gtsam 4.2.0
gtsam
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Public Member Functions | |
void | update (HybridGaussianFactorGraph graph, const Ordering &ordering, boost::optional< size_t > maxNrLeaves=boost::none) |
Given new factors, perform an incremental update. | |
std::pair< HybridGaussianFactorGraph, HybridBayesNet > | addConditionals (const HybridGaussianFactorGraph &graph, const HybridBayesNet &hybridBayesNet, const Ordering &ordering) const |
Add conditionals from previous timestep as part of liquefication. | |
GaussianMixture::shared_ptr | gaussianMixture (size_t index) const |
Get the Gaussian Mixture from the Bayes Net posterior at index . | |
const HybridBayesNet & | hybridBayesNet () const |
Return the Bayes Net posterior. | |
std::pair< HybridGaussianFactorGraph, HybridBayesNet > gtsam::HybridSmoother::addConditionals | ( | const HybridGaussianFactorGraph & | graph, |
const HybridBayesNet & | hybridBayesNet, | ||
const Ordering & | ordering | ||
) | const |
Add conditionals from previous timestep as part of liquefication.
graph | The new factor graph for the current time step. |
hybridBayesNet | The hybrid bayes net containing all conditionals so far. |
ordering | The elimination ordering. |
void gtsam::HybridSmoother::update | ( | HybridGaussianFactorGraph | graph, |
const Ordering & | ordering, | ||
boost::optional< size_t > | maxNrLeaves = boost::none |
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) |
Given new factors, perform an incremental update.
The relevant densities in the hybridBayesNet
will be added to the input graph (fragment), and then eliminated according to the ordering
presented. The remaining factor graph contains Gaussian mixture factors that are not connected to the variables in the ordering, or a single discrete factor on all discrete keys, plus all discrete factors in the original graph.
graph | The new factors, should be linear only |
ordering | The ordering for elimination, only continuous vars are allowed |
maxNrLeaves | The maximum number of leaves in the new discrete factor, if applicable |
Prune