%feature("docstring") OT::ImportanceSamplingExperiment
"Importance Sampling experiment.

Available constructors:
    ImportanceSamplingExperiment(*distribution*)

    ImportanceSamplingExperiment(*distribution, size*)

    ImportanceSamplingExperiment(*distribution, importanceDistribution, size*)

Parameters
----------
distribution : :class:`~openturns.Distribution`
    Distribution :math:`\mu` with an independent copula used to generate the
    set of input data.
size : positive int
    Number :math:`cardI` of points that will be generated in the experiment.
importanceDistribution : :class:`~openturns.Distribution`
    Distribution :math:`p` according to which the points of the design of
    experiments will be generated with the Importance Sampling technique.

Notes
-----
ImportanceSamplingExperiment is a random weighted design of experiments.
The :meth:`generate` method generates points :math:`(\Xi_i)_{i \in I}`
independently from the distribution :math:`\mu`. When the :meth:`generate`
method is recalled, the generated sample changes.
The weights associated to the points are all equal to:
:math:`\frac{1}{\Xi_i}\frac{\mu(\Xi_i)}{p(\Xi_i)}`

See also
--------
WeightedExperiment

Examples
--------
>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.ComposedDistribution([ot.Uniform(0, 1)] * 2)
>>> importanceDistribution = ot.ComposedDistribution([ot.Uniform(0, 1)] * 2)
>>> myPlane = ot.ImportanceSamplingExperiment(distribution, importanceDistribution, 5)
>>> print(myPlane.generate())
    [ X0        X1        ]
0 : [ 0.629877  0.882805  ]
1 : [ 0.135276  0.0325028 ]
2 : [ 0.347057  0.969423  ]
3 : [ 0.92068   0.50304   ]
4 : [ 0.0632061 0.292757  ]"

// ---------------------------------------------------------------------

%feature("docstring") OT::WeightedExperiment::getImportanceDistribution
"Accessor to the importance distribution.

Returns
-------
importanceDistribution : :class:`~openturns.Distribution`
    Distribution :math:`p` according to which the points of the design of
    experiments will be generated with the Importance Sampling technique."
