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additional clarifying comments

release/4.3a0
Varun Agrawal 2024-02-17 23:10:37 -05:00
parent 4b2a22eaa5
commit 6d7dc57599
2 changed files with 5 additions and 3 deletions
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2
gtsam/hybrid/HybridBayesNet.cpp
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@ -291,7 +291,7 @@ AlgebraicDecisionTree<Key> HybridBayesNet::modelSelection() const {
std::numeric_limits<double>::max()); std::numeric_limits<double>::max());
} }
// Compute the error for X* and the assignment // Compute the error at the MLE point X* for the current assignment
double error = double error =
this->error(HybridValues(mu, DiscreteValues(assignment))); this->error(HybridValues(mu, DiscreteValues(assignment)));

6
gtsam/hybrid/HybridGaussianFactorGraph.cpp
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@ -295,8 +295,9 @@ static std::shared_ptr<Factor> createDiscreteFactor(
if (!factor) return 1.0; // TODO(dellaert): not loving this. if (!factor) return 1.0; // TODO(dellaert): not loving this.
// Logspace version of: // Logspace version of:
// exp(-factor->error(kEmpty)) / conditional->normalizationConstant(); // exp(-factor->error(kEmpty)) * conditional->normalizationConstant();
return -factor->error(kEmpty) - conditional->logNormalizationConstant(); // We take negative of the logNormalizationConstant (1/k) to get k
return -factor->error(kEmpty) + (-conditional->logNormalizationConstant());
}; };
AlgebraicDecisionTree<Key> logProbabilities( AlgebraicDecisionTree<Key> logProbabilities(
@ -324,6 +325,7 @@ static std::shared_ptr<Factor> createGaussianMixtureFactor(
if (factor) { if (factor) {
auto hf = std::dynamic_pointer_cast<HessianFactor>(factor); auto hf = std::dynamic_pointer_cast<HessianFactor>(factor);
if (!hf) throw std::runtime_error("Expected HessianFactor!"); if (!hf) throw std::runtime_error("Expected HessianFactor!");
// Add 2.0 term since the constant term will be premultiplied by 0.5
hf->constantTerm() += 2.0 * conditional->logNormalizationConstant(); hf->constantTerm() += 2.0 * conditional->logNormalizationConstant();
} }
return factor; return factor;