sampling tests

python/gtsam/tests/test_DiscreteBayesNet.py

@@ -33,6 +33,39 @@ XRay = (2, 2)
 Dyspnea = (1, 2)
 
 
+class TestDiscreteConditional(GtsamTestCase):
+    """Tests for Discrete Conditional"""
+
+    def setUp(self):
+        self.key = (0, 2)
+        self.parent = (1, 2)
+        self.parents = DiscreteKeys()
+        self.parents.push_back(self.parent)
+
+    def test_sample(self):
+        """Tests to check sampling in DiscreteConditionals"""
+        rng = gtsam.MT19937(11)
+        niters = 1000
+
+        # Sample with only 1 variable
+        conditional = DiscreteConditional(self.key, "7/3")
+        p = 0
+        for _ in range(niters):
+            p += conditional.sample(rng)
+
+        self.assertAlmostEqual(p / niters, 0.3, 1)
+
+        # Sample with variable and parent
+        conditional = DiscreteConditional(self.key, self.parents, "7/3 2/8")
+        p = 0
+        parentValues = gtsam.DiscreteValues()
+        parentValues[self.parent[0]] = 1
+        for _ in range(niters):
+            p += conditional.sample(parentValues, rng)
+
+        self.assertAlmostEqual(p / niters, 0.8, 1)
+
+
 class TestDiscreteBayesNet(GtsamTestCase):
     """Tests for Discrete Bayes Nets."""
 
@@ -85,10 +118,12 @@ class TestDiscreteBayesNet(GtsamTestCase):
         # solve
         actualMPE = fg.optimize()
         expectedMPE = DiscreteValues()
-        for key in [Asia, Dyspnea, XRay, Tuberculosis, Smoking, Either, LungCancer, Bronchitis]:
+        for key in [
+                Asia, Dyspnea, XRay, Tuberculosis, Smoking, Either, LungCancer,
+                Bronchitis
+        ]:
             expectedMPE[key[0]] = 0
-        self.assertEqual(list(actualMPE.items()),
-                         list(expectedMPE.items()))
+        self.assertEqual(list(actualMPE.items()), list(expectedMPE.items()))
 
         # Check value for MPE is the same
         self.assertAlmostEqual(asia(actualMPE), fg(actualMPE))
@@ -104,8 +139,7 @@ class TestDiscreteBayesNet(GtsamTestCase):
             expectedMPE2[key[0]] = 0
         for key in [Asia, Dyspnea, Smoking, Bronchitis]:
             expectedMPE2[key[0]] = 1
-        self.assertEqual(list(actualMPE2.items()),
-                         list(expectedMPE2.items()))
+        self.assertEqual(list(actualMPE2.items()), list(expectedMPE2.items()))
 
         # now sample from it
         chordal2 = fg.eliminateSequential(ordering)
@@ -135,8 +169,9 @@ class TestDiscreteBayesNet(GtsamTestCase):
         # 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.at(i).logProbability(values),
+                math.log(fragment.at(i).evaluate(values)))
         self.assertAlmostEqual(fragment.logProbability(values),
                                math.log(fragment.evaluate(values)))
         actual = fragment.sample(given)

python/gtsam/tests/test_GaussianBayesNet.py

@@ -76,7 +76,7 @@ class TestGaussianBayesNet(GtsamTestCase):
         niters = 10000
         for _ in range(niters):
             val += conditional.sample(rng).at(_x_).item()
-        self.assertAlmostEqual(val / niters, 9.0, 2)
+        self.assertAlmostEqual(val / niters, 9.0, 1)
 
 
 if __name__ == "__main__":