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improved naming and documentation

release/4.3a0
Varun Agrawal 2024-01-07 15:49:33 -05:00
parent a80b5d4f5a
commit 0430fee377
2 changed files with 15 additions and 12 deletions
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18
gtsam/hybrid/HybridBayesNet.cpp
View File

@ -281,7 +281,7 @@ GaussianBayesNetValTree HybridBayesNet::assembleTree() const {
}
/* ************************************************************************* */
AlgebraicDecisionTree<Key> HybridBayesNet::model_selection() const {
AlgebraicDecisionTree<Key> HybridBayesNet::modelSelection() const {
/*
To perform model selection, we need:
q(mu; M, Z) * sqrt((2*pi)^n*det(Sigma))
@ -330,16 +330,16 @@ AlgebraicDecisionTree<Key> HybridBayesNet::model_selection() const {
});
// Compute model selection term (with help from ADT methods)
AlgebraicDecisionTree<Key> model_selection_term =
AlgebraicDecisionTree<Key> modelSelectionTerm =
(errorTree + log_norm_constants) * -1;
double max_log = model_selection_term.max();
AlgebraicDecisionTree<Key> model_selection = DecisionTree<Key, double>(
model_selection_term,
double max_log = modelSelectionTerm.max();
modelSelectionTerm = DecisionTree<Key, double>(
modelSelectionTerm,
[&max_log](const double &x) { return std::exp(x - max_log); });
model_selection = model_selection.normalize(model_selection.sum());
modelSelectionTerm = modelSelectionTerm.normalize(modelSelectionTerm.sum());
return model_selection;
return modelSelectionTerm;
}
/* ************************************************************************* */
@ -348,7 +348,7 @@ HybridValues HybridBayesNet::optimize() const {
DiscreteFactorGraph discrete_fg;
// Compute model selection term
AlgebraicDecisionTree<Key> model_selection_term = model_selection();
AlgebraicDecisionTree<Key> modelSelectionTerm = modelSelection();
// Get the set of all discrete keys involved in model selection
std::set<DiscreteKey> discreteKeySet;
@ -376,7 +376,7 @@ HybridValues HybridBayesNet::optimize() const {
if (discreteKeySet.size() > 0) {
discrete_fg.push_back(DecisionTreeFactor(
DiscreteKeys(discreteKeySet.begin(), discreteKeySet.end()),
model_selection_term));
modelSelectionTerm));
}
// Solve for the MPE

9
gtsam/hybrid/HybridBayesNet.h
View File

@ -129,8 +129,11 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
GaussianBayesNetValTree assembleTree() const;
/*
Perform the integration of L(X;M,Z)P(X|M)
which is the model selection term.
Compute L(M;Z), the likelihood of the discrete model M
given the measurements Z.
This is called the model selection term.
To do so, we perform the integration of L(M;Z) ∝ L(X;M,Z)P(X|M).
By Bayes' rule, P(X|M,Z) ∝ L(X;M,Z)P(X|M),
hence L(X;M,Z)P(X|M) is the unnormalized probabilty of
@ -139,7 +142,7 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
This can be computed by multiplying all the exponentiated errors
of each of the conditionals.
*/
AlgebraicDecisionTree<Key> model_selection() const;
AlgebraicDecisionTree<Key> modelSelection() const;
/**
* @brief Solve the HybridBayesNet by first computing the MPE of all the