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rename asMixture to asHybrid

release/4.3a0
Varun Agrawal 2024-09-16 14:42:35 -04:00
parent 4016de7942
commit 6929d62300
11 changed files with 39 additions and 39 deletions
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10
gtsam/hybrid/HybridBayesNet.cpp
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@ -180,7 +180,7 @@ HybridBayesNet HybridBayesNet::prune(size_t maxNrLeaves) {
// Go through all the conditionals in the
// Bayes Net and prune them as per prunedDiscreteProbs.
for (auto &&conditional : *this) {
if (auto gm = conditional->asMixture()) {
if (auto gm = conditional->asHybrid()) {
// Make a copy of the hybrid Gaussian conditional and prune it!
auto prunedHybridGaussianConditional =
std::make_shared<HybridGaussianConditional>(*gm);
@ -204,7 +204,7 @@ GaussianBayesNet HybridBayesNet::choose(
const DiscreteValues &assignment) const {
GaussianBayesNet gbn;
for (auto &&conditional : *this) {
if (auto gm = conditional->asMixture()) {
if (auto gm = conditional->asHybrid()) {
// If conditional is hybrid, select based on assignment.
gbn.push_back((*gm)(assignment));
} else if (auto gc = conditional->asGaussian()) {
@ -291,7 +291,7 @@ AlgebraicDecisionTree<Key> HybridBayesNet::errorTree(
// Iterate over each conditional.
for (auto &&conditional : *this) {
if (auto gm = conditional->asMixture()) {
if (auto gm = conditional->asHybrid()) {
// If conditional is hybrid, compute error for all assignments.
result = result + gm->errorTree(continuousValues);
@ -321,7 +321,7 @@ AlgebraicDecisionTree<Key> HybridBayesNet::logProbability(
// Iterate over each conditional.
for (auto &&conditional : *this) {
if (auto gm = conditional->asMixture()) {
if (auto gm = conditional->asHybrid()) {
// If conditional is hybrid, select based on assignment and compute
// logProbability.
result = result + gm->logProbability(continuousValues);
@ -369,7 +369,7 @@ HybridGaussianFactorGraph HybridBayesNet::toFactorGraph(
if (conditional->frontalsIn(measurements)) {
if (auto gc = conditional->asGaussian()) {
fg.push_back(gc->likelihood(measurements));
} else if (auto gm = conditional->asMixture()) {
} else if (auto gm = conditional->asHybrid()) {
fg.push_back(gm->likelihood(measurements));
} else {
throw std::runtime_error("Unknown conditional type");

4
gtsam/hybrid/HybridBayesTree.cpp
View File

@ -109,7 +109,7 @@ struct HybridAssignmentData {
GaussianConditional::shared_ptr conditional;
if (hybrid_conditional->isHybrid()) {
conditional = (*hybrid_conditional->asMixture())(parentData.assignment_);
conditional = (*hybrid_conditional->asHybrid())(parentData.assignment_);
} else if (hybrid_conditional->isContinuous()) {
conditional = hybrid_conditional->asGaussian();
} else {
@ -205,7 +205,7 @@ void HybridBayesTree::prune(const size_t maxNrLeaves) {
// If conditional is hybrid, we prune it.
if (conditional->isHybrid()) {
auto hybridGaussianCond = conditional->asMixture();
auto hybridGaussianCond = conditional->asHybrid();
hybridGaussianCond->prune(parentData.prunedDiscreteProbs);
}

10
gtsam/hybrid/HybridConditional.cpp
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@ -97,8 +97,8 @@ void HybridConditional::print(const std::string &s,
bool HybridConditional::equals(const HybridFactor &other, double tol) const {
const This *e = dynamic_cast<const This *>(&other);
if (e == nullptr) return false;
if (auto gm = asMixture()) {
auto other = e->asMixture();
if (auto gm = asHybrid()) {
auto other = e->asHybrid();
return other != nullptr && gm->equals(*other, tol);
}
if (auto gc = asGaussian()) {
@ -119,7 +119,7 @@ double HybridConditional::error(const HybridValues &values) const {
if (auto gc = asGaussian()) {
return gc->error(values.continuous());
}
if (auto gm = asMixture()) {
if (auto gm = asHybrid()) {
return gm->error(values);
}
if (auto dc = asDiscrete()) {
@ -134,7 +134,7 @@ double HybridConditional::logProbability(const HybridValues &values) const {
if (auto gc = asGaussian()) {
return gc->logProbability(values.continuous());
}
if (auto gm = asMixture()) {
if (auto gm = asHybrid()) {
return gm->logProbability(values);
}
if (auto dc = asDiscrete()) {
@ -149,7 +149,7 @@ double HybridConditional::logNormalizationConstant() const {
if (auto gc = asGaussian()) {
return gc->logNormalizationConstant();
}
if (auto gm = asMixture()) {
if (auto gm = asHybrid()) {
return gm->logNormalizationConstant(); // 0.0!
}
if (auto dc = asDiscrete()) {

