rename error to errorTree when it returns an AlgebraicDecisionTree

gtsam/discrete/DecisionTreeFactor.cpp

@@ -63,7 +63,7 @@ namespace gtsam {
   }
 
   /* ************************************************************************ */
-  AlgebraicDecisionTree<Key> DecisionTreeFactor::error() const {
+  AlgebraicDecisionTree<Key> DecisionTreeFactor::errorTree() const {
     // Get all possible assignments
     DiscreteKeys dkeys = discreteKeys();
     // Reverse to make cartesian product output a more natural ordering.

gtsam/discrete/DecisionTreeFactor.h

@@ -293,7 +293,7 @@ namespace gtsam {
   double error(const HybridValues& values) const override;
 
   /// Compute error for each assignment and return as a tree
-  AlgebraicDecisionTree<Key> error() const override;
+  AlgebraicDecisionTree<Key> errorTree() const override;
 
   /// @}
 

gtsam/discrete/DiscreteFactor.h

@@ -105,7 +105,7 @@ class GTSAM_EXPORT DiscreteFactor : public Factor {
   double error(const HybridValues& c) const override;
 
   /// Compute error for each assignment and return as a tree
-  virtual AlgebraicDecisionTree<Key> error() const = 0;
+  virtual AlgebraicDecisionTree<Key> errorTree() const = 0;
 
   /// Multiply in a DecisionTreeFactor and return the result as
   /// DecisionTreeFactor

gtsam/discrete/TableFactor.cpp

@@ -169,8 +169,8 @@ double TableFactor::error(const HybridValues& values) const {
 }
 
 /* ************************************************************************ */
-AlgebraicDecisionTree<Key> TableFactor::error() const {
+AlgebraicDecisionTree<Key> TableFactor::errorTree() const {
-  return toDecisionTreeFactor().error();
+  return toDecisionTreeFactor().errorTree();
 }
 
 /* ************************************************************************ */

gtsam/discrete/TableFactor.h

@@ -359,7 +359,7 @@ class GTSAM_EXPORT TableFactor : public DiscreteFactor {
   double error(const HybridValues& values) const override;
 
   /// Compute error for each assignment and return as a tree
-  AlgebraicDecisionTree<Key> error() const override;
+  AlgebraicDecisionTree<Key> errorTree() const override;
 
   /// @}
 };

gtsam/discrete/tests/testDecisionTreeFactor.cpp

@@ -75,7 +75,7 @@ TEST(DecisionTreeFactor, Error) {
   // Create factors
   DecisionTreeFactor f(X & Y & Z, "2 5 3 6 4 7 25 55 35 65 45 75");
 
-  auto errors = f.error();
+  auto errors = f.errorTree();
   // regression
   AlgebraicDecisionTree<Key> expected(
       {X, Y, Z},

gtsam/hybrid/GaussianMixture.cpp

@@ -313,14 +313,14 @@ AlgebraicDecisionTree<Key> GaussianMixture::logProbability(
 }
 
 /* *******************************************************************************/
-AlgebraicDecisionTree<Key> GaussianMixture::error(
+AlgebraicDecisionTree<Key> GaussianMixture::errorTree(
     const VectorValues &continuousValues) const {
   auto errorFunc = [&](const GaussianConditional::shared_ptr &conditional) {
     return conditional->error(continuousValues) +  //
            logConstant_ - conditional->logNormalizationConstant();
   };
-  DecisionTree<Key, double> errorTree(conditionals_, errorFunc);
+  DecisionTree<Key, double> error_tree(conditionals_, errorFunc);
-  return errorTree;
+  return error_tree;
 }
 
 /* *******************************************************************************/

gtsam/hybrid/GaussianMixture.h

@@ -214,7 +214,7 @@ class GTSAM_EXPORT GaussianMixture
    * @return AlgebraicDecisionTree<Key> A decision tree on the discrete keys
    * only, with the leaf values as the error for each assignment.
    */
-  AlgebraicDecisionTree<Key> error(const VectorValues &continuousValues) const;
+  AlgebraicDecisionTree<Key> errorTree(const VectorValues &continuousValues) const;
 
   /**
    * @brief Compute the logProbability of this Gaussian Mixture.

gtsam/hybrid/GaussianMixtureFactor.cpp

@@ -102,14 +102,14 @@ GaussianFactorGraphTree GaussianMixtureFactor::asGaussianFactorGraphTree()
 }
 
