replace errorConstant with negLogConstant

gtsam/discrete/DiscreteConditional.cpp

@@ -467,7 +467,7 @@ double DiscreteConditional::evaluate(const HybridValues& x) const {
 }
 
 /* ************************************************************************* */
-double DiscreteConditional::errorConstant() const {
+double DiscreteConditional::negLogConstant() const {
   return 0.0;
 }
 

gtsam/discrete/DiscreteConditional.h

@@ -264,12 +264,12 @@ class GTSAM_EXPORT DiscreteConditional
   }
 
   /**
-   * errorConstant is just zero, such that
+   * negLogConstant is just zero, such that
-   * logProbability(x) = log(evaluate(x)) = - error(x)
+   * -logProbability(x) = -log(evaluate(x)) = error(x)
-   * and hence error(x) = - log(evaluate(x)) > 0 for all x.
+   * and hence error(x) > 0 for all x.
    * Thus -log(K) for the normalization constant k is 0.
    */
-  double errorConstant() const override;
+  double negLogConstant() const override;
 
   /// @}
 

gtsam/discrete/discrete.i

@@ -112,7 +112,7 @@ virtual class DiscreteConditional : gtsam::DecisionTreeFactor {
                       const std::vector<double>& table);
 
   // Standard interface
-  double errorConstant() const;
+  double negLogConstant() const;
   double logNormalizationConstant() const;
   double logProbability(const gtsam::DiscreteValues& values) const;
   double evaluate(const gtsam::DiscreteValues& values) const;

gtsam/hybrid/HybridConditional.cpp

@@ -161,18 +161,18 @@ double HybridConditional::logProbability(const HybridValues &values) const {
 }
 
 /* ************************************************************************ */
-double HybridConditional::errorConstant() const {
+double HybridConditional::negLogConstant() const {
   if (auto gc = asGaussian()) {
-    return gc->errorConstant();
+    return gc->negLogConstant();
   }
   if (auto gm = asHybrid()) {
-    return gm->errorConstant();  // 0.0!
+    return gm->negLogConstant();  // 0.0!
   }
   if (auto dc = asDiscrete()) {
-    return dc->errorConstant();  // 0.0!
+    return dc->negLogConstant();  // 0.0!
   }
   throw std::runtime_error(
-      "HybridConditional::errorConstant: conditional type not handled");
+      "HybridConditional::negLogConstant: conditional type not handled");
 }
 
 /* ************************************************************************ */

gtsam/hybrid/HybridConditional.h

@@ -194,11 +194,11 @@ class GTSAM_EXPORT HybridConditional
 
   /**
    * Return the negative log of the normalization constant.
-   * This shows up in the error as -(error(x) + errorConstant)
+   * This shows up in the error as -(error(x) + negLogConstant)
    * Note this is 0.0 for discrete and hybrid conditionals, but depends
    * on the continuous parameters for Gaussian conditionals.
    */
-  double errorConstant() const override;
+  double negLogConstant() const override;
 
   /// Return the probability (or density) of the underlying conditional.
   double evaluate(const HybridValues& values) const override;

gtsam/hybrid/HybridGaussianConditional.cpp

@@ -36,7 +36,7 @@ HybridGaussianFactor::FactorValuePairs GetFactorValuePairs(
     // Check if conditional is pruned
     if (conditional) {
       // Assign log(\sqrt(|2πΣ|)) = -log(1 / sqrt(|2πΣ|))
-      value = conditional->errorConstant();
+      value = conditional->negLogConstant();
     }
     return {std::dynamic_pointer_cast<GaussianFactor>(conditional), value};
   };
@@ -58,7 +58,7 @@ HybridGaussianConditional::HybridGaussianConditional(
       [this](const GaussianConditional::shared_ptr &conditional) {
         if (conditional) {
           this->logConstant_ =
-              std::min(this->logConstant_, conditional->errorConstant());
+              std::min(this->logConstant_, conditional->negLogConstant());
         }
       });
 }
@@ -84,7 +84,7 @@ GaussianFactorGraphTree HybridGaussianConditional::asGaussianFactorGraphTree()
   auto wrap = [this](const GaussianConditional::shared_ptr &gc) {
     // First check if conditional has not been pruned
     if (gc) {
-      const double Cgm_Kgcm = gc->errorConstant() - this->logConstant_;
+      const double Cgm_Kgcm = gc->negLogConstant() - this->logConstant_;
       // 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) {
@@ -214,7 +214,7 @@ std::shared_ptr<HybridGaussianFactor> HybridGaussianConditional::likelihood(
       [&](const GaussianConditional::shared_ptr &conditional)
           -> GaussianFactorValuePair {
         const auto likelihood_m = conditional->likelihood(given);
-        const double Cgm_Kgcm = conditional->errorConstant() - logConstant_;
+        const double Cgm_Kgcm = conditional->negLogConstant() - logConstant_;
         if (Cgm_Kgcm == 0.0) {
           return {likelihood_m, 0.0};
         } else {

gtsam/hybrid/HybridGaussianConditional.h

@@ -158,7 +158,7 @@ class GTSAM_EXPORT HybridGaussianConditional
    *
    * @return double
    */
-  inline double errorConstant() const override { return logConstant_; }
+  inline double negLogConstant() const override { return logConstant_; }
 
