Merge pull request #1386 from borglab/hybrid/more_tests

Test and fix all conditionals

gtsam/discrete/DiscreteConditional.cpp

@@ -20,7 +20,7 @@
 #include <gtsam/base/debug.h>
 #include <gtsam/discrete/DiscreteConditional.h>
 #include <gtsam/discrete/Signature.h>
-#include <gtsam/inference/Conditional-inst.h>
+#include <gtsam/hybrid/HybridValues.h>
 
 #include <algorithm>
 #include <boost/make_shared.hpp>
@@ -510,6 +510,10 @@ string DiscreteConditional::html(const KeyFormatter& keyFormatter,
   return ss.str();
 }
 
+/* ************************************************************************* */
+double DiscreteConditional::evaluate(const HybridValues& x) const{
+  return this->evaluate(x.discrete());
+}
 /* ************************************************************************* */
 
 }  // namespace gtsam

gtsam/discrete/DiscreteConditional.h

@@ -160,10 +160,13 @@ class GTSAM_EXPORT DiscreteConditional
   }
 
   /// Evaluate, just look up in AlgebraicDecisonTree
-  double operator()(const DiscreteValues& values) const override {
+  double evaluate(const DiscreteValues& values) const {
     return ADT::operator()(values);
   }
 
+  using DecisionTreeFactor::error;       ///< DiscreteValues version
+  using DecisionTreeFactor::operator();  ///< DiscreteValues version
+
   /**
    * @brief restrict to given *parent* values.
    *
@@ -235,6 +238,14 @@ class GTSAM_EXPORT DiscreteConditional
   /// @name HybridValues methods.
   /// @{
 
+  /**
+   * Calculate probability for HybridValues `x`.
+   * Dispatches to DiscreteValues version.
+   */
+  double evaluate(const HybridValues& x) const override;
+
+  using BaseConditional::operator();  ///< HybridValues version
+
   /**
    * Calculate log-probability log(evaluate(x)) for HybridValues `x`.
    * This is actually just -error(x).
@@ -243,8 +254,6 @@ class GTSAM_EXPORT DiscreteConditional
     return -error(x);
   }
 
-  using DecisionTreeFactor::evaluate;
-
   /// @}
 
 #ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V42

gtsam/discrete/discrete.i

@@ -82,6 +82,7 @@ virtual class DecisionTreeFactor : gtsam::DiscreteFactor {
 };
 
 #include <gtsam/discrete/DiscreteConditional.h>
+#include <gtsam/hybrid/HybridValues.h>
 virtual class DiscreteConditional : gtsam::DecisionTreeFactor {
   DiscreteConditional();
   DiscreteConditional(size_t nFrontals, const gtsam::DecisionTreeFactor& f);
@@ -95,9 +96,12 @@ virtual class DiscreteConditional : gtsam::DecisionTreeFactor {
   DiscreteConditional(const gtsam::DecisionTreeFactor& joint,
                       const gtsam::DecisionTreeFactor& marginal,
                       const gtsam::Ordering& orderedKeys);
+
+  // Standard interface
+  double logNormalizationConstant() const;
   double logProbability(const gtsam::DiscreteValues& values) const;
   double evaluate(const gtsam::DiscreteValues& values) const;
-  double operator()(const gtsam::DiscreteValues& values) const;
+  double error(const gtsam::DiscreteValues& values) const;
   gtsam::DiscreteConditional operator*(
       const gtsam::DiscreteConditional& other) const;
   gtsam::DiscreteConditional marginal(gtsam::Key key) const;
@@ -119,6 +123,8 @@ virtual class DiscreteConditional : gtsam::DecisionTreeFactor {
   size_t sample(size_t value) const;
   size_t sample() const;
   void sampleInPlace(gtsam::DiscreteValues @parentsValues) const;
+
+  // Markdown and HTML
   string markdown(const gtsam::KeyFormatter& keyFormatter =
                       gtsam::DefaultKeyFormatter) const;
   string markdown(const gtsam::KeyFormatter& keyFormatter,
@@ -127,6 +133,11 @@ virtual class DiscreteConditional : gtsam::DecisionTreeFactor {
                   gtsam::DefaultKeyFormatter) const;
   string html(const gtsam::KeyFormatter& keyFormatter,
               std::map<gtsam::Key, std::vector<std::string>> names) const;
+
+  // Expose HybridValues versions
+  double logProbability(const gtsam::HybridValues& x) const;
+  double evaluate(const gtsam::HybridValues& x) const;
+  double error(const gtsam::HybridValues& x) const;
 };
 
