Add explicit HMM example

examples/DiscreteBayesNetExample.cpp

@@ -18,6 +18,7 @@
 
 #include <gtsam/discrete/DiscreteFactorGraph.h>
 #include <gtsam/discrete/DiscreteMarginals.h>
+#include <gtsam/inference/BayesNet-inst.h>
 
 #include <iomanip>
 

examples/HMMExample.cpp

@@ -0,0 +1,94 @@
+/* ----------------------------------------------------------------------------
+
+ * GTSAM Copyright 2010-2020, Georgia Tech Research Corporation,
+ * Atlanta, Georgia 30332-0415
+ * All Rights Reserved
+ * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
+
+ * See LICENSE for the license information
+
+ * -------------------------------------------------------------------------- */
+
+/**
+ * @file  DiscreteBayesNetExample.cpp
+ * @brief   Hidden Markov Model example, discrete.
+ * @author  Frank Dellaert
+ * @date  July 12, 2020
+ */
+
+#include <gtsam/discrete/DiscreteFactorGraph.h>
+#include <gtsam/discrete/DiscreteMarginals.h>
+#include <gtsam/inference/BayesNet-inst.h>
+
+#include <iomanip>
+#include <sstream>
+
+using namespace std;
+using namespace gtsam;
+
+int main(int argc, char **argv) {
+  const int nrNodes = 4;
+  const size_t nrStates = 3;
+
+  // Define variables as well as ordering
+  Ordering ordering;
+  vector<DiscreteKey> keys;
+  for (int k = 0; k < nrNodes; k++) {
+    DiscreteKey key_i(k, nrStates);
+    keys.push_back(key_i);
+    ordering.emplace_back(k);
+  }
+
+  // Create HMM as a DiscreteBayesNet
+  DiscreteBayesNet hmm;
+
+  // Define backbone
+  const string transition = "8/1/1 1/8/1 1/1/8";
+  for (int k = 1; k < nrNodes; k++) {
+    hmm.add(keys[k] | keys[k - 1] = transition);
+  }
+
+  // Add some measurements, not needed for all time steps!
+  hmm.add(keys[0] % "7/2/1");
+  hmm.add(keys[1] % "1/9/0");
+  hmm.add(keys.back() % "5/4/1");
+
+  // print
+  hmm.print("HMM");
+
+  // Convert to factor graph
+  DiscreteFactorGraph factorGraph(hmm);
+
+  // 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
+  DiscreteFactor::sharedValues mpe = chordal->optimize();
+  GTSAM_PRINT(*mpe);
+
+  // We can also sample from it
+  cout << "\n10 samples:" << endl;
+  for (size_t k = 0; k < 10; k++) {
+    DiscreteFactor::sharedValues sample = chordal->sample();
+    GTSAM_PRINT(*sample);
+  }
+
+  // Or compute the marginals. This re-eliminates the FG into a Bayes tree
+  cout << "\nComputing Node Marginals .." << endl;
+  DiscreteMarginals marginals(factorGraph);
+  for (int k = 0; k < nrNodes; k++) {
+    Vector margProbs = marginals.marginalProbabilities(keys[k]);
+    stringstream ss;
+    ss << "marginal " << k;
+    print(margProbs, ss.str());
+  }
+
+  // TODO(frank): put in the glue to have DiscreteMarginals produce *arbitrary*
+  // joints efficiently, by the Bayes tree shortcut magic. All the code is there
+  // but it's not yet connected.
+
+  return 0;
+}