Merge pull request #1050 from borglab/feature/betterMPE

examples/DiscreteBayesNetExample.cpp

@@ -53,10 +53,9 @@ int main(int argc, char **argv) {
   // Create solver and eliminate
   Ordering ordering;
   ordering += Key(0), Key(1), Key(2), Key(3), Key(4), Key(5), Key(6), Key(7);
-  DiscreteBayesNet::shared_ptr chordal = fg.eliminateSequential(ordering);
 
   // solve
-  auto mpe = chordal->optimize();
+  auto mpe = fg.optimize();
   GTSAM_PRINT(mpe);
 
   // We can also build a Bayes tree (directed junction tree).
@@ -69,14 +68,14 @@ int main(int argc, char **argv) {
   fg.add(Dyspnea, "0 1");
 
   // solve again, now with evidence
-  DiscreteBayesNet::shared_ptr chordal2 = fg.eliminateSequential(ordering);
-  auto mpe2 = chordal2->optimize();
+  auto mpe2 = fg.optimize();
   GTSAM_PRINT(mpe2);
 
   // We can also sample from it
+  DiscreteBayesNet::shared_ptr chordal = fg.eliminateSequential(ordering);
   cout << "\n10 samples:" << endl;
   for (size_t i = 0; i < 10; i++) {
-    auto sample = chordal2->sample();
+    auto sample = chordal->sample();
     GTSAM_PRINT(sample);
   }
   return 0;

examples/DiscreteBayesNet_FG.cpp

@@ -85,7 +85,7 @@ int main(int argc, char **argv) {
         }
 
   // "Most Probable Explanation", i.e., configuration with largest value
-  auto mpe = graph.eliminateSequential()->optimize();
+  auto mpe = graph.optimize();
   cout << "\nMost Probable Explanation (MPE):" << endl;
   print(mpe);
 
@@ -96,8 +96,7 @@ int main(int argc, char **argv) {
   graph.add(Cloudy, "1 0");
 
   // solve again, now with evidence
-  DiscreteBayesNet::shared_ptr chordal = graph.eliminateSequential();
-  auto mpe_with_evidence = chordal->optimize();
+  auto mpe_with_evidence = graph.optimize();
 
   cout << "\nMPE given C=0:" << endl;
   print(mpe_with_evidence);
@@ -110,7 +109,8 @@ int main(int argc, char **argv) {
   cout << "\nP(W=1|C=0):" << marginals.marginalProbabilities(WetGrass)[1]
        << endl;
 
-  // We can also sample from it
+  // We can also sample from the eliminated graph
+  DiscreteBayesNet::shared_ptr chordal = graph.eliminateSequential();
   cout << "\n10 samples:" << endl;
   for (size_t i = 0; i < 10; i++) {
     auto sample = chordal->sample();

examples/HMMExample.cpp

@@ -59,16 +59,16 @@ int main(int argc, char **argv) {
   // Convert to factor graph
   DiscreteFactorGraph factorGraph(hmm);
 
+  // Do max-prodcut
+  auto mpe = factorGraph.optimize();
+  GTSAM_PRINT(mpe);
+
   // Create solver and eliminate
   // This will create a DAG ordered with arrow of time reversed
   DiscreteBayesNet::shared_ptr chordal =
       factorGraph.eliminateSequential(ordering);
   chordal->print("Eliminated");
 
-  // solve
-  auto mpe = chordal->optimize();
-  GTSAM_PRINT(mpe);
-
   // We can also sample from it
   cout << "\n10 samples:" << endl;
   for (size_t k = 0; k < 10; k++) {

examples/UGM_chain.cpp

@@ -68,9 +68,8 @@ int main(int argc, char** argv) {
        << graph.size() << " factors (Unary+Edge).";
 
   // "Decoding", i.e., configuration with largest value
-  // We use sequential variable elimination
-  DiscreteBayesNet::shared_ptr chordal = graph.eliminateSequential();
-  auto optimalDecoding = chordal->optimize();
+  // Uses max-product.
+  auto optimalDecoding = graph.optimize();
   optimalDecoding.print("\nMost Probable Explanation (optimalDecoding)\n");
 
   // "Inference" Computing marginals for each node

examples/UGM_small.cpp

@@ -61,9 +61,8 @@ int main(int argc, char** argv) {
   }
 
   // "Decoding", i.e., configuration with largest value (MPE)
-  // We use sequential variable elimination
-  DiscreteBayesNet::shared_ptr chordal = graph.eliminateSequential();
-  auto optimalDecoding = chordal->optimize();
+  // Uses max-product
+  auto optimalDecoding = graph.optimize();
   GTSAM_PRINT(optimalDecoding);
 