 #include <gtsam/discrete/DiscreteDistribution.h>

gtsam/discrete/tests/testDiscreteConditional.cpp

@@ -96,6 +96,7 @@ TEST(DiscreteConditional, PriorProbability) {
   DiscreteConditional dc(Asia, "4/6");
   DiscreteValues values{{asiaKey, 0}};
   EXPECT_DOUBLES_EQUAL(0.4, dc.evaluate(values), 1e-9);
+  EXPECT(DiscreteConditional::CheckInvariants(dc, values));
 }
 
 /* ************************************************************************* */
@@ -109,6 +110,7 @@ TEST(DiscreteConditional, probability) {
   EXPECT_DOUBLES_EQUAL(0.2, C_given_DE(given), 1e-9);
   EXPECT_DOUBLES_EQUAL(log(0.2), C_given_DE.logProbability(given), 1e-9);
   EXPECT_DOUBLES_EQUAL(-log(0.2), C_given_DE.error(given), 1e-9);
+  EXPECT(DiscreteConditional::CheckInvariants(C_given_DE, given));
 }
 
 /* ************************************************************************* */

gtsam/hybrid/GaussianMixture.cpp

@@ -290,10 +290,22 @@ AlgebraicDecisionTree<Key> GaussianMixture::logProbability(
 }
 
 /* *******************************************************************************/
-double GaussianMixture::logProbability(const HybridValues &values) const {
+double GaussianMixture::error(const HybridValues &values) const {
   // Directly index to get the conditional, no need to build the whole tree.
+  auto conditional = conditionals_(values.discrete());
+  return conditional->error(values.continuous()) - conditional->logNormalizationConstant();
+}
+
+/* *******************************************************************************/
+double GaussianMixture::logProbability(const HybridValues &values) const {
   auto conditional = conditionals_(values.discrete());
   return conditional->logProbability(values.continuous());
 }
 
+/* *******************************************************************************/
+double GaussianMixture::evaluate(const HybridValues &values) const {
+  auto conditional = conditionals_(values.discrete());
+  return conditional->evaluate(values.continuous());
+}
+
 }  // namespace gtsam

gtsam/hybrid/GaussianMixture.h

@@ -174,20 +174,44 @@ class GTSAM_EXPORT GaussianMixture
       const VectorValues &continuousValues) const;
 
   /**
-   * @brief Compute the logProbability of this Gaussian Mixture given the
-   * continuous values and a discrete assignment.
+   * @brief Compute the error of this Gaussian Mixture.
+   *
+   * This requires some care, as different mixture components may have
+   * different normalization constants. Let's consider p(x|y,m), where m is
+   * discrete. We need the error to satisfy the invariant:
+   *
+   *    error(x;y,m) = K - log(probability(x;y,m))
+   *
+   * For all x,y,m. But note that K, for the GaussianMixture, cannot depend on
+   * any arguments. Hence, we delegate to the underlying Gaussian
+   * conditionals, indexed by m, which do satisfy:
+   * 
+   *    log(probability_m(x;y)) = K_m - error_m(x;y)
+   * 
+   * We resolve by having K == 0.0 and
+   * 
+   *    error(x;y,m) = error_m(x;y) - K_m
+   *
+   * @param values Continuous values and discrete assignment.
+   * @return double
+   */
+  double error(const HybridValues &values) const override;
+
+  /**
+   * @brief Compute the logProbability of this Gaussian Mixture.
    *
    * @param values Continuous values and discrete assignment.
    * @return double
    */
   double logProbability(const HybridValues &values) const override;
 
