]
distsOT = dists_to_ot(dists)
space = Space(corners)
#Get the Data Base for UQ
Case = Mascaret_new()
X = Case.data_input
x_l = X[0:799, :]
x_t = X[800:999, :]
# Build the learning sample
#x_l = ot.LHSExperiment(ot.ComposedDistribution(distsOT), Nl, True, True).generate() #LHS distribution
#x_l = [list(x_l[i]) for i in range(Nl)]
#x_l = np.array(x_l)
doe_l = doe(x_l)
doe_t = doe(x_t)
# Build the training sample
#x_t = ot.LHSExperiment(ot.ComposedDistribution(distsOT), Nl, True, True).generate() #LHS distribution
#x_t = [list(x_t[i]) for i in range(Nl)]
#x_t = np.array(x_t)
# Build Mascaret model via MascaretStudy class
y_l = Case(x_l)
y_t = Case(x_t)
print('y_l', y_l)
print('y_t', y_t)
# Build the surrogate Model with UQ and loo/rmse/q2 criteria for each Surrogate
# Kriging surrogate
from scipy.spatial import distance
from sklearn import preprocessing
from sklearn.neighbors import NearestNeighbors
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
from batman.space import Space
from batman.visualization import doe, response_surface, reshow
from batman.functions import Branin
import openturns as ot
# Problem definition: f(sample) -> data
corners = np.array([[-5, 0], [10, 14]])
sample = Space(corners)
sample.sampling(20)
doe(sample, fname='init_doe.pdf')
fun_branin = Branin()
def fun(x):
return -fun_branin(x)
data = fun(sample)
# Algo
def random_uniform_ring(
center=np.array([0, 0]), r_outer=1, r_inner=0, n_samples=1):
def test_doe_3D(self, ishigami_data, tmp):
fig, ax = doe(ishigami_data.space, fname=os.path.join(tmp, 'DOE.pdf'))
fig = reshow(fig)
ax[0].plot([0, 6], [4, -3])
fig.savefig(os.path.join(tmp, 'DOE_change.pdf'))
def test_doe_mufi(self, ishigami_data, tmp):
doe(ishigami_data.space,
multifidelity=True,
fname=os.path.join(tmp, 'DOE_mufi.pdf'))
def test_doe(self, mock_show, mascaret_data):
doe(mascaret_data.space)