def test_no_domain_or_measure_raises_error(input_dim):
"""Test that errors are correctly raised when both domain and a Gaussian measure is
given."""
fun = lambda x: x
nodes = np.linspace(0, 1, 3)
fun_evals = fun(nodes)
with pytest.raises(ValueError):
bayesquad(fun=fun, input_dim=input_dim)
with pytest.raises(ValueError):
bayesquad_from_data(nodes=nodes, fun_evals=fun_evals)
def test_domain_and_gaussian_measure_raises_error(measure, input_dim):
"""Test that errors are correctly raised when both domain and a Gaussian measure is
given."""
domain = (0, 1)
fun = lambda x: x
with pytest.raises(ValueError):
bayesquad(fun=fun, input_dim=input_dim, domain=domain, measure=measure)
nodes = np.linspace(0, 1, 3)
fun_evals = fun(nodes)
with pytest.raises(ValueError):
bayesquad_from_data(nodes=nodes,
fun_evals=fun_evals,
domain=domain,
measure=measure)
def test_type_1d(f1d, kernel, measure, input_dim):
"""Test that BQ outputs normal random variables for 1D integrands."""
integral, _ = bayesquad(fun=f1d,
input_dim=input_dim,
kernel=kernel,
measure=measure,
max_evals=10)
assert isinstance(integral, Normal)
def test_integral_values_1d(f1d, kernel, measure, input_dim):
"""Test numerically that BQ computes 1D integrals correctly."""
# numerical integral
# pylint: disable=invalid-name
def integrand(x):
return f1d(x) * measure(np.atleast_2d(x))
# pylint: disable=invalid-name
bq_integral, _ = bayesquad(fun=f1d,
input_dim=input_dim,
kernel=kernel,
measure=measure,
max_evals=250)
num_integral, _ = quad(integrand, measure.domain[0], measure.domain[1])
np.testing.assert_almost_equal(bq_integral.mean, num_integral, decimal=2)
def test_integral_values_kernel_translate(kernel, measure, input_dim, x):
"""Test numerical integration of kernel translates."""
kernel_embedding = KernelEmbedding(kernel, measure)
# pylint: disable=cell-var-from-loop
for translate_point in x:
fun = lambda y: kernel(translate_point, y)
bq_integral, _ = bayesquad(
fun=fun,
input_dim=input_dim,
kernel=kernel,
measure=measure,
var_tol=1e-8,
max_evals=1000,
batch_size=50,
)
true_integral = kernel_embedding.kernel_mean(
np.atleast_2d(translate_point))
np.testing.assert_almost_equal(bq_integral.mean,
true_integral,
decimal=2)
def test_domain_ignored_if_lebesgue(input_dim, measure):
domain = (0, 1)
fun = lambda x: x
# standard BQ
bq_integral, _ = bayesquad(fun=fun,
input_dim=input_dim,
domain=domain,
measure=measure)
assert isinstance(bq_integral, Normal)
# fixed nodes BQ
nodes = np.linspace(0, 1, 3).reshape((3, 1))
fun_evals = fun(nodes)
bq_integral, _ = bayesquad_from_data(nodes=nodes,
fun_evals=fun_evals,
domain=domain,
measure=measure)
assert isinstance(bq_integral, Normal)