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Fix python wrapper

release/4.3a0
Frank Dellaert 2023-01-05 12:58:29 -08:00
parent 8b8cde4230
commit d49bcce780
3 changed files with 18 additions and 38 deletions
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26
gtsam/hybrid/hybrid.i
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@ -135,29 +135,9 @@ class HybridBayesTree {
#include <gtsam/hybrid/HybridBayesNet.h>
class HybridBayesNet {
HybridBayesNet();
void add(const gtsam::HybridConditional& s);
void addMixture(const gtsam::GaussianMixture* s);
void addGaussian(const gtsam::GaussianConditional* s);
void addDiscrete(const gtsam::DiscreteConditional* s);
void emplaceMixture(const gtsam::GaussianMixture& s);
void emplaceMixture(const gtsam::KeyVector& continuousFrontals,
const gtsam::KeyVector& continuousParents,
const gtsam::DiscreteKeys& discreteParents,
const std::vector<gtsam::GaussianConditional::shared_ptr>&
conditionalsList);
void emplaceGaussian(const gtsam::GaussianConditional& s);
void emplaceDiscrete(const gtsam::DiscreteConditional& s);
void emplaceDiscrete(const gtsam::DiscreteKey& key, string spec);
void emplaceDiscrete(const gtsam::DiscreteKey& key,
const gtsam::DiscreteKeys& parents, string spec);
void emplaceDiscrete(const gtsam::DiscreteKey& key,
const std::vector<gtsam::DiscreteKey>& parents,
string spec);
gtsam::GaussianMixture* atMixture(size_t i) const;
gtsam::GaussianConditional* atGaussian(size_t i) const;
gtsam::DiscreteConditional* atDiscrete(size_t i) const;
void push_back(const gtsam::GaussianMixture* s);
void push_back(const gtsam::GaussianConditional* s);
void push_back(const gtsam::DiscreteConditional* s);
bool empty() const;
size_t size() const;

16
python/gtsam/tests/test_HybridBayesNet.py
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@ -16,13 +16,13 @@ import numpy as np
from gtsam.symbol_shorthand import A, X
from gtsam.utils.test_case import GtsamTestCase
import gtsam
from gtsam import (DiscreteKeys, GaussianConditional, GaussianMixture,
from gtsam import (DiscreteKeys, GaussianMixture, DiscreteConditional, GaussianConditional, GaussianMixture,
HybridBayesNet, HybridValues, noiseModel)
class TestHybridBayesNet(GtsamTestCase):
"""Unit tests for HybridValues."""
def test_evaluate(self):
"""Test evaluate for a hybrid Bayes net P(X0|X1) P(X1|Asia) P(Asia)."""
asiaKey = A(0)
@ -40,15 +40,15 @@ class TestHybridBayesNet(GtsamTestCase):
# Create the conditionals
conditional0 = GaussianConditional(X(1), [5], I_1x1, model0)
conditional1 = GaussianConditional(X(1), [2], I_1x1, model1)
dkeys = DiscreteKeys()
dkeys.push_back(Asia)
gm = GaussianMixture([X(1)], [], dkeys, [conditional0, conditional1])
discrete_keys = DiscreteKeys()
discrete_keys.push_back(Asia)
# Create hybrid Bayes net.
bayesNet = HybridBayesNet()
bayesNet.addGaussian(gc)
bayesNet.addMixture(gm)
bayesNet.emplaceDiscrete(Asia, "99/1")
bayesNet.push_back(gc)
bayesNet.push_back(GaussianMixture(
[X(1)], [], discrete_keys, [conditional0, conditional1]))
bayesNet.push_back(DiscreteConditional(Asia, "99/1"))
# Create values at which to evaluate.
values = HybridValues()

14
python/gtsam/tests/test_HybridFactorGraph.py
View File

@ -108,16 +108,16 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
I_1x1,
X(0), [0],
sigma=3)
bayesNet.emplaceMixture([Z(i)], [X(0)], keys,
[conditional0, conditional1])
bayesNet.push_back(GaussianMixture([Z(i)], [X(0)], keys,
[conditional0, conditional1]))
# Create prior on X(0).
prior_on_x0 = GaussianConditional.FromMeanAndStddev(
X(0), [prior_mean], prior_sigma)
bayesNet.addGaussian(prior_on_x0)
bayesNet.push_back(prior_on_x0)
# Add prior on mode.
bayesNet.emplaceDiscrete(mode, "4/6")
bayesNet.push_back(DiscreteConditional(mode, "4/6"))
return bayesNet
@ -163,11 +163,11 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
fg = HybridGaussianFactorGraph()
num_measurements = bayesNet.size() - 2
for i in range(num_measurements):
conditional = bayesNet.atMixture(i)
conditional = bayesNet.at(i).asMixture()
factor = conditional.likelihood(cls.measurements(sample, [i]))
fg.push_back(factor)
fg.push_back(bayesNet.atGaussian(num_measurements))
fg.push_back(bayesNet.atDiscrete(num_measurements+1))
fg.push_back(bayesNet.at(num_measurements).asGaussian())
fg.push_back(bayesNet.at(num_measurements+1).asDiscrete())
return fg
@classmethod