Merge pull request #1043 from borglab/feature/more_discrete_methods

gtsam/discrete/DiscreteBayesNet.cpp

@@ -25,65 +25,71 @@
 
 namespace gtsam {
 
-  // Instantiate base class
-  template class FactorGraph<DiscreteConditional>;
-
-  /* ************************************************************************* */
-  bool DiscreteBayesNet::equals(const This& bn, double tol) const
-  {
-    return Base::equals(bn, tol);
-  }
-
-  /* ************************************************************************* */
-  double DiscreteBayesNet::evaluate(const DiscreteValues & values) const {
-    // evaluate all conditionals and multiply
-    double result = 1.0;
-    for(const DiscreteConditional::shared_ptr& conditional: *this)
-      result *= (*conditional)(values);
-    return result;
-  }
-
-  /* ************************************************************************* */
-  DiscreteValues DiscreteBayesNet::optimize() const {
-    // solve each node in turn in topological sort order (parents first)
-    DiscreteValues result;
-    for (auto conditional: boost::adaptors::reverse(*this))
-      conditional->solveInPlace(&result);
-    return result;
-  }
-
-  /* ************************************************************************* */
-  DiscreteValues DiscreteBayesNet::sample() const {
-    // sample each node in turn in topological sort order (parents first)
-    DiscreteValues result;
-    for (auto conditional: boost::adaptors::reverse(*this))
-      conditional->sampleInPlace(&result);
-    return result;
-  }
-
-  /* *********************************************************************** */
-  std::string DiscreteBayesNet::markdown(
-      const KeyFormatter& keyFormatter,
-      const DiscreteFactor::Names& names) const {
-    using std::endl;
-    std::stringstream ss;
-    ss << "`DiscreteBayesNet` of size " << size() << endl << endl;
-    for (const DiscreteConditional::shared_ptr& conditional : *this)
-      ss << conditional->markdown(keyFormatter, names) << endl;
-    return ss.str();
-  }
-
-  /* *********************************************************************** */
-  std::string DiscreteBayesNet::html(
-      const KeyFormatter& keyFormatter,
-      const DiscreteFactor::Names& names) const {
-    using std::endl;
-    std::stringstream ss;
-    ss << "<div><p><tt>DiscreteBayesNet</tt> of size " << size() << "</p>";
-    for (const DiscreteConditional::shared_ptr& conditional : *this)
-      ss << conditional->html(keyFormatter, names) << endl;
-    return ss.str();
-  }
+// Instantiate base class
+template class FactorGraph<DiscreteConditional>;
 
 /* ************************************************************************* */
-} // namespace
+bool DiscreteBayesNet::equals(const This& bn, double tol) const {
+  return Base::equals(bn, tol);
+}
+
+/* ************************************************************************* */
+double DiscreteBayesNet::evaluate(const DiscreteValues& values) const {
+  // evaluate all conditionals and multiply
+  double result = 1.0;
+  for (const DiscreteConditional::shared_ptr& conditional : *this)
+    result *= (*conditional)(values);
+  return result;
+}
+
+/* ************************************************************************* */
+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)
+  for (auto conditional : boost::adaptors::reverse(*this))
+    conditional->solveInPlace(&result);
+  return result;
+}
+
+/* ************************************************************************* */
+DiscreteValues DiscreteBayesNet::sample() const {
+  DiscreteValues result;
+  return sample(result);
+}
+
+DiscreteValues DiscreteBayesNet::sample(DiscreteValues result) const {
+  // sample each node in turn in topological sort order (parents first)
+  for (auto conditional : boost::adaptors::reverse(*this))
+    conditional->sampleInPlace(&result);
+  return result;
+}
+
+/* *********************************************************************** */
+std::string DiscreteBayesNet::markdown(
+    const KeyFormatter& keyFormatter,
+    const DiscreteFactor::Names& names) const {
+  using std::endl;
+  std::stringstream ss;
+  ss << "`DiscreteBayesNet` of size " << size() << endl << endl;
+  for (const DiscreteConditional::shared_ptr& conditional : *this)
+    ss << conditional->markdown(keyFormatter, names) << endl;
+  return ss.str();
+}
+
+/* *********************************************************************** */
+std::string DiscreteBayesNet::html(const KeyFormatter& keyFormatter,
+                                   const DiscreteFactor::Names& names) const {
+  using std::endl;
+  std::stringstream ss;
+  ss << "<div><p><tt>DiscreteBayesNet</tt> of size " << size() << "</p>";
+  for (const DiscreteConditional::shared_ptr& conditional : *this)
+    ss << conditional->html(keyFormatter, names) << endl;
+  return ss.str();
+}
+
+/* ************************************************************************* */
+}  // namespace gtsam

