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Reject NaN Gaussian hypothesis log weights #2676

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Reject NaN Gaussian hypothesis log weights #2676

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ecc4ff6
Reject NaN Gaussian hypothesis log weights
FlorianPfaff Jun 14, 2026
c3fb4c3
Add regression test for NaN log weights
FlorianPfaff Jun 14, 2026
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2 changes: 2 additions & 0 deletions src/pyrecest/filters/gaussian_hypothesis_mixture.py
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Original file line number Diff line number Diff line change
Expand Up @@ -59,6 +59,8 @@ def normalize_log_weights(log_weights: list[float] | np.ndarray) -> np.ndarray:
values = np.asarray(log_weights, dtype=float).reshape(-1)
if values.size == 0:
raise ValueError("log_weights must not be empty")
if np.any(np.isnan(values)):
raise ValueError("log_weights must not contain NaN values")

positive_infinite = np.isposinf(values)
if np.any(positive_infinite):
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16 changes: 16 additions & 0 deletions tests/filters/test_gaussian_hypothesis_mixture.py
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Expand Up @@ -24,6 +24,22 @@ def test_multiple_positive_infinite_log_weights_share_mass(self):

self.assertTrue(np.allclose(weights, np.array([0.5, 0.0, 0.5])))

def test_nan_log_weights_are_rejected(self):
with self.assertRaisesRegex(ValueError, "NaN"):
normalize_log_weights(np.array([0.0, np.nan]))

with self.assertRaisesRegex(ValueError, "NaN"):
moment_match_gaussian_hypotheses(
[
WeightedGaussianHypothesis(
np.array([0.0]), np.array([[1.0]]), log_weight=0.0
),
WeightedGaussianHypothesis(
np.array([1.0]), np.array([[1.0]]), log_weight=np.nan
),
]
)

def test_moment_matching_respects_dominant_infinite_weight(self):
mean, covariance, weights = moment_match_gaussian_hypotheses(
[
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