Added convenience constructors and python wrappers

gtsam/hybrid/HybridBayesNet.h

@@ -69,10 +69,25 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
   /// Add HybridConditional to Bayes Net
   using Base::add;
 
+  /// Add a Gaussian Mixture to the Bayes Net.
+  template <typename... T>
+  void addMixture(T &&...args) {
+    push_back(HybridConditional(
+        boost::make_shared<GaussianMixture>(std::forward<T>(args)...)));
+  }
+
+  /// Add a Gaussian conditional to the Bayes Net.
+  template <typename... T>
+  void addGaussian(T &&...args) {
+    push_back(HybridConditional(
+        boost::make_shared<GaussianConditional>(std::forward<T>(args)...)));
+  }
+
   /// Add a discrete conditional to the Bayes Net.
-  void add(const DiscreteKey &key, const std::string &table) {
-    push_back(
-        HybridConditional(boost::make_shared<DiscreteConditional>(key, table)));
+  template <typename... T>
+  void addDiscrete(T &&...args) {
+    push_back(HybridConditional(
+        boost::make_shared<DiscreteConditional>(std::forward<T>(args)...)));
   }
 
   using Base::push_back;

gtsam/hybrid/hybrid.i

@@ -87,7 +87,6 @@ class HybridBayesTreeClique {
   // double evaluate(const gtsam::HybridValues& values) const;
 };
 
-#include <gtsam/hybrid/HybridBayesTree.h>
 class HybridBayesTree {
   HybridBayesTree();
   void print(string s = "HybridBayesTree\n",
@@ -105,14 +104,29 @@ class HybridBayesTree {
                  gtsam::DefaultKeyFormatter) const;
 };
 
+#include <gtsam/hybrid/HybridBayesTree.h>
 class HybridBayesNet {
   HybridBayesNet();
   void add(const gtsam::HybridConditional& s);
+  void addMixture(const gtsam::GaussianMixture& s);
+  void addGaussian(const gtsam::GaussianConditional& s);
+  void addDiscrete(const gtsam::DiscreteConditional& s);
+  void addDiscrete(const gtsam::DiscreteKey& key, string spec);
+  void addDiscrete(const gtsam::DiscreteKey& key,
+                   const gtsam::DiscreteKeys& parents, string spec);
+  void addDiscrete(const gtsam::DiscreteKey& key,
+                   const std::vector<gtsam::DiscreteKey>& parents, string spec);
+
   bool empty() const;
   size_t size() const;
   gtsam::KeySet keys() const;
   const gtsam::HybridConditional* at(size_t i) const;
+
+  double evaluate(const gtsam::HybridValues& x) const;
   gtsam::HybridValues optimize() const;
+  gtsam::HybridValues sample(const gtsam::HybridValues &given) const;
+  gtsam::HybridValues sample() const;
+
   void print(string s = "HybridBayesNet\n",
              const gtsam::KeyFormatter& keyFormatter =
                  gtsam::DefaultKeyFormatter) const;

gtsam/hybrid/tests/testHybridBayesNet.cpp

@@ -43,7 +43,7 @@ static const DiscreteKey Asia(asiaKey, 2);
 // Test creation of a pure discrete Bayes net.
 TEST(HybridBayesNet, Creation) {
   HybridBayesNet bayesNet;
-  bayesNet.add(Asia, "99/1");
+  bayesNet.addDiscrete(Asia, "99/1");
 
   DiscreteConditional expected(Asia, "99/1");
   CHECK(bayesNet.atDiscrete(0));
@@ -54,7 +54,7 @@ TEST(HybridBayesNet, Creation) {
 // Test adding a Bayes net to another one.
 TEST(HybridBayesNet, Add) {
   HybridBayesNet bayesNet;
-  bayesNet.add(Asia, "99/1");
+  bayesNet.addDiscrete(Asia, "99/1");
 
   HybridBayesNet other;
   other.push_back(bayesNet);
@@ -65,7 +65,7 @@ TEST(HybridBayesNet, Add) {
 // Test evaluate for a pure discrete Bayes net P(Asia).
 TEST(HybridBayesNet, evaluatePureDiscrete) {
   HybridBayesNet bayesNet;
-  bayesNet.add(Asia, "99/1");
+  bayesNet.addDiscrete(Asia, "99/1");
   HybridValues values;
   values.insert(asiaKey, 0);
   EXPECT_DOUBLES_EQUAL(0.99, bayesNet.evaluate(values), 1e-9);
@@ -85,17 +85,12 @@ TEST(HybridBayesNet, evaluateHybrid) {
              conditional1 = boost::make_shared<GaussianConditional>(
                  X(1), Vector1::Constant(2), I_1x1, model1);
 
