def test_update_cov(self):
__, options, parameters, __ = gf.setup_mcmc()
CP = CovarianceProcedures()
CP._initialize_covariance_settings(parameters=parameters,
options=options)
check = {
'R': np.random.random_sample(size=(2, 2)),
'covchain': np.random.random_sample(size=(2, 2)),
'meanchain': np.random.random_sample(size=(1, 2)),
'wsum': np.random.random_sample(size=(2, 1)),
'last_index_since_adaptation': 0,
'iiadapt': 100
}
CP._update_covariance_from_adaptation(**check)
CPD = CP.__dict__
items = ['last_index_since_adaptation', 'iiadapt']
for __, ai in enumerate(items):
self.assertEqual(CPD[str('_{}'.format(ai))],
check[ai],
msg=str('{}: {} != {}'.format(
ai, CPD[str('_{}'.format(ai))], check[ai])))
array_items = ['R', 'covchain', 'meanchain', 'wsum']
for __, ai in enumerate(array_items):
self.assertTrue(np.array_equal(CPD[str('_{}'.format(ai))],
check[ai]),
msg=str('{}: {} != {}'.format(
ai, CPD[str('_{}'.format(ai))], check[ai])))
def test_init_CP_original_cov(self):
CP = CovarianceProcedures()
__, options, parameters, __ = gf.setup_mcmc()
CP = CovarianceProcedures()
CP._initialize_covariance_settings(parameters=parameters,
options=options)
self.assertTrue(hasattr(CP, '_qcov_original'),
msg='Has assigned original covariance matrix')
original_covariance = CP._qcov_original.copy()
check = {
'R': np.random.random_sample(size=(2, 2)),
'covchain': np.random.random_sample(size=(2, 2)),
'meanchain': np.random.random_sample(size=(1, 2)),
'wsum': np.random.random_sample(size=(2, 1)),
'last_index_since_adaptation': 0,
'iiadapt': 100
}
CP._update_covariance_from_adaptation(**check)
self.assertTrue(
np.array_equal(CP._qcov_original, original_covariance),
msg='Expect original cov. mtx. unchanged after update.')