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add model_selection method to HybridBayesNet

release/4.3a0
Varun Agrawal 2024-01-03 16:32:21 -05:00
parent 82e0c0dae1
commit 8a61c49bb3
2 changed files with 37 additions and 26 deletions
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50
gtsam/hybrid/HybridBayesNet.cpp
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@ -283,35 +283,20 @@ GaussianBayesNetValTree HybridBayesNet::assembleTree() const {
}
/* ************************************************************************* */
HybridValues HybridBayesNet::optimize() const {
// Collect all the discrete factors to compute MPE
DiscreteFactorGraph discrete_fg;
AlgebraicDecisionTree<Key> HybridBayesNet::model_selection() const {
/*
Perform the integration of L(X;M,Z)P(X|M)
which is the model selection term.
To perform model selection, we need:
q(mu; M, Z) * sqrt((2*pi)^n*det(Sigma))
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
the joint Gaussian distribution.
If q(mu; M, Z) = exp(-error) & k = 1.0 / sqrt((2*pi)^n*det(Sigma))
thus, q * sqrt((2*pi)^n*det(Sigma)) = q/k = exp(log(q/k))
= exp(log(q) - log(k)) = exp(-error - log(k))
= exp(-(error + log(k))),
where error is computed at the corresponding MAP point, gbn.error(mu).
This can be computed by multiplying all the exponentiated errors
of each of the conditionals, which we do below in hybrid case.
*/
/*
To perform model selection, we need:
q(mu; M, Z) * sqrt((2*pi)^n*det(Sigma))
So we compute (error + log(k)) and exponentiate later
*/
If q(mu; M, Z) = exp(-error) & k = 1.0 / sqrt((2*pi)^n*det(Sigma))
thus, q * sqrt((2*pi)^n*det(Sigma)) = q/k = exp(log(q/k))
= exp(log(q) - log(k)) = exp(-error - log(k))
= exp(-(error + log(k))),
where error is computed at the corresponding MAP point, gbn.error(mu).
So we compute (error + log(k)) and exponentiate later
*/
std::set<DiscreteKey> discreteKeySet;
GaussianBayesNetValTree bnTree = assembleTree();
GaussianBayesNetValTree bn_error = bnTree.apply(
@ -356,6 +341,19 @@ HybridValues HybridBayesNet::optimize() const {
[&max_log](const double &x) { return std::exp(x - max_log); });
model_selection = model_selection.normalize(model_selection.sum());
return model_selection;
}
/* ************************************************************************* */
HybridValues HybridBayesNet::optimize() const {
// Collect all the discrete factors to compute MPE
DiscreteFactorGraph discrete_fg;
// Compute model selection term
AlgebraicDecisionTree<Key> model_selection_term = model_selection();
// Get the set of all discrete keys involved in model selection
std::set<DiscreteKey> discreteKeySet;
for (auto &&conditional : *this) {
if (conditional->isDiscrete()) {
discrete_fg.push_back(conditional->asDiscrete());
@ -380,7 +378,7 @@ HybridValues HybridBayesNet::optimize() const {
if (discreteKeySet.size() > 0) {
discrete_fg.push_back(DecisionTreeFactor(
DiscreteKeys(discreteKeySet.begin(), discreteKeySet.end()),
model_selection));
model_selection_term));
}
// Solve for the MPE

13
gtsam/hybrid/HybridBayesNet.h
View File

@ -120,6 +120,19 @@ 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.
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
the joint Gaussian distribution.
This can be computed by multiplying all the exponentiated errors
of each of the conditionals.
*/
AlgebraicDecisionTree<Key> model_selection() const;
/**
* @brief Solve the HybridBayesNet by first computing the MPE of all the
* discrete variables and then optimizing the continuous variables based on