-  // TODO(dellaert): creating and adding mixture is clumsy.
-  const auto mixture = GaussianMixture::FromConditionals(
-      {X(1)}, {}, {Asia}, {conditional0, conditional1});
-
   // Create hybrid Bayes net.
   HybridBayesNet bayesNet;
-  bayesNet.push_back(HybridConditional(
-      boost::make_shared<GaussianConditional>(continuousConditional)));
-  bayesNet.push_back(
-      HybridConditional(boost::make_shared<GaussianMixture>(mixture)));
-  bayesNet.add(Asia, "99/1");
+  bayesNet.addGaussian(continuousConditional);
+  bayesNet.addMixture(GaussianMixture::FromConditionals(
+      {X(1)}, {}, {Asia}, {conditional0, conditional1}));
+  bayesNet.addDiscrete(Asia, "99/1");
 
   // Create values at which to evaluate.
   HybridValues values;

gtsam/linear/GaussianBayesNet.cpp

@@ -46,7 +46,8 @@ namespace gtsam {
     return optimize(solution);
   }
 
-  VectorValues GaussianBayesNet::optimize(VectorValues solution) const {
+  VectorValues GaussianBayesNet::optimize(const VectorValues& given) const {
+    VectorValues solution = given;
     // (R*x)./sigmas = y by solving x=inv(R)*(y.*sigmas)
     // solve each node in reverse topological sort order (parents first)
     for (auto cg : boost::adaptors::reverse(*this)) {

gtsam/linear/GaussianBayesNet.h

@@ -113,7 +113,7 @@ namespace gtsam {
     VectorValues optimize() const;
 
     /// Version of optimize for incomplete BayesNet, given missing variables
-    VectorValues optimize(const VectorValues given) const;
+    VectorValues optimize(const VectorValues& given) const;
 
     /**
      * Sample using ancestral sampling

gtsam/linear/linear.i

@@ -490,6 +490,8 @@ virtual class GaussianConditional : gtsam::JacobianFactor {
   bool equals(const gtsam::GaussianConditional& cg, double tol) const;
 
   // Standard Interface
+  double evaluate(const gtsam::VectorValues& x) const;
+  double logDensity(const gtsam::VectorValues& x) const;
   gtsam::Key firstFrontalKey() const;
   gtsam::VectorValues solve(const gtsam::VectorValues& parents) const;
   gtsam::JacobianFactor* likelihood(
@@ -543,17 +545,20 @@ virtual class GaussianBayesNet {
   bool equals(const gtsam::GaussianBayesNet& other, double tol) const;
   size_t size() const;
 
-  // Standard interface
   void push_back(gtsam::GaussianConditional* conditional);
   void push_back(const gtsam::GaussianBayesNet& bayesNet);
   gtsam::GaussianConditional* front() const;
   gtsam::GaussianConditional* back() const;
 
+  // Standard interface
+  double evaluate(const gtsam::VectorValues& x) const;
+  double logDensity(const gtsam::VectorValues& x) const;
+
   gtsam::VectorValues optimize() const;
-  gtsam::VectorValues optimize(gtsam::VectorValues given) const;
+  gtsam::VectorValues optimize(const gtsam::VectorValues& given) const;
   gtsam::VectorValues optimizeGradientSearch() const;
 
-  gtsam::VectorValues sample(gtsam::VectorValues given) const;
+  gtsam::VectorValues sample(const gtsam::VectorValues& given) const;
   gtsam::VectorValues sample() const;
   gtsam::VectorValues backSubstitute(const gtsam::VectorValues& gx) const;
   gtsam::VectorValues backSubstituteTranspose(const gtsam::VectorValues& gx) const;

python/gtsam/preamble/hybrid.h

@@ -19,3 +19,4 @@ PYBIND11_MAKE_OPAQUE(std::vector<gtsam::Key>);
 #endif
 
 PYBIND11_MAKE_OPAQUE(std::vector<gtsam::GaussianFactor::shared_ptr>);
+PYBIND11_MAKE_OPAQUE(std::vector<gtsam::GaussianConditional::shared_ptr>);

python/gtsam/specializations/hybrid.h

@@ -1,4 +1,6 @@
 
 py::bind_vector<std::vector<gtsam::GaussianFactor::shared_ptr> >(m_, "GaussianFactorVector");
-
 py::implicitly_convertible<py::list, std::vector<gtsam::GaussianFactor::shared_ptr> >();
+
+py::bind_vector<std::vector<gtsam::GaussianConditional::shared_ptr> >(m_, "GaussianConditionalVector");
+py::implicitly_convertible<py::list, std::vector<gtsam::GaussianConditional::shared_ptr> >();

python/gtsam/tests/test_GaussianBayesNet.py

@@ -29,8 +29,7 @@ def smallBayesNet():
     """Create a small Bayes Net for testing"""
     bayesNet = GaussianBayesNet()
     I_1x1 = np.eye(1, dtype=float)
-    bayesNet.push_back(GaussianConditional(
-        _x_, [9.0], I_1x1, _y_, I_1x1))
+    bayesNet.push_back(GaussianConditional(_x_, [9.0], I_1x1, _y_, I_1x1))
     bayesNet.push_back(GaussianConditional(_y_, [5.0], I_1x1))
     return bayesNet
 
