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remove unnecessary code in child class

release/4.3a0
Varun Agrawal 2024-09-21 03:24:29 -04:00
parent d81cd82b9a
commit 821b22f6f8
2 changed files with 0 additions and 75 deletions
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34
gtsam/hybrid/HybridGaussianConditional.cpp
View File

@ -323,40 +323,6 @@ AlgebraicDecisionTree<Key> HybridGaussianConditional::logProbability(
return DecisionTree<Key, double>(conditionals_, probFunc);
}
/* ************************************************************************* */
double HybridGaussianConditional::conditionalError(
const GaussianConditional::shared_ptr &conditional,
const VectorValues &continuousValues) const {
// Check if valid pointer
if (conditional) {
return conditional->error(continuousValues) + //
-logConstant_ - conditional->logNormalizationConstant();
} else {
// If not valid, pointer, it means this conditional was pruned,
// so we return maximum error.
// This way the negative exponential will give
// a probability value close to 0.0.
return std::numeric_limits<double>::max();
}
}
/* *******************************************************************************/
AlgebraicDecisionTree<Key> HybridGaussianConditional::errorTree(
const VectorValues &continuousValues) const {
auto errorFunc = [&](const GaussianConditional::shared_ptr &conditional) {
return conditionalError(conditional, continuousValues);
};
DecisionTree<Key, double> error_tree(conditionals_, errorFunc);
return error_tree;
}
/* *******************************************************************************/
double HybridGaussianConditional::error(const HybridValues &values) const {
// Directly index to get the conditional, no need to build the whole tree.
auto conditional = conditionals_(values.discrete());
return conditionalError(conditional, values.continuous());
}
/* *******************************************************************************/
double HybridGaussianConditional::logProbability(
const HybridValues &values) const {

41
gtsam/hybrid/HybridGaussianConditional.h
View File

@ -174,43 +174,6 @@ class GTSAM_EXPORT HybridGaussianConditional
AlgebraicDecisionTree<Key> logProbability(
const VectorValues &continuousValues) const;
/**
* @brief Compute the error of this hybrid Gaussian conditional.
*
* This requires some care, as different components may have
* different normalization constants. Let's consider p(x|y,m), where m is
* discrete. We need the error to satisfy the invariant:
*
* error(x;y,m) = K - log(probability(x;y,m))
*
* For all x,y,m. But note that K, the (log) normalization constant defined
* in Conditional.h, should not depend on x, y, or m, only on the parameters
* of the density. Hence, we delegate to the underlying Gaussian
* conditionals, indexed by m, which do satisfy:
*
* log(probability_m(x;y)) = K_m - error_m(x;y)
*
* We resolve by having K == max(K_m) and
*
* error(x;y,m) = error_m(x;y) + K - K_m
*
* which also makes error(x;y,m) >= 0 for all x,y,m.
*
* @param values Continuous values and discrete assignment.
* @return double
*/
double error(const HybridValues &values) const override;
/**
* @brief Compute error of the HybridGaussianConditional as a tree.
*
* @param continuousValues The continuous VectorValues.
* @return AlgebraicDecisionTree<Key> A decision tree on the discrete keys
* only, with the leaf values as the error for each assignment.
*/
virtual AlgebraicDecisionTree<Key> errorTree(
const VectorValues &continuousValues) const override;
/**
* @brief Compute the logProbability of this hybrid Gaussian conditional.
*
@ -241,10 +204,6 @@ class GTSAM_EXPORT HybridGaussianConditional
/// Check whether `given` has values for all frontal keys.
bool allFrontalsGiven(const VectorValues &given) const;
/// Helper method to compute the error of a conditional.
double conditionalError(const GaussianConditional::shared_ptr &conditional,
const VectorValues &continuousValues) const;
#ifdef GTSAM_ENABLE_BOOST_SERIALIZATION
/** Serialization function */
friend class boost::serialization::access;