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, 2])
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, 1, 2])
print("model2 active parameter: ", [
model2.getFullParameterDescription()[i]
for i in model2.getActiveParameter()
])
print("Product covariance model")
d = 3
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()
])
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
if ot.ExponentiallyDampedCosineModel(
).__class__.__name__ == 'ExponentialModel':
covarianceModel = ot.ExponentialModel([0.5], [5.0])
elif ot.ExponentiallyDampedCosineModel(
).__class__.__name__ == 'GeneralizedExponential':
covarianceModel = ot.GeneralizedExponential([2.0], [3.0], 1.5)
elif ot.ExponentiallyDampedCosineModel(
).__class__.__name__ == 'ProductCovarianceModel':
amplitude = [1.0]
scale1 = [4.0]
scale2 = [4.0]
cov1 = ot.ExponentialModel(scale1, amplitude)
cov2 = ot.ExponentialModel(scale2, amplitude)
covarianceModel = ot.ProductCovarianceModel([cov1, cov2])
elif ot.ExponentiallyDampedCosineModel(
).__class__.__name__ == 'RankMCovarianceModel':
variance = [1.0, 2.0]
basis = ot.LinearBasisFactory().build()
covarianceModel = ot.RankMCovarianceModel(variance, basis)
else:
covarianceModel = ot.ExponentiallyDampedCosineModel()
title = str(covarianceModel)[:100]
if covarianceModel.getInputDimension() == 1:
scale = covarianceModel.getScale()[0]
if covarianceModel.isStationary():
def f(x):
return [covarianceModel(x)[0, 0]]
myDefautModel = ot.AbsoluteExponential([2.0], [3.0])
print('myDefautModel = ', myDefautModel)
test_model(myDefautModel)
myModel = ot.AbsoluteExponential([2.0] * inputDimension, [3.0])
test_model(myModel)
myDefautModel = ot.MaternModel([2.0], [3.0], 1.5)
print('myDefautModel = ', myDefautModel)
test_model(myDefautModel)
myModel = ot.MaternModel([2.0] * inputDimension, [3.0], 1.5)
test_model(myModel)
myDefautModel = ot.ExponentiallyDampedCosineModel([2.0], [3.0], 1.5)
print('myDefautModel = ', myDefautModel)
test_model(myDefautModel)
myModel = ot.ExponentiallyDampedCosineModel([2.0] * inputDimension, [3.0], 1.5)
test_model(myModel)
myDefautModel = ot.SphericalModel([2.0], [3.0], 4.5)
print('myDefautModel = ', myDefautModel)
test_model(myDefautModel)
myModel = ot.SphericalModel([2.0] * inputDimension, [3.0], 4.5)
test_model(myModel)
myDefautModel = ot.FractionalBrownianMotionModel(2.0, 3.0, 0.25)
print('myDefautModel = ', myDefautModel)
# 4) MaternModel myModel = ot.MaternModel([2.0], [3.0], 1.5) ott.assert_almost_equal(myModel.getScale(), [2], 0, 0) ott.assert_almost_equal(myModel.getAmplitude(), [3], 0, 0) ott.assert_almost_equal(myModel.getNu(), 1.5, 0, 0) test_model(myModel) myModel = ot.MaternModel([2.0] * inputDimension, [3.0], 1.5) ott.assert_almost_equal(myModel.getScale(), [2, 2], 0, 0) ott.assert_almost_equal(myModel.getAmplitude(), [3], 0, 0) ott.assert_almost_equal(myModel.getNu(), 1.5, 0, 0) test_model(myModel) # 5) ExponentiallyDampedCosineModel myModel = ot.ExponentiallyDampedCosineModel([2.0], [3.0], 1) ott.assert_almost_equal(myModel.getScale(), [2], 0, 0) ott.assert_almost_equal(myModel.getAmplitude(), [3], 0, 0) ott.assert_almost_equal(myModel.getFrequency(), 1, 0, 0) test_model(myModel) myModel.setFrequency(3) ott.assert_almost_equal(myModel.getFrequency(), 3, 0, 0) myModel = ot.ExponentiallyDampedCosineModel([2.0] * inputDimension, [3.0], 1) ott.assert_almost_equal(myModel.getScale(), [2, 2], 0, 0) ott.assert_almost_equal(myModel.getAmplitude(), [3], 0, 0) ott.assert_almost_equal(myModel.getFrequency(), 1, 0, 0) test_model(myModel) # 6) SphericalModel myModel = ot.SphericalModel([2.0], [3.0], 4.5)
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
covarianceModel = ot.ExponentiallyDampedCosineModel()
if covarianceModel.getInputDimension() == 1:
scale = covarianceModel.getScale()[0]
if covarianceModel.isStationary():
def f(x):
return [covarianceModel(x)[0, 0]]
func = ot.PythonFunction(1, 1, f)
func.setDescription(['$tau$', '$cov$'])
cov_graph = func.draw(-3.0 * scale, 3.0 * scale, 129)
fig = plt.figure(figsize=(10, 4))
plt.suptitle(str(covarianceModel))
cov_axis = fig.add_subplot(111)
View(cov_graph, figure=fig, axes=[cov_axis], add_legend=False)
else:
def f(x):
return [covarianceModel([x[0]], [x[1]])[0, 0]]
func = ot.PythonFunction(2, 1, f)
func.setDescription(['$s$', '$t$', '$cov$'])
cov_graph = func.draw([-3.0 * scale] * 2, [3.0 * scale] * 2, [129] * 2)
fig = plt.figure(figsize=(10, 4))
plt.suptitle(str(covarianceModel))
cov_axis = fig.add_subplot(111)
View(cov_graph,
figure=fig,