 /* *******************************************************************************/
-AlgebraicDecisionTree<Key> GaussianMixtureFactor::error(
+AlgebraicDecisionTree<Key> GaussianMixtureFactor::errorTree(
     const VectorValues &continuousValues) const {
   // functor to convert from sharedFactor to double error value.
   auto errorFunc = [&continuousValues](const sharedFactor &gf) {
     return gf->error(continuousValues);
   };
-  DecisionTree<Key, double> errorTree(factors_, errorFunc);
+  DecisionTree<Key, double> error_tree(factors_, errorFunc);
-  return errorTree;
+  return error_tree;
 }
 
 /* *******************************************************************************/

gtsam/hybrid/GaussianMixtureFactor.h

@@ -135,7 +135,7 @@ class GTSAM_EXPORT GaussianMixtureFactor : public HybridFactor {
    * @return AlgebraicDecisionTree<Key> A decision tree with the same keys
    * as the factors involved, and leaf values as the error.
    */
-  AlgebraicDecisionTree<Key> error(const VectorValues &continuousValues) const;
+  AlgebraicDecisionTree<Key> errorTree(const VectorValues &continuousValues) const;
 
   /**
    * @brief Compute the log-likelihood, including the log-normalizing constant.

gtsam/hybrid/HybridGaussianFactorGraph.cpp

@@ -420,7 +420,7 @@ EliminateHybrid(const HybridGaussianFactorGraph &factors,
 }
 
 /* ************************************************************************ */
-AlgebraicDecisionTree<Key> HybridGaussianFactorGraph::error(
+AlgebraicDecisionTree<Key> HybridGaussianFactorGraph::errorTree(
     const VectorValues &continuousValues) const {
   AlgebraicDecisionTree<Key> error_tree(0.0);
 
@@ -431,7 +431,7 @@ AlgebraicDecisionTree<Key> HybridGaussianFactorGraph::error(
 
     if (auto gaussianMixture = dynamic_pointer_cast<GaussianMixtureFactor>(f)) {
       // Compute factor error and add it.
-      error_tree = error_tree + gaussianMixture->error(continuousValues);
+      error_tree = error_tree + gaussianMixture->errorTree(continuousValues);
     } else if (auto gaussian = dynamic_pointer_cast<GaussianFactor>(f)) {
       // If continuous only, get the (double) error
       // and add it to the error_tree
@@ -460,7 +460,7 @@ double HybridGaussianFactorGraph::probPrime(const HybridValues &values) const {
 /* ************************************************************************ */
 AlgebraicDecisionTree<Key> HybridGaussianFactorGraph::probPrime(
     const VectorValues &continuousValues) const {
-  AlgebraicDecisionTree<Key> error_tree = this->error(continuousValues);
+  AlgebraicDecisionTree<Key> error_tree = this->errorTree(continuousValues);
   AlgebraicDecisionTree<Key> prob_tree = error_tree.apply([](double error) {
     // NOTE: The 0.5 term is handled by each factor
     return exp(-error);

gtsam/hybrid/HybridGaussianFactorGraph.h

@@ -161,7 +161,8 @@ class GTSAM_EXPORT HybridGaussianFactorGraph
    * @param continuousValues Continuous values at which to compute the error.
    * @return AlgebraicDecisionTree<Key>
    */
-  AlgebraicDecisionTree<Key> error(const VectorValues& continuousValues) const;
+  AlgebraicDecisionTree<Key> errorTree(
+      const VectorValues& continuousValues) const;
 
   /**
    * @brief Compute unnormalized probability \f$ P(X | M, Z) \f$

gtsam/hybrid/MixtureFactor.h

@@ -131,13 +131,13 @@ class MixtureFactor : public HybridFactor {
    * @return AlgebraicDecisionTree<Key> A decision tree with the same keys
    * as the factor, and leaf values as the error.
    */
-  AlgebraicDecisionTree<Key> error(const Values& continuousValues) const {
+  AlgebraicDecisionTree<Key> errorTree(const Values& continuousValues) const {
     // functor to convert from sharedFactor to double error value.
     auto errorFunc = [continuousValues](const sharedFactor& factor) {
       return factor->error(continuousValues);
     };
-    DecisionTree<Key, double> errorTree(factors_, errorFunc);
+    DecisionTree<Key, double> result(factors_, errorFunc);
-    return errorTree;
+    return result;
   }
 
   /**

gtsam/hybrid/tests/testGaussianMixture.cpp

@@ -97,7 +97,7 @@ TEST(GaussianMixture, LogProbability) {
 /// Check error.
 TEST(GaussianMixture, Error) {
   using namespace equal_constants;
-  auto actual = mixture.error(vv);
+  auto actual = mixture.errorTree(vv);
 
