def design_random(problem, field, n_iterations, nodes=None):
print('design_random ', end='')
if nodes is None:
nodes = Nodes()
n_eval = np.arange(n_iterations + 1) + nodes.idx.size
grid = problem.grid
n_sample = grid.shape[0]
this_field = field.condition_to(nodes, grid)
loglikelihoods = np.zeros((n_sample, n_iterations + 1))
loglikelihoods[:,
0] = this_field.estimate_loglikelihood(grid, problem.data)
for i_iteration in range(n_iterations):
print('.', end='')
# choose random point
new_index = pick_random_node_without_duplicates(n_sample, nodes)
y = problem.evaluate_model(new_index)
nodes.append(new_index, y)
this_field = field.condition_to(nodes, grid)
this_ll = this_field.estimate_loglikelihood(grid, problem.data)
loglikelihoods[:, i_iteration + 1] = this_ll
print('')
return loglikelihoods, nodes, n_eval
def design_old(problem, field, n_iterations, nodes=None):
print('design_linearized ', end='')
if nodes is None:
nodes = Nodes()
else:
nodes = copy.deepcopy(
nodes) # make a copy in order to not overwrite the node
n_eval = np.arange(n_iterations + 1) + nodes.idx.size
this_field = field.condition_to(nodes)
loglikelihoods = np.zeros((field.n_sample, n_iterations + 1))
loglikelihoods[:, 0] = this_field.estimate_loglikelihood(problem.data)
for i_iteration in range(n_iterations):
print('.', end='')
new_index = find_optimal_node_linear_old(nodes, this_field,
problem.data)
y = problem.evaluate_model(new_index)
nodes.append(new_index, y)
this_field = field.condition_to(nodes)
this_ll = this_field.estimate_loglikelihood(problem.data)
loglikelihoods[:, i_iteration + 1] = this_ll
print('')
return loglikelihoods, nodes, n_eval
def design_hybrid(problem, field, n_iterations, nodes=None):
print('design_hybrid ', end='')
# same as design sampled, but if 95% of weights are concentrated on one
# subfield, then discard all other subfields and use linearized criterion
if nodes is None:
nodes = Nodes()
n_eval = np.arange(n_iterations + 1) + nodes.idx.size
this_field = field.condition_to(nodes)
loglikelihoods = np.zeros((field.n_sample, n_iterations + 1))
loglikelihoods[:, 0] = this_field.estimate_loglikelihood(problem.data)
for i_iteration in range(n_iterations):
print('.', end='')
if this_field.is_almost_gpe():
map_field = this_field.get_map_field()
new_index = find_optimal_node_linear(nodes, map_field,
problem.data)
else:
new_index = find_optimal_node_sampled(nodes, this_field,
problem.data)
y = problem.evaluate_model(new_index)
nodes.append(new_index, y)
this_field = field.condition_to(nodes)
this_ll = this_field.estimate_loglikelihood(problem.data)
loglikelihoods[:, i_iteration + 1] = this_ll
print('')
return loglikelihoods, nodes, n_eval
def design_sampled(problem,
field,
n_iterations,
nodes=None,
use_dimension_trick=False):
print('design_sampled ', end='')
if nodes is None:
nodes = Nodes()
n_eval = np.arange(n_iterations + 1) + nodes.idx.size
this_field = field.condition_to(nodes)
loglikelihoods = np.zeros((field.n_sample, n_iterations + 1))
loglikelihoods[:, 0] = this_field.estimate_loglikelihood(problem.data)
for i_iteration in range(n_iterations):
print('.', end='')
new_index = find_optimal_node_sampled(nodes, this_field, problem.data)
y = problem.evaluate_model(new_index)
nodes.append(new_index, y)
this_field = field.condition_to(nodes)
this_ll = this_field.estimate_loglikelihood(problem.data)
loglikelihoods[:, i_iteration + 1] = this_ll
print('')
return loglikelihoods, nodes, n_eval
def design_map(problem, fields, n_iterations, nodes=None, n_subsample=None):
print('design_map ', end='')
if nodes is None:
nodes = Nodes()
else:
nodes = copy.deepcopy(nodes)
n_eval = np.arange(n_iterations + 1) + nodes.idx.size
grid = problem.grid
n_sample = grid.shape[0]
this_prior_field = fields.get_map_field(nodes, grid)
this_field = this_prior_field.condition_to(nodes, grid)
loglikelihoods = np.zeros((n_sample, n_iterations + 1))
loglikelihoods[:,
0] = this_field.estimate_loglikelihood(grid, problem.data)
for i_iteration in range(n_iterations):
print('.', end='')
subgrid, subindex = make_subgrid(grid, n_subsample, nodes)
discrete_field = this_field.discretize(subgrid)
with warnings.catch_warnings():
warnings.filterwarnings('error')
try:
new_sub_index = find_optimal_node_linear(
discrete_field, problem.data)
new_index = subindex[new_sub_index]
except Warning:
print(subindex)
print(nodes.idx)
y = problem.evaluate_model(new_index)
nodes.append(new_index, y)
this_prior_field = fields.get_map_field(nodes, grid)
this_field = this_prior_field.condition_to(nodes, grid)
this_ll = this_field.estimate_loglikelihood(grid, problem.data)
loglikelihoods[:, i_iteration + 1] = this_ll
print('')
return loglikelihoods, nodes, n_eval
def design_linearized(problem,
field,
n_iterations,
nodes=None,
n_subsample=None):
print('design_linearized ', end='')
if nodes is None:
nodes = Nodes()
else:
nodes = copy.deepcopy(nodes)
n_eval = np.arange(n_iterations + 1) + nodes.idx.size
grid = problem.grid
n_sample = grid.shape[0]
this_field = field.condition_to(nodes, grid)
loglikelihoods = np.zeros((n_sample, n_iterations + 1))
loglikelihoods[:,
0] = this_field.estimate_loglikelihood(grid, problem.data)
for i_iteration in range(n_iterations):
print('.', end='')
subgrid, subindex = make_subgrid(grid, n_subsample, nodes)
discrete_field = this_field.discretize(subgrid)
new_sub_index = find_optimal_node_linear(discrete_field, problem.data)
# index here is global index (of the global grid)
new_index = subindex[new_sub_index]
y = problem.evaluate_model(new_index)
nodes.append(new_index, y)
this_field = field.condition_to(nodes, grid)
#print(nodes.idx)
this_ll = this_field.estimate_loglikelihood(grid, problem.data)
loglikelihoods[:, i_iteration + 1] = this_ll
print('')
return loglikelihoods, nodes, n_eval
def design_heuristic(problem, field, n_iterations, nodes=None):
print('design_heuristic ', end='')
if nodes is None:
nodes = Nodes()
n_eval = np.arange(n_iterations + 1) + nodes.idx.size
this_field = field.condition_to(nodes)
loglikelihoods = np.zeros((field.n_sample, n_iterations + 1))
loglikelihoods[:, 0] = this_field.estimate_loglikelihood(problem.data)
for i_iteration in range(n_iterations):
print('.', end='')
# choose index according to heuristic
new_index = find_heuristic_node(this_field, problem.data)
y = problem.evaluate_model(new_index)
nodes.append(new_index, y)
this_field = field.condition_to(nodes)
this_ll = this_field.estimate_loglikelihood(problem.data)
loglikelihoods[:, i_iteration + 1] = this_ll
print('')
return loglikelihoods, nodes, n_eval