def test_consistency(oterm, mean, data):
x, diag, y, t = data
# Setup the original GP
original_gp = original_celerite.GP(oterm, mean=mean)
original_gp.compute(x, np.sqrt(diag))
# Setup the new GP
term = terms.OriginalCeleriteTerm(oterm)
gp = celerite2.GaussianProcess(term, mean=mean)
gp.compute(x, diag=diag)
# "log_likelihood" method
assert np.allclose(original_gp.log_likelihood(y), gp.log_likelihood(y))
# "predict" method
for args in [
dict(return_cov=False, return_var=False),
dict(return_cov=False, return_var=True),
dict(return_cov=True, return_var=False),
]:
assert all(
np.allclose(a, b) for a, b in zip(
original_gp.predict(y, **args),
gp.predict(y, **args),
))
assert all(
np.allclose(a, b) for a, b in zip(
original_gp.predict(y, t=t, **args),
gp.predict(y, t=t, **args),
))
# "sample" method
seed = 5938
np.random.seed(seed)
a = original_gp.sample()
np.random.seed(seed)
b = gp.sample()
assert np.allclose(a, b)
np.random.seed(seed)
a = original_gp.sample(size=10)
np.random.seed(seed)
b = gp.sample(size=10)
assert np.allclose(a, b)
# "sample_conditional" method, numerics make this one a little unstable;
# just check the shape
a = original_gp.sample_conditional(y, t=t)
b = gp.sample_conditional(y, t=t)
assert a.shape == b.shape
a = original_gp.sample_conditional(y, size=10)
b = gp.sample_conditional(y, size=10)
assert a.shape == b.shape
def test_consistency(oterm, mean, data):
x, diag, y, t = data
# Setup the original GP
original_gp = original_celerite.GP(oterm, mean=mean)
original_gp.compute(x, np.sqrt(diag))
# Setup the new GP
term = terms.OriginalCeleriteTerm(oterm)
gp = celerite2.GaussianProcess(term, mean=mean)
gp.compute(x, diag=diag)
# "log_likelihood" method
assert np.allclose(original_gp.log_likelihood(y), gp.log_likelihood(y))
# Apply inverse
assert np.allclose(
np.squeeze(original_gp.apply_inverse(y)), gp.apply_inverse(y)
)
conditional_t = gp.condition(y, t=t)
mu, cov = original_gp.predict(y, t=t, return_cov=True)
assert np.allclose(conditional_t.mean, mu)
assert np.allclose(conditional_t.variance, np.diag(cov))
assert np.allclose(conditional_t.covariance, cov)
conditional = gp.condition(y)
mu, cov = original_gp.predict(y, return_cov=True)
assert np.allclose(conditional.mean, mu)
assert np.allclose(conditional.variance, np.diag(cov))
assert np.allclose(conditional.covariance, cov)
# "sample" method
seed = 5938
np.random.seed(seed)
a = original_gp.sample()
np.random.seed(seed)
b = gp.sample()
assert np.allclose(a, b)
np.random.seed(seed)
a = original_gp.sample(size=10)
np.random.seed(seed)
b = gp.sample(size=10)
assert np.allclose(a, b)
# "sample_conditional" method, numerics make this one a little unstable;
# just check the shape
a = original_gp.sample_conditional(y, t=t)
b = conditional_t.sample()
assert a.shape == b.shape
a = original_gp.sample_conditional(y, size=10)
b = conditional.sample(size=10)
assert a.shape == b.shape
def test_consistency(oterm):
# Check that the coefficients are all correct
term = _convert_kernel(oterm)
for v1, v2 in zip(oterm.get_all_coefficients(), term.get_coefficients()):
assert np.allclose(v1, v2)
for v1, v2 in zip(
terms.OriginalCeleriteTerm(oterm).get_coefficients(),
term.get_coefficients(),
):
assert np.allclose(v1, v2)
# Make sure that the covariance matrix is right
np.random.seed(40582)
x = np.sort(np.random.uniform(0, 10, 50))
diag = np.random.uniform(0.1, 0.3, len(x))
assert np.allclose(oterm.get_value(x), term.get_value(x))
tau = x[:, None] - x[None, :]
K = term.get_value(tau)
assert np.allclose(oterm.get_value(tau), K)
# And the power spectrum
omega = np.linspace(-10, 10, 500)
assert np.allclose(oterm.get_psd(omega), term.get_psd(omega))
# Add in the diagonal
K[np.diag_indices_from(K)] += diag
# Matrix vector multiply
y = np.sin(x)
value = term.dot(x, diag, y)
assert np.allclose(y, np.sin(x))
assert np.allclose(value, np.dot(K, y))
# Matrix-matrix multiply
y = np.vstack([x]).T
value = term.dot(x, diag, y)
assert np.allclose(value, np.dot(K, y))