Discrete Improvements

gtsam/discrete/DiscreteFactorGraph.cpp

@@ -112,24 +112,23 @@ namespace gtsam {
 //  }
 
   /**
-   * @brief Multiply all the `factors` and normalize the
-   * product to prevent underflow.
+   * @brief Multiply all the `factors`.
    *
    * @param factors The factors to multiply as a DiscreteFactorGraph.
    * @return DecisionTreeFactor
    */
-  static DecisionTreeFactor ProductAndNormalize(
+  static DecisionTreeFactor DiscreteProduct(
       const DiscreteFactorGraph& factors) {
     // PRODUCT: multiply all factors
     gttic(product);
     DecisionTreeFactor product = factors.product();
     gttoc(product);
 
     // Max over all the potentials by pretending all keys are frontal:
-    auto normalization = product.max(product.size());
+    auto denominator = product.max(product.size());
 
     // Normalize the product factor to prevent underflow.
-    product = product / (*normalization);
+    product = product / (*denominator);
 
     return product;
   }
@@ -139,7 +138,7 @@ namespace gtsam {
   std::pair<DiscreteConditional::shared_ptr, DiscreteFactor::shared_ptr>  //
   EliminateForMPE(const DiscreteFactorGraph& factors,
                   const Ordering& frontalKeys) {
-    DecisionTreeFactor product = ProductAndNormalize(factors);
+    DecisionTreeFactor product = DiscreteProduct(factors);
 
     // max out frontals, this is the factor on the separator
     gttic(max);
@@ -207,8 +206,7 @@ namespace gtsam {
     return dag.argmax();
   }
 
-  DiscreteValues DiscreteFactorGraph::optimize(
-      const Ordering& ordering) const {
+  DiscreteValues DiscreteFactorGraph::optimize(const Ordering& ordering) const {
     gttic(DiscreteFactorGraph_optimize);
     DiscreteLookupDAG dag = maxProduct(ordering);
     return dag.argmax();
@@ -218,7 +216,7 @@ namespace gtsam {
   std::pair<DiscreteConditional::shared_ptr, DiscreteFactor::shared_ptr>  //
   EliminateDiscrete(const DiscreteFactorGraph& factors,
                     const Ordering& frontalKeys) {
-    DecisionTreeFactor product = ProductAndNormalize(factors);
+    DecisionTreeFactor product = DiscreteProduct(factors);
 
     // sum out frontals, this is the factor on the separator
     gttic(sum);

gtsam/discrete/TableFactor.cpp

@@ -252,41 +252,12 @@ DecisionTreeFactor TableFactor::operator*(const DecisionTreeFactor& f) const {
 DecisionTreeFactor TableFactor::toDecisionTreeFactor() const {
   DiscreteKeys dkeys = discreteKeys();
 
-  // Record key assignment and value pairs in pair_table.
-  // The assignments are stored in descending order of keys so that the order of
-  // the values matches what is expected by a DecisionTree.
-  // This is why we reverse the keys and then
-  // query for the key value/assignment.
-  DiscreteKeys rdkeys(dkeys.rbegin(), dkeys.rend());
-  std::vector<std::pair<uint64_t, double>> pair_table;
-  for (auto i = 0; i < sparse_table_.size(); i++) {
-    std::stringstream ss;
-    for (auto&& [key, _] : rdkeys) {
-      ss << keyValueForIndex(key, i);
-    }
-    // k will be in reverse key order already
-    uint64_t k;
-    ss >> k;
-    pair_table.push_back(std::make_pair(k, sparse_table_.coeff(i)));
-  }
-
-  // Sort the pair_table (of assignment-value pairs) based on assignment so we
-  // get values in reverse key order.
-  std::sort(
-      pair_table.begin(), pair_table.end(),
-      [](const std::pair<uint64_t, double>& a,
-         const std::pair<uint64_t, double>& b) { return a.first < b.first; });
-
-  // Create the table vector by extracting the values from pair_table.
-  // The pair_table has already been sorted in the desired order,
-  // so the values will be in descending key order.
   std::vector<double> table;
-  std::for_each(pair_table.begin(), pair_table.end(),
-                [&table](const std::pair<uint64_t, double>& pair) {
-                  table.push_back(pair.second);
-                });
+  for (auto i = 0; i < sparse_table_.size(); i++) {
+    table.push_back(sparse_table_.coeff(i));
+  }
 
-  AlgebraicDecisionTree<Key> tree(rdkeys, table);
+  AlgebraicDecisionTree<Key> tree(dkeys, table);
   DecisionTreeFactor f(dkeys, tree);
   return f;
 }

gtsam/discrete/tests/testDiscreteFactorGraph.cpp

@@ -117,9 +117,9 @@ TEST(DiscreteFactorGraph, test) {
       *std::dynamic_pointer_cast<DecisionTreeFactor>(newFactorPtr);
 
   // Normalize newFactor by max for comparison with expected
-  auto normalization = newFactor.max(newFactor.size());
+  auto normalizer = newFactor.max(newFactor.size());
 
-  newFactor = newFactor / *normalization;
+  newFactor = newFactor / *normalizer;
 
   // Check Conditional
   CHECK(conditional);
@@ -131,9 +131,9 @@ TEST(DiscreteFactorGraph, test) {
   CHECK(&newFactor);
   DecisionTreeFactor expectedFactor(B & A, "10 6 6 10");
   // Normalize by max.
-  normalization = expectedFactor.max(expectedFactor.size());
-  // Ensure normalization is correct.
-  expectedFactor = expectedFactor / *normalization;
+  normalizer = expectedFactor.max(expectedFactor.size());
+  // Ensure normalizer is correct.
+  expectedFactor = expectedFactor / *normalizer;
   EXPECT(assert_equal(expectedFactor, newFactor));
 
   // Test using elimination tree

gtsam/hybrid/HybridGaussianFactorGraph.cpp

@@ -282,7 +282,7 @@ discreteElimination(const HybridGaussianFactorGraph &factors,
     } else if (auto hc = dynamic_pointer_cast<HybridConditional>(f)) {
       auto dc = hc->asDiscrete();
       if (!dc) throwRuntimeError("discreteElimination", dc);
-      dfg.push_back(hc->asDiscrete());
+      dfg.push_back(dc);
     } else {
       throwRuntimeError("discreteElimination", f);
     }