   /**
    * Create a likelihood factor for a hybrid Gaussian conditional,

gtsam/hybrid/HybridGaussianFactorGraph.cpp

@@ -329,9 +329,9 @@ static std::shared_ptr<Factor> createDiscreteFactor(
 
     // Logspace version of:
     // exp(-factor->error(kEmpty)) / conditional->normalizationConstant();
-    // errorConstant gives `-log(k)`
+    // negLogConstant gives `-log(k)`
     // which is `-log(k) = log(1/k) = log(\sqrt{|2πΣ|})`.
-    return -factor->error(kEmpty) + conditional->errorConstant();
+    return -factor->error(kEmpty) + conditional->negLogConstant();
   };
 
   AlgebraicDecisionTree<Key> logProbabilities(
@@ -357,7 +357,7 @@ static std::shared_ptr<Factor> createHybridGaussianFactor(
       // Add 2.0 term since the constant term will be premultiplied by 0.5
       // as per the Hessian definition,
       // and negative since we want log(k)
-      hf->constantTerm() += -2.0 * conditional->errorConstant();
+      hf->constantTerm() += -2.0 * conditional->negLogConstant();
     }
     return {factor, 0.0};
   };

gtsam/hybrid/hybrid.i

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

gtsam/hybrid/tests/testHybridGaussianConditional.cpp

@@ -180,16 +180,16 @@ TEST(HybridGaussianConditional, Error2) {
 
   // Check result.
   DiscreteKeys discrete_keys{mode};
-  double errorConstant0 = conditionals[0]->errorConstant();
+  double negLogConstant0 = conditionals[0]->negLogConstant();
-  double errorConstant1 = conditionals[1]->errorConstant();
+  double negLogConstant1 = conditionals[1]->negLogConstant();
-  double minErrorConstant = std::min(errorConstant0, errorConstant1);
+  double minErrorConstant = std::min(negLogConstant0, negLogConstant1);
 
   // Expected error is e(X) + log(sqrt(|2πΣ|)).
-  // We normalize log(sqrt(|2πΣ|)) with min(errorConstant)
+  // We normalize log(sqrt(|2πΣ|)) with min(negLogConstant)
   // so it is non-negative.
   std::vector<double> leaves = {
-      conditionals[0]->error(vv) + errorConstant0 - minErrorConstant,
+      conditionals[0]->error(vv) + negLogConstant0 - minErrorConstant,
-      conditionals[1]->error(vv) + errorConstant1 - minErrorConstant};
+      conditionals[1]->error(vv) + negLogConstant1 - minErrorConstant};
   AlgebraicDecisionTree<Key> expected(discrete_keys, leaves);
 
   EXPECT(assert_equal(expected, actual, 1e-6));
@@ -198,7 +198,7 @@ TEST(HybridGaussianConditional, Error2) {
   for (size_t mode : {0, 1}) {
     const HybridValues hv{vv, {{M(0), mode}}};
     EXPECT_DOUBLES_EQUAL(conditionals[mode]->error(vv) +
-                             conditionals[mode]->errorConstant() -
+                             conditionals[mode]->negLogConstant() -
                              minErrorConstant,
                          hybrid_conditional.error(hv), 1e-8);
   }

gtsam/hybrid/tests/testHybridGaussianFactor.cpp

@@ -780,8 +780,8 @@ static HybridGaussianFactorGraph CreateFactorGraph(
   // Create HybridGaussianFactor
   // We take negative since we want
   // the underlying scalar to be log(\sqrt(|2πΣ|))
-  std::vector<GaussianFactorValuePair> factors{{f0, model0->errorConstant()},
+  std::vector<GaussianFactorValuePair> factors{{f0, model0->negLogConstant()},
-                                               {f1, model1->errorConstant()}};
+                                               {f1, model1->negLogConstant()}};
   HybridGaussianFactor motionFactor({X(0), X(1)}, m1, factors);
 
   HybridGaussianFactorGraph hfg;

gtsam/hybrid/tests/testHybridNonlinearFactorGraph.cpp

@@ -902,8 +902,8 @@ static HybridNonlinearFactorGraph CreateFactorGraph(
   // Create HybridNonlinearFactor
   // We take negative since we want
   // the underlying scalar to be log(\sqrt(|2πΣ|))
-  std::vector<NonlinearFactorValuePair> factors{{f0, model0->errorConstant()},
+  std::vector<NonlinearFactorValuePair> factors{{f0, model0->negLogConstant()},
-                                                {f1, model1->errorConstant()}};
+                                                {f1, model1->negLogConstant()}};
 
