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 polynomial, result in self.cases:
assert_equal(tools.companion_matrix(polynomial), result)
def test_cases(self):
for polynomial, result in self.cases:
assert_equal(tools.companion_matrix(polynomial), result)