def test_wrong_number_of_noises(self):
def me(prm):
return np.array(prm - 10)
# That should work:
correct_noise = bayem.Gamma(1, 2)
info = bayem.vba(me, bayem.MVN(7, 12), correct_noise)
self.assertTrue(info.success)
# Providing a wrong dimension in the noise pattern should fail.
wrong_noise = bayem.Gamma((1, 1), (2, 2))
with self.assertRaises(Exception):
bayem.vba(me, bayem.MVN(7, 12), wrong_noise)
def test_given_mu(self):
"""
We infer the gamma distribution of the noise precision for a
given, fixed parameter mu. This is done by setting a prior with
a very high precision.
For a super noninformative noise prior (shape=0, scale->inf), the
analytic solution for the INVERSE gamma distribution for the noise
VARIANCE reads
"""
a = n / 2
b = np.sum((data - prior_mean) ** 2) / 2
# The parameters for the corresponding gamma distribution for the
# PRECISION then read (a, 1/b)
big_but_not_nan = 1e20
prior = bayem.MVN(prior_mean, big_but_not_nan)
gamma = bayem.Gamma(shape=1.0e-20, scale=big_but_not_nan)
result = bayem.vba(model_error, prior, gamma, update_noise=True)
self.assertTrue(result.success)
gamma = result.noise
self.assertAlmostEqual(gamma.shape, a)
self.assertAlmostEqual(gamma.scale, 1 / b)
def test_custom_objects():
data = {}
data["parameter_prior"] = bayem.MVN(
mean=np.r_[1, 2, 3],
precision=np.diag([1, 2, 3]),
parameter_names=["A", "B", "C"],
)
data["noise_prior"] = bayem.Gamma()
data["non bayes thing"] = {"best number": 6174.0}
string = bayem.save_json(data)
loaded = bayem.load_json(string)
A, B = data["parameter_prior"], loaded["parameter_prior"]
CHECK = np.testing.assert_array_equal
assert A.name == B.name
assert A.parameter_names == B.parameter_names
CHECK(A.mean, B.mean)
CHECK(A.precision, B.precision)
C, D = data["noise_prior"], loaded["noise_prior"]
assert C.name == D.name
assert C.shape == D.shape
assert C.scale == D.scale
def test_inconsistent_noise(self):
def dict_model_error(numbers):
return {"A": np.ones(5), "B": np.zeros(5)}
param0 = bayem.MVN()
noise0 = {"A": bayem.Gamma(1, 1), "B": bayem.Gamma(2, 2)}
# You may provide a single update_noise flag
result = bayem.vba(dict_model_error, param0, noise0, update_noise=False)
self.assert_gamma_equal(result.noise["A"], noise0["A"])
self.assert_gamma_equal(result.noise["B"], noise0["B"])
# Alternatively, you can provide a dict containing _all_ noise keys
result = bayem.vba(
dict_model_error, param0, noise0, update_noise={"A": True, "B": False}
)
self.assert_not_gamma_equal(result.noise["A"], noise0["A"])
self.assert_gamma_equal(result.noise["B"], noise0["B"])
# There will be an error, if you forget one
with self.assertRaises(KeyError):
bayem.vba(dict_model_error, param0, noise0, update_noise={"A": True})
test_img = imread(test_img_name)
ref_img = imread(ref_img_name)
assert np.linalg.norm(test_img - ref_img) == pytest.approx(0)
np.random.seed(6174)
t = np.linspace(1, 2, 10)
noise = np.random.normal(0, 0.42, len(t))
def f(x):
return t * x[0]**2 - t * 9 + noise
info = bayem.vba(f, x0=bayem.MVN([2], [0.5]), noise0=bayem.Gamma(1, 2))
def test_pair_plot(generate_ref_img=False):
visu.pair_plot(info, show=False)
compare_plt("ref_pair_plot.png", generate_ref_img=generate_ref_img)
def test_trace_plot(generate_ref_img=False):
visu.result_trace(info, show=False)
compare_plt("ref_trace_plot.png", generate_ref_img=generate_ref_img)
if __name__ == "__main__":
generate = True
test_pair_plot(generate_ref_img=generate)
def test_print():
print(bayem.Gamma(42, 2))
def test_from_mean_and_sd():
gamma_ref = bayem.Gamma(6174, 42)
gamma = bayem.Gamma.FromMeanStd(gamma_ref.mean, gamma_ref.std)
assert gamma == gamma_ref
def test_sd():
scale, shape = 42, 6174
gamma = bayem.Gamma(shape=shape, scale=scale)
assert gamma.mean == pytest.approx(shape * scale)
assert gamma.std**2 == pytest.approx(shape * scale**2) # variance