Merge pull request #1294 from borglab/hybrid/check-elimination
commit
3407f9798b
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@ -73,6 +73,8 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
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HybridConditional(boost::make_shared<DiscreteConditional>(key, table)));
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}
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using Base::push_back;
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/// Get a specific Gaussian mixture by index `i`.
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GaussianMixture::shared_ptr atMixture(size_t i) const;
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@ -135,6 +135,28 @@ class HybridFactorGraph : public FactorGraph<HybridFactor> {
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push_hybrid(p);
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}
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}
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/// Get all the discrete keys in the factor graph.
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const KeySet discreteKeys() const {
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KeySet discrete_keys;
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for (auto& factor : factors_) {
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for (const DiscreteKey& k : factor->discreteKeys()) {
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discrete_keys.insert(k.first);
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}
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}
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return discrete_keys;
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}
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/// Get all the continuous keys in the factor graph.
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const KeySet continuousKeys() const {
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KeySet keys;
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for (auto& factor : factors_) {
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for (const Key& key : factor->continuousKeys()) {
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keys.insert(key);
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}
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}
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return keys;
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}
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};
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} // namespace gtsam
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@ -404,31 +404,9 @@ void HybridGaussianFactorGraph::add(DecisionTreeFactor::shared_ptr factor) {
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FactorGraph::add(boost::make_shared<HybridDiscreteFactor>(factor));
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}
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/* ************************************************************************ */
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const KeySet HybridGaussianFactorGraph::getDiscreteKeys() const {
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KeySet discrete_keys;
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for (auto &factor : factors_) {
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for (const DiscreteKey &k : factor->discreteKeys()) {
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discrete_keys.insert(k.first);
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}
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}
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return discrete_keys;
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}
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/* ************************************************************************ */
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const KeySet HybridGaussianFactorGraph::getContinuousKeys() const {
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KeySet keys;
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for (auto &factor : factors_) {
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for (const Key &key : factor->continuousKeys()) {
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keys.insert(key);
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}
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}
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return keys;
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}
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/* ************************************************************************ */
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const Ordering HybridGaussianFactorGraph::getHybridOrdering() const {
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KeySet discrete_keys = getDiscreteKeys();
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KeySet discrete_keys = discreteKeys();
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for (auto &factor : factors_) {
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for (const DiscreteKey &k : factor->discreteKeys()) {
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discrete_keys.insert(k.first);
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@ -161,12 +161,6 @@ class GTSAM_EXPORT HybridGaussianFactorGraph
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}
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}
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/// Get all the discrete keys in the factor graph.
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const KeySet getDiscreteKeys() const;
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/// Get all the continuous keys in the factor graph.
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const KeySet getContinuousKeys() const;
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/**
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* @brief Return a Colamd constrained ordering where the discrete keys are
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* eliminated after the continuous keys.
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@ -62,23 +62,24 @@ void HybridGaussianISAM::updateInternal(
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for (const sharedClique& orphan : *orphans)
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factors += boost::make_shared<BayesTreeOrphanWrapper<Node> >(orphan);
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KeySet allDiscrete;
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for (auto& factor : factors) {
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for (auto& k : factor->discreteKeys()) {
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allDiscrete.insert(k.first);
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}
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}
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// Get all the discrete keys from the factors
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KeySet allDiscrete = factors.discreteKeys();
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// Create KeyVector with continuous keys followed by discrete keys.
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KeyVector newKeysDiscreteLast;
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// Insert continuous keys first.
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for (auto& k : newFactorKeys) {
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if (!allDiscrete.exists(k)) {
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newKeysDiscreteLast.push_back(k);
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}
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}
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// Insert discrete keys at the end
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std::copy(allDiscrete.begin(), allDiscrete.end(),
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std::back_inserter(newKeysDiscreteLast));
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// Get an ordering where the new keys are eliminated last
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const VariableIndex index(factors);
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Ordering elimination_ordering;
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if (ordering) {
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elimination_ordering = *ordering;
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@ -52,6 +52,21 @@ TEST(HybridBayesNet, Creation) {
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EXPECT(df.equals(expected));
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}
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/* ****************************************************************************/
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// Test adding a bayes net to another one.
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TEST(HybridBayesNet, Add) {
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HybridBayesNet bayesNet;
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bayesNet.add(Asia, "99/1");
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DiscreteConditional expected(Asia, "99/1");
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HybridBayesNet other;
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other.push_back(bayesNet);
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EXPECT(bayesNet.equals(other));
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}
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/* ****************************************************************************/
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// Test choosing an assignment of conditionals
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TEST(HybridBayesNet, Choose) {
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