   HybridNonlinearFactor mixtureFactor({X(0), X(1)}, m1, factors);
 

gtsam/inference/Conditional-inst.h

@@ -59,17 +59,17 @@ double Conditional<FACTOR, DERIVEDCONDITIONAL>::evaluate(
 
 /* ************************************************************************* */
 template <class FACTOR, class DERIVEDCONDITIONAL>
-double Conditional<FACTOR, DERIVEDCONDITIONAL>::errorConstant()
+double Conditional<FACTOR, DERIVEDCONDITIONAL>::negLogConstant()
     const {
   throw std::runtime_error(
-      "Conditional::errorConstant is not implemented");
+      "Conditional::negLogConstant is not implemented");
 }
 
 /* ************************************************************************* */
 template <class FACTOR, class DERIVEDCONDITIONAL>
 double Conditional<FACTOR, DERIVEDCONDITIONAL>::logNormalizationConstant()
     const {
-  return -errorConstant();
+  return -negLogConstant();
 }
 
 /* ************************************************************************* */
@@ -96,7 +96,7 @@ bool Conditional<FACTOR, DERIVEDCONDITIONAL>::CheckInvariants(
                    // normalization constant
   const double error = conditional.error(values);
   if (error < 0.0) return false;  // prob_or_density is negative.
-  const double expected = -(conditional.errorConstant() + error);
+  const double expected = -(conditional.negLogConstant() + error);
   if (std::abs(logProb - expected) > 1e-9)
     return false;  // logProb is not consistent with error
   return true;

gtsam/inference/Conditional.h

@@ -169,7 +169,7 @@ namespace gtsam {
      *
      * @return double
      */
-    virtual double errorConstant() const;
+    virtual double negLogConstant() const;
 
     /**
      * All conditional types need to implement a log normalization constant to

gtsam/linear/GaussianConditional.cpp

@@ -182,7 +182,7 @@ namespace gtsam {
   /* ************************************************************************* */
   //  normalization constant = 1.0 / sqrt((2*pi)^n*det(Sigma))
   //  neg-log = 0.5 * n*log(2*pi) + 0.5 * log det(Sigma)
-  double GaussianConditional::errorConstant() const {
+  double GaussianConditional::negLogConstant() const {
     constexpr double log2pi = 1.8378770664093454835606594728112;
     size_t n = d().size();
     // Sigma = (R'R)^{-1}, det(Sigma) = det((R'R)^{-1}) = det(R'R)^{-1}

gtsam/linear/GaussianConditional.h

@@ -140,7 +140,7 @@ namespace gtsam {
      *
      * @return double 
      */
-    double errorConstant() const override;
+    double negLogConstant() const override;
 
     /**
      * Calculate log-probability log(evaluate(x)) for given values `x`:

gtsam/linear/NoiseModel.cpp

@@ -255,7 +255,7 @@ double Gaussian::logDeterminant() const {
 }
 
 /* *******************************************************************************/
-double Gaussian::errorConstant() const {
+double Gaussian::negLogConstant() const {
   // log(det(Sigma)) = -2.0 * logDetR
   // which gives neg-log = 0.5*n*log(2*pi) + 0.5*(-2.0 * logDetR())
   //     = 0.5*n*log(2*pi) - (0.5*2.0 * logDetR())
@@ -268,7 +268,7 @@ double Gaussian::errorConstant() const {
 
 /* *******************************************************************************/
 double Gaussian::logNormalizationConstant() const {
-  return -errorConstant();
+  return -negLogConstant();
 }
 
 /* ************************************************************************* */

gtsam/linear/NoiseModel.h

@@ -277,7 +277,7 @@ namespace gtsam {
        * 
        * @return double 
        */
-      double errorConstant() const;
+      double negLogConstant() const;
 
       /**
        * @brief Method to compute the normalization constant

gtsam/linear/linear.i

@@ -548,7 +548,7 @@ virtual class GaussianConditional : gtsam::JacobianFactor {
   bool equals(const gtsam::GaussianConditional& cg, double tol) const;
   
   // Standard Interface
-  double errorConstant() const;
+  double negLogConstant() const;
   double logNormalizationConstant() const;
   double logProbability(const gtsam::VectorValues& x) const;
   double evaluate(const gtsam::VectorValues& x) const;

gtsam/linear/tests/testGaussianDensity.cpp

@@ -55,7 +55,7 @@ TEST(GaussianDensity, FromMeanAndStddev) {
   double expected1 = 0.5 * e.dot(e);
   EXPECT_DOUBLES_EQUAL(expected1, density.error(values), 1e-9);
 
-  double expected2 = -(density.errorConstant() + 0.5 * e.dot(e));
+  double expected2 = -(density.negLogConstant() + 0.5 * e.dot(e));
   EXPECT_DOUBLES_EQUAL(expected2, density.logProbability(values), 1e-9);
 }
 

python/gtsam/tests/test_HybridBayesNet.py

@@ -90,7 +90,7 @@ class TestHybridBayesNet(GtsamTestCase):
         self.assertTrue(probability >= 0.0)
         logProb = conditional.logProbability(values)
         self.assertAlmostEqual(probability, np.exp(logProb))
-        expected = -(conditional.errorConstant() + conditional.error(values))
+        expected = -(conditional.negLogConstant() + conditional.error(values))
         self.assertAlmostEqual(logProb, expected)