def test_cases(self):
for constrained in self.constrained_cases:
if type(constrained) == list:
cov = np.eye(constrained[0].shape[0])
else:
cov = np.eye(constrained.shape[0])
unconstrained, _ = tools.unconstrain_stationary_multivariate(constrained, cov)
reconstrained, _ = tools.constrain_stationary_multivariate(unconstrained, cov)
assert_allclose(reconstrained, constrained)
for unconstrained in self.unconstrained_cases:
if type(unconstrained) == list:
cov = np.eye(unconstrained[0].shape[0])
else:
cov = np.eye(unconstrained.shape[0])
constrained, _ = tools.constrain_stationary_multivariate(unconstrained, cov)
reunconstrained, _ = tools.unconstrain_stationary_multivariate(constrained, cov)
# Note: low tolerance comes from last example in unconstrained_cases,
# but is not a real problem
assert_allclose(reunconstrained, unconstrained, atol=1e-4)
def test_cases(self):
for constrained in self.constrained_cases:
if type(constrained) == list:
cov = np.eye(constrained[0].shape[0])
else:
cov = np.eye(constrained.shape[0])
unconstrained, _ = tools.unconstrain_stationary_multivariate(constrained, cov) # noqa:E501
reconstrained, _ = tools.constrain_stationary_multivariate(unconstrained, cov) # noqa:E501
assert_allclose(reconstrained, constrained)
for unconstrained in self.unconstrained_cases:
if type(unconstrained) == list:
cov = np.eye(unconstrained[0].shape[0])
else:
cov = np.eye(unconstrained.shape[0])
constrained, _ = tools.constrain_stationary_multivariate(unconstrained, cov) # noqa:E501
reunconstrained, _ = tools.unconstrain_stationary_multivariate(constrained, cov) # noqa:E501
# Note: low tolerance comes from last example in
# unconstrained_cases, but is not a real problem
assert_allclose(reunconstrained, unconstrained, atol=1e-4)
def test_cases(self):
for constrained, error_variance, unconstrained in self.cases:
result = tools.unconstrain_stationary_multivariate(
constrained, error_variance)
assert_allclose(result[0], unconstrained)
def test_cases(self):
for constrained, error_variance, unconstrained in self.cases:
result = tools.unconstrain_stationary_multivariate(
constrained, error_variance)
assert_allclose(result[0], unconstrained)
