unique = true) const {
+      NodePtr newRoot = root_->choose(label, index, make_unique);
       return DecisionTree(newRoot);
     }
 

gtsam/discrete/tests/testDecisionTree.cpp

@@ -236,7 +236,7 @@ TEST(DecisionTree, Example) {
 #ifdef GTSAM_DT_MERGING
   EXPECT(assert_equal(DT(0.0), actual0));
 #else
-  // EXPECT(assert_equal(DT({0.0, 0.0}), actual0));
+  EXPECT(assert_equal(DT({0.0, 0.0}), actual0));
 #endif
   DOT(actual0);
 

gtsam/discrete/tests/testDiscreteFactorGraph.cpp

@@ -349,6 +349,7 @@ TEST(DiscreteFactorGraph, markdown) {
   EXPECT_DOUBLES_EQUAL(0.3, graph[0]->operator()(values), 1e-9);
 }
 
+/* ************************************************************************* */
 TEST(DiscreteFactorGraph, NrAssignments) {
 #ifdef GTSAM_DT_MERGING
   string expected_dfg = R"(
@@ -358,13 +359,13 @@ factor 0:  f[ (m0,2), (m1,2), (m2,2), ]
  0 Choice(m1) 
  0 0 Leaf [2]    0
  0 1 Choice(m0) 
- 0 1 0 Leaf [1] 0.27527634
+ 0 1 0 Leaf [1] 0.17054468
  0 1 1 Leaf [1]    0
  1 Choice(m1) 
  1 0 Leaf [2]    0
  1 1 Choice(m0) 
- 1 1 0 Leaf [1] 0.44944733
- 1 1 1 Leaf [1] 0.27527634
+ 1 1 0 Leaf [1] 0.27845056
+ 1 1 1 Leaf [1] 0.17054468
 factor 1:  f[ (m0,2), (m1,2), (m2,2), (m3,2), ]
  Choice(m3) 
  0 Choice(m2) 
@@ -445,9 +446,7 @@ factor 1:  f[ (m0,2), (m1,2), (m2,2), (m3,2), ]
   DiscreteKeys d0{{M(0), 2}, {M(1), 2}, {M(2), 2}};
   std::vector<double> p0 = {0, 0, 0.17054468, 0.27845056, 0, 0, 0, 0.17054468};
   AlgebraicDecisionTree<Key> dt(d0, p0);
-  //TODO(Varun) Passing ADT to DiscreteConditional causes nrAssignments to get messed up
-  // Issue seems to be in DecisionTreeFactor.cpp L104
-  DiscreteConditional f0(3, DecisionTreeFactor(d0, dt));
+  DiscreteConditional f0(3, d0, dt);
 
   DiscreteKeys d1{{M(0), 2}, {M(1), 2}, {M(2), 2}, {M(3), 2}};
   std::vector<double> p1 = {