rename logConstant_ to logNormalizer_ and add test for error with mode-dependent constants

gtsam/hybrid/HybridGaussianConditional.cpp

@@ -33,8 +33,10 @@ HybridGaussianFactor::FactorValuePairs GetFactorValuePairs(
   auto func = [](const GaussianConditional::shared_ptr &conditional)
       -> GaussianFactorValuePair {
     double value = 0.0;
-    if (conditional) {  // Check if conditional is pruned
-      value = conditional->logNormalizationConstant();
+    // Check if conditional is pruned
+    if (conditional) {
+      // Assign log(|2πΣ|) = -2*log(1 / sqrt(|2πΣ|))
+      value = -2.0 * conditional->logNormalizationConstant();
     }
     return {std::dynamic_pointer_cast<GaussianFactor>(conditional), value};
   };
@@ -49,14 +51,14 @@ HybridGaussianConditional::HybridGaussianConditional(
                  discreteParents, GetFactorValuePairs(conditionals)),
       BaseConditional(continuousFrontals.size()),
       conditionals_(conditionals) {
-  // Calculate logConstant_ as the maximum of the log constants of the
+  // Calculate logNormalizer_ as the minimum of the log normalizers of the
   // conditionals, by visiting the decision tree:
-  logConstant_ = -std::numeric_limits<double>::infinity();
+  logNormalizer_ = std::numeric_limits<double>::infinity();
   conditionals_.visit(
       [this](const GaussianConditional::shared_ptr &conditional) {
         if (conditional) {
-          this->logConstant_ = std::max(
-              this->logConstant_, conditional->logNormalizationConstant());
+          this->logNormalizer_ = std::min(
+              this->logNormalizer_, -conditional->logNormalizationConstant());
         }
       });
 }
@@ -98,7 +100,7 @@ GaussianFactorGraphTree HybridGaussianConditional::asGaussianFactorGraphTree()
     // First check if conditional has not been pruned
     if (gc) {
       const double Cgm_Kgcm =
-          this->logConstant_ - gc->logNormalizationConstant();
+          -gc->logNormalizationConstant() - this->logNormalizer_;
       // If there is a difference in the covariances, we need to account for
       // that since the error is dependent on the mode.
       if (Cgm_Kgcm > 0.0) {
@@ -169,7 +171,8 @@ void HybridGaussianConditional::print(const std::string &s,
     std::cout << "(" << formatter(dk.first) << ", " << dk.second << "), ";
   }
   std::cout << std::endl
-            << " logNormalizationConstant: " << logConstant_ << std::endl
+            << " logNormalizationConstant: " << logNormalizationConstant()
+            << std::endl
             << std::endl;
   conditionals_.print(
       "", [&](Key k) { return formatter(k); },
@@ -228,7 +231,7 @@ std::shared_ptr<HybridGaussianFactor> HybridGaussianConditional::likelihood(
           -> GaussianFactorValuePair {
         const auto likelihood_m = conditional->likelihood(given);
         const double Cgm_Kgcm =
-            logConstant_ - conditional->logNormalizationConstant();
+            -conditional->logNormalizationConstant() - logNormalizer_;
         if (Cgm_Kgcm == 0.0) {
           return {likelihood_m, 0.0};
         } else {
@@ -342,7 +345,7 @@ double HybridGaussianConditional::conditionalError(
   // Check if valid pointer
   if (conditional) {
     return conditional->error(continuousValues) +  //
-           logConstant_ - conditional->logNormalizationConstant();
+           -conditional->logNormalizationConstant() - logNormalizer_;
   } else {
     // If not valid, pointer, it means this conditional was pruned,
     // so we return maximum error.

gtsam/hybrid/HybridGaussianConditional.h

@@ -64,7 +64,8 @@ class GTSAM_EXPORT HybridGaussianConditional
 
  private:
   Conditionals conditionals_;  ///< a decision tree of Gaussian conditionals.
-  double logConstant_;         ///< log of the normalization constant.
+  double logNormalizer_;       ///< log of the normalization constant
+                               ///< (log(\sqrt(|2πΣ|))).
 
   /**
    * @brief Convert a HybridGaussianConditional of conditionals into
@@ -149,7 +150,7 @@ class GTSAM_EXPORT HybridGaussianConditional
 
   /// The log normalization constant is max of the the individual
   /// log-normalization constants.
-  double logNormalizationConstant() const override { return logConstant_; }
+  double logNormalizationConstant() const override { return -logNormalizer_; }
 
   /**
    * Create a likelihood factor for a hybrid Gaussian conditional,

gtsam/hybrid/tests/testHybridGaussianConditional.cpp

@@ -100,7 +100,7 @@ TEST(HybridGaussianConditional, Error) {
   auto actual = hybrid_conditional.errorTree(vv);
 
   // Check result.
-  std::vector<DiscreteKey> discrete_keys = {mode};
+  DiscreteKeys discrete_keys{mode};
   std::vector<double> leaves = {conditionals[0]->error(vv),
                                 conditionals[1]->error(vv)};
   AlgebraicDecisionTree<Key> expected(discrete_keys, leaves);
@@ -172,6 +172,37 @@ TEST(HybridGaussianConditional, ContinuousParents) {
   EXPECT(continuousParentKeys[0] == X(0));
 }
 
+/* ************************************************************************* */
+/// Check error with mode dependent constants.
+TEST(HybridGaussianConditional, Error2) {
+  using namespace mode_dependent_constants;
+  auto actual = mixture.errorTree(vv);
+
+  // Check result.
+  DiscreteKeys discrete_keys{mode};
+  double logNormalizer0 = -conditionals[0]->logNormalizationConstant();
+  double logNormalizer1 = -conditionals[1]->logNormalizationConstant();
+  double minLogNormalizer = std::min(logNormalizer0, logNormalizer1);
+
+  // Expected error is e(X) + log(|2πΣ|).
+  // We normalize log(|2πΣ|) with min(logNormalizers) so it is non-negative.
+  std::vector<double> leaves = {
+      conditionals[0]->error(vv) + logNormalizer0 - minLogNormalizer,
+      conditionals[1]->error(vv) + logNormalizer1 - minLogNormalizer};
+  AlgebraicDecisionTree<Key> expected(discrete_keys, leaves);
+
+  EXPECT(assert_equal(expected, actual, 1e-6));
+
+  // Check for non-tree version.
+  for (size_t mode : {0, 1}) {
+    const HybridValues hv{vv, {{M(0), mode}}};
+    EXPECT_DOUBLES_EQUAL(conditionals[mode]->error(vv) -
+                             conditionals[mode]->logNormalizationConstant() -
+                             minLogNormalizer,
+                         mixture.error(hv), 1e-8);
+  }
+}
+
 /* ************************************************************************* */
 /// Check that the likelihood is proportional to the conditional density given
 /// the measurements.