-  //   /// Calculate probability density for given values `x`.
-  //   double evaluate(const HybridValues &values) const;
+  /// Calculate probability density for given `values`.
+  double evaluate(const HybridValues &values) const override;
 
-  //   /// Evaluate probability density, sugar.
-  //   double operator()(const HybridValues &values) const { return
-  //   evaluate(values); }
+  /// Evaluate probability density, sugar.
+  double operator()(const HybridValues &values) const {
+    return evaluate(values);
+  }
 
   /**
    * @brief Prune the decision tree of Gaussian factors as per the discrete

gtsam/hybrid/HybridConditional.cpp

@@ -121,6 +121,21 @@ bool HybridConditional::equals(const HybridFactor &other, double tol) const {
                 : !(e->inner_);
 }
 
+/* ************************************************************************ */
+double HybridConditional::error(const HybridValues &values) const {
+  if (auto gc = asGaussian()) {
+    return gc->error(values.continuous());
+  }
+  if (auto gm = asMixture()) {
+    return gm->error(values);
+  }
+  if (auto dc = asDiscrete()) {
+    return dc->error(values.discrete());
+  }
+  throw std::runtime_error(
+      "HybridConditional::error: conditional type not handled");
+}
+
 /* ************************************************************************ */
 double HybridConditional::logProbability(const HybridValues &values) const {
   if (auto gc = asGaussian()) {
@@ -136,4 +151,24 @@ double HybridConditional::logProbability(const HybridValues &values) const {
       "HybridConditional::logProbability: conditional type not handled");
 }
 
+/* ************************************************************************ */
+double HybridConditional::logNormalizationConstant() const {
+  if (auto gc = asGaussian()) {
+    return gc->logNormalizationConstant();
+  }
+  if (auto gm = asMixture()) {
+    return gm->logNormalizationConstant(); // 0.0!
+  }
+  if (auto dc = asDiscrete()) {
+    return dc->logNormalizationConstant(); // 0.0!
+  }
+  throw std::runtime_error(
+      "HybridConditional::logProbability: conditional type not handled");
+}
+
+/* ************************************************************************ */
+double HybridConditional::evaluate(const HybridValues &values) const {
+  return std::exp(logProbability(values));
+}
+
 }  // namespace gtsam

gtsam/hybrid/HybridConditional.h

@@ -176,9 +176,22 @@ class GTSAM_EXPORT HybridConditional
   /// Get the type-erased pointer to the inner type
   boost::shared_ptr<Factor> inner() const { return inner_; }
 
-  /// Return the logProbability of the underlying conditional.
+  /// Return the error of the underlying conditional.
+  double error(const HybridValues& values) const override;
+
+  /// Return the log-probability (or density) of the underlying conditional.
   double logProbability(const HybridValues& values) const override;
 
+  /**
+   * Return the log normalization constant.
+   * Note this is 0.0 for discrete and hybrid conditionals, but depends
+   * on the continuous parameters for Gaussian conditionals.
+   */ 
+  double logNormalizationConstant() const override;
+
+  /// Return the probability (or density) of the underlying conditional.
+  double evaluate(const HybridValues& values) const override;
+
   /// Check if VectorValues `measurements` contains all frontal keys.
   bool frontalsIn(const VectorValues& measurements) const {
     for (Key key : frontals()) {

gtsam/hybrid/hybrid.i

@@ -61,6 +61,7 @@ virtual class HybridConditional {
   size_t nrParents() const;
 
   // Standard interface:
+  double logNormalizationConstant() const;
   double logProbability(const gtsam::HybridValues& values) const;
   double evaluate(const gtsam::HybridValues& values) const;
   double operator()(const gtsam::HybridValues& values) const;

