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@ -128,7 +128,7 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
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* value assignment. Note this corresponds to the Gaussian posterior p(X|M=m)
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* of the continuous variables given the discrete assignment M=m.
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*
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* @note Any pure discrete factors are ignored.
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* @note Be careful, as any factors not Gaussian are ignored.
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*
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* @param assignment The discrete value assignment for the discrete keys.
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* @return Gaussian posterior as a GaussianBayesNet
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@ -542,7 +542,7 @@ AlgebraicDecisionTree<Key> HybridGaussianFactorGraph::discretePosterior(
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}
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/* ************************************************************************ */
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GaussianFactorGraph HybridGaussianFactorGraph::operator()(
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GaussianFactorGraph HybridGaussianFactorGraph::choose(
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const DiscreteValues &assignment) const {
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GaussianFactorGraph gfg;
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for (auto &&f : *this) {
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@ -230,8 +230,23 @@ class GTSAM_EXPORT HybridGaussianFactorGraph
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eliminate(const Ordering& keys) const;
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/// @}
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/// Get the GaussianFactorGraph at a given discrete assignment.
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GaussianFactorGraph operator()(const DiscreteValues& assignment) const;
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/**
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@brief Get the GaussianFactorGraph at a given discrete assignment. Note this
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* corresponds to the Gaussian posterior p(X|M=m, Z=z) of the continuous
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* variables X given the discrete assignment M=m and whatever measurements z
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* where assumed in the creation of the factor Graph.
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*
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* @note Be careful, as any factors not Gaussian are ignored.
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*
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* @param assignment The discrete value assignment for the discrete keys.
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* @return Gaussian factors as a GaussianFactorGraph
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*/
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GaussianFactorGraph choose(const DiscreteValues& assignment) const;
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/// Syntactic sugar for choose
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GaussianFactorGraph operator()(const DiscreteValues& assignment) const {
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return choose(assignment);
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}
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};
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// traits
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