2
gtsam/hybrid/HybridConditional.h
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@ -151,7 +151,7 @@ class GTSAM_EXPORT HybridConditional
* @return nullptr if not a conditional
* @return HybridGaussianConditional::shared_ptr otherwise
*/
HybridGaussianConditional::shared_ptr asMixture() const {
HybridGaussianConditional::shared_ptr asHybrid() const {
return std::dynamic_pointer_cast<HybridGaussianConditional>(inner_);
}

4
gtsam/hybrid/HybridGaussianFactorGraph.cpp
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@ -115,7 +115,7 @@ void HybridGaussianFactorGraph::printErrors(
} else {
// Is hybrid
auto conditionalComponent =
hc->asMixture()->operator()(values.discrete());
hc->asHybrid()->operator()(values.discrete());
conditionalComponent->print(ss.str(), keyFormatter);
std::cout << "error = " << conditionalComponent->error(values)
<< "\n";
@ -184,7 +184,7 @@ GaussianFactorGraphTree HybridGaussianFactorGraph::assembleGraphTree() const {
} else if (auto gm = dynamic_pointer_cast<HybridGaussianConditional>(f)) {
result = gm->add(result);
} else if (auto hc = dynamic_pointer_cast<HybridConditional>(f)) {
if (auto gm = hc->asMixture()) {
if (auto gm = hc->asHybrid()) {
result = gm->add(result);
} else if (auto g = hc->asGaussian()) {
result = addGaussian(result, g);

2
gtsam/hybrid/HybridSmoother.cpp
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@ -140,7 +140,7 @@ HybridSmoother::addConditionals(const HybridGaussianFactorGraph &originalGraph,
/* ************************************************************************* */
HybridGaussianConditional::shared_ptr HybridSmoother::gaussianMixture(
size_t index) const {
return hybridBayesNet_.at(index)->asMixture();
return hybridBayesNet_.at(index)->asHybrid();
}
/* ************************************************************************* */

2
gtsam/hybrid/hybrid.i
View File

@ -65,7 +65,7 @@ virtual class HybridConditional {
double logProbability(const gtsam::HybridValues& values) const;
double evaluate(const gtsam::HybridValues& values) const;
double operator()(const gtsam::HybridValues& values) const;
gtsam::HybridGaussianConditional* asMixture() const;
gtsam::HybridGaussianConditional* asHybrid() const;
gtsam::GaussianConditional* asGaussian() const;
gtsam::DiscreteConditional* asDiscrete() const;
gtsam::Factor* inner();

14
gtsam/hybrid/tests/testHybridBayesNet.cpp
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@ -144,13 +144,13 @@ TEST(HybridBayesNet, Choose) {
EXPECT_LONGS_EQUAL(4, gbn.size());
EXPECT(assert_equal(*(*hybridBayesNet->at(0)->asMixture())(assignment),
EXPECT(assert_equal(*(*hybridBayesNet->at(0)->asHybrid())(assignment),
*gbn.at(0)));
EXPECT(assert_equal(*(*hybridBayesNet->at(1)->asMixture())(assignment),
EXPECT(assert_equal(*(*hybridBayesNet->at(1)->asHybrid())(assignment),
*gbn.at(1)));
EXPECT(assert_equal(*(*hybridBayesNet->at(2)->asMixture())(assignment),
EXPECT(assert_equal(*(*hybridBayesNet->at(2)->asHybrid())(assignment),
*gbn.at(2)));
EXPECT(assert_equal(*(*hybridBayesNet->at(3)->asMixture())(assignment),
EXPECT(assert_equal(*(*hybridBayesNet->at(3)->asHybrid())(assignment),
*gbn.at(3)));
}
@ -280,9 +280,9 @@ TEST(HybridBayesNet, Pruning) {
const DiscreteValues discrete_values{{M(0), 1}, {M(1), 1}};
const HybridValues hybridValues{delta.continuous(), discrete_values};
double logProbability = 0;
logProbability += posterior->at(0)->asMixture()->logProbability(hybridValues);
logProbability += posterior->at(1)->asMixture()->logProbability(hybridValues);
logProbability += posterior->at(2)->asMixture()->logProbability(hybridValues);
logProbability += posterior->at(0)->asHybrid()->logProbability(hybridValues);
logProbability += posterior->at(1)->asHybrid()->logProbability(hybridValues);
logProbability += posterior->at(2)->asHybrid()->logProbability(hybridValues);
// NOTE(dellaert): the discrete errors were not added in logProbability tree!
logProbability +=
posterior->at(3)->asDiscrete()->logProbability(hybridValues);

2
gtsam/hybrid/tests/testHybridConditional.cpp
View File

@ -44,7 +44,7 @@ TEST(HybridConditional, Invariants) {
CHECK(hc0->isHybrid());
// Check invariants as a HybridGaussianConditional.
const auto conditional = hc0->asMixture();
const auto conditional = hc0->asHybrid();
EXPECT(HybridGaussianConditional::CheckInvariants(*conditional, values));
// Check invariants as a HybridConditional.

