def test_three_variances(self):
model, options, parameters, data = gf.setup_mcmc()
CP = CovarianceProcedures()
CP._qcov = np.atleast_2d([0.2, 0.3, 0.4])
CP._CovarianceProcedures__setup_R_based_on_variances(parind=[0, 1, 2])
self.assertTrue(np.array_equal(CP._R,
np.diagflat(np.sqrt([0.2, 0.3, 0.4]))),
msg=str('Expect sqrt of variances: {}'.format(CP._R)))
self.assertTrue(np.array_equal(CP._qcovorig,
np.diagflat([0.2, 0.3, 0.4])),
msg='Arrays should match')
def test_3x3_cov_mtx(self):
model, options, parameters, data = gf.setup_mcmc()
CP = CovarianceProcedures()
testmtx = np.atleast_2d([[0.2, 0.01, 0.05], [0.01, 0.3, 0.024],
[0.05, 0.024, 0.04]])
CP._qcov = testmtx
CP._CovarianceProcedures__setup_R_based_on_covariance_matrix(
parind=[0, 1, 2])
self.assertTrue(np.array_equal(CP._R,
np.linalg.cholesky(testmtx).T),
msg='Expect sqrt of variance')
self.assertTrue(np.array_equal(CP._qcovorig, testmtx),
msg='Expect sqrt of variance')
def test_2x2_cov_mtx(self):
model, options, parameters, data = gf.setup_mcmc()
CP = CovarianceProcedures()
CP._qcov = np.atleast_2d([[0.2, 0.1], [0.1, 0.3]])
CP._CovarianceProcedures__setup_R_based_on_covariance_matrix(
parind=[0, 1])
self.assertTrue(np.array_equal(
CP._R,
np.linalg.cholesky(np.atleast_2d([[0.2, 0.1], [0.1, 0.3]])).T),
msg='Expect sqrt of variance')
self.assertTrue(np.array_equal(CP._qcovorig,
np.atleast_2d([[0.2, 0.1], [0.1,
0.3]])),
msg='Expect sqrt of variance')
def test_update_cov_wsum_none(self):
__, options, parameters, data = gf.setup_mcmc()
CP = CovarianceProcedures()
CP._initialize_covariance_settings(parameters=parameters,
options=options)
theta = np.array([2., 5.])
CP._wsum = None
CP._covchain = 1
CP._meanchain = 2
CP._qcov = 0
CP._update_covariance_settings(parameter_set=theta)
CPD = CP.__dict__
self.assertEqual(CPD['_covchain'], 1, msg='_covchain unchanged.')
self.assertEqual(CPD['_meanchain'], 2, msg='_meanchain unchanged.')
def test_update_cov_wsum_not_none(self):
model, options, parameters, data = gf.setup_mcmc()
CP = CovarianceProcedures()
CP._initialize_covariance_settings(parameters=parameters,
options=options)
theta = np.array([2., 5.])
CP._wsum = 10
CP._covchain = 1
CP._meanchain = 2
CP._qcov = 0
CP._update_covariance_settings(parameter_set=theta)
CPD = CP.__dict__
self.assertEqual(CPD['_covchain'], 0, msg='_covchain = _qcov.')
self.assertTrue(np.array_equal(CPD['_meanchain'], theta),
msg='_meanchain = parameter_set.')
def test_one_variance(self):
model, options, parameters, data = gf.setup_mcmc()
CP = CovarianceProcedures()
CP._qcov = np.atleast_2d([0.2])
CP._CovarianceProcedures__setup_R_based_on_variances(parind=[0])
self.assertEqual(CP._R, np.sqrt(0.2), msg='Expect sqrt of variance')