rename asMixture to asHybrid

gtsam/hybrid/HybridBayesNet.cpp

@@ -180,7 +180,7 @@ HybridBayesNet HybridBayesNet::prune(size_t maxNrLeaves) {
   // Go through all the conditionals in the
   // Bayes Net and prune them as per prunedDiscreteProbs.
   for (auto &&conditional : *this) {
-    if (auto gm = conditional->asMixture()) {
+    if (auto gm = conditional->asHybrid()) {
       // Make a copy of the hybrid Gaussian conditional and prune it!
       auto prunedHybridGaussianConditional =
           std::make_shared<HybridGaussianConditional>(*gm);
@@ -204,7 +204,7 @@ GaussianBayesNet HybridBayesNet::choose(
     const DiscreteValues &assignment) const {
   GaussianBayesNet gbn;
   for (auto &&conditional : *this) {
-    if (auto gm = conditional->asMixture()) {
+    if (auto gm = conditional->asHybrid()) {
       // If conditional is hybrid, select based on assignment.
       gbn.push_back((*gm)(assignment));
     } else if (auto gc = conditional->asGaussian()) {
@@ -291,7 +291,7 @@ AlgebraicDecisionTree<Key> HybridBayesNet::errorTree(
 
   // Iterate over each conditional.
   for (auto &&conditional : *this) {
-    if (auto gm = conditional->asMixture()) {
+    if (auto gm = conditional->asHybrid()) {
       // If conditional is hybrid, compute error for all assignments.
       result = result + gm->errorTree(continuousValues);
 
@@ -321,7 +321,7 @@ AlgebraicDecisionTree<Key> HybridBayesNet::logProbability(
 
   // Iterate over each conditional.
   for (auto &&conditional : *this) {
-    if (auto gm = conditional->asMixture()) {
+    if (auto gm = conditional->asHybrid()) {
       // If conditional is hybrid, select based on assignment and compute
       // logProbability.
       result = result + gm->logProbability(continuousValues);
@@ -369,7 +369,7 @@ HybridGaussianFactorGraph HybridBayesNet::toFactorGraph(
     if (conditional->frontalsIn(measurements)) {
       if (auto gc = conditional->asGaussian()) {
         fg.push_back(gc->likelihood(measurements));
-      } else if (auto gm = conditional->asMixture()) {
+      } else if (auto gm = conditional->asHybrid()) {
         fg.push_back(gm->likelihood(measurements));
       } else {
         throw std::runtime_error("Unknown conditional type");

gtsam/hybrid/HybridBayesTree.cpp

@@ -109,7 +109,7 @@ struct HybridAssignmentData {
 
     GaussianConditional::shared_ptr conditional;
     if (hybrid_conditional->isHybrid()) {
-      conditional = (*hybrid_conditional->asMixture())(parentData.assignment_);
+      conditional = (*hybrid_conditional->asHybrid())(parentData.assignment_);
     } else if (hybrid_conditional->isContinuous()) {
       conditional = hybrid_conditional->asGaussian();
     } else {
@@ -205,7 +205,7 @@ void HybridBayesTree::prune(const size_t maxNrLeaves) {
 
       // If conditional is hybrid, we prune it.
       if (conditional->isHybrid()) {
-        auto hybridGaussianCond = conditional->asMixture();
+        auto hybridGaussianCond = conditional->asHybrid();
 
         hybridGaussianCond->prune(parentData.prunedDiscreteProbs);
       }

gtsam/hybrid/HybridConditional.cpp

@@ -97,8 +97,8 @@ void HybridConditional::print(const std::string &s,
 bool HybridConditional::equals(const HybridFactor &other, double tol) const {
   const This *e = dynamic_cast<const This *>(&other);
   if (e == nullptr) return false;
-  if (auto gm = asMixture()) {
+  if (auto gm = asHybrid()) {
-    auto other = e->asMixture();
+    auto other = e->asHybrid();
     return other != nullptr && gm->equals(*other, tol);
   }
   if (auto gc = asGaussian()) {
@@ -119,7 +119,7 @@ double HybridConditional::error(const HybridValues &values) const {
   if (auto gc = asGaussian()) {
     return gc->error(values.continuous());
   }
-  if (auto gm = asMixture()) {
+  if (auto gm = asHybrid()) {
     return gm->error(values);
   }
   if (auto dc = asDiscrete()) {
@@ -134,7 +134,7 @@ double HybridConditional::logProbability(const HybridValues &values) const {
   if (auto gc = asGaussian()) {
     return gc->logProbability(values.continuous());
   }
-  if (auto gm = asMixture()) {
+  if (auto gm = asHybrid()) {
     return gm->logProbability(values);
   }
   if (auto dc = asDiscrete()) {
@@ -149,7 +149,7 @@ double HybridConditional::logNormalizationConstant() const {
   if (auto gc = asGaussian()) {
     return gc->logNormalizationConstant();
   }
-  if (auto gm = asMixture()) {
+  if (auto gm = asHybrid()) {
     return gm->logNormalizationConstant();  // 0.0!
   }
   if (auto dc = asDiscrete()) {

