/usr/include/openturns/swig/GaussianProcess_doc.i is in libopenturns-dev 1.9-5.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | %feature("docstring") OT::GaussianProcess
"Gaussian processes.
Available constructor:
GaussianProcess(*trend, secondOrderModel, mesh*)
GaussianProcess(*trend, covarianceModel, mesh*)
GaussianProcess(*secondOrderModel, mesh*)
GaussianProcess(*covarianceModel, mesh*)
Parameters
----------
trend : :class:`~openturns.TrendTransform`
Trend function of the process. By default the trend is null.
secondOrderModel : :class:`~openturns.SecondOrderModel`
Stationary second order model that insures the coherence between the
covariance function and the spectral density function.
covarianceModel : :class:`~openturns.CovarianceModel`
Temporal covariance model :math:`C`.
mesh : :class:`~openturns.Mesh`
Mesh :math:`\\\\cM` over which the domain :math:`\\\\cD` is discretized.
Notes
-----
GaussianProcess creates the processes,
:math:`X: \\\\Omega \\\\times\\\\cD \\\\mapsto \\\\Rset^d` where :math:`\\\\cD \\\\in \\\\Rset^n`,
from their temporal covariance function
:math:`\\\\cC: \\\\cD \\\\times \\\\cD \\\\mapsto \\\\cM_{d \\\\times d}(\\\\Rset)`, which writes, in
the stationary case: :math:`\\\\cC^{stat}: \\\\cD \\\\mapsto \\\\cM_{d \\\\times d}(\\\\Rset)`. A
process is *normal*, if all its finite dimensional joint distributions are
normal (See the method :meth:`~openturns.Process.isNormal` for a detailed definition).
The gaussian processes may have a trend: in that case, the normal
process is the sum of the trend function
:math:`f_{trend}: \\\\Rset^n \\\\mapsto \\\\Rset^d` and a zero-mean normal process.
If the zero-mean process is stationary, in order to manipulate the same
normal process through both the temporal and spectral views, it is necessary to
create a second order model *secondOrderModel* that insures the coherence
between the covariance function :math:`C^{stat}` and the spectral density
function :math:`S: \\\\Rset \\\\mapsto \\\\cH^+(d)`. :math:`\\\\cH^+(d) \\\\in \\\\cM_d(\\\\Cset)`
is the set of :math:`d`-dimensional positive definite hermitian matrices.
Examples
--------
>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> # Default dimension parameter to evaluate the model
>>> defaultDimension = 1
>>> # Amplitude values
>>> amplitude = [1.0]*defaultDimension
>>> # Scale values
>>> scale = [1.0]*defaultDimension
>>> # Second order model with parameters
>>> myModel = ot.ExponentialCauchy(scale, amplitude)
>>> # Time grid
>>> tmin = 0.0
>>> step = 0.1
>>> n = 11
>>> myTimeGrid = ot.RegularGrid(tmin, step, n)
>>> size = 100
>>> myProcess = ot.GaussianProcess(myModel, myTimeGrid)"
// ---------------------------------------------------------------------
%feature("docstring") OT::GaussianProcess::getCovarianceModel
"Get the covariance model.
Returns
-------
covarianceModel : :class:`~openturns.CovarianceModel`
Temporal covariance model :math:`C`."
// ---------------------------------------------------------------------
%feature("docstring") OT::GaussianProcess::getTrend
"Get the trend function.
Returns
-------
trend : :class:`~openturns.TrendTransform`
Trend function."
// ---------------------------------------------------------------------
%feature("docstring") OT::GaussianProcess::isTrendStationary
"Tell if the process is trend stationary or not.
Returns
-------
isTrendStationary : bool
*True* if the process is trend stationary."
// ---------------------------------------------------------------------
%feature("docstring") OT::GaussianProcess::setSamplingMethod
"Set the used method for getRealization.
Available parameters are :
* 0 : Cholesky factor sampling (default method)
* 1 : H-Matrix method (if H-Mat available)
* 2 : Gibbs method (in dimension 1 only)
Parameters
----------
samplingMethod : int
Fix a method for sampling.
"
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