Wrapper and tests for logProbability

gtsam/discrete/discrete.i

@@ -95,6 +95,9 @@ virtual class DiscreteConditional : gtsam::DecisionTreeFactor {
   DiscreteConditional(const gtsam::DecisionTreeFactor& joint,
                       const gtsam::DecisionTreeFactor& marginal,
                       const gtsam::Ordering& orderedKeys);
+  double logProbability(const gtsam::DiscreteValues& values) const;
+  double evaluate(const gtsam::DiscreteValues& values) const;
+  double operator()(const gtsam::DiscreteValues& values) const;
   gtsam::DiscreteConditional operator*(
       const gtsam::DiscreteConditional& other) const;
   gtsam::DiscreteConditional marginal(gtsam::Key key) const;
@@ -157,7 +160,12 @@ class DiscreteBayesNet {
              const gtsam::KeyFormatter& keyFormatter =
                  gtsam::DefaultKeyFormatter) const;
   bool equals(const gtsam::DiscreteBayesNet& other, double tol = 1e-9) const;
+
+  // Standard interface.
+  double logProbability(const gtsam::DiscreteValues& values) const;
+  double evaluate(const gtsam::DiscreteValues& values) const;
   double operator()(const gtsam::DiscreteValues& values) const;
+
   gtsam::DiscreteValues sample() const;
   gtsam::DiscreteValues sample(gtsam::DiscreteValues given) const;
 

gtsam/hybrid/hybrid.i

@@ -61,6 +61,9 @@ virtual class HybridConditional {
   size_t nrParents() const;
 
   // Standard interface:
+  double logProbability(const gtsam::HybridValues& values) const;
+  double evaluate(const gtsam::HybridValues& values) const;
+  double operator()(const gtsam::HybridValues& values) const;
   gtsam::GaussianMixture* asMixture() const;
   gtsam::GaussianConditional* asGaussian() const;
   gtsam::DiscreteConditional* asDiscrete() const;
@@ -133,7 +136,10 @@ class HybridBayesNet {
   gtsam::KeySet keys() const;
   const gtsam::HybridConditional* at(size_t i) const;
   
-  double evaluate(const gtsam::HybridValues& x) const;
+  // Standard interface:
+  double logProbability(const gtsam::HybridValues& values) const;
+  double evaluate(const gtsam::HybridValues& values) const;
+
   gtsam::HybridValues optimize() const;
   gtsam::HybridValues sample(const gtsam::HybridValues &given) const;
   gtsam::HybridValues sample() const;

gtsam/linear/linear.i

@@ -497,6 +497,7 @@ virtual class GaussianConditional : gtsam::JacobianFactor {
   bool equals(const gtsam::GaussianConditional& cg, double tol) const;
   
   // Standard Interface
+  double logProbability(const gtsam::VectorValues& x) const;
   double evaluate(const gtsam::VectorValues& x) const;
   double error(const gtsam::VectorValues& x) const;
   gtsam::Key firstFrontalKey() const;
@@ -558,6 +559,8 @@ virtual class GaussianBayesNet {
   gtsam::GaussianConditional* back() const;
 
   // Standard interface
+  // Standard Interface
+  double logProbability(const gtsam::VectorValues& x) const;
   double evaluate(const gtsam::VectorValues& x) const;
   double error(const gtsam::VectorValues& x) const;
 

python/gtsam/tests/test_DiscreteBayesNet.py

@@ -11,13 +11,15 @@ Author: Frank Dellaert
 
 # pylint: disable=no-name-in-module, invalid-name
 
+import math
 import textwrap
 import unittest
 
+from gtsam.utils.test_case import GtsamTestCase
+
 import gtsam
 from gtsam import (DiscreteBayesNet, DiscreteConditional, DiscreteDistribution,
                    DiscreteFactorGraph, DiscreteKeys, DiscreteValues, Ordering)
-from gtsam.utils.test_case import GtsamTestCase
 
 # Some keys:
 Asia = (0, 2)
@@ -111,7 +113,7 @@ class TestDiscreteBayesNet(GtsamTestCase):
         self.assertEqual(len(actualSample), 8)
 
     def test_fragment(self):
-        """Test sampling and optimizing for Asia fragment."""
+        """Test evaluate/sampling/optimizing for Asia fragment."""
 
