make inference tests have no timeout
@@ -13,7 +13,7 @@ import numpy as np
from dit.inference.knn_estimators import differential_entropy_knn, total_correlation_ksg
-@settings(deadline=None, max_examples=25)
+@settings(deadline=None, timeout=unlimited, max_examples=25)
@given(mean=floats(min_value=-2.0, max_value=2.0),
std=floats(min_value=0.1, max_value=2.0))
def test_entropy_knn(mean, std):
@@ -26,7 +26,7 @@ def test_entropy_knn(mean, std):
assert h == pytest.approx(np.log2(2*np.pi*np.e*std**2)/2, abs=5e-2)
-@settings(deadline=None, max_examples=25)
+@settings(deadline=None, timeout=unlimited, max_examples=25)
@given(means=lists(floats(min_value=-2.0, max_value=2.0), min_size=2, max_size=2),
stds=lists(floats(min_value=0.1, max_value=2.0), min_size=2, max_size=2),
rho=floats(min_value=-0.9, max_value=0.9))
@@ -41,7 +41,7 @@ def test_mi_knn(means, stds, rho):
assert mi == pytest.approx(-np.log2(1-rho**2)/2, abs=5e-2)
-@settings(deadline=None, max_examples=25)
+@settings(deadline=None, timeout=unlimited, max_examples=25)
@given(means=lists(floats(min_value=-2.0, max_value=2.0), min_size=3, max_size=3),
stds=lists(floats(min_value=0.1, max_value=2.0), min_size=3, max_size=3),
rho=floats(min_value=-0.9, max_value=0.9))
