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_MKS_elastic_delta():
from pymks import MKSRegressionModel
from pymks.bases import DiscreteIndicatorBasis
nx, ny = 21, 21
X, y_test = get_delta_data(nx, ny)
basis = DiscreteIndicatorBasis(n_states=2)
model = MKSRegressionModel(basis)
model.fit(X, y_test)
y_pred = model.predict(X)
assert np.allclose(y_pred, y_test, rtol=1e-3, atol=1e-3)
def test_resize_coeff():
from pymks import MKSRegressionModel
from pymks.bases import DiscreteIndicatorBasis
nx, ny = 21, 21
resize = 3
X_delta, y_delta = get_delta_data(nx, ny)
X_big_delta, y_big_delta = get_delta_data(resize * nx, resize * ny)
basis = DiscreteIndicatorBasis(n_states=2)
model = MKSRegressionModel(basis)
big_model = MKSRegressionModel(basis)
model.fit(X_delta, y_delta)
big_model.fit(X_big_delta, y_big_delta)
model.resize_coeff((resize * nx, resize * ny))
assert np.allclose(model.coef_, big_model.coef_, rtol=1e-2, atol=2.1e-3)
def test_resize_pred():
from pymks import MKSRegressionModel
from pymks.bases import DiscreteIndicatorBasis
nx, ny = 21, 21
resize = 3
X_delta, y_delta = get_delta_data(nx, ny)
X_test, y_test = get_random_data(nx, ny)
X_big_test, y_big_test = get_random_data(resize * nx, resize * ny)
basis = DiscreteIndicatorBasis(n_states=2)
model = MKSRegressionModel(basis)
model.fit(X_delta, y_delta)
y_pred = model.predict(X_test)
assert np.allclose(y_pred, y_test, rtol=1e-2, atol=6.1e-3)
model.resize_coeff((resize * nx, resize * ny))
y_big_pred = model.predict(X_big_test)
assert np.allclose(y_big_pred, y_big_test, rtol=1e-2, atol=6.1e-2)