   // Check result.
   std::vector<DiscreteKey> discrete_keys = {mode};
@@ -134,7 +134,7 @@ TEST(GaussianMixture, Likelihood) {
   std::vector<double> leaves = {conditionals[0]->likelihood(vv)->error(vv),
                                 conditionals[1]->likelihood(vv)->error(vv)};
   AlgebraicDecisionTree<Key> expected(discrete_keys, leaves);
-  EXPECT(assert_equal(expected, likelihood->error(vv), 1e-6));
+  EXPECT(assert_equal(expected, likelihood->errorTree(vv), 1e-6));
 
   // Check that the ratio of probPrime to evaluate is the same for all modes.
   std::vector<double> ratio(2);

gtsam/hybrid/tests/testGaussianMixtureFactor.cpp

@@ -178,7 +178,7 @@ TEST(GaussianMixtureFactor, Error) {
   continuousValues.insert(X(2), Vector2(1, 1));
 
   // error should return a tree of errors, with nodes for each discrete value.
-  AlgebraicDecisionTree<Key> error_tree = mixtureFactor.error(continuousValues);
+  AlgebraicDecisionTree<Key> error_tree = mixtureFactor.errorTree(continuousValues);
 
   std::vector<DiscreteKey> discrete_keys = {m1};
   // Error values for regression test

gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp

@@ -580,7 +580,7 @@ TEST(HybridGaussianFactorGraph, ErrorAndProbPrimeTree) {
   HybridBayesNet::shared_ptr hybridBayesNet = graph.eliminateSequential();
 
   HybridValues delta = hybridBayesNet->optimize();
-  auto error_tree = graph.error(delta.continuous());
+  auto error_tree = graph.errorTree(delta.continuous());
 
   std::vector<DiscreteKey> discrete_keys = {{M(0), 2}, {M(1), 2}};
   std::vector<double> leaves = {0.9998558, 0.4902432, 0.5193694, 0.0097568};

gtsam/hybrid/tests/testMixtureFactor.cpp

@@ -97,7 +97,8 @@ TEST(MixtureFactor, Error) {
   continuousValues.insert<double>(X(1), 0);
   continuousValues.insert<double>(X(2), 1);
 
-  AlgebraicDecisionTree<Key> error_tree = mixtureFactor.error(continuousValues);
+  AlgebraicDecisionTree<Key> error_tree =
+      mixtureFactor.errorTree(continuousValues);
 
   DiscreteKey m1(1, 2);
   std::vector<DiscreteKey> discrete_keys = {m1};

gtsam_unstable/discrete/AllDiff.h

@@ -54,7 +54,7 @@ class GTSAM_UNSTABLE_EXPORT AllDiff : public Constraint {
   DecisionTreeFactor operator*(const DecisionTreeFactor& f) const override;
 
   /// Compute error for each assignment and return as a tree
-  AlgebraicDecisionTree<Key> error() const override {
+  AlgebraicDecisionTree<Key> errorTree() const override {
     throw std::runtime_error("AllDiff::error not implemented");
   }
 

gtsam_unstable/discrete/BinaryAllDiff.h

@@ -93,7 +93,7 @@ class BinaryAllDiff : public Constraint {
   }
 
   /// Compute error for each assignment and return as a tree
-  AlgebraicDecisionTree<Key> error() const override {
+  AlgebraicDecisionTree<Key> errorTree() const override {
     throw std::runtime_error("BinaryAllDiff::error not implemented");
   }
 };

gtsam_unstable/discrete/Domain.h

@@ -70,7 +70,7 @@ class GTSAM_UNSTABLE_EXPORT Domain : public Constraint {
   }
 
   /// Compute error for each assignment and return as a tree
-  AlgebraicDecisionTree<Key> error() const override {
+  AlgebraicDecisionTree<Key> errorTree() const override {
     throw std::runtime_error("Domain::error not implemented");
   }
 

gtsam_unstable/discrete/SingleValue.h

@@ -50,7 +50,7 @@ class GTSAM_UNSTABLE_EXPORT SingleValue : public Constraint {
   }
 
   /// Compute error for each assignment and return as a tree
-  AlgebraicDecisionTree<Key> error() const override {
+  AlgebraicDecisionTree<Key> errorTree() const override {
     throw std::runtime_error("SingleValue::error not implemented");
   }