gtsam/hybrid/HybridConditional.h

@@ -151,7 +151,7 @@ class GTSAM_EXPORT HybridConditional
    * @return nullptr if not a conditional
    * @return HybridGaussianConditional::shared_ptr otherwise
    */
-  HybridGaussianConditional::shared_ptr asMixture() const {
+  HybridGaussianConditional::shared_ptr asHybrid() const {
     return std::dynamic_pointer_cast<HybridGaussianConditional>(inner_);
   }
 

gtsam/hybrid/HybridGaussianFactorGraph.cpp

@@ -115,7 +115,7 @@ void HybridGaussianFactorGraph::printErrors(
         } else {
           // Is hybrid
           auto conditionalComponent =
-              hc->asMixture()->operator()(values.discrete());
+              hc->asHybrid()->operator()(values.discrete());
           conditionalComponent->print(ss.str(), keyFormatter);
           std::cout << "error = " << conditionalComponent->error(values)
                     << "\n";
@@ -184,7 +184,7 @@ GaussianFactorGraphTree HybridGaussianFactorGraph::assembleGraphTree() const {
     } else if (auto gm = dynamic_pointer_cast<HybridGaussianConditional>(f)) {
       result = gm->add(result);
     } else if (auto hc = dynamic_pointer_cast<HybridConditional>(f)) {
-      if (auto gm = hc->asMixture()) {
+      if (auto gm = hc->asHybrid()) {
         result = gm->add(result);
       } else if (auto g = hc->asGaussian()) {
         result = addGaussian(result, g);

gtsam/hybrid/HybridSmoother.cpp

@@ -140,7 +140,7 @@ HybridSmoother::addConditionals(const HybridGaussianFactorGraph &originalGraph,
 /* ************************************************************************* */
 HybridGaussianConditional::shared_ptr HybridSmoother::gaussianMixture(
     size_t index) const {
-  return hybridBayesNet_.at(index)->asMixture();
+  return hybridBayesNet_.at(index)->asHybrid();
 }
 
 /* ************************************************************************* */

gtsam/hybrid/hybrid.i

@@ -65,7 +65,7 @@ virtual class HybridConditional {
   double logProbability(const gtsam::HybridValues& values) const;
   double evaluate(const gtsam::HybridValues& values) const;
   double operator()(const gtsam::HybridValues& values) const;
-  gtsam::HybridGaussianConditional* asMixture() const;
+  gtsam::HybridGaussianConditional* asHybrid() const;
   gtsam::GaussianConditional* asGaussian() const;
   gtsam::DiscreteConditional* asDiscrete() const;
   gtsam::Factor* inner();

gtsam/hybrid/tests/testHybridBayesNet.cpp

@@ -144,13 +144,13 @@ TEST(HybridBayesNet, Choose) {
 
   EXPECT_LONGS_EQUAL(4, gbn.size());
 
-  EXPECT(assert_equal(*(*hybridBayesNet->at(0)->asMixture())(assignment),
+  EXPECT(assert_equal(*(*hybridBayesNet->at(0)->asHybrid())(assignment),
                       *gbn.at(0)));
-  EXPECT(assert_equal(*(*hybridBayesNet->at(1)->asMixture())(assignment),
+  EXPECT(assert_equal(*(*hybridBayesNet->at(1)->asHybrid())(assignment),
                       *gbn.at(1)));
-  EXPECT(assert_equal(*(*hybridBayesNet->at(2)->asMixture())(assignment),
+  EXPECT(assert_equal(*(*hybridBayesNet->at(2)->asHybrid())(assignment),
                       *gbn.at(2)));
-  EXPECT(assert_equal(*(*hybridBayesNet->at(3)->asMixture())(assignment),
+  EXPECT(assert_equal(*(*hybridBayesNet->at(3)->asHybrid())(assignment),
                       *gbn.at(3)));
 }
 
@@ -280,9 +280,9 @@ TEST(HybridBayesNet, Pruning) {
   const DiscreteValues discrete_values{{M(0), 1}, {M(1), 1}};
   const HybridValues hybridValues{delta.continuous(), discrete_values};
   double logProbability = 0;
-  logProbability += posterior->at(0)->asMixture()->logProbability(hybridValues);
+  logProbability += posterior->at(0)->asHybrid()->logProbability(hybridValues);
-  logProbability += posterior->at(1)->asMixture()->logProbability(hybridValues);
+  logProbability += posterior->at(1)->asHybrid()->logProbability(hybridValues);
-  logProbability += posterior->at(2)->asMixture()->logProbability(hybridValues);
+  logProbability += posterior->at(2)->asHybrid()->logProbability(hybridValues);
   // NOTE(dellaert): the discrete errors were not added in logProbability tree!
   logProbability +=
       posterior->at(3)->asDiscrete()->logProbability(hybridValues);

gtsam/hybrid/tests/testHybridConditional.cpp

@@ -44,7 +44,7 @@ TEST(HybridConditional, Invariants) {
   CHECK(hc0->isHybrid());
 
   // Check invariants as a HybridGaussianConditional.
-  const auto conditional = hc0->asMixture();
+  const auto conditional = hc0->asHybrid();
   EXPECT(HybridGaussianConditional::CheckInvariants(*conditional, values));
 
