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Better way of handling assignments

release/4.3a0
Varun Agrawal 2023-12-25 18:47:44 -05:00
parent ebcf958d69
commit 1e298be3b3
1 changed files with 43 additions and 33 deletions
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76
gtsam/hybrid/HybridBayesNet.cpp
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@ -285,8 +285,6 @@ HybridValues HybridBayesNet::optimize() const {
DiscreteFactorGraph discrete_fg; DiscreteFactorGraph discrete_fg;
VectorValues continuousValues; VectorValues continuousValues;
// Error values for each hybrid factor
AlgebraicDecisionTree<Key> error(0.0);
std::set<DiscreteKey> discreteKeySet; std::set<DiscreteKey> discreteKeySet;
// this->print(); // this->print();
@ -313,7 +311,8 @@ HybridValues HybridBayesNet::optimize() const {
If q(mu; M, Z) = exp(-error) & k = 1.0 / sqrt((2*pi)^n*det(Sigma)) 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)) thus, q * sqrt((2*pi)^n*det(Sigma)) = q/k = exp(log(q/k))
= exp(log(q) - log(k)) = exp(-error - log(k)) = exp(log(q) - log(k)) = exp(-error - 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 So we compute (error + log(k)) and exponentiate later
*/ */
@ -325,29 +324,45 @@ HybridValues HybridBayesNet::optimize() const {
AlgebraicDecisionTree<Key> log_norm_constants = AlgebraicDecisionTree<Key> log_norm_constants =
DecisionTree<Key, double>(bnTree, [](const GaussianBayesNet &gbn) { DecisionTree<Key, double>(bnTree, [](const GaussianBayesNet &gbn) {
if (gbn.size() == 0) { if (gbn.size() == 0) {
return -std::numeric_limits<double>::max(); return 0.0;
} }
return -gbn.logNormalizationConstant(); return gbn.logNormalizationConstant();
}); });
// Compute unnormalized error term and compute model selection term
AlgebraicDecisionTree<Key> model_selection_term = log_norm_constants.apply( // Compute unnormalized error term
[this, &x_map](const Assignment<Key> &assignment, double x) { std::vector<DiscreteKey> labels;
double error = 0.0; for (auto &&key : x_map.labels()) {
for (auto &&f : *this) { labels.push_back(std::make_pair(key, 2));
if (auto gm = dynamic_pointer_cast<GaussianMixture>(f)) { }
error += gm->error(
HybridValues(x_map(assignment), DiscreteValues(assignment))); std::vector<double> errors;
} else if (auto hc = dynamic_pointer_cast<HybridConditional>(f)) { x_map.visitWith([this, &errors](const Assignment<Key> &assignment,
if (auto gm = hc->asMixture()) { const VectorValues &mu) {
error += gm->error( double error = 0.0;
HybridValues(x_map(assignment), DiscreteValues(assignment))); for (auto &&f : *this) {
} else if (auto g = hc->asGaussian()) { if (auto gm = dynamic_pointer_cast<GaussianMixture>(f)) {
error += g->error(x_map(assignment)); error += gm->error(HybridValues(mu, DiscreteValues(assignment)));
} } else if (auto hc = dynamic_pointer_cast<HybridConditional>(f)) {
} if (auto gm = hc->asMixture()) {
error += gm->error(HybridValues(mu, DiscreteValues(assignment)));
} else if (auto g = hc->asGaussian()) {
error += g->error(mu);
} }
return -(error + x); }
}
errors.push_back(error);
});
AlgebraicDecisionTree<Key> errorTree =
DecisionTree<Key, double>(labels, errors);
// Compute model selection term
AlgebraicDecisionTree<Key> model_selection_term = errorTree.apply(
[&log_norm_constants](const Assignment<Key> assignment, double err) {
return -(err + log_norm_constants(assignment));
}); });
// std::cout << "model selection term" << std::endl;
// model_selection_term.print("", DefaultKeyFormatter); // model_selection_term.print("", DefaultKeyFormatter);
double max_log = model_selection_term.max(); double max_log = model_selection_term.max();
@ -355,6 +370,7 @@ HybridValues HybridBayesNet::optimize() const {
model_selection_term, model_selection_term,
[&max_log](const double &x) { return std::exp(x - max_log); }); [&max_log](const double &x) { return std::exp(x - max_log); });
model_selection = model_selection.normalize(model_selection.sum()); model_selection = model_selection.normalize(model_selection.sum());
// std::cout << "normalized model selection" << std::endl; // std::cout << "normalized model selection" << std::endl;
// model_selection.print("", DefaultKeyFormatter); // model_selection.print("", DefaultKeyFormatter);
@ -363,17 +379,11 @@ HybridValues HybridBayesNet::optimize() const {
discrete_fg.push_back(conditional->asDiscrete()); discrete_fg.push_back(conditional->asDiscrete());
} else { } else {
if (conditional->isContinuous()) { if (conditional->isContinuous()) {
// /* /*
// If we are here, it means there are no discrete variables in If we are here, it means there are no discrete variables in
// the Bayes net (due to strong elimination ordering). the Bayes net (due to strong elimination ordering).
// This is a continuous-only problem hence model selection doesn't matter. This is a continuous-only problem hence model selection doesn't matter.
// */ */
// auto gc = conditional->asGaussian();
// for (GaussianConditional::const_iterator frontal = gc->beginFrontals();
// frontal != gc->endFrontals(); ++frontal) {
// continuousValues.insert_or_assign(*frontal,
// Vector::Zero(gc->getDim(frontal)));
// }
} else if (conditional->isHybrid()) { } else if (conditional->isHybrid()) {
auto gm = conditional->asMixture(); auto gm = conditional->asMixture();