# Create an equilateral triangular grid in 2 dims.
# Number of respones.
my_grid = TriangularGrid(40)
my_square_grid = SquareGrid(50, 2)
my_discrete_grf = DiscreteGRF.from_model(myGRF, my_grid)
# Observe some data.
S_y = torch.tensor([[0.2, 0.1], [0.2, 0.2], [0.2, 0.3],
[0.2, 0.4], [0.2, 0.5], [0.2, 0.6],
[0.2, 0.7], [0.2, 0.8], [0.2, 0.9], [0.2, 1.0],
[0.6, 0.5]])
L_y = torch.tensor([0, 0, 0, 0, 0, 1, 1, 0 ,0 ,0, 0])
y = torch.tensor(11*[-6]).float()
# Since we are working with a discrete GRF, we can only observa data at grid
# nodes. Hence get the ones corresponding to the measured data.
S_y_inds = my_grid.get_closest(S_y)
my_sensor = DiscreteSensor(my_grid, my_discrete_grf)
# Excursion threshold.
lower = torch.tensor([2.5, 20.0]).float()
my_sensor.update_design(S_y_inds, L_y, y, noise_std=0.05)
excursion_probas = my_sensor.compute_exursion_prob(lower)
plot_grid_probas(my_grid, excursion_probas,
my_sensor.S_y_tot)
noise_realization = noise_distr.sample()
return ground_truth[node_ind] + noise_realization
my_sensor = DiscreteSensor(my_discrete_grf)
# Excursion threshold.
lower = torch.tensor([2.3, 22.0]).float()
# Get the real excursion set and plot it.
excursion_ground_truth = (sample.isotopic > lower).float()
plot_grid_values(my_grid, excursion_ground_truth.sum(dim=1), cmap="proba")
# Plot the prior excursion probability.
excu_probas = my_sensor.compute_exursion_prob(lower)
plot_grid_probas(my_grid, excu_probas)
print(my_sensor.grf.mean_vec.isotopic.shape)
# Start from lower middle corner.
my_sensor.set_location([0.0, 0.5])
# First initialize the myopic strategy.
my_sensor.neighbors_eibv, my_sensor.neighbors_inds = my_sensor.get_neighbors_isotopic_eibv(
noise_std, lower)
# Run the myopic strategy for a while
n_steps = 4
for i in range(n_steps):
my_sensor.run_myopic_stragegy(n_steps=1,
data_feed=data_feed,
lower=lower,
from meslas.plotting import plot_grid_values, plot_grid_probas
plot_grid_values(my_grid, mu_cond, S_y, L_y)
plot_grid_values(my_square_grid, mu_cond_sq, S_y, L_y)
# Try the variance part.
mu_cond, var_cond = myGRF.krig_isotopic(my_grid.points,
S_y,
L_y,
y,
noise_std=0.05,
compute_post_var=True)
plot_grid_values(my_grid, var_cond, S_y, L_y, cmap="proba")
variance_reduction = myGRF.variance_reduction_isotopic(my_grid.points,
S_y,
L_y,
noise_std=0.05)
plot_grid_values(my_grid, variance_reduction, S_y, L_y, cmap="proba")
K_cond_diag = torch.diagonal(K_cond.isotopic, dim1=0, dim2=1).T
lower = torch.tensor([-1.0, -1.0]).double()
coverage = coverage_fct_fixed_location(mu_cond.isotopic,
K_cond_diag,
lower,
upper=None)
plot_grid_probas(my_grid, coverage, title="Excursion Probability")