@@ -41,13 +40,21 @@ class TestGaussianBayesNet(GtsamTestCase):
     def test_matrix(self):
         """Test matrix method"""
         R, d = smallBayesNet().matrix()  # get matrix and RHS
-        R1 = np.array([
-            [1.0, 1.0],
-            [0.0, 1.0]])
+        R1 = np.array([[1.0, 1.0], [0.0, 1.0]])
         d1 = np.array([9.0, 5.0])
         np.testing.assert_equal(R, R1)
         np.testing.assert_equal(d, d1)
 
+    def test_sample(self):
+        """Test sample method"""
+        bayesNet = smallBayesNet()
+        sample = bayesNet.sample()
+        self.assertIsInstance(sample, gtsam.VectorValues)
 
-if __name__ == '__main__':
+        # standard deviation is 1.0 for both, so we set tolerance to 3*sigma
+        mean = bayesNet.optimize()
+        self.gtsamAssertEquals(sample, mean, tol=3.0)
+
+
+if __name__ == "__main__":
     unittest.main()

python/gtsam/tests/test_HybridBayesNet.py

@@ -0,0 +1,66 @@
+"""
+GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,
+Atlanta, Georgia 30332-0415
+All Rights Reserved
+
+See LICENSE for the license information
+
+Unit tests for Hybrid Values.
+Author: Frank Dellaert
+"""
+# pylint: disable=invalid-name, no-name-in-module, no-member
+
+from __future__ import print_function
+
+import unittest
+
+import numpy as np
+from gtsam.symbol_shorthand import A, X
+from gtsam.utils.test_case import GtsamTestCase
+
+import gtsam
+from gtsam import GaussianConditional, GaussianMixture, HybridBayesNet, HybridValues, noiseModel
+
+
+class TestHybridBayesNet(GtsamTestCase):
+    """Unit tests for HybridValues."""
+
+    def test_evaluate(self):
+        """Test evaluate for a hybrid Bayes net P(X0|X1) P(X1|Asia) P(Asia)."""
+        asiaKey = A(0)
+        Asia = (asiaKey, 2)
+
+        # Create the continuous conditional
+        I_1x1 = np.eye(1)
+        gc = GaussianConditional.FromMeanAndStddev(X(0), 2 * I_1x1, X(1), [-4], 5.0)
+
+        # Create the noise models
+        model0 = noiseModel.Diagonal.Sigmas([2.0])
+        model1 = noiseModel.Diagonal.Sigmas([3.0])
+
+        # Create the conditionals
+        conditional0 = GaussianConditional(X(1), [5], I_1x1, model0)
+        conditional1 = GaussianConditional(X(1), [2], I_1x1, model1)
+        # gm = GaussianMixture.FromConditionals([X(1)], [], [Asia], [conditional0, conditional1])  #
+
+        # Create hybrid Bayes net.
+        bayesNet = HybridBayesNet()
+        bayesNet.addGaussian(gc)
+        # bayesNet.addMixture(gm)
+        bayesNet.addDiscrete(Asia, "99/1")
+
+        # Create values at which to evaluate.
+        values = HybridValues()
+        values.insert(asiaKey, 0)
+        values.insert(X(0), [-6])
+        values.insert(X(1), [1])
+
+        conditionalProbability = gc.evaluate(values.continuous())
+        mixtureProbability = conditional0.evaluate(values.continuous())
+        assert self.assertAlmostEqual(
+            conditionalProbability * mixtureProbability * 0.99, bayesNet.evaluate(values), places=5
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()

python/gtsam/tests/test_HybridValues.py

@@ -1,5 +1,5 @@
 """
-GTSAM Copyright 2010-2019, Georgia Tech Research Corporation,
+GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,
 Atlanta, Georgia 30332-0415
 All Rights Reserved
 
@@ -20,22 +20,23 @@
 from gtsam.utils.test_case import GtsamTestCase
 
 
-class TestHybridGaussianFactorGraph(GtsamTestCase):
+class TestHybridValues(GtsamTestCase):
     """Unit tests for HybridValues."""
 
     def test_basic(self):
-        """Test contruction and basic methods of hybrid values."""
-        
+        """Test construction and basic methods of hybrid values."""
+
         hv1 = gtsam.HybridValues()
-        hv1.insert(X(0), np.ones((3,1)))
+        hv1.insert(X(0), np.ones((3, 1)))
         hv1.insert(C(0), 2)
 
         hv2 = gtsam.HybridValues()
         hv2.insert(C(0), 2)
-        hv2.insert(X(0), np.ones((3,1)))
+        hv2.insert(X(0), np.ones((3, 1)))
 
         self.assertEqual(hv1.atDiscrete(C(0)), 2)
-        self.assertEqual(hv1.at(X(0))[0], np.ones((3,1))[0])
+        self.assertEqual(hv1.at(X(0))[0], np.ones((3, 1))[0])
+
 
 if __name__ == "__main__":
     unittest.main()