def test_cases(self):
# Test against known results
for unconstrained, error_variance, constrained in self.cases:
result = tools.constrain_stationary_multivariate(
unconstrained, error_variance)
assert_allclose(result[0], constrained)
# Test that the constrained results correspond to companion matrices
# with eigenvalues less than 1 in modulus
for unconstrained in self.eigval_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)
companion = tools.companion_matrix(
[1] + [-constrained[i] for i in range(len(constrained))]
).T
assert_equal(np.max(np.abs(np.linalg.eigvals(companion))) < 1, True)
def test_cases(self):
# Test against known results
for unconstrained, error_variance, constrained in self.cases:
result = tools.constrain_stationary_multivariate(
unconstrained, error_variance)
assert_allclose(result[0], constrained)
# Test that the constrained results correspond to companion matrices
# with eigenvalues less than 1 in modulus
for unconstrained in self.eigval_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)
companion = tools.companion_matrix(
[1] + [-constrained[i] for i in range(len(constrained))]
).T
assert_equal(np.max(np.abs(np.linalg.eigvals(companion))) < 1, True)
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)