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Check marginals in addition to ratios for non-uniform mode prior

release/4.3a0
Frank Dellaert 2022-12-30 15:20:10 -05:00
parent b798f3ebb5
commit cec26d16ea
1 changed files with 25 additions and 15 deletions
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40
python/gtsam/tests/test_HybridFactorGraph.py
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@ -114,7 +114,7 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
bayesNet.addGaussian(prior_on_x0) bayesNet.addGaussian(prior_on_x0)
# Add prior on mode. # Add prior on mode.
bayesNet.emplaceDiscrete(mode, "6/4") bayesNet.emplaceDiscrete(mode, "4/6")
return bayesNet return bayesNet
@ -136,15 +136,8 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
fg.push_back(bayesNet.atDiscrete(num_measurements+1)) fg.push_back(bayesNet.atDiscrete(num_measurements+1))
return fg return fg
@staticmethod
def calculate_ratio(bayesNet: HybridBayesNet,
fg: HybridGaussianFactorGraph,
sample: HybridValues):
"""Calculate ratio between Bayes net probability and the factor graph."""
return bayesNet.evaluate(sample) / fg.probPrime(sample) if fg.probPrime(sample) > 0 else 0
@classmethod @classmethod
def estimate_marginals(cls, bayesNet: HybridBayesNet, sample: HybridValues, N=1000): def estimate_marginals(cls, bayesNet: HybridBayesNet, sample: HybridValues, N=10000):
"""Do importance sampling to get an estimate of the discrete marginal P(mode).""" """Do importance sampling to get an estimate of the discrete marginal P(mode)."""
# Use prior on x0, mode as proposal density. # Use prior on x0, mode as proposal density.
prior = cls.tiny(num_measurements=0) # just P(x0)P(mode) prior = cls.tiny(num_measurements=0) # just P(x0)P(mode)
@ -174,13 +167,24 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
# Estimate marginals using importance sampling. # Estimate marginals using importance sampling.
marginals = self.estimate_marginals(bayesNet, sample) marginals = self.estimate_marginals(bayesNet, sample)
print(f"True mode: {sample.atDiscrete(M(0))}") # print(f"True mode: {sample.atDiscrete(M(0))}")
print(f"P(mode=0; z0) = {marginals[0]}") # print(f"P(mode=0; z0) = {marginals[0]}")
print(f"P(mode=1; z0) = {marginals[1]}") # print(f"P(mode=1; z0) = {marginals[1]}")
# Check that the estimate is close to the true value.
self.assertAlmostEqual(marginals[0], 0.4, delta=0.1)
self.assertAlmostEqual(marginals[1], 0.6, delta=0.1)
fg = self.factor_graph_from_bayes_net(bayesNet, sample) fg = self.factor_graph_from_bayes_net(bayesNet, sample)
self.assertEqual(fg.size(), 3) self.assertEqual(fg.size(), 3)
@staticmethod
def calculate_ratio(bayesNet: HybridBayesNet,
fg: HybridGaussianFactorGraph,
sample: HybridValues):
"""Calculate ratio between Bayes net probability and the factor graph."""
return bayesNet.evaluate(sample) / fg.probPrime(sample) if fg.probPrime(sample) > 0 else 0
def test_ratio(self): def test_ratio(self):
""" """
Given a tiny two variable hybrid model, with 2 measurements, Given a tiny two variable hybrid model, with 2 measurements,
@ -196,9 +200,15 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
# Estimate marginals using importance sampling. # Estimate marginals using importance sampling.
marginals = self.estimate_marginals(bayesNet, sample) marginals = self.estimate_marginals(bayesNet, sample)
print(f"True mode: {sample.atDiscrete(M(0))}") # print(f"True mode: {sample.atDiscrete(M(0))}")
print(f"P(mode=0; z0, z1) = {marginals[0]}") # print(f"P(mode=0; z0, z1) = {marginals[0]}")
print(f"P(mode=1; z0, z1) = {marginals[1]}") # print(f"P(mode=1; z0, z1) = {marginals[1]}")
# Check marginals based on sampled mode.
if sample.atDiscrete(M(0)) == 0:
self.assertGreater(marginals[0], marginals[1])
else:
self.assertGreater(marginals[1], marginals[0])
fg = self.factor_graph_from_bayes_net(bayesNet, sample) fg = self.factor_graph_from_bayes_net(bayesNet, sample)
self.assertEqual(fg.size(), 4) self.assertEqual(fg.size(), 4)