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#! /usr/bin/env python
import openturns as ot
import openturns.testing as ott
ot.TESTPREAMBLE()
def test_active_parameter():
# Define product of matern 1d
cov_model_1d = ot.MaternModel([0.5], 2.5)
print("1D Full parameter : ", cov_model_1d.getFullParameter())
print(
"1D active cov. param.: ",
[
cov_model_1d.getFullParameterDescription()[i]
for i in cov_model_1d.getActiveParameter()
],
)
print("Activate nu parameter")
cov_model_1d.setActiveParameter([0, 2, 3])
print(
"active cov. param.: ",
[
cov_model_1d.getFullParameterDescription()[i]
for i in cov_model_1d.getActiveParameter()
],
)
print("Matern d-dimensional covariance as product")
d = 3
cov_model = ot.ProductCovarianceModel([cov_model_1d] * d)
marginal0 = cov_model.getMarginal(0)
assert marginal0.getInputDimension() == d, "wrong marginal input dim"
assert marginal0.getOutputDimension() == 1, "wrong marginal output dim"
print("Full parameter : ", cov_model.getFullParameter())
print(
"active cov. param.: ",
[
cov_model.getFullParameterDescription()[i]
for i in cov_model.getActiveParameter()
],
)
print("Disable nu for marginals 0 & 1 parameter : ", cov_model.getFullParameter())
cov_model.setActiveParameter([0, 1, 2, 4, 7])
print(
"active cov. param.: ",
[
cov_model.getFullParameterDescription()[i]
for i in cov_model.getActiveParameter()
],
)
print("Check that active parameter is correctly propagated")
for k in range(3):
print(
"Model ",
k,
" : active cov. param.: ",
[
cov_model.getCollection()[k].getFullParameterDescription()[i]
for i in cov_model.getCollection()[k].getActiveParameter()
],
)
def test_active_amplitude_parameter():
# Define product of matern 1d
model1 = ot.MaternModel([1.0], 2.5)
print("Model 1 : ", model1.getFullParameterDescription())
print("Activate nu parameter and disable sigma2")
model1.setActiveParameter([0, 1, 3])
print(
"model1 active parameter: ",
[model1.getFullParameterDescription()[i] for i in model1.getActiveParameter()],
)
model2 = ot.ExponentiallyDampedCosineModel()
print("Model 2 : ", model2.getFullParameterDescription())
print("Activate freq parameter")
model2.setActiveParameter([0, 2, 3])
print(
"model2 active parameter: ",
[model2.getFullParameterDescription()[i] for i in model2.getActiveParameter()],
)
print("Activate nuggetFactor parameter")
model2.setActiveParameter([0, 1, 2, 3])
print(
"model2 active parameter: ",
[model2.getFullParameterDescription()[i] for i in model2.getActiveParameter()],
)
print("Product covariance model")
cov_model = ot.ProductCovarianceModel([model1, model2])
print("Full parameter : ", cov_model.getFullParameter())
print(
"active cov. param.: ",
[
cov_model.getFullParameterDescription()[i]
for i in cov_model.getActiveParameter()
],
)
def test_parameters_iso():
scale = []
nuggetFactor = 1e-12
amplitude = 1.0
extraParameter = []
# model 1
atom_ex = ot.IsotropicCovarianceModel(ot.MaternModel(), 2)
atom_ex.setScale([5])
atom_ex.setAmplitude([1.5])
scale.append(5)
amplitude *= 1.5
extraParameter.append(atom_ex.getKernel().getFullParameter()[-1])
# model2
m = ot.MaternModel()
m.setNu(2.5)
m.setScale([3])
m.setAmplitude([3])
scale.append(3)
amplitude *= 3
extraParameter.append(m.getNu())
# model 3
atom = ot.IsotropicCovarianceModel(ot.AbsoluteExponential(), 2)
atom.setScale([2])
atom.setAmplitude([2.5])
scale.append(2)
amplitude *= 2.5
model = ot.ProductCovarianceModel([atom_ex, m, atom])
ott.assert_almost_equal(model.getScale(), scale, 1e-16, 1e-16)
ott.assert_almost_equal(model.getAmplitude(), [amplitude], 1e-16, 1e-16)
ott.assert_almost_equal(
model.getFullParameter(),
scale + [nuggetFactor, amplitude] + extraParameter,
1e-16,
1e-16,
)
# active parameter should be scale + amplitude
ott.assert_almost_equal(model.getActiveParameter(), [0, 1, 2, 4], 1e-16, 1e-16)
# setting new parameters
extraParameter = [2.5, 0.5]
model.setFullParameter([6, 7, 8, 0.01, 2] + extraParameter)
ott.assert_almost_equal(model.getCollection()[0].getScale()[0], 6, 1e-16, 1e-16)
ott.assert_almost_equal(model.getCollection()[1].getScale()[0], 7, 1e-16, 1e-16)
ott.assert_almost_equal(model.getCollection()[2].getScale()[0], 8, 1e-16, 1e-16)
ott.assert_almost_equal(model.getNuggetFactor(), 0.01, 0.0, 0.0)
ott.assert_almost_equal(model.getAmplitude()[0], 2, 1e-16, 1e-16)
ott.assert_almost_equal(
model.getCollection()[0].getFullParameter()[-1], extraParameter[0], 1e-16, 1e-16
)
ott.assert_almost_equal(
model.getCollection()[1].getFullParameter()[-1], extraParameter[1], 1e-16, 1e-16
)
# checking active par setting
model.setActiveParameter([0, 1, 2, 4, 6])
ott.assert_almost_equal(
model.getParameter(), [6, 7, 8, 2, extraParameter[-1]], 1e-16, 1e-16
)
test_active_parameter()
test_active_amplitude_parameter()
test_parameters_iso()
|
