12345qiupeng
/
gtsam
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Inline lambda

release/4.3a0
Frank Dellaert 2024-09-29 23:08:00 -07:00
parent d2880e9913
commit 28f5ed0a6e
2 changed files with 4 additions and 73 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

66
gtsam/hybrid/HybridGaussianConditional.cpp
View File

@ -288,59 +288,6 @@ std::set<DiscreteKey> DiscreteKeysAsSet(const DiscreteKeys &discreteKeys) {
return s;
}
/* ************************************************************************* */
std::function<GaussianConditional::shared_ptr(
const Assignment<Key> &, const GaussianConditional::shared_ptr &)>
HybridGaussianConditional::prunerFunc(const DecisionTreeFactor &discreteProbs) {
// Get the discrete keys as sets for the decision tree
// and the hybrid gaussian conditional.
auto discreteProbsKeySet = DiscreteKeysAsSet(discreteProbs.discreteKeys());
auto hybridGaussianCondKeySet = DiscreteKeysAsSet(this->discreteKeys());
auto pruner = [discreteProbs, discreteProbsKeySet, hybridGaussianCondKeySet](
const Assignment<Key> &choices,
const GaussianConditional::shared_ptr &conditional)
-> GaussianConditional::shared_ptr {
// typecast so we can use this to get probability value
const DiscreteValues values(choices);
// Case where the hybrid gaussian conditional has the same
// discrete keys as the decision tree.
if (hybridGaussianCondKeySet == discreteProbsKeySet) {
if (discreteProbs(values) == 0.0) {
// empty aka null pointer
std::shared_ptr<GaussianConditional> null;
return null;
} else {
return conditional;
}
} else {
std::vector<DiscreteKey> set_diff;
std::set_difference(
discreteProbsKeySet.begin(), discreteProbsKeySet.end(),
hybridGaussianCondKeySet.begin(), hybridGaussianCondKeySet.end(),
std::back_inserter(set_diff));
const std::vector<DiscreteValues> assignments =
DiscreteValues::CartesianProduct(set_diff);
for (const DiscreteValues &assignment : assignments) {
DiscreteValues augmented_values(values);
augmented_values.insert(assignment);
// If any one of the sub-branches are non-zero,
// we need this conditional.
if (discreteProbs(augmented_values) > 0.0) {
return conditional;
}
}
// If we are here, it means that all the sub-branches are 0,
// so we prune.
return nullptr;
}
};
return pruner;
}
/* *******************************************************************************/
HybridGaussianConditional::shared_ptr HybridGaussianConditional::prune(
const DecisionTreeFactor &discreteProbs) const {
@ -358,14 +305,10 @@ HybridGaussianConditional::shared_ptr HybridGaussianConditional::prune(
// Case where the hybrid gaussian conditional has the same
// discrete keys as the decision tree.
if (hybridGaussianCondKeySet == discreteProbsKeySet) {
if (discreteProbs(values) == 0.0) {
// empty aka null pointer
std::shared_ptr<GaussianConditional> null;
return null;
} else {
return conditional;
}
return (discreteProbs(values) == 0.0) ? nullptr : conditional;
} else {
// TODO(Frank): It might be faster to "choose" based on values
// and then check whether the resulting tree has non-nullptrs.
std::vector<DiscreteKey> set_diff;
std::set_difference(
discreteProbsKeySet.begin(), discreteProbsKeySet.end(),
@ -384,8 +327,7 @@ HybridGaussianConditional::shared_ptr HybridGaussianConditional::prune(
return conditional;
}
}
// If we are here, it means that all the sub-branches are 0,
// so we prune.
// If we are here, it means that all the sub-branches are 0, so we prune.
return nullptr;
}
};

11
gtsam/hybrid/HybridGaussianConditional.h
View File

@ -243,17 +243,6 @@ class GTSAM_EXPORT HybridGaussianConditional
/// Convert to a DecisionTree of Gaussian factor graphs.
GaussianFactorGraphTree asGaussianFactorGraphTree() const;
/**
* @brief Get the pruner function from discrete probabilities.
*
* @param discreteProbs The probabilities of only discrete keys.
* @return std::function<GaussianConditional::shared_ptr(
* const Assignment<Key> &, const GaussianConditional::shared_ptr &)>
*/
std::function<GaussianConditional::shared_ptr(
const Assignment<Key> &, const GaussianConditional::shared_ptr &)>
prunerFunc(const DecisionTreeFactor &prunedProbabilities);
/// Check whether `given` has values for all frontal keys.
bool allFrontalsGiven(const VectorValues &given) const;