sum and normalize helper methods for the AlgebraicDecisionTree

gtsam/discrete/AlgebraicDecisionTree.h

@@ -196,6 +196,25 @@ namespace gtsam {
       return this->apply(g, &Ring::div);
     }
 
+    /// Compute sum of all values
+    double sum() const {
+      double sum = 0;
+      auto visitor = [&](int y) { sum += y; };
+      this->visit(visitor);
+      return sum;
+    }
+
+    /**
+     * @brief Helper method to perform normalization such that all leaves in the
+     * tree sum to 1
+     *
+     * @param sum
+     * @return AlgebraicDecisionTree
+     */
+    AlgebraicDecisionTree normalize(double sum) const {
+      return this->apply([&sum](const double& x) { return x / sum; });
+    }
+
     /** sum out variable */
     AlgebraicDecisionTree sum(const L& label, size_t cardinality) const {
       return this->combine(label, cardinality, &Ring::add);

gtsam/hybrid/HybridBayesNet.cpp

@@ -283,16 +283,17 @@ HybridValues HybridBayesNet::optimize() const {
         error = error + gm->error(continuousValues);
 
         // Add the logNormalization constant to the error
-        // Also compute the mean for normalization (for numerical stability)
+        // Also compute the sum for discrete probability normalization
-        double mean = 0.0;
+        // (normalization trick for numerical stability)
-        auto addConstant = [&gm, &mean](const double &error) {
+        double sum = 0.0;
+        auto addConstant = [&gm, &sum](const double &error) {
           double e = error + gm->logNormalizationConstant();
-          mean += e;
+          sum += e;
           return e;
         };
         error = error.apply(addConstant);
-        // Normalize by the mean
+        // Normalize by the sum
-        error = error.apply([&mean](double x) { return x / mean; });
+        error = error.normalize(sum);
 
         // Include the discrete keys
         std::copy(gm->discreteKeys().begin(), gm->discreteKeys().end(),