def get_random_data(nx, ny):
from pymks.datasets import make_elastic_FE_strain_random
np.random.seed(8)
return make_elastic_FE_strain_random(elastic_modulus=(1., 1.1),
poissons_ratio=(0.3, 0.3),
n_samples=1,
size=(nx, ny))
def test_multiphase_FE_strain():
from pymks import MKSRegressionModel
from pymks.datasets import make_elastic_FE_strain_delta
from pymks.datasets import make_elastic_FE_strain_random
from pymks.bases import DiscreteIndicatorBasis
L = 21
i = 3
elastic_modulus = (80, 100, 120)
poissons_ratio = (0.3, 0.3, 0.3)
macro_strain = 0.02
size = (L, L)
X_delta, strains_delta = \
make_elastic_FE_strain_delta(elastic_modulus=elastic_modulus,
poissons_ratio=poissons_ratio,
size=size, macro_strain=macro_strain)
basis = DiscreteIndicatorBasis(len(elastic_modulus))
MKSmodel = MKSRegressionModel(basis)
MKSmodel.fit(X_delta, strains_delta)
np.random.seed(99)
X, strain = make_elastic_FE_strain_random(n_samples=1,
elastic_modulus=elastic_modulus,
poissons_ratio=poissons_ratio,
size=size,
macro_strain=macro_strain)
strain_pred = MKSmodel.predict(X)
assert np.allclose(strain_pred[0, i:-i],
strain[0, i:-i],
rtol=1e-2,
atol=6.1e-3)
def get_random_data(nx, ny):
from pymks.datasets import make_elastic_FE_strain_random
np.random.seed(8)
return make_elastic_FE_strain_random(elastic_modulus=(1., 1.1),
poissons_ratio=(0.3, 0.3),
n_samples=1,
size=(nx, ny))
def test_multiphase_FE_strain():
from pymks import MKSRegressionModel
from pymks.datasets import make_elastic_FE_strain_delta
from pymks.datasets import make_elastic_FE_strain_random
from pymks.bases import DiscreteIndicatorBasis
L = 21
i = 3
elastic_modulus = (80, 100, 120)
poissons_ratio = (0.3, 0.3, 0.3)
macro_strain = 0.02
size = (L, L)
X_delta, strains_delta = \
make_elastic_FE_strain_delta(elastic_modulus=elastic_modulus,
poissons_ratio=poissons_ratio,
size=size, macro_strain=macro_strain)
basis = DiscreteIndicatorBasis(len(elastic_modulus))
MKSmodel = MKSRegressionModel(basis)
MKSmodel.fit(X_delta, strains_delta)
np.random.seed(99)
X, strain = make_elastic_FE_strain_random(n_samples=1,
elastic_modulus=elastic_modulus,
poissons_ratio=poissons_ratio,
size=size,
macro_strain=macro_strain)
strain_pred = MKSmodel.predict(X)
assert np.allclose(strain_pred[0, i:-i], strain[0, i:-i],
rtol=1e-2, atol=6.1e-3)
def test_make_elastic_FE_strain_random():
elastic_modulus = (1., 2.)
poissons_ratio = (0.3, 0.3)
X, y = make_elastic_FE_strain_random(n_samples=1,
elastic_modulus=elastic_modulus,
poissons_ratio=poissons_ratio,
size=(5, 5))
