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rename discreteFactor to decisionTree

release/4.3a0
Varun Agrawal 2022-10-14 16:44:38 -04:00
parent d387d2cf3f
commit 40d38651cc
1 changed files with 3 additions and 3 deletions
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6
gtsam/hybrid/HybridBayesNet.cpp
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@ -45,7 +45,7 @@ DecisionTreeFactor::shared_ptr HybridBayesNet::discreteConditionals() const {
HybridBayesNet HybridBayesNet::prune(size_t maxNrLeaves) const { HybridBayesNet HybridBayesNet::prune(size_t maxNrLeaves) const {
// Get the decision tree of only the discrete keys // Get the decision tree of only the discrete keys
auto discreteConditionals = this->discreteConditionals(); auto discreteConditionals = this->discreteConditionals();
const DecisionTreeFactor::shared_ptr discreteFactor = const DecisionTreeFactor::shared_ptr decisionTree =
boost::make_shared<DecisionTreeFactor>( boost::make_shared<DecisionTreeFactor>(
discreteConditionals->prune(maxNrLeaves)); discreteConditionals->prune(maxNrLeaves));
@ -59,7 +59,7 @@ HybridBayesNet HybridBayesNet::prune(size_t maxNrLeaves) const {
HybridBayesNet prunedBayesNetFragment; HybridBayesNet prunedBayesNetFragment;
// Go through all the conditionals in the // Go through all the conditionals in the
// Bayes Net and prune them as per discreteFactor. // Bayes Net and prune them as per decisionTree.
for (size_t i = 0; i < this->size(); i++) { for (size_t i = 0; i < this->size(); i++) {
HybridConditional::shared_ptr conditional = this->at(i); HybridConditional::shared_ptr conditional = this->at(i);
@ -69,7 +69,7 @@ HybridBayesNet HybridBayesNet::prune(size_t maxNrLeaves) const {
// Make a copy of the gaussian mixture and prune it! // Make a copy of the gaussian mixture and prune it!
auto prunedGaussianMixture = auto prunedGaussianMixture =
boost::make_shared<GaussianMixture>(*gaussianMixture); boost::make_shared<GaussianMixture>(*gaussianMixture);
prunedGaussianMixture->prune(*discreteFactor); prunedGaussianMixture->prune(*decisionTree);
// Type-erase and add to the pruned Bayes Net fragment. // Type-erase and add to the pruned Bayes Net fragment.
prunedBayesNetFragment.push_back( prunedBayesNetFragment.push_back(