def test_space_evaluation(settings_ishigami):
f_3d = Ishigami()
space = Space(settings_ishigami['space']['corners'])
space.sampling(2, 'halton')
targets_space = f_3d(space)
f_data_base = np.array([5.25, 4.2344145]).reshape(2, 1)
npt.assert_almost_equal(targets_space, f_data_base)
def test_moment_independant(self, ishigami_data, tmp):
ishigami_data_ = copy.deepcopy(ishigami_data)
ishigami_data_.space.max_points_nb = 5000
X = ishigami_data_.space.sampling(5000, 'olhs')
Y = ishigami_data_.func(X).flatten()
momi = moment_independent(X,
Y,
plabels=['x1', 'x2', 'x3'],
fname=os.path.join(tmp,
'moment_independent.pdf'))
npt.assert_almost_equal(momi[2]['Kolmogorov'], [0.236, 0.377, 0.107],
decimal=2)
npt.assert_almost_equal(momi[2]['Kuiper'], [0.257, 0.407, 0.199],
decimal=2)
npt.assert_almost_equal(momi[2]['Delta'], [0.211, 0.347, 0.162],
decimal=2)
npt.assert_almost_equal(momi[2]['Sobol'], [0.31, 0.421, 0.002],
decimal=2)
# Cramer
space = Space(corners=[[-5, -5], [5, 5]], sample=5000)
space.sampling(dists=['Normal(0, 1)', 'Normal(0, 1)'])
Y = [np.exp(x_i[0] + 2 * x_i[1]) for x_i in space]
X = np.array(space)
momi = moment_independent(X,
Y,
fname=os.path.join(tmp,
'moment_independent.pdf'))
npt.assert_almost_equal(momi[2]['Cramer'], [0.113, 0.572], decimal=2)
init_size3 = 100 init_size2 = 80 init_size1 = 60 #sample size used for trunctation error init_size = 1000 indim = 2 # inputs dim plabels = ['Ks', 'Q'] space = Space(corners) # Build the learning samples #training sample for truncation error (1 sample) x_train = np.array(space.sampling(init_size, 'halton')) #training samples for sampling error (init_size varies) x_train5 = np.array(space.sampling(init_size5, 'halton')) x_train4 = np.array(space.sampling(init_size4, 'halton')) x_train3 = np.array(space.sampling(init_size3, 'halton')) x_train2 = np.array(space.sampling(init_size2, 'halton')) x_train1 = np.array(space.sampling(init_size1, 'halton')) #training sample for estimation of LC metrics (large init_size) x_trainr = np.array(space.sampling(init_size, 'halton')) #Creaing empty arrays for ouptut data y_train = [] y_train5 = []
def test_space(settings_ishigami, seed):
corners = settings_ishigami['space']['corners']
space = Space(corners)
assert space.max_points_nb == np.inf
space = Space(corners, sample=10)
assert space.max_points_nb == 10
space = Space(corners, sample=10, nrefine=6,
plabels=['x', 'y', 'z'])
assert space.max_points_nb == 16
space += (1, 2, 3)
npt.assert_array_equal(space.values, [(1, 2, 3)])
space.empty()
npt.assert_array_equal(space.values, np.empty((0, 3)))
space += [(1, 2, 3), (1, 1, 3)]
npt.assert_array_equal(space.values, [(1, 2, 3), (1, 1, 3)])
space2 = Space(corners, space.values)
npt.assert_array_equal(space2.values, [(1, 2, 3), (1, 1, 3)])
s1 = space.sampling()
assert len(s1) == 10
space2 = Space(corners,
sample=settings_ishigami['space']['sampling']['init_size'],
nrefine=settings_ishigami['space']['resampling']['resamp_size'])
s2 = space2.sampling(10, kind='lhsc')
assert len(s2) == 10
assert np.any(s1 != s2)
space.empty()
space += (1, 2, 3)
space += (1, 2, 3)
assert len(space) == 1
space = Space(corners, sample=16, duplicate=True)
space += (1, 2, 3)
space += (1, 2, 3)
assert len(space) == 2
with pytest.raises(ValueError):
space += (1, 2)
assert len(space) == 2
space += (1, 7, 3)
assert len(space) == 2
space.sampling(17)
assert len(space) == 16
space.empty()
dists = ['Uniform(0., 1.)', 'Uniform(-1., 2.)', 'Uniform(-2., 3.)']
space.sampling(5, kind='halton', dists=dists)
out = [(0.5, 0.0, -1.0), (0.25, 1.0, 0.0), (0.75, -0.67, 1.0),
(0.125, 0.33, 2.0), (0.625, 1.33, -1.8)]
npt.assert_almost_equal(space, out, decimal=1)
space = Space(corners, sample=np.array([(1, 2, 3), (1, 1, 3)]))
assert space.doe_init == 2
assert space.max_points_nb == 2
test_settings = copy.deepcopy(settings_ishigami)
test_settings['space']['corners'][1] = [np.pi, -np.pi, np.pi]
with pytest.raises(ValueError):
Space(test_settings['space']['corners'])
import numpy as np
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(
init_size4 = 150
init_size3 = 100
init_size2 = 50
init_size1 = 40
#sample size used for trunctation error
init_size = 1000
indim = 2 # inputs dim
plabels = ['Ks', 'Q']
space = Space(corners)
# Build the learning samples
x_train = np.array(space.sampling(
init_size, 'halton')) #training sample for truncation error (1 sample)
x_train5 = np.array(space.sampling(
init_size5,
'halton')) #training samples for sampling error (init_size varies)
x_train4 = np.array(space.sampling(init_size4, 'halton'))
x_train3 = np.array(space.sampling(init_size3, 'halton'))
x_train2 = np.array(space.sampling(init_size2, 'halton'))
x_train1 = np.array(space.sampling(init_size1, 'halton'))
x_trainr = np.array(space.sampling(
1000,
'halton')) #training sample for estimation of LC metrics (large init_size)
#Build the solution vector with y = f(x)