   // "Inference" Computing marginals

gtsam/discrete/DecisionTreeFactor.cpp

@@ -22,6 +22,7 @@
 #include <gtsam/base/FastSet.h>
 
 #include <boost/make_shared.hpp>
+#include <boost/format.hpp>
 #include <utility>
 
 using namespace std;
@@ -65,9 +66,13 @@ namespace gtsam {
 
   /* ************************************************************************* */
   void DecisionTreeFactor::print(const string& s,
-      const KeyFormatter& formatter) const {
+                                 const KeyFormatter& formatter) const {
     cout << s;
-    ADT::print("Potentials:",formatter);
+    cout << " f[";
+    for (auto&& key : keys())
+      cout << boost::format(" (%1%,%2%),") % formatter(key) % cardinality(key);
+    cout << " ]" << endl;
+    ADT::print("Potentials:", formatter);
   }
 
   /* ************************************************************************* */

gtsam/discrete/DecisionTreeFactor.h

@@ -127,11 +127,16 @@ namespace gtsam {
       return combine(keys, ADT::Ring::add);
     }
 
-    /// Create new factor by maximizing over all values with the same separator values
+    /// Create new factor by maximizing over all values with the same separator.
     shared_ptr max(size_t nrFrontals) const {
       return combine(nrFrontals, ADT::Ring::max);
     }
 
+    /// Create new factor by maximizing over all values with the same separator.
+    shared_ptr max(const Ordering& keys) const {
+      return combine(keys, ADT::Ring::max);
+    }
+
     /// @}
     /// @name Advanced Interface
     /// @{

gtsam/discrete/DiscreteBayesNet.cpp

@@ -43,17 +43,24 @@ double DiscreteBayesNet::evaluate(const DiscreteValues& values) const {
 }
 
 /* ************************************************************************* */
+#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V42
 DiscreteValues DiscreteBayesNet::optimize() const {
   DiscreteValues result;
   return optimize(result);
 }
 
 DiscreteValues DiscreteBayesNet::optimize(DiscreteValues result) const {
   // solve each node in turn in topological sort order (parents first)
+#ifdef _MSC_VER
+#pragma message("DiscreteBayesNet::optimize (deprecated) does not compute MPE!")
+#else
+#warning "DiscreteBayesNet::optimize (deprecated) does not compute MPE!"
+#endif
   for (auto conditional : boost::adaptors::reverse(*this))
     conditional->solveInPlace(&result);
   return result;
 }
+#endif
 
 /* ************************************************************************* */
 DiscreteValues DiscreteBayesNet::sample() const {

gtsam/discrete/DiscreteBayesNet.h

@@ -31,12 +31,12 @@
 
 namespace gtsam {
 
-/** A Bayes net made from linear-Discrete densities */
+/** A Bayes net made from discrete conditional distributions. */
   class GTSAM_EXPORT DiscreteBayesNet: public BayesNet<DiscreteConditional>
   {
   public:
 
-    typedef FactorGraph<DiscreteConditional> Base;
+    typedef BayesNet<DiscreteConditional> Base;
     typedef DiscreteBayesNet This;
     typedef DiscreteConditional ConditionalType;
     typedef boost::shared_ptr<This> shared_ptr;
@@ -45,7 +45,7 @@ namespace gtsam {
     /// @name Standard Constructors
     /// @{
 
-    /** Construct empty factor graph */
+    /// Construct empty Bayes net.
     DiscreteBayesNet() {}
 
     /** Construct from iterator over conditionals */
@@ -98,27 +98,6 @@ namespace gtsam {
       return evaluate(values);
     }
 
-    /**
-     * @brief solve by back-substitution.
-     *
-     * Assumes the Bayes net is reverse topologically sorted, i.e. last
-     * conditional will be optimized first. If the Bayes net resulted from
-     * eliminating a factor graph, this is true for the elimination ordering.
-     *
-     * @return a sampled value for all variables.
-    */
-    DiscreteValues optimize() const;
-
-    /**
-     * @brief solve by back-substitution, given certain variables.
-     *
-     * Assumes the Bayes net is reverse topologically sorted *and* that the
-     * Bayes net does not contain any conditionals for the given values.
-     *
-     * @return given values extended with optimized value for other variables.
-     */
-    DiscreteValues optimize(DiscreteValues given) const;
-
     /**
      * @brief do ancestral sampling
      *
@@ -152,7 +131,16 @@ namespace gtsam {
     std::string html(const KeyFormatter& keyFormatter = DefaultKeyFormatter,
                      const DiscreteFactor::Names& names = {}) const;
 
+    ///@}
+
+#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V42
+    /// @name Deprecated functionality
+    /// @{
+
+    DiscreteValues GTSAM_DEPRECATED optimize() const;
+    DiscreteValues GTSAM_DEPRECATED optimize(DiscreteValues given) const;
     /// @}
+#endif
 
  private:
     /** Serialization function */