   // Check invariants as a HybridConditional.

gtsam/hybrid/tests/testHybridGaussianISAM.cpp

@@ -333,13 +333,13 @@ TEST(HybridGaussianElimination, Incremental_approximate) {
   // each with 2, 4, 8, and 5 (pruned) leaves respetively.
   EXPECT_LONGS_EQUAL(4, incrementalHybrid.size());
   EXPECT_LONGS_EQUAL(
-      2, incrementalHybrid[X(0)]->conditional()->asMixture()->nrComponents());
+      2, incrementalHybrid[X(0)]->conditional()->asHybrid()->nrComponents());
   EXPECT_LONGS_EQUAL(
-      3, incrementalHybrid[X(1)]->conditional()->asMixture()->nrComponents());
+      3, incrementalHybrid[X(1)]->conditional()->asHybrid()->nrComponents());
   EXPECT_LONGS_EQUAL(
-      5, incrementalHybrid[X(2)]->conditional()->asMixture()->nrComponents());
+      5, incrementalHybrid[X(2)]->conditional()->asHybrid()->nrComponents());
   EXPECT_LONGS_EQUAL(
-      5, incrementalHybrid[X(3)]->conditional()->asMixture()->nrComponents());
+      5, incrementalHybrid[X(3)]->conditional()->asHybrid()->nrComponents());
 
   /***** Run Round 2 *****/
   HybridGaussianFactorGraph graph2;
@@ -354,9 +354,9 @@ TEST(HybridGaussianElimination, Incremental_approximate) {
   // with 5 (pruned) leaves.
   CHECK_EQUAL(5, incrementalHybrid.size());
   EXPECT_LONGS_EQUAL(
-      5, incrementalHybrid[X(3)]->conditional()->asMixture()->nrComponents());
+      5, incrementalHybrid[X(3)]->conditional()->asHybrid()->nrComponents());
   EXPECT_LONGS_EQUAL(
-      5, incrementalHybrid[X(4)]->conditional()->asMixture()->nrComponents());
+      5, incrementalHybrid[X(4)]->conditional()->asHybrid()->nrComponents());
 }
 
 /* ************************************************************************/
@@ -548,7 +548,7 @@ TEST(HybridGaussianISAM, NonTrivial) {
 
   // Test if pruning worked correctly by checking that we only have 3 leaves in
   // the last node.
-  auto lastConditional = inc[X(3)]->conditional()->asMixture();
+  auto lastConditional = inc[X(3)]->conditional()->asHybrid();
   EXPECT_LONGS_EQUAL(3, lastConditional->nrComponents());
 }
 

gtsam/hybrid/tests/testHybridNonlinearISAM.cpp

@@ -358,13 +358,13 @@ TEST(HybridNonlinearISAM, Incremental_approximate) {
   // each with 2, 4, 8, and 5 (pruned) leaves respetively.
   EXPECT_LONGS_EQUAL(4, bayesTree.size());
   EXPECT_LONGS_EQUAL(
-      2, bayesTree[X(0)]->conditional()->asMixture()->nrComponents());
+      2, bayesTree[X(0)]->conditional()->asHybrid()->nrComponents());
   EXPECT_LONGS_EQUAL(
-      3, bayesTree[X(1)]->conditional()->asMixture()->nrComponents());
+      3, bayesTree[X(1)]->conditional()->asHybrid()->nrComponents());
   EXPECT_LONGS_EQUAL(
-      5, bayesTree[X(2)]->conditional()->asMixture()->nrComponents());
+      5, bayesTree[X(2)]->conditional()->asHybrid()->nrComponents());
   EXPECT_LONGS_EQUAL(
-      5, bayesTree[X(3)]->conditional()->asMixture()->nrComponents());
+      5, bayesTree[X(3)]->conditional()->asHybrid()->nrComponents());
 
   /***** Run Round 2 *****/
   HybridGaussianFactorGraph graph2;
@@ -382,9 +382,9 @@ TEST(HybridNonlinearISAM, Incremental_approximate) {
   // with 5 (pruned) leaves.
   CHECK_EQUAL(5, bayesTree.size());
   EXPECT_LONGS_EQUAL(
-      5, bayesTree[X(3)]->conditional()->asMixture()->nrComponents());
+      5, bayesTree[X(3)]->conditional()->asHybrid()->nrComponents());
   EXPECT_LONGS_EQUAL(
-      5, bayesTree[X(4)]->conditional()->asMixture()->nrComponents());
+      5, bayesTree[X(4)]->conditional()->asHybrid()->nrComponents());
 }
 
 /* ************************************************************************/
@@ -569,7 +569,7 @@ TEST(HybridNonlinearISAM, NonTrivial) {
 
   // Test if pruning worked correctly by checking that
   // we only have 3 leaves in the last node.
-  auto lastConditional = bayesTree[X(3)]->conditional()->asMixture();
+  auto lastConditional = bayesTree[X(3)]->conditional()->asHybrid();
   EXPECT_LONGS_EQUAL(3, lastConditional->nrComponents());
 }