Now doing MPE with DAG class

gtsam/discrete/DiscreteFactorGraph.cpp

@@ -21,6 +21,7 @@
 #include <gtsam/discrete/DiscreteEliminationTree.h>
 #include <gtsam/discrete/DiscreteFactorGraph.h>
 #include <gtsam/discrete/DiscreteJunctionTree.h>
+#include <gtsam/discrete/DiscreteLookupDAG.h>
 #include <gtsam/inference/EliminateableFactorGraph-inst.h>
 #include <gtsam/inference/FactorGraph-inst.h>
 
@@ -96,18 +97,6 @@ namespace gtsam {
 //  }
 
   /* ************************************************************************ */
-  /**
-   * @brief Lookup table for max-product
-   * 
-   * This inherits from a DiscreteConditional but is not normalized to 1
-   * 
-   */
-  class Lookup : public DiscreteConditional {
-   public:
-    Lookup(size_t nFrontals, const DiscreteKeys& keys, const ADT& potentials)
-        : DiscreteConditional(nFrontals, keys, potentials) {}
-  };
-
   // Alternate eliminate function for MPE
   std::pair<DiscreteConditional::shared_ptr, DecisionTreeFactor::shared_ptr>  //
   EliminateForMPE(const DiscreteFactorGraph& factors,
@@ -133,7 +122,8 @@ namespace gtsam {
     // Make lookup with product
     gttic(lookup);
     size_t nrFrontals = frontalKeys.size();
-    auto lookup = boost::make_shared<Lookup>(nrFrontals, orderedKeys, product);
+    auto lookup = boost::make_shared<DiscreteLookupTable>(nrFrontals,
+                                                          orderedKeys, product);
     gttoc(lookup);
 
     return std::make_pair(
@@ -141,18 +131,37 @@ namespace gtsam {
   }
 
   /* ************************************************************************ */
-  DiscreteBayesNet::shared_ptr DiscreteFactorGraph::maxProduct(
+  DiscreteLookupDAG DiscreteFactorGraph::maxProduct(
       OptionalOrderingType orderingType) const {
     gttic(DiscreteFactorGraph_maxProduct);
-    return BaseEliminateable::eliminateSequential(orderingType,
+
-                                                  EliminateForMPE);
+    // The solution below is a bitclunky: the elimination machinery does not
+    // allow for differently *typed* versions of elimination, so we eliminate
+    // into a Bayes Net using the special eliminate function above, and then
+    // create the DiscreteLookupDAG after the fact, in linear time.
+    auto bayesNet =
+        BaseEliminateable::eliminateSequential(orderingType, EliminateForMPE);
+
+    // Copy to the DAG
+    DiscreteLookupDAG dag;
+    for (auto&& conditional : *bayesNet) {
+      if (auto lookupTable =
+              boost::dynamic_pointer_cast<DiscreteLookupTable>(conditional)) {
+        dag.push_back(lookupTable);
+      } else {
+        throw std::runtime_error(
+            "DiscreteFactorGraph::maxProduct: Expected look up table.");
+      }
+    }
+    return dag;
   }
 
   /* ************************************************************************ */
   DiscreteValues DiscreteFactorGraph::optimize(
       OptionalOrderingType orderingType) const {
     gttic(DiscreteFactorGraph_optimize);
-    return maxProduct()->optimize();
+    DiscreteLookupDAG dag = maxProduct();
+    return dag.argmax();
   }
 
   /* ************************************************************************ */

gtsam/discrete/DiscreteFactorGraph.h

@@ -18,10 +18,11 @@
 
 #pragma once
 
-#include <gtsam/inference/FactorGraph.h>
-#include <gtsam/inference/EliminateableFactorGraph.h>
-#include <gtsam/inference/Ordering.h>
 #include <gtsam/discrete/DecisionTreeFactor.h>
+#include <gtsam/discrete/DiscreteLookupDAG.h>
+#include <gtsam/inference/EliminateableFactorGraph.h>
+#include <gtsam/inference/FactorGraph.h>
+#include <gtsam/inference/Ordering.h>
 #include <gtsam/base/FastSet.h>
 
 #include <boost/make_shared.hpp>
@@ -132,9 +133,9 @@ class GTSAM_EXPORT DiscreteFactorGraph
    * @brief Implement the max-product algorithm
    *
    * @param orderingType : one of COLAMD, METIS, NATURAL, CUSTOM
-   * @return DiscreteBayesNet::shared_ptr DAG with lookup tables
+   * @return DiscreteLookupDAG::shared_ptr DAG with lookup tables
    */
-  boost::shared_ptr<DiscreteBayesNet> maxProduct(
+  DiscreteLookupDAG maxProduct(
       OptionalOrderingType orderingType = boost::none) const;
 
   /**

gtsam/discrete/tests/testDiscreteFactorGraph.cpp

@@ -52,13 +52,6 @@ TEST_UNSAFE(DiscreteFactorGraph, debugScheduler) {
   DiscreteValues mpe;
   insert(mpe)(0, 2)(1, 1)(2, 0)(3, 0);
   EXPECT(assert_equal(mpe, actualMPE));
-
-  // Check Bayes Net
-  Ordering ordering;
-  ordering += Key(0), Key(1), Key(2), Key(3);
-  auto chordal = graph.eliminateSequential(ordering);
-  // happens to be the same, but not in general!
-  EXPECT(assert_equal(mpe, chordal->optimize()));
 }
 
 /* ************************************************************************* */
@@ -125,57 +118,46 @@ TEST(DiscreteFactorGraph, test) {
   DecisionTreeFactor::shared_ptr newFactor;
   boost::tie(conditional, newFactor) = EliminateDiscrete(graph, frontalKeys);
 
