Python ExponentiallyDampedCosineModel示例

示例#1

文件： t_ProductCovarianceModel_std.py 项目： vchabri/openturns

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()
    ])

示例#2

文件： openturns-ExponentiallyDampedCosineModel-1.py 项目： ThibaultDelage/openturns.github.io

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]]

示例#3

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)

示例#4

# 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)

示例#5

文件： openturns-ExponentiallyDampedCosineModel-1.py 项目： ThibaultDelage/openturns.github.io

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,