n_out = 2
# Spatial Covariance.
matern_cov = Matern32(lmbda=0.5, sigma=1.0)
# Cross covariance.
cross_cov = UniformMixing(gamma0=0.2, sigmas=[2.25, 2.25])
covariance = FactorCovariance(spatial_cov=matern_cov,
cross_cov=cross_cov,
n_out=n_out)
# Specify mean function, here it is a linear trend that decreases with the
# horizontal coordinate.
beta0s = np.array([5.8, 24.0])
beta1s = np.array([[0, -4.0], [0, -3.8]])
mean = LinearMean(beta0s, beta1s)
# Create the GRF.
myGRF = GRF(mean, covariance)
# ------------------------------------------------------
# DISCRETIZE EVERYTHING
# ------------------------------------------------------
# Create a regular square grid in 2 dims.
my_grid = TriangularGrid(31)
print("Working on an equilateral triangular grid with {} nodes.".format(
my_grid.n_points))
# Discretize the GRF on a grid and be done with it.
# From now on we only consider locatoins on the grid.
my_discrete_grf = DiscreteGRF.from_model(myGRF, my_grid)
""" Test script for meslas.means """ import torch from meslas.means import ConstantMean, LinearMean # Array of locations. S1 = torch.Tensor([[0, 0], [0, 1], [0, 2], [3, 0], [2, 2]]).float() S2 = torch.Tensor([[0, 0], [3, 0], [5, 4]]).float() # Corresponding response indices. L1 = torch.Tensor([0, 0, 0, 1, 1]).long() L2 = torch.Tensor([0, 3, 0]).long() # Constant mean of each component. means = torch.tensor([1.0, -2.0, 4.0, 33.0]) my_mean = ConstantMean(means) print(my_mean(S1, L1)) # Test the linear trend mean function. beta0s = means beta1s = torch.tensor([[1.0, 1.0], [2.0, 2.0], [0.0, 0.0], [-1.0, 0.0]]) my_linear_mean = LinearMean(beta0s, beta1s) print(my_linear_mean(S1, L1))