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Viewing changes to python/test/t_IsoProbabilisticTransformation_EllipticalDistribution.atpy

  • Committer: Bazaar Package Importer
  • Author(s): Fabrice Coutadeur
  • Date: 2010-05-10 17:27:55 UTC
  • mfrom: (1.1.4 upstream) (5.1.5 sid)
  • Revision ID: james.westby@ubuntu.com-20100510172755-cb5ynskknqqi5rhp
Tags: 0.13.2-2ubuntu1
* Merge with Debian testing. No changes left.
* ubuntu_fix-python-2.6.patch: fix detection of python 2.6 libs, to not use
  LOCALMODLIBS. This pulls a dependency on SSL and makes the package FTBFS.

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python/test/t_IsoProbabilisticTransformation_EllipticalDistribution.atpy
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# List of executables that should be run to perform the tests
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#AT_TESTED([t_IsoProbabilisticTransformation_EllipticalDistribution.py])
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AT_CHECK([t_IsoProbabilisticTransformation_EllipticalDistribution.py],
 
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AT_CHECK([python ${examplesdir}/t_IsoProbabilisticTransformation_EllipticalDistribution.py],
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         [0],
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[[sample first= class=NumericalPoint name=Unnamed dimension=3 implementation=class=NumericalPointImplementation name=Unnamed dimension=3 values=[1.7164,-2.87731,-0.0888678]  last= class=NumericalPoint name=Unnamed dimension=3 implementation=class=NumericalPointImplementation name=Unnamed dimension=3 values=[-0.227791,-2.59096,1.4601]
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sample mean= class=NumericalPoint name=Unnamed dimension=3 implementation=class=NumericalPointImplementation name=Unnamed dimension=3 values=[0.476599,-0.538382,0.997853]
 
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[[sample first= class=NumericalPoint name=Unnamed dimension=3 values=[1.7164,-2.87731,-0.0888678]  last= class=NumericalPoint name=Unnamed dimension=3 values=[-0.227791,-2.59096,1.4601]
 
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sample mean= class=NumericalPoint name=Unnamed dimension=3 values=[0.476599,-0.538382,0.997853]
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sample covariance= class=CovarianceMatrix dimension=3 implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[3.93982,2.85907,-0.0162011,2.85907,8.94638,1.48782,-0.0162011,1.48782,1.00509]
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isoprobabilistic transformation= class=NumericalMathFunction name=Unnamed implementation=class=NumericalMathFunctionImplementation name=Unnamed description=[x0,x1,x2,y0,y1,y2] evaluationImplementation=class=NatafEllipticalDistributionEvaluation mean=class=NumericalPoint name=Unnamed dimension=3 implementation=class=NumericalPointImplementation name=Unnamed dimension=3 values=[0.5,-0.5,1] inverseCholesky=class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[0.5,-0.288675,0.204124,0,0.3849,-0.272166,0,0,1.22474] gradientImplementation=class=NatafEllipticalDistributionGradient inverseCholesky=class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[0.5,-0.288675,0.204124,0,0.3849,-0.272166,0,0,1.22474] hessianImplementation=class=NatafEllipticalDistributionHessian
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transformed sample first= class=NumericalPoint name=Unnamed dimension=3 implementation=class=NumericalPointImplementation name=Unnamed dimension=3 values=[0.608202,-1.26617,-0.438266]  last= class=NumericalPoint name=Unnamed dimension=3 implementation=class=NumericalPointImplementation name=Unnamed dimension=3 values=[-0.363896,-0.594717,0.984039]
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transformed sample mean= class=NumericalPoint name=Unnamed dimension=3 implementation=class=NumericalPointImplementation name=Unnamed dimension=3 values=[-0.0117004,-0.00801788,0.0030405]
 
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isoprobabilistic transformation= class=NumericalMathFunction name=Unnamed implementation=class=NumericalMathFunctionImplementation name=Unnamed description=[x0,x1,x2,y0,y1,y2] evaluationImplementation=class=NatafEllipticalDistributionEvaluation mean=class=NumericalPoint name=Unnamed dimension=3 values=[0.5,-0.5,1] inverseCholesky=class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[0.5,-0.288675,0.204124,0,0.3849,-0.272166,0,0,1.22474] gradientImplementation=class=NatafEllipticalDistributionGradient inverseCholesky=class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[0.5,-0.288675,0.204124,0,0.3849,-0.272166,0,0,1.22474] hessianImplementation=class=NatafEllipticalDistributionHessian
 
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transformed sample first= class=NumericalPoint name=Unnamed dimension=3 values=[0.608202,-1.26617,-0.438266]  last= class=NumericalPoint name=Unnamed dimension=3 values=[-0.363896,-0.594717,0.984039]
 
