De-clutter header
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@ -64,47 +64,11 @@ class GTSAM_EXPORT HybridGaussianConditional
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private:
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Conditionals conditionals_; ///< a decision tree of Gaussian conditionals.
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///< Negative-log of the normalization constant (log(\sqrt(|2πΣ|))).
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///< Take advantage of the neg-log space so everything is a minimization
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double negLogConstant_;
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/// Helper struct for private constructor.
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struct ConstructorHelper {
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KeyVector frontals;
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KeyVector parents;
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KeyVector continuousKeys;
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HybridGaussianFactor::FactorValuePairs pairs;
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double negLogConstant;
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ConstructorHelper(const Conditionals &conditionals);
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};
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/// Private constructor
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HybridGaussianConditional(
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const DiscreteKeys &discreteParents,
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const HybridGaussianConditional::Conditionals &conditionals,
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const ConstructorHelper &helper)
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: BaseFactor(helper.continuousKeys, discreteParents, helper.pairs),
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BaseConditional(helper.frontals.size()),
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conditionals_(conditionals),
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negLogConstant_(helper.negLogConstant) {}
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/**
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* @brief Convert a HybridGaussianConditional of conditionals into
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* a DecisionTree of Gaussian factor graphs.
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*/
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GaussianFactorGraphTree asGaussianFactorGraphTree() const;
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/**
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* @brief Helper function to get the pruner functor.
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*
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* @param discreteProbs The pruned discrete probabilities.
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* @return std::function<GaussianConditional::shared_ptr(
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* const Assignment<Key> &, const GaussianConditional::shared_ptr &)>
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*/
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std::function<GaussianConditional::shared_ptr(
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const Assignment<Key> &, const GaussianConditional::shared_ptr &)>
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prunerFunc(const DecisionTreeFactor &discreteProbs);
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public:
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/// @name Constructors
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/// @{
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@ -220,6 +184,33 @@ class GTSAM_EXPORT HybridGaussianConditional
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/// @}
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private:
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/// Helper struct for private constructor.
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struct ConstructorHelper {
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KeyVector frontals, parents, continuousKeys;
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HybridGaussianFactor::FactorValuePairs pairs;
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double negLogConstant;
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/// Compute all variables needed for the private constructor below.
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ConstructorHelper(const Conditionals &conditionals);
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};
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/// Private constructor that uses helper struct above.
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HybridGaussianConditional(
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const DiscreteKeys &discreteParents,
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const HybridGaussianConditional::Conditionals &conditionals,
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const ConstructorHelper &helper)
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: BaseFactor(helper.continuousKeys, discreteParents, helper.pairs),
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BaseConditional(helper.frontals.size()),
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conditionals_(conditionals),
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negLogConstant_(helper.negLogConstant) {}
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/// Convert to a DecisionTree of Gaussian factor graphs.
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GaussianFactorGraphTree asGaussianFactorGraphTree() const;
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//// Get the pruner functor from pruned discrete probabilities.
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std::function<GaussianConditional::shared_ptr(
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const Assignment<Key> &, const GaussianConditional::shared_ptr &)>
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prunerFunc(const DecisionTreeFactor &prunedProbabilities);
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/// Check whether `given` has values for all frontal keys.
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bool allFrontalsGiven(const VectorValues &given) const;
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@ -39,8 +39,6 @@ TEST(HybridConditional, Invariants) {
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const DiscreteValues d{{M(0), 1}};
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const HybridValues values{c, d};
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GTSAM_PRINT(bn);
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// Check invariants for p(z|x,m)
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auto hc0 = bn.at(0);
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CHECK(hc0->isHybrid());
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