Fix tests once more

gtsam/hybrid/tests/testGaussianMixture.cpp

@@ -179,8 +179,9 @@ TEST(GaussianMixture, Likelihood) {
   const GaussianMixtureFactor::Factors factors(
       gm.conditionals(),
       [measurements](const GaussianConditional::shared_ptr& conditional) {
-        return std::make_pair(conditional->likelihood(measurements),
-                              0.5 * conditional->logDeterminant());
+        return GaussianMixtureFactor::FactorAndConstant{
+            conditional->likelihood(measurements),
+            conditional->logNormalizationConstant()};
       });
   const GaussianMixtureFactor expected({X(0)}, {mode}, factors);
   EXPECT(assert_equal(*factor, expected));

gtsam/hybrid/tests/testGaussianMixtureFactor.cpp

@@ -22,6 +22,7 @@
 #include <gtsam/discrete/DiscreteValues.h>
 #include <gtsam/hybrid/GaussianMixture.h>
 #include <gtsam/hybrid/GaussianMixtureFactor.h>
+#include <gtsam/hybrid/HybridValues.h>
 #include <gtsam/inference/Symbol.h>
 #include <gtsam/linear/GaussianFactorGraph.h>
 
@@ -188,7 +189,8 @@ TEST(GaussianMixtureFactor, Error) {
   DiscreteValues discreteValues;
   discreteValues[m1.first] = 1;
   EXPECT_DOUBLES_EQUAL(
-      4.0, mixtureFactor.error({continuousValues, discreteValues}), 1e-9);
+      4.0, mixtureFactor.error({continuousValues, discreteValues}),
+      1e-9);
 }
 
 /* ************************************************************************* */

gtsam/hybrid/tests/testHybridBayesNet.cpp

@@ -188,14 +188,14 @@ TEST(HybridBayesNet, Optimize) {
 
   HybridValues delta = hybridBayesNet->optimize();
 
-  //TODO(Varun) The expectedAssignment should be 111, not 101
+  // TODO(Varun) The expectedAssignment should be 111, not 101
   DiscreteValues expectedAssignment;
   expectedAssignment[M(0)] = 1;
   expectedAssignment[M(1)] = 0;
   expectedAssignment[M(2)] = 1;
   EXPECT(assert_equal(expectedAssignment, delta.discrete()));
 
-  //TODO(Varun) This should be all -Vector1::Ones()
+  // TODO(Varun) This should be all -Vector1::Ones()
   VectorValues expectedValues;
   expectedValues.insert(X(0), -0.999904 * Vector1::Ones());
   expectedValues.insert(X(1), -0.99029 * Vector1::Ones());
@@ -243,8 +243,8 @@ TEST(HybridBayesNet, Error) {
   double total_error = 0;
   for (size_t idx = 0; idx < hybridBayesNet->size(); idx++) {
     if (hybridBayesNet->at(idx)->isHybrid()) {
-      double error = hybridBayesNet->atMixture(idx)->error(delta.continuous(),
-                                                           discrete_values);
+      double error = hybridBayesNet->atMixture(idx)->error(
+          {delta.continuous(), discrete_values});
       total_error += error;
     } else if (hybridBayesNet->at(idx)->isContinuous()) {
       double error = hybridBayesNet->atGaussian(idx)->error(delta.continuous());
@@ -253,7 +253,7 @@ TEST(HybridBayesNet, Error) {
   }
 
   EXPECT_DOUBLES_EQUAL(
-      total_error, hybridBayesNet->error(delta.continuous(), discrete_values),
+      total_error, hybridBayesNet->error({delta.continuous(), discrete_values}),
       1e-9);
   EXPECT_DOUBLES_EQUAL(total_error, error_tree(discrete_values), 1e-9);
   EXPECT_DOUBLES_EQUAL(total_error, pruned_error_tree(discrete_values), 1e-9);

gtsam/hybrid/tests/testHybridEstimation.cpp

@@ -273,7 +273,7 @@ AlgebraicDecisionTree<Key> getProbPrimeTree(
       continue;
     }
 
-    double error = graph.error(delta, assignment);
+    double error = graph.error({delta, assignment});
     probPrimes.push_back(exp(-error));
   }
   AlgebraicDecisionTree<Key> probPrimeTree(discrete_keys, probPrimes);
@@ -487,8 +487,8 @@ TEST(HybridEstimation, CorrectnessViaSampling) {
          const HybridValues& sample) -> double {
     const DiscreteValues assignment = sample.discrete();
     // Compute in log form for numerical stability
-    double log_ratio = bayesNet->error(sample.continuous(), assignment) -
-                       factorGraph->error(sample.continuous(), assignment);
+    double log_ratio = bayesNet->error({sample.continuous(), assignment}) -
+                       factorGraph->error({sample.continuous(), assignment});
     double ratio = exp(-log_ratio);
     return ratio;
   };

gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp

@@ -575,15 +575,14 @@ TEST(HybridGaussianFactorGraph, ErrorAndProbPrime) {
   HybridBayesNet::shared_ptr hybridBayesNet =
       graph.eliminateSequential(hybridOrdering);
 
-  HybridValues delta = hybridBayesNet->optimize();
-  double error = graph.error(delta.continuous(), delta.discrete());
+  const HybridValues delta = hybridBayesNet->optimize();
+  const double error = graph.error(delta);
 
-  double expected_error = 0.490243199;
   // regression
-  EXPECT(assert_equal(expected_error, error, 1e-9));
+  EXPECT(assert_equal(0.490243199, error, 1e-9));
 
   double probs = exp(-error);
-  double expected_probs = graph.probPrime(delta.continuous(), delta.discrete());
+  double expected_probs = graph.probPrime(delta);
 
   // regression
   EXPECT(assert_equal(expected_probs, probs, 1e-7));