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add documentation for additive scalar in the error and remove the 0.5 since it gets cancelled out during normalization

release/4.3a0
Varun Agrawal 2024-09-19 15:33:24 -04:00
parent 2937533128
commit 9b6facd262
2 changed files with 22 additions and 2 deletions
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9
gtsam/hybrid/HybridGaussianFactor.h
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@ -45,6 +45,15 @@ using GaussianFactorValuePair = std::pair<GaussianFactor::shared_ptr, double>;
* where the set of discrete variables indexes to
* the continuous gaussian distribution.
*
* In factor graphs the error function typically returns 0.5*|h(x)-z|^2, i.e.,
* the negative log-likelihood for a Gaussian noise model.
* In hybrid factor graphs we allow *adding* an arbitrary scalar dependent on
* the discrete assignment.
* For example, adding a 70/30 mode probability is supported by providing the
* scalars $-log(.7)$ and $-log(.3)$.
* Note that adding a common constant will not make any difference in the
* optimization, so $-log(70)$ and $-log(30)$ work just as well.
*
* @ingroup hybrid
*/
class GTSAM_EXPORT HybridGaussianFactor : public HybridFactor {

15
gtsam/hybrid/HybridNonlinearFactor.h
View File

@ -45,6 +45,17 @@ using NonlinearFactorValuePair = std::pair<NonlinearFactor::shared_ptr, double>;
* This class stores all factors as HybridFactors which can then be typecast to
* one of (NonlinearFactor, GaussianFactor) which can then be checked to perform
* the correct operation.
*
* In factor graphs the error function typically returns 0.5*|h(x)-z|^2, i.e.,
* the negative log-likelihood for a Gaussian noise model.
* In hybrid factor graphs we allow *adding* an arbitrary scalar dependent on
* the discrete assignment.
* For example, adding a 70/30 mode probability is supported by providing the
* scalars $-log(.7)$ and $-log(.3)$.
* Note that adding a common constant will not make any difference in the
* optimization, so $-log(70)$ and $-log(30)$ work just as well.
*
* @ingroup hybrid
*/
class HybridNonlinearFactor : public HybridFactor {
public:
@ -134,7 +145,7 @@ class HybridNonlinearFactor : public HybridFactor {
auto errorFunc =
[continuousValues](const std::pair<sharedFactor, double>& f) {
auto [factor, val] = f;
return factor->error(continuousValues) + (0.5 * val);
return factor->error(continuousValues) + val;
};
DecisionTree<Key, double> result(factors_, errorFunc);
return result;
@ -153,7 +164,7 @@ class HybridNonlinearFactor : public HybridFactor {
auto [factor, val] = factors_(discreteValues);
// Compute the error for the selected factor
const double factorError = factor->error(continuousValues);
return factorError + (0.5 * val);
return factorError + val;
}
/**