         # Create a reverse-topologically sorted fragment:
         fragment = DiscreteBayesNet()
@@ -125,8 +127,14 @@ class TestDiscreteBayesNet(GtsamTestCase):
             given[key[0]] = 0
 
         # Now sample from fragment:
-        actual = fragment.sample(given)
+        values = fragment.sample(given)
-        self.assertEqual(len(actual), 5)
+        self.assertEqual(len(values), 5)
+
+        for i in [0, 1, 2]:
+            self.assertAlmostEqual(fragment.at(i).logProbability(values),
+                                   math.log(fragment.at(i).evaluate(values)))
+        self.assertAlmostEqual(fragment.logProbability(values),
+                               math.log(fragment.evaluate(values)))
 
     def test_dot(self):
         """Check that dot works with position hints."""

python/gtsam/tests/test_GaussianBayesNet.py

@@ -12,13 +12,15 @@ Author: Frank Dellaert
 
 from __future__ import print_function
 
+import math
 import unittest
 
-import gtsam
 import numpy as np
-from gtsam import GaussianBayesNet, GaussianConditional
 from gtsam.utils.test_case import GtsamTestCase
 
+import gtsam
+from gtsam import GaussianBayesNet, GaussianConditional
+
 # some keys
 _x_ = 11
 _y_ = 22
@@ -45,6 +47,18 @@ class TestGaussianBayesNet(GtsamTestCase):
         np.testing.assert_equal(R, R1)
         np.testing.assert_equal(d, d1)
 
+    def test_evaluate(self):
+        """Test evaluate method"""
+        bayesNet = smallBayesNet()
+        values = gtsam.VectorValues()
+        values.insert(_x_, np.array([9.0]))
+        values.insert(_y_, np.array([5.0]))
+        for i in [0, 1]:
+            self.assertAlmostEqual(bayesNet.at(i).logProbability(values),
+                                   math.log(bayesNet.at(i).evaluate(values)))
+        self.assertAlmostEqual(bayesNet.logProbability(values),
+                               math.log(bayesNet.evaluate(values)))
+
     def test_sample(self):
         """Test sample method"""
         bayesNet = smallBayesNet()

python/gtsam/tests/test_HybridBayesNet.py

@@ -10,14 +10,15 @@ Author: Frank Dellaert
 """
 # pylint: disable=invalid-name, no-name-in-module, no-member
 
+import math
 import unittest
 
 import numpy as np
 from gtsam.symbol_shorthand import A, X
 from gtsam.utils.test_case import GtsamTestCase
 
-from gtsam import (DiscreteKeys, GaussianMixture, DiscreteConditional, GaussianConditional, GaussianMixture,
+from gtsam import (DiscreteConditional, DiscreteKeys, GaussianConditional,
-                   HybridBayesNet, HybridValues, noiseModel)
+                   GaussianMixture, HybridBayesNet, HybridValues, noiseModel)
 
 
 class TestHybridBayesNet(GtsamTestCase):
@@ -30,7 +31,7 @@ class TestHybridBayesNet(GtsamTestCase):
 
         # Create the continuous conditional
         I_1x1 = np.eye(1)
-        gc = GaussianConditional.FromMeanAndStddev(X(0), 2 * I_1x1, X(1), [-4],
+        conditional = GaussianConditional.FromMeanAndStddev(X(0), 2 * I_1x1, X(1), [-4],
                                                             5.0)
 
         # Create the noise models
@@ -45,7 +46,7 @@ class TestHybridBayesNet(GtsamTestCase):
 
         # Create hybrid Bayes net.
         bayesNet = HybridBayesNet()
-        bayesNet.push_back(gc)
+        bayesNet.push_back(conditional)
         bayesNet.push_back(GaussianMixture(
             [X(1)], [], discrete_keys, [conditional0, conditional1]))
         bayesNet.push_back(DiscreteConditional(Asia, "99/1"))
@@ -56,13 +57,17 @@ class TestHybridBayesNet(GtsamTestCase):
         values.insert(X(0), [-6])
         values.insert(X(1), [1])
 
-        conditionalProbability = gc.evaluate(values.continuous())
+        conditionalProbability = conditional.evaluate(values.continuous())
         mixtureProbability = conditional0.evaluate(values.continuous())
         self.assertAlmostEqual(conditionalProbability * mixtureProbability *
                                0.99,
                                bayesNet.evaluate(values),
                                places=5)
 
+        # Check logProbability
+        self.assertAlmostEqual(bayesNet.logProbability(values),
+                               math.log(bayesNet.evaluate(values)))
+
 
 if __name__ == "__main__":
     unittest.main()