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remove prints

release/4.3a0
Varun Agrawal 2023-07-28 15:51:00 -04:00
parent b35fb0f668
commit 4e9d849595
2 changed files with 2 additions and 16 deletions
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15
gtsam/hybrid/HybridBayesNet.cpp
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@ -139,27 +139,16 @@ DecisionTreeFactor HybridBayesNet::pruneDiscreteConditionals(
for (size_t i = 0; i < this->size(); i++) { for (size_t i = 0; i < this->size(); i++) {
auto conditional = this->at(i); auto conditional = this->at(i);
if (conditional->isDiscrete()) { if (conditional->isDiscrete()) {
std::cout << ">>>" << std::endl;
conditional->print();
discreteProbs = discreteProbs * (*conditional->asDiscrete()); discreteProbs = discreteProbs * (*conditional->asDiscrete());
// discreteProbs.print();
// std::cout << "================\n" << std::endl;
Ordering conditional_keys(conditional->frontals()); Ordering conditional_keys(conditional->frontals());
discrete_frontals += conditional_keys; discrete_frontals += conditional_keys;
discrete_factor_idxs.push_back(i); discrete_factor_idxs.push_back(i);
} }
} }
std::cout << "Original Joint Prob:" << std::endl;
std::cout << discreteProbs.nrAssignments() << std::endl;
discreteProbs.print();
const DecisionTreeFactor prunedDiscreteProbs = const DecisionTreeFactor prunedDiscreteProbs =
discreteProbs.prune(maxNrLeaves); discreteProbs.prune(maxNrLeaves);
std::cout << "Pruned Joint Prob:" << std::endl;
std::cout << prunedDiscreteProbs.nrAssignments() << std::endl;
prunedDiscreteProbs.print();
std::cout << "\n\n\n";
gttoc_(HybridBayesNet_PruneDiscreteConditionals);
// Eliminate joint probability back into conditionals // Eliminate joint probability back into conditionals
DiscreteFactorGraph dfg{prunedDiscreteProbs}; DiscreteFactorGraph dfg{prunedDiscreteProbs};
@ -168,8 +157,6 @@ DecisionTreeFactor HybridBayesNet::pruneDiscreteConditionals(
// Assign pruned discrete conditionals back at the correct indices. // Assign pruned discrete conditionals back at the correct indices.
for (size_t i = 0; i < discrete_factor_idxs.size(); i++) { for (size_t i = 0; i < discrete_factor_idxs.size(); i++) {
size_t idx = discrete_factor_idxs.at(i); size_t idx = discrete_factor_idxs.at(i);
// std::cout << i << std::endl;
// dbn->at(i)->print();
this->at(idx) = std::make_shared<HybridConditional>(dbn->at(i)); this->at(idx) = std::make_shared<HybridConditional>(dbn->at(i));
} }

3
gtsam/hybrid/HybridGaussianFactorGraph.cpp
View File

@ -96,7 +96,6 @@ static GaussianFactorGraphTree addGaussian(
// TODO(dellaert): it's probably more efficient to first collect the discrete // TODO(dellaert): it's probably more efficient to first collect the discrete
// keys, and then loop over all assignments to populate a vector. // keys, and then loop over all assignments to populate a vector.
GaussianFactorGraphTree HybridGaussianFactorGraph::assembleGraphTree() const { GaussianFactorGraphTree HybridGaussianFactorGraph::assembleGraphTree() const {
GaussianFactorGraphTree result; GaussianFactorGraphTree result;
for (auto &f : factors_) { for (auto &f : factors_) {
@ -175,7 +174,7 @@ discreteElimination(const HybridGaussianFactorGraph &factors,
throwRuntimeError("continuousElimination", f); throwRuntimeError("continuousElimination", f);
} }
} }
dfg.print("The DFG to eliminate");
// NOTE: This does sum-product. For max-product, use EliminateForMPE. // NOTE: This does sum-product. For max-product, use EliminateForMPE.
auto result = EliminateDiscrete(dfg, frontalKeys); auto result = EliminateDiscrete(dfg, frontalKeys);