Kill obsolete prunerFunc
Frank Dellaert
parent
e15c44ec5c
commit
d2880e9913
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@ -37,94 +37,6 @@ bool HybridBayesNet::equals(const This &bn, double tol) const {
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return Base::equals(bn, tol);
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}
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/* ************************************************************************* */
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/**
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* @brief Helper function to get the pruner functional.
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*
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* @param prunedDiscreteProbs The prob. decision tree of only discrete keys.
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* @param conditional Conditional to prune. Used to get full assignment.
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* @return std::function<double(const Assignment<Key> &, double)>
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*/
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std::function<double(const Assignment<Key> &, double)> prunerFunc(
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const DecisionTreeFactor &prunedDiscreteProbs,
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const HybridConditional &conditional) {
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// Get the discrete keys as sets for the decision tree
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// and the hybrid Gaussian conditional.
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std::set<DiscreteKey> discreteProbsKeySet =
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DiscreteKeysAsSet(prunedDiscreteProbs.discreteKeys());
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std::set<DiscreteKey> conditionalKeySet =
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DiscreteKeysAsSet(conditional.discreteKeys());
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auto pruner = [prunedDiscreteProbs, discreteProbsKeySet, conditionalKeySet](
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const Assignment<Key> &choices,
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double probability) -> double {
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// This corresponds to 0 probability
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double pruned_prob = 0.0;
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// typecast so we can use this to get probability value
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DiscreteValues values(choices);
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// Case where the hybrid Gaussian conditional has the same
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// discrete keys as the decision tree.
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if (conditionalKeySet == discreteProbsKeySet) {
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if (prunedDiscreteProbs(values) == 0) {
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return pruned_prob;
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} else {
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return probability;
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}
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} else {
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// Due to branch merging (aka pruning) in DecisionTree, it is possible we
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// get a `values` which doesn't have the full set of keys.
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std::set<Key> valuesKeys;
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for (auto kvp : values) {
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valuesKeys.insert(kvp.first);
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}
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std::set<Key> conditionalKeys;
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for (auto kvp : conditionalKeySet) {
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conditionalKeys.insert(kvp.first);
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}
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// If true, then values is missing some keys
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if (conditionalKeys != valuesKeys) {
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// Get the keys present in conditionalKeys but not in valuesKeys
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std::vector<Key> missing_keys;
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std::set_difference(conditionalKeys.begin(), conditionalKeys.end(),
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valuesKeys.begin(), valuesKeys.end(),
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std::back_inserter(missing_keys));
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// Insert missing keys with a default assignment.
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for (auto missing_key : missing_keys) {
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values[missing_key] = 0;
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}
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}
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// Now we generate the full assignment by enumerating
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// over all keys in the prunedDiscreteProbs.
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// First we find the differing keys
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std::vector<DiscreteKey> set_diff;
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std::set_difference(discreteProbsKeySet.begin(),
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discreteProbsKeySet.end(), conditionalKeySet.begin(),
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conditionalKeySet.end(),
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std::back_inserter(set_diff));
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// Now enumerate over all assignments of the differing keys
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const std::vector<DiscreteValues> assignments =
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DiscreteValues::CartesianProduct(set_diff);
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for (const DiscreteValues &assignment : assignments) {
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DiscreteValues augmented_values(values);
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augmented_values.insert(assignment);
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// If any one of the sub-branches are non-zero,
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// we need this probability.
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if (prunedDiscreteProbs(augmented_values) > 0.0) {
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return probability;
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}
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}
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// If we are here, it means that all the sub-branches are 0,
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// so we prune.
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return pruned_prob;
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}
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};
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return pruner;
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}
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/* ************************************************************************* */
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DecisionTreeFactor HybridBayesNet::pruneDiscreteConditionals(
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size_t maxNrLeaves) {
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@ -164,9 +76,10 @@ DecisionTreeFactor HybridBayesNet::pruneDiscreteConditionals(
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}
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/* ************************************************************************* */
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HybridBayesNet HybridBayesNet::prune(size_t maxNrLeaves) {
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HybridBayesNet HybridBayesNet::prune(size_t maxNrLeaves) const {
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HybridBayesNet copy(*this);
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DecisionTreeFactor prunedDiscreteProbs =
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this->pruneDiscreteConditionals(maxNrLeaves);
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copy.pruneDiscreteConditionals(maxNrLeaves);
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/* To prune, we visitWith every leaf in the HybridGaussianConditional.
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* For each leaf, using the assignment we can check the discrete decision tree
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@ -179,13 +92,10 @@ HybridBayesNet HybridBayesNet::prune(size_t maxNrLeaves) {
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// Go through all the conditionals in the
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// Bayes Net and prune them as per prunedDiscreteProbs.
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for (auto &&conditional : *this) {
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for (auto &&conditional : copy) {
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if (auto gm = conditional->asHybrid()) {
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// Make a copy of the hybrid Gaussian conditional and prune it!
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auto prunedHybridGaussianConditional =
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std::make_shared<HybridGaussianConditional>(*gm);
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prunedHybridGaussianConditional->prune(
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prunedDiscreteProbs); // imperative :-(
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auto prunedHybridGaussianConditional = gm->prune(prunedDiscreteProbs);
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// Type-erase and add to the pruned Bayes Net fragment.
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prunedBayesNetFragment.push_back(prunedHybridGaussianConditional);
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@ -336,10 +246,14 @@ AlgebraicDecisionTree<Key> HybridBayesNet::logProbability(
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});
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} else if (auto dc = conditional->asDiscrete()) {
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// If discrete, add the discrete logProbability in the right branch
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result = result.apply(
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[dc](const Assignment<Key> &assignment, double leaf_value) {
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return leaf_value + dc->logProbability(DiscreteValues(assignment));
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});
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if (result.nrLeaves() == 1) {
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result = dc->errorTree().apply([](double error) { return -error; });
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} else {
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result = result.apply([dc](const Assignment<Key> &assignment,
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double leaf_value) {
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return leaf_value + dc->logProbability(DiscreteValues(assignment));
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});
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}
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}
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}
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@ -201,8 +201,13 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
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*/
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HybridValues sample() const;
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/// Prune the Hybrid Bayes Net such that we have at most maxNrLeaves leaves.
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HybridBayesNet prune(size_t maxNrLeaves);
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/**
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* @brief Prune the Bayes Net such that we have at most maxNrLeaves leaves.
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*
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* @param maxNrLeaves Continuous values at which to compute the error.
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* @return A pruned HybridBayesNet
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*/
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HybridBayesNet prune(size_t maxNrLeaves) const;
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/**
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* @brief Compute conditional error for each discrete assignment,
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