1%feature("docstring") OT::MetaModelAlgorithm
2"Base class to compute a metamodel.
3
4Available constructor:
5    MetaModelAlgorithm(*distribution, model*)
6
7Parameters
8----------
9distribution : :class:`~openturns.Distribution`
10    Joint probability density function of the physical input vector.
11model : :class:`~openturns.Function`
12    Physical model to be approximated by a metamodel.
13
14Notes
15-----
16A MetaModelAlgorithm object can be used only through its derived classes:
17
18- :class:`~openturns.KrigingAlgorithm`
19
20- :class:`~openturns.FunctionalChaosAlgorithm`"
21
22// ---------------------------------------------------------------------
23
24
25%feature("docstring") OT::MetaModelAlgorithm::BuildDistribution
26"Recover the distribution, with metamodel performance in mind.
27
28For each marginal, find the best 1-d continuous parametric model
29else fallback to the use of a nonparametric one.
30
31The selection is done as follow:
32
33  - We start with a list of all parametric models (all factories)
34  - For each model, we estimate its parameters if feasible.
35  - We check then if model is `valid`, ie if its Kolmogorov score exceeds a threshold
36    fixed in the `MetaModelAlgorithm-PValueThreshold` ResourceMap key. Default value is 5%
37  - We sort all `valid` models and return the one with the optimal criterion.
38
39For the last step, the criterion might be `BIC`, `AIC` or `AICC`. The specification of the criterion is
40done through the `MetaModelAlgorithm-ModelSelectionCriterion` ResourceMap key. Default value is fixed to `BIC`.
41Note that if there is no `valid` candidate, we estimate a non-parametric model (:class:`~openturns.KernelSmoothing`
42or :class:`~openturns.Histogram`). The `MetaModelAlgorithm-NonParametricModel` ResourceMap key allows selecting
43the preferred one. Default value is `Histogram`
44
45One each marginal is estimated, we use the Spearman independence test on each component pair to decide whether an
46independent copula. In case of non independence, we rely on a :class:`~openturns.NormalCopula`.
47
48Parameters
49----------
50sample : :class:`~openturns.Sample`
51    Input sample.
52
53Returns
54-------
55distribution : :class:`~openturns.Distribution`
56    Input distribution."
57// ---------------------------------------------------------------------
58
59%feature("docstring") OT::MetaModelAlgorithm::getDistribution
60"Accessor to the joint probability density function of the physical input vector.
61
62Returns
63-------
64distribution : :class:`~openturns.Distribution`
65    Joint probability density function of the physical input vector."
66
67// ---------------------------------------------------------------------
68
69%feature("docstring") OT::MetaModelAlgorithm::setDistribution
70"Accessor to the joint probability density function of the physical input vector.
71
72Parameters
73----------
74distribution : :class:`~openturns.Distribution`
75    Joint probability density function of the physical input vector."
76
77// ---------------------------------------------------------------------
78
79%feature("docstring") OT::MetaModelAlgorithm::getInputSample
80"Accessor to the input sample.
81
82Returns
83-------
84inputSample : :class:`~openturns.Sample`
85    Input sample of a model evaluated apart."
86
87// ---------------------------------------------------------------------
88
89%feature("docstring") OT::MetaModelAlgorithm::getOutputSample
90"Accessor to the output sample.
91
92Returns
93-------
94outputSample : :class:`~openturns.Sample`
95    Output sample of a model evaluated apart."
96
97// ---------------------------------------------------------------------
98
99%feature("docstring") OT::MetaModelAlgorithm::run
100"Compute the response surfaces.
101
102Notes
103-----
104It computes the response surfaces and creates a
105:class:`~openturns.MetaModelResult` structure containing all the results."