def test_dv_finite_space_model(finite_space_model, one_point_design, true_eig):
pyro.set_rng_seed(42)
pyro.clear_param_store()
donsker_varadhan_eig(
finite_space_model,
one_point_design,
"y",
"theta",
num_samples=100,
num_steps=250,
T=dv_critic,
optim=optim.Adam({"lr": 0.1}),
)
estimated_eig = donsker_varadhan_eig(
finite_space_model,
one_point_design,
"y",
"theta",
num_samples=100,
num_steps=250,
T=dv_critic,
optim=optim.Adam({"lr": 0.01}),
final_num_samples=2000,
)
assert_equal(estimated_eig, true_eig, prec=1e-2)
def test_dv_linear_model(linear_model, one_point_design):
pyro.set_rng_seed(42)
pyro.clear_param_store()
donsker_varadhan_eig(
linear_model,
one_point_design,
"y",
"w",
num_samples=100,
num_steps=500,
T=dv_critic,
optim=optim.Adam({"lr": 0.1}),
)
estimated_eig = donsker_varadhan_eig(
linear_model,
one_point_design,
"y",
"w",
num_samples=100,
num_steps=650,
T=dv_critic,
optim=optim.Adam({"lr": 0.001}),
final_num_samples=2000,
)
expected_eig = linear_model_ground_truth(linear_model, one_point_design, "y", "w")
assert_equal(estimated_eig, expected_eig, prec=5e-2)