gtsam/hybrid/tests/testHybridConditional.cpp

@@ -0,0 +1,83 @@
+/* ----------------------------------------------------------------------------
+
+ * GTSAM Copyright 2010, 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    testHybridConditional.cpp
+ * @brief   Unit tests for HybridConditional class
+ * @date    January 2023
+ */
+
+#include <gtsam/hybrid/HybridConditional.h>
+
+#include "TinyHybridExample.h"
+
+// Include for test suite
+#include <CppUnitLite/TestHarness.h>
+
+using namespace gtsam;
+
+using symbol_shorthand::M;
+using symbol_shorthand::X;
+using symbol_shorthand::Z;
+
+/* ****************************************************************************/
+// Check invariants for all conditionals in a tiny Bayes net.
+TEST(HybridConditional, Invariants) {
+  // Create hybrid Bayes net p(z|x,m)p(x)P(m)
+  auto bn = tiny::createHybridBayesNet();
+
+  // Create values to check invariants.
+  const VectorValues c{{X(0), Vector1(5.1)}, {Z(0), Vector1(4.9)}};
+  const DiscreteValues d{{M(0), 1}};
+  const HybridValues values{c, d};
+
+  // Check invariants for p(z|x,m)
+  auto hc0 = bn.at(0);
+  CHECK(hc0->isHybrid());
+
+  // Check invariants as a GaussianMixture.
+  const auto mixture = hc0->asMixture();
+  EXPECT(GaussianMixture::CheckInvariants(*mixture, values));
+
+  // Check invariants as a HybridConditional.
+  EXPECT(HybridConditional::CheckInvariants(*hc0, values));
+
+  // Check invariants for p(x)
+  auto hc1 = bn.at(1);
+  CHECK(hc1->isContinuous());
+
+  // Check invariants as a GaussianConditional.
+  const auto gaussian = hc1->asGaussian();
+  EXPECT(GaussianConditional::CheckInvariants(*gaussian, c));
+  EXPECT(GaussianConditional::CheckInvariants(*gaussian, values));
+
+  // Check invariants as a HybridConditional.
+  EXPECT(HybridConditional::CheckInvariants(*hc1, values));
+
+  // Check invariants for p(m)
+  auto hc2 = bn.at(2);
+  CHECK(hc2->isDiscrete());
+
+  // Check invariants as a DiscreteConditional.
+  const auto discrete = hc2->asDiscrete();
+  EXPECT(DiscreteConditional::CheckInvariants(*discrete, d));
+  EXPECT(DiscreteConditional::CheckInvariants(*discrete, values));
+
+  // Check invariants as a HybridConditional.
+  EXPECT(HybridConditional::CheckInvariants(*hc2, values));
+}
+
+/* ************************************************************************* */
+int main() {
+  TestResult tr;
+  return TestRegistry::runAllTests(tr);
+}
+/* ************************************************************************* */

gtsam/inference/Conditional-inst.h

@@ -56,4 +56,30 @@ double Conditional<FACTOR, DERIVEDCONDITIONAL>::evaluate(
     const HybridValues& c) const {
   throw std::runtime_error("Conditional::evaluate is not implemented");
 }
+
+/* ************************************************************************* */
+template <class FACTOR, class DERIVEDCONDITIONAL>
+double Conditional<FACTOR, DERIVEDCONDITIONAL>::normalizationConstant() const {
+  return std::exp(logNormalizationConstant());
+}
+
+/* ************************************************************************* */
+template <class FACTOR, class DERIVEDCONDITIONAL>
+template <class VALUES>
+bool Conditional<FACTOR, DERIVEDCONDITIONAL>::CheckInvariants(
+    const DERIVEDCONDITIONAL& conditional, const VALUES& values) {
+  const double prob_or_density = conditional.evaluate(values);
+  if (prob_or_density < 0.0) return false;  // prob_or_density is negative.
+  if (std::abs(prob_or_density - conditional(values)) > 1e-9)
+    return false;  // operator and evaluate differ
+  const double logProb = conditional.logProbability(values);
+  if (std::abs(prob_or_density - std::exp(logProb)) > 1e-9)
+    return false;  // logProb is not consistent with prob_or_density
+  const double expected =
+      conditional.logNormalizationConstant() - conditional.error(values);
+  if (std::abs(logProb - expected) > 1e-9)
+    return false;  // logProb is not consistent with error
+  return true;
+}
+
 }  // namespace gtsam

