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consistent naming

release/4.3a0
Varun Agrawal 2024-09-20 10:00:49 -04:00
parent 97a7121c37
commit f3bfe7e1a1
2 changed files with 11 additions and 10 deletions
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14
gtsam/hybrid/HybridGaussianConditional.cpp
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@ -51,14 +51,14 @@ HybridGaussianConditional::HybridGaussianConditional(
discreteParents, GetFactorValuePairs(conditionals)), discreteParents, GetFactorValuePairs(conditionals)),
BaseConditional(continuousFrontals.size()), BaseConditional(continuousFrontals.size()),
conditionals_(conditionals) { conditionals_(conditionals) {
// Calculate logNormalizer_ as the minimum of the log normalizers of the // Calculate logConstant_ as the minimum of the log normalizers of the
// conditionals, by visiting the decision tree: // conditionals, by visiting the decision tree:
logNormalizer_ = std::numeric_limits<double>::infinity(); logConstant_ = std::numeric_limits<double>::infinity();
conditionals_.visit( conditionals_.visit(
[this](const GaussianConditional::shared_ptr &conditional) { [this](const GaussianConditional::shared_ptr &conditional) {
if (conditional) { if (conditional) {
this->logNormalizer_ = std::min( this->logConstant_ = std::min(
this->logNormalizer_, -conditional->logNormalizationConstant()); this->logConstant_, -conditional->logNormalizationConstant());
} }
}); });
} }
@ -85,7 +85,7 @@ GaussianFactorGraphTree HybridGaussianConditional::asGaussianFactorGraphTree()
// First check if conditional has not been pruned // First check if conditional has not been pruned
if (gc) { if (gc) {
const double Cgm_Kgcm = const double Cgm_Kgcm =
-gc->logNormalizationConstant() - this->logNormalizer_; -this->logConstant_ - gc->logNormalizationConstant();
// If there is a difference in the covariances, we need to account for // If there is a difference in the covariances, we need to account for
// that since the error is dependent on the mode. // that since the error is dependent on the mode.
if (Cgm_Kgcm > 0.0) { if (Cgm_Kgcm > 0.0) {
@ -216,7 +216,7 @@ std::shared_ptr<HybridGaussianFactor> HybridGaussianConditional::likelihood(
-> GaussianFactorValuePair { -> GaussianFactorValuePair {
const auto likelihood_m = conditional->likelihood(given); const auto likelihood_m = conditional->likelihood(given);
const double Cgm_Kgcm = const double Cgm_Kgcm =
-conditional->logNormalizationConstant() - logNormalizer_; -logConstant_ - conditional->logNormalizationConstant();
if (Cgm_Kgcm == 0.0) { if (Cgm_Kgcm == 0.0) {
return {likelihood_m, 0.0}; return {likelihood_m, 0.0};
} else { } else {
@ -330,7 +330,7 @@ double HybridGaussianConditional::conditionalError(
// Check if valid pointer // Check if valid pointer
if (conditional) { if (conditional) {
return conditional->error(continuousValues) + // return conditional->error(continuousValues) + //
-conditional->logNormalizationConstant() - logNormalizer_; -logConstant_ - conditional->logNormalizationConstant();
} else { } else {
// If not valid, pointer, it means this conditional was pruned, // If not valid, pointer, it means this conditional was pruned,
// so we return maximum error. // so we return maximum error.

7
gtsam/hybrid/HybridGaussianConditional.h
View File

@ -64,8 +64,9 @@ class GTSAM_EXPORT HybridGaussianConditional
private: private:
Conditionals conditionals_; ///< a decision tree of Gaussian conditionals. Conditionals conditionals_; ///< a decision tree of Gaussian conditionals.
double logNormalizer_; ///< log of the normalization constant ///< Negative-log of the normalization constant (log(\sqrt(|2πΣ|))).
///< (log(\sqrt(|2πΣ|))). ///< Take advantage of the neg-log space so everything is a minimization
double logConstant_;
/** /**
* @brief Convert a HybridGaussianConditional of conditionals into * @brief Convert a HybridGaussianConditional of conditionals into
@ -151,7 +152,7 @@ class GTSAM_EXPORT HybridGaussianConditional
/// The log normalization constant is max of the the individual /// The log normalization constant is max of the the individual
/// log-normalization constants. /// log-normalization constants.
double logNormalizationConstant() const override { return -logNormalizer_; } double logNormalizationConstant() const override { return -logConstant_; }
/** /**
* Create a likelihood factor for a hybrid Gaussian conditional, * Create a likelihood factor for a hybrid Gaussian conditional,