-  // Check Bayes net
+  // Check Conditional
   CHECK(conditional);
-  DiscreteBayesNet expected;
   Signature signature((C | B, A) = "9/1 1/1 1/1 1/9");
-
   DiscreteConditional expectedConditional(signature);
   EXPECT(assert_equal(expectedConditional, *conditional));
-  expected.add(signature);
 
   // Check Factor
   CHECK(newFactor);
   DecisionTreeFactor expectedFactor(B & A, "10 6 6 10");
   EXPECT(assert_equal(expectedFactor, *newFactor));
 
-  // add conditionals to complete expected Bayes net
+  // Test using elimination tree
-  expected.add(B | A = "5/3 3/5");
-  expected.add(A % "1/1");
-
-  // Test elimination tree
   Ordering ordering;
   ordering += Key(0), Key(1), Key(2);
   DiscreteEliminationTree etree(graph, ordering);
   DiscreteBayesNet::shared_ptr actual;
   DiscreteFactorGraph::shared_ptr remainingGraph;
   boost::tie(actual, remainingGraph) = etree.eliminate(&EliminateDiscrete);
-  EXPECT(assert_equal(expected, *actual));
 
-  DiscreteValues mpe;
+  // Check Bayes net
-  insert(mpe)(0, 0)(1, 0)(2, 0);
+  DiscreteBayesNet expectedBayesNet;
-  EXPECT_DOUBLES_EQUAL(9, graph(mpe), 1e-5);  // regression
+  expectedBayesNet.add(signature);
-
+  expectedBayesNet.add(B | A = "5/3 3/5");
-  // Check Bayes Net
+  expectedBayesNet.add(A % "1/1");
-  auto chordal = graph.eliminateSequential();
+  EXPECT(assert_equal(expectedBayesNet, *actual));
-  auto notOptimal = chordal->optimize();
-  // happens to be the same but not in general!
-  EXPECT(assert_equal(mpe, notOptimal));
 
   // Test eliminateSequential
   DiscreteBayesNet::shared_ptr actual2 = graph.eliminateSequential(ordering);
-  EXPECT(assert_equal(expected, *actual2));
+  EXPECT(assert_equal(expectedBayesNet, *actual2));
-  auto notOptimal2 = actual2->optimize();
-  // happens to be the same but not in general!
-  EXPECT(assert_equal(mpe, notOptimal2));
 
   // Test mpe
+  DiscreteValues mpe;
+  insert(mpe)(0, 0)(1, 0)(2, 0);
   auto actualMPE = graph.optimize();
   EXPECT(assert_equal(mpe, actualMPE));
+  EXPECT_DOUBLES_EQUAL(9, graph(mpe), 1e-5);  // regression
 }
 
 /* ************************************************************************* */
-TEST_UNSAFE(DiscreteFactorGraph, testMPE) {
+TEST_UNSAFE(DiscreteFactorGraph, testMaxProduct) {
   // Declare a bunch of keys
   DiscreteKey C(0, 2), A(1, 2), B(2, 2);
 
@@ -184,17 +166,20 @@ TEST_UNSAFE(DiscreteFactorGraph, testMPE) {
   graph.add(C & A, "0.2 0.8 0.3 0.7");
   graph.add(C & B, "0.1 0.9 0.4 0.6");
 
-  // Check MPE.
+  // Created expected MPE
-  auto actualMPE = graph.optimize();
   DiscreteValues mpe;
   insert(mpe)(0, 0)(1, 1)(2, 1);
-  EXPECT(assert_equal(mpe, actualMPE));
 
-  // Check Bayes Net
+  // Do max-product with different orderings
-  auto chordal = graph.eliminateSequential();
+  for (Ordering::OrderingType orderingType :
-  auto notOptimal = chordal->optimize();
+       {Ordering::COLAMD, Ordering::METIS, Ordering::NATURAL,
-  // happens to be the same but not in general
+        Ordering::CUSTOM}) {
-  EXPECT(assert_equal(mpe, notOptimal));
+    DiscreteLookupDAG dag = graph.maxProduct(orderingType);
+    auto actualMPE = dag.argmax();
+    EXPECT(assert_equal(mpe, actualMPE));
+    auto actualMPE2 = graph.optimize();  // all in one
+    EXPECT(assert_equal(mpe, actualMPE2));
+  }
 }
 
 /* ************************************************************************* */
@@ -218,10 +203,12 @@ TEST(DiscreteFactorGraph, marginalIsNotMPE) {
   EXPECT(assert_equal(mpe, actualMPE));
   EXPECT_DOUBLES_EQUAL(0.315789, graph(mpe), 1e-5);  // regression
 
+#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V42
   // Optimize on BayesNet maximizes marginal, then the conditional marginals:
   auto notOptimal = bayesNet.optimize();
   EXPECT(graph(notOptimal) < graph(mpe));
   EXPECT_DOUBLES_EQUAL(0.263158, graph(notOptimal), 1e-5);  // regression
+#endif
 }
 
 /* ************************************************************************* */
@@ -252,8 +239,11 @@ TEST(DiscreteFactorGraph, testMPE_Darwiche09book_p244) {
   Ordering ordering;
   ordering += Key(0), Key(1), Key(2), Key(3), Key(4);
   auto chordal = graph.eliminateSequential(ordering);
+  EXPECT_LONGS_EQUAL(2, chordal->size());
+#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V42
   auto notOptimal = chordal->optimize();  // not MPE !
   EXPECT(graph(notOptimal) < graph(mpe));
+#endif
 
   // Let us create the Bayes tree here, just for fun, because we don't use it
   DiscreteBayesTree::shared_ptr bayesTree =