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)