gtsam/inference/Conditional.h

@@ -141,6 +141,15 @@ namespace gtsam {
       return evaluate(x);
     }
 
+    /**
+     * By default, log normalization constant = 0.0.
+     * Override if this depends on the parameters.
+     */
+    virtual double logNormalizationConstant() const { return 0.0; }
+
+    /** Non-virtual, exponentiate logNormalizationConstant. */
+    double normalizationConstant() const;
+
     /// @}
     /// @name Advanced Interface
     /// @{
@@ -172,7 +181,17 @@ namespace gtsam {
     /** Mutable iterator pointing past the last parent key. */
     typename FACTOR::iterator endParents() { return asFactor().end(); }
 
+    template <class VALUES>
+    static bool CheckInvariants(const DERIVEDCONDITIONAL& conditional,
+                                const VALUES& values);
+
+    /// @}
+
   private:
+
+    /// @name Serialization
+    /// @{
+
     // Cast to factor type (non-const) (casts down to derived conditional type, then up to factor type)
     FACTOR& asFactor() { return static_cast<FACTOR&>(static_cast<DERIVEDCONDITIONAL&>(*this)); }
 

gtsam/linear/GaussianConditional.cpp

@@ -205,9 +205,14 @@ namespace gtsam {
   }
 
   /* ************************************************************************* */
-  double GaussianConditional::evaluate(const VectorValues& c) const {
-    return exp(logProbability(c));
+  double GaussianConditional::evaluate(const VectorValues& x) const {
+    return exp(logProbability(x));
   }
+
+  double GaussianConditional::evaluate(const HybridValues& x) const {
+    return evaluate(x.continuous());
+  }
+
   /* ************************************************************************* */
   VectorValues GaussianConditional::solve(const VectorValues& x) const {
     // Concatenate all vector values that correspond to parent variables

gtsam/linear/GaussianConditional.h

@@ -34,7 +34,7 @@ namespace gtsam {
   /**
   * A GaussianConditional functions as the node in a Bayes network.
   * It has a set of parents y,z, etc. and implements a probability density on x.
-  * The negative log-probability is given by \f$ \frac{1}{2} |Rx - (d - Sy - Tz - ...)|^2 \f$
+  * The negative log-density is given by \f$ \frac{1}{2} |Rx - (d - Sy - Tz - ...)|^2 \f$
   * @ingroup linear
   */
   class GTSAM_EXPORT GaussianConditional :
@@ -136,14 +136,7 @@ namespace gtsam {
      * normalization constant = 1.0 / sqrt((2*pi)^n*det(Sigma))
      * log = - 0.5 * n*log(2*pi) - 0.5 * log det(Sigma)
      */
-    double logNormalizationConstant() const;
-
-    /**
-     * normalization constant = 1.0 / sqrt((2*pi)^n*det(Sigma))
-     */
-    inline double normalizationConstant() const {
-      return exp(logNormalizationConstant());
-    }
+    double logNormalizationConstant() const override;
 
     /**
      * Calculate log-probability log(evaluate(x)) for given values `x`:
@@ -269,9 +262,14 @@ namespace gtsam {
      */
     double logProbability(const HybridValues& x) const override;
 
-    using Conditional::evaluate; // Expose evaluate(const HybridValues&) method..
+    /**
+     * Calculate probability for HybridValues `x`.
+     * Simply dispatches to VectorValues version.
+     */
+    double evaluate(const HybridValues& x) const override;
+
     using Conditional::operator(); // Expose evaluate(const HybridValues&) method..
-    using Base::error; // Expose error(const HybridValues&) method..
+    using JacobianFactor::error; // Expose error(const HybridValues&) method..
 