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transformed sample mean= class=NumericalPoint name=Unnamed dimension=3 values=[-0.0117004,-0.00801788,0.0030405]
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transformed sample covariance= class=CovarianceMatrix dimension=3 implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[0.984954,-0.0184348,0.00311426,-0.0184348,1.01836,-0.0129937,0.00311426,-0.0129937,1.01684]
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inverse isoprobabilistic transformation= class=NumericalMathFunction name=Unnamed implementation=class=NumericalMathFunctionImplementation name=Unnamed description=[x0,x1,x2,y0,y1,y2] evaluationImplementation=class=InverseNatafEllipticalDistributionEvaluation mean=class=NumericalPoint name=Unnamed dimension=3 implementation=class=NumericalPointImplementation name=Unnamed dimension=3 values=[0,0,0] cholesky=class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[2,1.5,0,0,2.59808,0.57735,0,0,0.816497] gradientImplementation=class=InverseNatafEllipticalDistributionGradient cholesky=class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[2,1.5,0,0,2.59808,0.57735,0,0,0.816497] hessianImplementation=class=InverseNatafEllipticalDistributionHessian
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transformed back sample first= class=NumericalPoint name=Unnamed dimension=3 implementation=class=NumericalPointImplementation name=Unnamed dimension=3 values=[1.7164,-2.87731,-0.0888678]  last= class=NumericalPoint name=Unnamed dimension=3 implementation=class=NumericalPointImplementation name=Unnamed dimension=3 values=[-0.227791,-2.59096,1.4601]
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transformed back sample mean= class=NumericalPoint name=Unnamed dimension=3 implementation=class=NumericalPointImplementation name=Unnamed dimension=3 values=[0.476599,-0.538382,0.997853]
 
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inverse isoprobabilistic transformation= class=NumericalMathFunction name=Unnamed implementation=class=NumericalMathFunctionImplementation name=Unnamed description=[x0,x1,x2,y0,y1,y2] evaluationImplementation=class=InverseNatafEllipticalDistributionEvaluation mean=class=NumericalPoint name=Unnamed dimension=3 values=[0,0,0] cholesky=class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[2,1.5,0,0,2.59808,0.57735,0,0,0.816497] gradientImplementation=class=InverseNatafEllipticalDistributionGradient cholesky=class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[2,1.5,0,0,2.59808,0.57735,0,0,0.816497] hessianImplementation=class=InverseNatafEllipticalDistributionHessian
 
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transformed back sample first= class=NumericalPoint name=Unnamed dimension=3 values=[1.7164,-2.87731,-0.0888678]  last= class=NumericalPoint name=Unnamed dimension=3 values=[-0.227791,-2.59096,1.4601]
 
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transformed back sample mean= class=NumericalPoint name=Unnamed dimension=3 values=[0.476599,-0.538382,0.997853]
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transformed back sample covariance= class=CovarianceMatrix dimension=3 implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[3.93982,2.85907,-0.0162011,2.85907,8.94638,1.48782,-0.0162011,1.48782,1.00509]
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point= class=NumericalPoint name=Unnamed dimension=3 implementation=class=NumericalPointImplementation name=Unnamed dimension=3 values=[1,1,1]
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transform value at point        = class=NumericalPoint name=Unnamed dimension=3 implementation=class=NumericalPointImplementation name=Unnamed dimension=3 values=[0.25,0.433013,-0.306186]
 
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point= class=NumericalPoint name=Unnamed dimension=3 values=[1,1,1]
 
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transform value at point        = class=NumericalPoint name=Unnamed dimension=3 values=[0.25,0.433013,-0.306186]
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transform gradient at point     = class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[0.5,0,0,-0.288675,0.3849,0,0.204124,-0.272166,1.22474]
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transform gradient at point (FD)= class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[0.5,0,0,-0.288675,0.3849,0,0.204124,-0.272166,1.22474]
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transform hessian at point      = class=SymmetricTensor implementation=class=TensorImplementation name=Unnamed rows=3 columns=3 sheets=3 values=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
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transform hessian at point (FD) = class=SymmetricTensor implementation=class=TensorImplementation name=Unnamed rows=3 columns=3 sheets=3 values=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
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inverse transform value at transformed point        = class=NumericalPoint name=Unnamed dimension=3 implementation=class=NumericalPointImplementation name=Unnamed dimension=3 values=[1,1,1]
 
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inverse transform value at transformed point        = class=NumericalPoint name=Unnamed dimension=3 values=[1,1,1]
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inverse transform gradient at transformed point (FD)= class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[2,0,0,1.5,2.59808,0,0,0.57735,0.816497]
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inverse transform gradient at transformed point     = class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[2,0,0,1.5,2.59808,0,0,0.57735,0.816497]
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inverse transform hessian at transformed point      = class=SymmetricTensor implementation=class=TensorImplementation name=Unnamed rows=3 columns=3 sheets=3 values=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]