def __init__(self, V, chol, randobs):
'''
Parameters:
V - FEniCS FunctionSpace
chol - Covariance matrix to define Gaussian field over V
'''
self._V = V
self._solver = Fin(self._V, randobs)
self._pred_k = dl.Function(self._V)
# Setup synthetic observations
self.k_true = dl.Function(self._V)
# Random Gaussian field as true function
# norm = np.random.randn(len(chol))
# nodal_vals = np.exp(0.5 * chol.T @ norm)
nodal_vals = np.load('res_x.npy')
self.k_true.vector().set_local(nodal_vals)
w, y, A, B, C = self._solver.forward(self.k_true)
self.obs_data = self._solver.qoi_operator(w)
# Setup DL error model
# self._err_model = load_parametric_model_avg('elu', Adam,
#0.0003, 5, 58, 200, 2000, V.dim())
self._err_model = load_bn_model(randobs)
# Initialize reduced order model
self.phi = np.loadtxt('../data/basis_nine_param.txt', delimiter=",")
self._solver_r = AffineROMFin(self._V, self._err_model, self.phi,
randobs)
self._solver_r.set_data(self.obs_data)
# ROMML imports
from fom.forward_solve_exp import Fin
from fom.thermal_fin import get_space
from rom.averaged_affine_ROM import AffineROMFin
from deep_learning.dl_model import load_parametric_model_avg, load_bn_model
from gaussian_field import make_cov_chol
randobs = True
resolution = 40
V = get_space(resolution)
chol = make_cov_chol(V, length=1.2)
# Setup DL error model
# err_model = load_parametric_model_avg('elu', Adam, 0.0003, 5, 58, 200, 2000, V.dim())
err_model = load_bn_model()
# Initialize reduced order model
phi = np.loadtxt('../data/basis_nine_param.txt', delimiter=",")
solver_r = AffineROMFin(V, err_model, phi, randobs)
# Setup synthetic observations
solver = Fin(V, randobs)
z_true = dl.Function(V)
#Generate random Gaussian field
# norm = np.random.randn(len(chol))
# nodal_vals = np.exp(0.5 * chol.T @ norm)
#Load random Gaussian field
nodal_vals = np.load('res_x.npy')
from fom.forward_solve import Fin
from fom.thermal_fin import get_space
from rom.averaged_affine_ROM import AffineROMFin
from deep_learning.dl_model import load_bn_model, load_surrogate_model
from bayesian_inference.gaussian_field import make_cov_chol
# randobs = True
randobs = True
resolution = 40
V = get_space(resolution)
chol = make_cov_chol(V, length=1.6)
# Setup DL error model
# err_model = load_parametric_model_avg('elu', Adam, 0.0003, 5, 58, 200, 2000, V.dim())
err_model = load_bn_model(randobs)
surrogate_model = load_surrogate_model(randobs)
# Initialize reduced order model
phi = np.loadtxt('../data/basis_nine_param.txt',delimiter=",")
solver_r = AffineROMFin(V, err_model, phi, randobs)
# Setup synthetic observations
solver = Fin(V, randobs)
z_true = dl.Function(V)
prior_covariance = np.load('prior_covariance_0.07_0.07.npy')
L = np.linalg.cholesky(prior_covariance)
# draw = np.random.randn(V.dim())
# nodal_vals = np.dot(L, draw)