     /// @}
 

gtsam/linear/HessianFactor.h

@@ -196,6 +196,9 @@ namespace gtsam {
     /** Compare to another factor for testing (implementing Testable) */
     bool equals(const GaussianFactor& lf, double tol = 1e-9) const override;
 
+    /// HybridValues simply extracts the \class VectorValues and calls error.
+    using GaussianFactor::error;
+
     /** 
      * Evaluate the factor error f(x). 
      * returns 0.5*[x -1]'*H*[x -1] (also see constructor documentation)

gtsam/linear/JacobianFactor.h

@@ -198,7 +198,12 @@ namespace gtsam {
 
     Vector unweighted_error(const VectorValues& c) const; /** (A*x-b) */
     Vector error_vector(const VectorValues& c) const; /** (A*x-b)/sigma */
-    double error(const VectorValues& c) const override; /**  0.5*(A*x-b)'*D*(A*x-b) */
+
+    /// HybridValues simply extracts the \class VectorValues and calls error.
+    using GaussianFactor::error;
+
+    //// 0.5*(A*x-b)'*D*(A*x-b).
+    double error(const VectorValues& c) const override; 
 
     /** Return the augmented information matrix represented by this GaussianFactor.
      * The augmented information matrix contains the information matrix with an

gtsam/linear/linear.i

@@ -456,6 +456,7 @@ class GaussianFactorGraph {
 };
 
 #include <gtsam/linear/GaussianConditional.h>
+#include <gtsam/hybrid/HybridValues.h>
 virtual class GaussianConditional : gtsam::JacobianFactor {
   // Constructors
   GaussianConditional(size_t key, Vector d, Matrix R,
@@ -497,6 +498,7 @@ virtual class GaussianConditional : gtsam::JacobianFactor {
   bool equals(const gtsam::GaussianConditional& cg, double tol) const;
   
   // Standard Interface
+  double logNormalizationConstant() const;
   double logProbability(const gtsam::VectorValues& x) const;
   double evaluate(const gtsam::VectorValues& x) const;
   double error(const gtsam::VectorValues& x) const;
@@ -518,6 +520,11 @@ virtual class GaussianConditional : gtsam::JacobianFactor {
 
   // enabling serialization functionality
   void serialize() const;
+
+  // Expose HybridValues versions
+  double logProbability(const gtsam::HybridValues& x) const;
+  double evaluate(const gtsam::HybridValues& x) const;
+  double error(const gtsam::HybridValues& x) const;
 };
 
 #include <gtsam/linear/GaussianDensity.h>

gtsam/linear/tests/testGaussianConditional.cpp

@@ -25,6 +25,7 @@
 #include <gtsam/linear/GaussianConditional.h>
 #include <gtsam/linear/GaussianDensity.h>
 #include <gtsam/linear/GaussianBayesNet.h>
+#include <gtsam/hybrid/HybridValues.h>
 
 #include <boost/make_shared.hpp>
 
@@ -154,6 +155,13 @@ TEST(GaussianConditional, Evaluate1) {
   using density::key;
   using density::sigma;
 
+  // Check Invariants at the mean and a different value
+  for (auto vv : {mean, VectorValues{{key, Vector1(4)}}}) {
+    EXPECT(GaussianConditional::CheckInvariants(density::unitPrior, vv));
+    EXPECT(GaussianConditional::CheckInvariants(density::unitPrior,
+                                                HybridValues{vv, {}, {}}));
+  }
+
   // Let's numerically integrate and see that we integrate to 1.0.
   double integral = 0.0;
   // Loop from -5*sigma to 5*sigma in 0.1*sigma steps:
@@ -180,6 +188,13 @@ TEST(GaussianConditional, Evaluate2) {
   using density::key;
   using density::sigma;
 
+  // Check Invariants at the mean and a different value
+  for (auto vv : {mean, VectorValues{{key, Vector1(4)}}}) {
+    EXPECT(GaussianConditional::CheckInvariants(density::widerPrior, vv));
+    EXPECT(GaussianConditional::CheckInvariants(density::widerPrior,
+                                                HybridValues{vv, {}, {}}));
+  }
+
   // Let's numerically integrate and see that we integrate to 1.0.
   double integral = 0.0;
   // Loop from -5*sigma to 5*sigma in 0.1*sigma steps:
@@ -384,17 +399,18 @@ TEST(GaussianConditional, FromMeanAndStddev) {
   double expected1 = 0.5 * e1.dot(e1);
   EXPECT_DOUBLES_EQUAL(expected1, conditional1.error(values), 1e-9);
 
