def test_quadratic_misfit_model(self):
num_dims = 10
rank = 3
num_qoi = 3
obs = np.random.normal(0., 1., (num_qoi))
model = QuadraticMisfitModel(num_dims, rank, num_qoi, obs)
sample = np.random.normal(0., 1., (num_dims))
helper_gradient(model.value, model.gradient, sample)
def test_neg_log_posterior(self):
num_dims = 10
rank = 3
num_qoi = 3
obs = np.random.normal(0., 1., (num_qoi))
noise_covariance = np.eye(num_qoi)*0.1
misfit_model = QuadraticMisfitModel(
num_dims, rank, num_qoi, obs, noise_covariance=noise_covariance)
prior_mean = np.ones((num_dims), float)
prior_covariance = np.eye(num_dims)*0.25
prior_density = NormalDensity(prior_mean, covariance=prior_covariance)
objective = LogUnormalizedPosterior(
misfit_model, misfit_model.gradient_set, prior_density.pdf,
prior_density.log_pdf, prior_density.log_pdf_gradient)
sample = np.random.normal(0., 1., (num_dims))
helper_gradient(misfit_model.value, misfit_model.gradient, sample)