use MaxProduct to compute Discrete Bayes Net mode

gtsam/discrete/DiscreteBayesNet.cpp

@@ -18,6 +18,7 @@
 
 #include <gtsam/discrete/DiscreteBayesNet.h>
 #include <gtsam/discrete/DiscreteConditional.h>
+#include <gtsam/discrete/DiscreteFactorGraph.h>
 #include <gtsam/discrete/DiscreteLookupDAG.h>
 #include <gtsam/inference/FactorGraph-inst.h>
 
@@ -65,7 +66,7 @@ DiscreteValues DiscreteBayesNet::sample(DiscreteValues result) const {
 }
 
 DiscreteValues DiscreteBayesNet::mode() const {
-  return DiscreteLookupDAG::FromBayesNet(*this).argmax();
+  return DiscreteFactorGraph(*this).optimize();
 }
 
 /* *********************************************************************** */

gtsam/discrete/DiscreteConditional.cpp

@@ -238,7 +238,8 @@ DecisionTreeFactor::shared_ptr DiscreteConditional::likelihood(
 size_t DiscreteConditional::argmax(const DiscreteValues& parentsValues) const {
   ADT pFS = choose(parentsValues, true);  // P(F|S=parentsValues)
 
-  // Initialize
+  // Then, find the max over all remaining
+  // TODO(Duy): only works for one key now, seems horribly slow this way
   size_t maxValue = 0;
   double maxP = 0;
   DiscreteValues values = parentsValues;
@@ -247,7 +248,7 @@ size_t DiscreteConditional::argmax(const DiscreteValues& parentsValues) const {
   Key j = firstFrontalKey();
   for (size_t value = 0; value < cardinality(j); value++) {
     values[j] = value;
-    double pValueS = (*this)(values);
+    double pValueS = pFS(values);  // P(F=value|S=parentsValues)
     // Update MPE solution if better
     if (pValueS > maxP) {
       maxP = pValueS;

gtsam/discrete/tests/testDiscreteBayesNet.cpp

@@ -147,6 +147,28 @@ TEST(DiscreteBayesNet, Mode) {
   EXPECT(assert_equal(expected, actual));
 }
 
+/* ************************************************************************* */
+TEST(DiscreteBayesNet, ModeEdgeCase) {
+  // Declare 2 keys
+  DiscreteKey A(0, 2), B(1, 2);
+
+  // Create Bayes net such that marginal on A is bigger for 0 than 1, but the
+  // MPE does not have A=0.
+  DiscreteBayesNet bayesNet;
+  bayesNet.add(B | A = "1/1 1/2");
+  bayesNet.add(A % "10/9");
+
+  // Which we verify using max-product:
+  DiscreteFactorGraph graph(bayesNet);
+  // The expected MPE is A=1, B=1
+  DiscreteValues expectedMPE = graph.optimize();
+
+  auto actualMPE = bayesNet.mode();
+
+  EXPECT(assert_equal(expectedMPE, actualMPE));
+  EXPECT_DOUBLES_EQUAL(0.315789, bayesNet(expectedMPE), 1e-5);  // regression
+}
+
 /* ************************************************************************* */
 TEST(DiscreteBayesNet, Sugar) {
   DiscreteKey T(0, 2), L(1, 2), E(2, 2), C(8, 3), S(7, 2);