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update HybridGaussianConditional to use single discrete parent

release/4.3a0
Varun Agrawal 2024-09-18 15:08:09 -04:00
parent c1ebdb200c
commit fa353840b3
7 changed files with 19 additions and 29 deletions
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6
gtsam/hybrid/HybridGaussianConditional.cpp
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@ -58,11 +58,11 @@ HybridGaussianConditional::conditionals() const {
/* *******************************************************************************/
HybridGaussianConditional::HybridGaussianConditional(
const KeyVector &continuousFrontals, const KeyVector &continuousParents,
const DiscreteKeys &discreteParents,
const DiscreteKey &discreteParent,
const std::vector<GaussianConditional::shared_ptr> &conditionals)
: HybridGaussianConditional(continuousFrontals, continuousParents,
discreteParents,
Conditionals(discreteParents, conditionals)) {}
DiscreteKeys{discreteParent},
Conditionals({discreteParent}, conditionals)) {}
/* *******************************************************************************/
// TODO(dellaert): This is copy/paste: HybridGaussianConditional should be

7
gtsam/hybrid/HybridGaussianConditional.h
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@ -112,12 +112,13 @@ class GTSAM_EXPORT HybridGaussianConditional
*
* @param continuousFrontals The continuous frontal variables
* @param continuousParents The continuous parent variables
* @param discreteParents Discrete parents variables
* @param conditionals Vector of conditionals
* @param discreteParent Single discrete parent variable
* @param conditionals Vector of conditionals with the same size as the
* cardinality of the discrete parent.
*/
HybridGaussianConditional(
const KeyVector &continuousFrontals, const KeyVector &continuousParents,
const DiscreteKeys &discreteParents,
const DiscreteKey &discreteParent,
const std::vector<GaussianConditional::shared_ptr> &conditionals);
/// @}

2
gtsam/hybrid/hybrid.i
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@ -95,7 +95,7 @@ class HybridGaussianConditional : gtsam::HybridFactor {
HybridGaussianConditional(
const gtsam::KeyVector& continuousFrontals,
const gtsam::KeyVector& continuousParents,
const gtsam::DiscreteKeys& discreteParents,
const gtsam::DiscreteKey& discreteParent,
const std::vector<gtsam::GaussianConditional::shared_ptr>& conditionals);
gtsam::HybridGaussianFactor* likelihood(

4
gtsam/hybrid/tests/TinyHybridExample.h
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@ -43,13 +43,11 @@ inline HybridBayesNet createHybridBayesNet(size_t num_measurements = 1,
// Create Gaussian mixture z_i = x0 + noise for each measurement.
for (size_t i = 0; i < num_measurements; i++) {
const auto mode_i = manyModes ? DiscreteKey{M(i), 2} : mode;
DiscreteKeys modes{mode_i};
std::vector<GaussianConditional::shared_ptr> conditionals{
GaussianConditional::sharedMeanAndStddev(Z(i), I_1x1, X(0), Z_1x1, 0.5),
GaussianConditional::sharedMeanAndStddev(Z(i), I_1x1, X(0), Z_1x1, 3)};
bayesNet.emplace_shared<HybridGaussianConditional>(
KeyVector{Z(i)}, KeyVector{X(0)}, DiscreteKeys{mode_i},
HybridGaussianConditional::Conditionals(modes, conditionals));
KeyVector{Z(i)}, KeyVector{X(0)}, mode_i, conditionals);
}
// Create prior on X(0).

21
gtsam/hybrid/tests/testHybridBayesNet.cpp
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@ -107,11 +107,9 @@ TEST(HybridBayesNet, evaluateHybrid) {
// Create hybrid Bayes net.
HybridBayesNet bayesNet;
bayesNet.push_back(continuousConditional);
DiscreteKeys discreteParents{Asia};
bayesNet.emplace_shared<HybridGaussianConditional>(
KeyVector{X(1)}, KeyVector{}, discreteParents,
HybridGaussianConditional::Conditionals(
discreteParents, std::vector{conditional0, conditional1}));
KeyVector{X(1)}, KeyVector{}, Asia,
std::vector{conditional0, conditional1});
bayesNet.emplace_shared<DiscreteConditional>(Asia, "99/1");
// Create values at which to evaluate.
@ -170,11 +168,9 @@ TEST(HybridBayesNet, Error) {
conditional1 = std::make_shared<GaussianConditional>(
X(1), Vector1::Constant(2), I_1x1, model1);
DiscreteKeys discreteParents{Asia};
auto gm = std::make_shared<HybridGaussianConditional>(
KeyVector{X(1)}, KeyVector{}, discreteParents,
HybridGaussianConditional::Conditionals(
discreteParents, std::vector{conditional0, conditional1}));
KeyVector{X(1)}, KeyVector{}, Asia,
std::vector{conditional0, conditional1});
// Create hybrid Bayes net.
HybridBayesNet bayesNet;
bayesNet.push_back(continuousConditional);
@ -393,11 +389,10 @@ TEST(HybridBayesNet, Sampling) {
std::make_shared<BetweenFactor<double>>(X(0), X(1), 0, noise_model);
auto one_motion =
std::make_shared<BetweenFactor<double>>(X(0), X(1), 1, noise_model);
DiscreteKeys discreteKeys{DiscreteKey(M(0), 2)};
HybridNonlinearFactor::Factors factors(
discreteKeys, {{zero_motion, 0.0}, {one_motion, 0.0}});
nfg.emplace_shared<HybridNonlinearFactor>(KeyVector{X(0), X(1)}, discreteKeys,
factors);
nfg.emplace_shared<HybridNonlinearFactor>(
KeyVector{X(0), X(1)}, DiscreteKey(M(0), 2),
std::vector<NonlinearFactorValuePair>{{zero_motion, 0.0},
{one_motion, 0.0}});
DiscreteKey mode(M(0), 2);
nfg.emplace_shared<DiscreteDistribution>(mode, "1/1");

4
python/gtsam/tests/test_HybridBayesNet.py
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@ -43,14 +43,12 @@ class TestHybridBayesNet(GtsamTestCase):
# Create the conditionals
conditional0 = GaussianConditional(X(1), [5], I_1x1, model0)
conditional1 = GaussianConditional(X(1), [2], I_1x1, model1)
discrete_keys = DiscreteKeys()
discrete_keys.push_back(Asia)
# Create hybrid Bayes net.
bayesNet = HybridBayesNet()
bayesNet.push_back(conditional)
bayesNet.push_back(
HybridGaussianConditional([X(1)], [], discrete_keys,
HybridGaussianConditional([X(1)], [], Asia,
[conditional0, conditional1]))
bayesNet.push_back(DiscreteConditional(Asia, "99/1"))

4
python/gtsam/tests/test_HybridFactorGraph.py
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@ -101,10 +101,8 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
I_1x1,
X(0), [0],
sigma=3)
discreteParents = DiscreteKeys()
discreteParents.push_back(mode)
bayesNet.push_back(
HybridGaussianConditional([Z(i)], [X(0)], discreteParents,
HybridGaussianConditional([Z(i)], [X(0)], mode,
[conditional0, conditional1]))
# Create prior on X(0).