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Added DEBUG_MARGINALS flag

release/4.3a0
Frank Dellaert 2023-01-16 18:36:28 -08:00
parent e31884c9a1
commit 9af7236980
1 changed files with 14 additions and 10 deletions
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24
python/gtsam/tests/test_HybridFactorGraph.py
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@ -22,6 +22,8 @@ from gtsam import (DiscreteConditional, DiscreteKeys, GaussianConditional,
HybridGaussianFactorGraph, HybridValues, JacobianFactor,
Ordering, noiseModel)
DEBUG_MARGINALS = False
class TestHybridGaussianFactorGraph(GtsamTestCase):
"""Unit tests for HybridGaussianFactorGraph."""
@ -201,9 +203,10 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
# Estimate marginals using importance sampling.
marginals = self.estimate_marginals(target=unnormalized_posterior,
proposal_density=proposal_density)
print(f"True mode: {values.atDiscrete(M(0))}")
print(f"P(mode=0; Z) = {marginals[0]}")
print(f"P(mode=1; Z) = {marginals[1]}")
if DEBUG_MARGINALS:
print(f"True mode: {values.atDiscrete(M(0))}")
print(f"P(mode=0; Z) = {marginals[0]}")
print(f"P(mode=1; Z) = {marginals[1]}")
# Check that the estimate is close to the true value.
self.assertAlmostEqual(marginals[0], 0.74, delta=0.01)
@ -232,9 +235,10 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
# Estimate marginals using importance sampling.
marginals = self.estimate_marginals(target=true_posterior,
proposal_density=proposal_density)
print(f"True mode: {values.atDiscrete(M(0))}")
print(f"P(mode=0; z0) = {marginals[0]}")
print(f"P(mode=1; z0) = {marginals[1]}")
if DEBUG_MARGINALS:
print(f"True mode: {values.atDiscrete(M(0))}")
print(f"P(mode=0; z0) = {marginals[0]}")
print(f"P(mode=1; z0) = {marginals[1]}")
# Check that the estimate is close to the true value.
self.assertAlmostEqual(marginals[0], 0.74, delta=0.01)
@ -247,7 +251,6 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
return bayesNet.evaluate(sample) / fg.probPrime(sample) if \
fg.probPrime(sample) > 0 else 0
@unittest.skip
def test_ratio(self):
"""
Given a tiny two variable hybrid model, with 2 measurements, test the
@ -283,9 +286,10 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
# Estimate marginals using importance sampling.
marginals = self.estimate_marginals(target=unnormalized_posterior,
proposal_density=proposal_density)
print(f"True mode: {values.atDiscrete(M(0))}")
print(f"P(mode=0; Z) = {marginals[0]}")
print(f"P(mode=1; Z) = {marginals[1]}")
if DEBUG_MARGINALS:
print(f"True mode: {values.atDiscrete(M(0))}")
print(f"P(mode=0; Z) = {marginals[0]}")
print(f"P(mode=1; Z) = {marginals[1]}")
# Check that the estimate is close to the true value.
self.assertAlmostEqual(marginals[0], 0.23, delta=0.01)