Python

gtsam/discrete/discrete.i

@@ -277,7 +277,12 @@ class DiscreteFactorGraph {
   double operator()(const gtsam::DiscreteValues& values) const;
   gtsam::DiscreteValues optimize() const;
 
+  gtsam::DiscreteLookupDAG sumProduct();
+  gtsam::DiscreteLookupDAG sumProduct(gtsam::Ordering::OrderingType type);
+  gtsam::DiscreteLookupDAG sumProduct(const gtsam::Ordering& ordering);
+
   gtsam::DiscreteLookupDAG maxProduct();
+  gtsam::DiscreteLookupDAG maxProduct(gtsam::Ordering::OrderingType type);
   gtsam::DiscreteLookupDAG maxProduct(const gtsam::Ordering& ordering);
 
   gtsam::DiscreteBayesNet eliminateSequential();

gtsam/nonlinear/nonlinear.i

@@ -111,6 +111,11 @@ size_t mrsymbolIndex(size_t key);
 
 #include <gtsam/inference/Ordering.h>
 class Ordering {
+  /// Type of ordering to use
+  enum OrderingType {
+    COLAMD, METIS, NATURAL, CUSTOM
+  };
+
   // Standard Constructors and Named Constructors
   Ordering();
   Ordering(const gtsam::Ordering& other);

python/gtsam/tests/test_DiscreteFactorGraph.py

@@ -13,9 +13,11 @@ Author: Frank Dellaert
 
 import unittest
 
-from gtsam import DiscreteFactorGraph, DiscreteKeys, DiscreteValues
+from gtsam import DiscreteFactorGraph, DiscreteKeys, DiscreteValues, Ordering
 from gtsam.utils.test_case import GtsamTestCase
 
+OrderingType = Ordering.OrderingType
+
 
 class TestDiscreteFactorGraph(GtsamTestCase):
     """Tests for Discrete Factor Graphs."""
@@ -108,14 +110,50 @@ class TestDiscreteFactorGraph(GtsamTestCase):
         graph.add([C, A], "0.2 0.8 0.3 0.7")
         graph.add([C, B], "0.1 0.9 0.4 0.6")
 
-        actualMPE = graph.optimize()
+        # We know MPE
+        mpe = DiscreteValues()
+        mpe[0] = 0
+        mpe[1] = 1
+        mpe[2] = 1
 
-        expectedMPE = DiscreteValues()
+        # Use maxProduct
-        expectedMPE[0] = 0
+        dag = graph.maxProduct(OrderingType.COLAMD)
-        expectedMPE[1] = 1
+        actualMPE = dag.argmax()
-        expectedMPE[2] = 1
         self.assertEqual(list(actualMPE.items()),
-                         list(expectedMPE.items()))
+                         list(mpe.items()))
+
+        # All in one
+        actualMPE2 = graph.optimize()
+        self.assertEqual(list(actualMPE2.items()),
+                         list(mpe.items()))
+
+    def test_sumProduct(self):
+        """Test sumProduct."""
+
+        # Declare a bunch of keys
+        C, A, B = (0, 2), (1, 2), (2, 2)
+
+        # Create Factor graph
+        graph = DiscreteFactorGraph()
+        graph.add([C, A], "0.2 0.8 0.3 0.7")
+        graph.add([C, B], "0.1 0.9 0.4 0.6")
+
+        # We know MPE
+        mpe = DiscreteValues()
+        mpe[0] = 0
+        mpe[1] = 1
+        mpe[2] = 1
+
+        # Use default sumProduct
+        bayesNet = graph.sumProduct()
+        mpeProbability = bayesNet(mpe)
+        self.assertAlmostEqual(mpeProbability, 0.36)  # regression
+
+        # Use sumProduct
+        for ordering_type in [OrderingType.COLAMD, OrderingType.METIS, OrderingType.NATURAL,
+                              OrderingType.CUSTOM]:
+            bayesNet = graph.sumProduct(ordering_type)
+            self.assertEqual(bayesNet(mpe), mpeProbability)
 
 
 if __name__ == "__main__":