14
gtsam/hybrid/tests/testHybridGaussianISAM.cpp
View File

@ -333,13 +333,13 @@ TEST(HybridGaussianElimination, Incremental_approximate) {
// each with 2, 4, 8, and 5 (pruned) leaves respetively.
EXPECT_LONGS_EQUAL(4, incrementalHybrid.size());
EXPECT_LONGS_EQUAL(
2, incrementalHybrid[X(0)]->conditional()->asMixture()->nrComponents());
2, incrementalHybrid[X(0)]->conditional()->asHybrid()->nrComponents());
EXPECT_LONGS_EQUAL(
3, incrementalHybrid[X(1)]->conditional()->asMixture()->nrComponents());
3, incrementalHybrid[X(1)]->conditional()->asHybrid()->nrComponents());
EXPECT_LONGS_EQUAL(
5, incrementalHybrid[X(2)]->conditional()->asMixture()->nrComponents());
5, incrementalHybrid[X(2)]->conditional()->asHybrid()->nrComponents());
EXPECT_LONGS_EQUAL(
5, incrementalHybrid[X(3)]->conditional()->asMixture()->nrComponents());
5, incrementalHybrid[X(3)]->conditional()->asHybrid()->nrComponents());
/***** Run Round 2 *****/
HybridGaussianFactorGraph graph2;
@ -354,9 +354,9 @@ TEST(HybridGaussianElimination, Incremental_approximate) {
// with 5 (pruned) leaves.
CHECK_EQUAL(5, incrementalHybrid.size());
EXPECT_LONGS_EQUAL(
5, incrementalHybrid[X(3)]->conditional()->asMixture()->nrComponents());
5, incrementalHybrid[X(3)]->conditional()->asHybrid()->nrComponents());
EXPECT_LONGS_EQUAL(
5, incrementalHybrid[X(4)]->conditional()->asMixture()->nrComponents());
5, incrementalHybrid[X(4)]->conditional()->asHybrid()->nrComponents());
}
/* ************************************************************************/
@ -548,7 +548,7 @@ TEST(HybridGaussianISAM, NonTrivial) {
// Test if pruning worked correctly by checking that we only have 3 leaves in
// the last node.
auto lastConditional = inc[X(3)]->conditional()->asMixture();
auto lastConditional = inc[X(3)]->conditional()->asHybrid();
EXPECT_LONGS_EQUAL(3, lastConditional->nrComponents());
}

14
gtsam/hybrid/tests/testHybridNonlinearISAM.cpp
View File

@ -358,13 +358,13 @@ TEST(HybridNonlinearISAM, Incremental_approximate) {
// each with 2, 4, 8, and 5 (pruned) leaves respetively.
EXPECT_LONGS_EQUAL(4, bayesTree.size());
EXPECT_LONGS_EQUAL(
2, bayesTree[X(0)]->conditional()->asMixture()->nrComponents());
2, bayesTree[X(0)]->conditional()->asHybrid()->nrComponents());
EXPECT_LONGS_EQUAL(
3, bayesTree[X(1)]->conditional()->asMixture()->nrComponents());
3, bayesTree[X(1)]->conditional()->asHybrid()->nrComponents());
EXPECT_LONGS_EQUAL(
5, bayesTree[X(2)]->conditional()->asMixture()->nrComponents());
5, bayesTree[X(2)]->conditional()->asHybrid()->nrComponents());
EXPECT_LONGS_EQUAL(
5, bayesTree[X(3)]->conditional()->asMixture()->nrComponents());
5, bayesTree[X(3)]->conditional()->asHybrid()->nrComponents());
/***** Run Round 2 *****/
HybridGaussianFactorGraph graph2;
@ -382,9 +382,9 @@ TEST(HybridNonlinearISAM, Incremental_approximate) {
// with 5 (pruned) leaves.
CHECK_EQUAL(5, bayesTree.size());
EXPECT_LONGS_EQUAL(
5, bayesTree[X(3)]->conditional()->asMixture()->nrComponents());
5, bayesTree[X(3)]->conditional()->asHybrid()->nrComponents());
EXPECT_LONGS_EQUAL(
5, bayesTree[X(4)]->conditional()->asMixture()->nrComponents());
5, bayesTree[X(4)]->conditional()->asHybrid()->nrComponents());
}
/* ************************************************************************/
@ -569,7 +569,7 @@ TEST(HybridNonlinearISAM, NonTrivial) {
// Test if pruning worked correctly by checking that
// we only have 3 leaves in the last node.
auto lastConditional = bayesTree[X(3)]->conditional()->asMixture();
auto lastConditional = bayesTree[X(3)]->conditional()->asHybrid();
EXPECT_LONGS_EQUAL(3, lastConditional->nrComponents());
}