gtsam/discrete/DiscreteBayesNet.h

@@ -99,13 +99,47 @@ namespace gtsam {
     }
 
     /**
-    * Solve the DiscreteBayesNet by back-substitution
+     * @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;
 
-    /** Do ancestral sampling */
+    /**
+     * @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
+     *
+     * Assumes the Bayes net is reverse topologically sorted, i.e. last
+     * conditional will be sampled 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 sample() const;
 
+    /**
+     * @brief do ancestral sampling, 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 sampled value for all other variables.
+     */
+    DiscreteValues sample(DiscreteValues given) const;
+
     ///@}
     /// @name Wrapper support
     /// @{

gtsam/discrete/discrete.i

@@ -165,7 +165,9 @@ class DiscreteBayesNet {
                                gtsam::DefaultKeyFormatter) const;
   double operator()(const gtsam::DiscreteValues& values) const;
   gtsam::DiscreteValues optimize() const;
+  gtsam::DiscreteValues optimize(gtsam::DiscreteValues given) const;
   gtsam::DiscreteValues sample() const;
+  gtsam::DiscreteValues sample(gtsam::DiscreteValues given) const;
   string markdown(const gtsam::KeyFormatter& keyFormatter =
                       gtsam::DefaultKeyFormatter) const;
   string markdown(const gtsam::KeyFormatter& keyFormatter,

python/gtsam/tests/test_DiscreteBayesNet.py

@@ -17,6 +17,17 @@
                    DiscreteKeys, DiscreteDistribution, DiscreteValues, Ordering)
 from gtsam.utils.test_case import GtsamTestCase
 
+# Some keys:
+Asia = (0, 2)
+Smoking = (4, 2)
+Tuberculosis = (3, 2)
+LungCancer = (6, 2)
+
+Bronchitis = (7, 2)
+Either = (5, 2)
+XRay = (2, 2)
+Dyspnea = (1, 2)
+
 
 class TestDiscreteBayesNet(GtsamTestCase):
     """Tests for Discrete Bayes Nets."""
@@ -43,16 +54,6 @@ def test_constructor(self):
     def test_Asia(self):
         """Test full Asia example."""
 
-        Asia = (0, 2)
-        Smoking = (4, 2)
-        Tuberculosis = (3, 2)
-        LungCancer = (6, 2)
-
-        Bronchitis = (7, 2)
-        Either = (5, 2)
-        XRay = (2, 2)
-        Dyspnea = (1, 2)
-
         asia = DiscreteBayesNet()
         asia.add(Asia, "99/1")
         asia.add(Smoking, "50/50")
@@ -107,6 +108,28 @@ def test_Asia(self):
         actualSample = chordal2.sample()
         self.assertEqual(len(actualSample), 8)
 
+    def test_fragment(self):
+        """Test sampling and optimizing for Asia fragment."""
+
+        # Create a reverse-topologically sorted fragment:
+        fragment = DiscreteBayesNet()
+        fragment.add(Either, [Tuberculosis, LungCancer], "F T T T")
+        fragment.add(Tuberculosis, [Asia], "99/1 95/5")
+        fragment.add(LungCancer, [Smoking], "99/1 90/10")
+
+        # Create assignment with missing values:
+        given = DiscreteValues()
+        for key in [Asia, Smoking]:
+            given[key[0]] = 0
+
+        # Now optimize fragment:
+        actual = fragment.optimize(given)
+        self.assertEqual(len(actual), 5)
+
+        # Now sample from fragment:
+        actual = fragment.sample(given)
+        self.assertEqual(len(actual), 5)
+
 
 if __name__ == "__main__":
     unittest.main()