-  double expected2 = conditional1.logNormalizationConstant() - 0.5 * e1.dot(e1);
-  EXPECT_DOUBLES_EQUAL(expected2, conditional1.logProbability(values), 1e-9);
-
   auto conditional2 = GaussianConditional::FromMeanAndStddev(X(0), A1, X(1), A2,
                                                              X(2), b, sigma);
   Vector2 e2 = (x0 - (A1 * x1 + A2 * x2 + b)) / sigma;
-  double expected3 = 0.5 * e2.dot(e2);
-  EXPECT_DOUBLES_EQUAL(expected3, conditional2.error(values), 1e-9);
+  double expected2 = 0.5 * e2.dot(e2);
+  EXPECT_DOUBLES_EQUAL(expected2, conditional2.error(values), 1e-9);
 
-  double expected4 = conditional2.logNormalizationConstant() - 0.5 * e2.dot(e2);
-  EXPECT_DOUBLES_EQUAL(expected4, conditional2.logProbability(values), 1e-9);
+  // Check Invariants for both conditionals
+  for (auto conditional : {conditional1, conditional2}) {
+    EXPECT(GaussianConditional::CheckInvariants(conditional, values));
+    EXPECT(GaussianConditional::CheckInvariants(conditional,
+                                                HybridValues{values, {}, {}}));
+  }
 }
 
 /* ************************************************************************* */

python/gtsam/tests/test_HybridBayesNet.py

@@ -17,8 +17,8 @@ import numpy as np
 from gtsam.symbol_shorthand import A, X
 from gtsam.utils.test_case import GtsamTestCase
 
-from gtsam import (DiscreteConditional, DiscreteKeys, GaussianConditional,
-                   GaussianMixture, HybridBayesNet, HybridValues, noiseModel)
+from gtsam import (DiscreteConditional, DiscreteKeys, DiscreteValues, GaussianConditional,
+                   GaussianMixture, HybridBayesNet, HybridValues, noiseModel, VectorValues)
 
 
 class TestHybridBayesNet(GtsamTestCase):
@@ -53,9 +53,13 @@ class TestHybridBayesNet(GtsamTestCase):
 
         # Create values at which to evaluate.
         values = HybridValues()
-        values.insert(asiaKey, 0)
-        values.insert(X(0), [-6])
-        values.insert(X(1), [1])
+        continuous = VectorValues()
+        continuous.insert(X(0), [-6])
+        continuous.insert(X(1), [1])
+        values.insert(continuous)
+        discrete = DiscreteValues()
+        discrete[asiaKey] = 0
+        values.insert(discrete)
 
         conditionalProbability = conditional.evaluate(values.continuous())
         mixtureProbability = conditional0.evaluate(values.continuous())
@@ -68,6 +72,26 @@ class TestHybridBayesNet(GtsamTestCase):
         self.assertAlmostEqual(bayesNet.logProbability(values),
                                math.log(bayesNet.evaluate(values)))
 
+        # Check invariance for all conditionals:
+        self.check_invariance(bayesNet.at(0).asGaussian(), continuous)
+        self.check_invariance(bayesNet.at(0).asGaussian(), values)
+        self.check_invariance(bayesNet.at(0), values)
+        
+        self.check_invariance(bayesNet.at(1), values)
+
+        self.check_invariance(bayesNet.at(2).asDiscrete(), discrete)
+        self.check_invariance(bayesNet.at(2).asDiscrete(), values)
+        self.check_invariance(bayesNet.at(2), values)
+
+    def check_invariance(self, conditional, values):
+        """Check invariance for given conditional."""
+        probability = conditional.evaluate(values)
+        self.assertTrue(probability >= 0.0)
+        logProb = conditional.logProbability(values)
+        self.assertAlmostEqual(probability, np.exp(logProb))
+        expected = conditional.logNormalizationConstant() - conditional.error(values)
+        self.assertAlmostEqual(logProb, expected)
+
 
 if __name__ == "__main__":
     unittest.main()