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myDefautModel = SquaredExponential(input dimension=1, theta=[10], sigma=[1])
myModel = SquaredExponential(input dimension=2, theta=[10,10], sigma=[1])
myModel( [-1,-2] , [3,5] )= [[ 0.722527 ]]
dCov = [[ 0.0289011 ]
[ 0.0505769 ]]
dCov (FD)= [0.0289011,0.0505769]
myDefautModel = GeneralizedExponential(input dimension=1, theta=[10], sigma=[1], p=1)
myModel = GeneralizedExponential(input dimension=2, theta=[10,10], sigma=[1], p=1.5)
myModel( [-1,-2] , [3,5] )= [[ 0.484852 ]]
dCov = [[ 0.0323991 ]
[ 0.0566984 ]]
dCov (FD)= [0.032399,0.0566984]
myDefautModel = AbsoluteExponential(input dimension=1, theta=[10], sigma=[1])
myModel = AbsoluteExponential(input dimension=2, theta=[10,10], sigma=[1])
myModel( [-1,-2] , [3,5] )= [[ 0.332871 ]]
dCov = [[ -0.0332871 ]
[ -0.0332871 ]]
dCov (FD)= [0.0332871,0.0332871]
myDefautModel = MaternModel(input dimension=1, theta=[10], sigma=[1], nu=1.5)
myModel = MaternModel(input dimension=2, theta=[8,8], sigma=[1], nu=2)
myModel( [-1,-2] , [3,5] )= [[ 0.503176 ]]
dCov = [[ 0.0345258 ]
[ 0.0604202 ]]
dCov (FD)= [0.0345258,0.0604202]
myDefautModel = class=ProductCovarianceModel input dimension=1 models=[class=AbsoluteExponential input dimension=1 theta=class=NumericalPoint name=Unnamed dimension=1 values=[10] sigma=class=NumericalPoint name=Unnamed dimension=1 values=[1]]
myModel = class=ProductCovarianceModel input dimension=2 models=[class=AbsoluteExponential input dimension=1 theta=class=NumericalPoint name=Unnamed dimension=1 values=[3] sigma=class=NumericalPoint name=Unnamed dimension=1 values=[1],class=SquaredExponential input dimension=1 theta=class=NumericalPoint name=Unnamed dimension=1 values=[2] sigma=class=NumericalPoint name=Unnamed dimension=1 values=[1]]
myModel( [-1,-2] , [3,5] )= [[ 0.000576616 ]]
dCov = [[ -0.000192205 ]
[ 0.00100908 ]]
dCov (FD)= [0.000192206,0.00100909]
myModel = class=TensorizedCovarianceModel input dimension=2, dimension = 4, models=[class=AbsoluteExponential input dimension=2 theta=class=NumericalPoint name=Unnamed dimension=2 values=[1,1] sigma=class=NumericalPoint name=Unnamed dimension=1 values=[1],class=SquaredExponential input dimension=2 theta=class=NumericalPoint name=Unnamed dimension=2 values=[1,1] sigma=class=NumericalPoint name=Unnamed dimension=1 values=[1],class=ExponentialModel input dimension=2 amplitude=class=NumericalPoint name=Unnamed dimension=2 values=[4,2] scale=class=NumericalPoint name=Unnamed dimension=2 values=[1,1] spatial correlation=class=CorrelationMatrix dimension=2 implementation=class=MatrixImplementation name=Unnamed rows=2 columns=2 values=[1,0.3,0.3,1] isDiagonal=false]
myModel( [-1,-2] , [3,5] )= class=CovarianceMatrix dimension=4 implementation=class=MatrixImplementation name=Unnamed rows=4 columns=4 values=[1.67017e-05,0,0,0,0,7.6812e-15,0,0,0,0,0.00504343,0.000756514,0,0,0.000756514,0.00126086]
dCov = class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=2 columns=16 values=[-1.67017e-05,-1.67017e-05,0,0,0,0,0,0,0,0,3.07248e-14,5.37684e-14,0,0,0,0,0,0,0,0,0.00250224,0.00437892,0.000375336,0.000656838,0,0,0,0,0.000375336,0.000656838,0.00062556,0.00109473]
dCov (FD)= class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=2 columns=16 values=[1.67018e-05,1.67018e-05,0,0,0,0,0,0,0,0,3.07254e-14,5.37703e-14,0,0,0,0,0,0,0,0,0.00250225,0.00437894,0.000375337,0.000656841,0,0,0,0,0.000375337,0.000656841,0.000625561,0.00109474]
myModel = class=TensorizedCovarianceModel input dimension=2, dimension = 4, models=[class=AbsoluteExponential input dimension=2 theta=class=NumericalPoint name=Unnamed dimension=2 values=[2.5,1.5] sigma=class=NumericalPoint name=Unnamed dimension=1 values=[1],class=SquaredExponential input dimension=2 theta=class=NumericalPoint name=Unnamed dimension=2 values=[2.5,1.5] sigma=class=NumericalPoint name=Unnamed dimension=1 values=[1],class=ExponentialModel input dimension=2 amplitude=class=NumericalPoint name=Unnamed dimension=2 values=[4,2] scale=class=NumericalPoint name=Unnamed dimension=2 values=[2.5,1.5] spatial correlation=class=CorrelationMatrix dimension=2 implementation=class=MatrixImplementation name=Unnamed rows=2 columns=2 values=[1,0.3,0.3,1] isDiagonal=false]
myModel( [-1,-2] , [3,5] )= class=CovarianceMatrix dimension=4 implementation=class=MatrixImplementation name=Unnamed rows=4 columns=4 values=[0.00189855,0,0,0,0,5.18942e-06,0,0,0,0,0.115239,0.0172859,0,0,0.0172859,0.0288098]
dCov = class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=2 columns=16 values=[-0.000759419,-0.0012657,0,0,0,0,0,0,0,0,3.32123e-06,1.61449e-05,0,0,0,0,0,0,0,0,0.01495,0.0726734,0.00224249,0.010901,0,0,0,0,0.00224249,0.010901,0.00373749,0.0181684]
dCov (FD)= class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=2 columns=16 values=[0.00075942,0.0012657,0,0,0,0,0,0,0,0,3.32123e-06,1.61451e-05,0,0,0,0,0,0,0,0,0.01495,0.0726736,0.00224249,0.010901,0,0,0,0,0.00224249,0.010901,0.00373749,0.0181684]
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