def test_replace_shared_variables():
x = aesara.shared(5, name="shared_x")
new_x = replace_shared_variables([x])
shared_variables = [var for var in graph_inputs(new_x) if isinstance(var, SharedVariable)]
assert not shared_variables
x.default_update = x + 1
with pytest.raises(ValueError, match="shared variables with default_update"):
replace_shared_variables([x])
def test_missing_symmetric():
"""Check that logpt works when partially observed variable have equal observed and
unobserved dimensions.
This would fail in a previous implementation because the two variables would be
equivalent and one of them would be discarded during MergeOptimization while
buling the logpt graph
"""
with Model() as m:
x = Gamma("x", alpha=3, beta=10, observed=np.array([1, np.nan]))
x_obs_rv = m["x_observed"]
x_obs_vv = m.rvs_to_values[x_obs_rv]
x_unobs_rv = m["x_missing"]
x_unobs_vv = m.rvs_to_values[x_unobs_rv]
logp = logpt([x_obs_rv, x_unobs_rv], {x_obs_rv: x_obs_vv, x_unobs_rv: x_unobs_vv})
logp_inputs = list(graph_inputs([logp]))
assert x_obs_vv in logp_inputs
assert x_unobs_vv in logp_inputs