libopenturns-dev 1.9-5 / usr / include / openturns / swig / Simulation_doc.i

%feature("docstring") OT::Simulation
"Base class for sampling methods.

Available constructor:
    Simulation(*event, verbose=True, convergenceStrategy=ot.Compact()*)

Parameters
----------
event : :class:`~openturns.Event`
    The event we are computing the probability of.
verbose : bool
    If *True*, make the computation verbose.
convergenceStrategy : :class:`~openturns.HistoryStrategy`
    Storage strategy used to store the values of the probability estimator and
    its variance during the simulation algorithm.

Notes
-----
Base class for sampling methods, using the probability distribution of a random
vector :math:`\\\\vect{X}` to evaluate the failure probability:

.. math::

    P_f = \\\\int_{\\\\Rset^{n_X}} \\\\mathbf{1}_{\\\\{g(\\\\ux,\\\\underline{d}) \\\\leq 0 \\\\}}f_{\\\\uX}(\\\\ux)\\\\di{\\\\ux}
        = \\\\Prob{g\\\\left( \\\\vect{X},\\\\vect{d} \\\\right) \\\\leq 0}

Here, :math:`\\\\vect{X}` is a random vector, :math:`\\\\vect{d}` a deterministic
vector, :math:`g(\\\\vect{X},\\\\vect{d})` the function known as *limit state function*
which enables the definition of the event
:math:`\\\\cD_f = \\\\{\\\\vect{X} \\\\in \\\\Rset^n \\\\, | \\\\, g(\\\\vect{X},\\\\vect{d}) \\\\le 0\\\\}`.
:math:`\\\\mathbf{1}_{ \\\\left\\\\{ g(\\\\vect{x}_i,\\\\vect{d}) \\\\leq 0 \\\\right\\\\} }`
describes the indicator function equal to 1 if
:math:`g(\\\\vect{x}_i,\\\\vect{d}) \\\\leq 0` and equal to 0 otherwise.

A Simulation object can be created only through its derived classes:

- :class:`~openturns.DirectionalSampling`

- :class:`~openturns.ImportanceSampling`,

- :class:`~openturns.LHS`,

- :class:`~openturns.MonteCarlo`,

- :class:`~openturns.QuasiMonteCarlo`,

- :class:`~openturns.PostAnalyticalControlledImportanceSampling`,

- :class:`~openturns.PostAnalyticalImportanceSampling`,

- :class:`~openturns.RandomizedLHS`,

- :class:`~openturns.RandomizedQuasiMonteCarlo`.

See also
--------
SimulationResult"

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%feature("docstring") OT::Simulation::getBlockSize
"Accessor to the block size.

Returns
-------
blockSize : int
    Number of terms in the probability simulation estimator grouped together.
    It is set by default to 1."

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%feature("docstring") OT::Simulation::setBlockSize
"Accessor to the block size.

Parameters
----------
blockSize : int, :math:`blockSize \\\\geq 1`
    Number of terms in the probability simulation estimator grouped together.
    It is set by default to 1.

Notes
-----
For Monte Carlo, LHS and Importance Sampling methods, this allows to save space
while allowing multithreading, when available we recommend
to use the number of available CPUs; for the Directional Sampling, we recommend
to set it to 1."

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%feature("docstring") OT::Simulation::getConvergenceStrategy
"Accessor to the convergence strategy.

Returns
-------
storage_strategy : :class:`~openturns.HistoryStrategy`
    Storage strategy used to store the values of the probability estimator
    and its variance during the simulation algorithm."

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%feature("docstring") OT::Simulation::setConvergenceStrategy
"Accessor to the convergence strategy.

Parameters
----------
storage_strategy : :class:`~openturns.HistoryStrategy`
    Storage strategy used to store the values of the probability estimator
    and its variance during the simulation algorithm."

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%feature("docstring") OT::Simulation::getMaximumCoefficientOfVariation
"Accessor to the maximum coefficient of variation.

Returns
-------
coefficient : float
    Maximum coefficient of variation of the simulated sample."

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%feature("docstring") OT::Simulation::setMaximumCoefficientOfVariation
"Accessor to the maximum coefficient of variation.

Parameters
----------
coefficient : float
    Maximum coefficient of variation of the simulated sample."

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%feature("docstring") OT::Simulation::getMaximumOuterSampling
"Accessor to the maximum sample size.

Returns
-------
outerSampling : int
    Maximum number of groups of terms in the probability simulation estimator."

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%feature("docstring") OT::Simulation::setMaximumOuterSampling
"Accessor to the maximum sample size.

Parameters
----------
outerSampling : int
    Maximum number of groups of terms in the probability simulation estimator."

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%feature("docstring") OT::Simulation::getMaximumStandardDeviation
"Accessor to the maximum standard deviation.

Returns
-------
sigma : float, :math:`\\\\sigma > 0`
    Maximum standard deviation of the estimator."

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%feature("docstring") OT::Simulation::setMaximumStandardDeviation
"Accessor to the maximum standard deviation.

Parameters
----------
sigma : float, :math:`\\\\sigma > 0`
    Maximum standard deviation of the estimator."

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%feature("docstring") OT::Simulation::getResult
"Accessor to the results.

Returns
-------
results : :class:`~openturns.SimulationResult`
    Structure containing all the results obtained after simulation and created
    by the method :py:meth:`run`."

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%feature("docstring") OT::Simulation::getEvent
"Accessor to the event.

Returns
-------
event : :class:`~openturns.Event`
    Event we want to evaluate the probability."

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%feature("docstring") OT::Simulation::getVerbose
"Accessor to verbosity.

Returns
-------
verbosity_enabled : bool
    If *True*, the computation is verbose. By default it is verbose."

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%feature("docstring") OT::Simulation::setVerbose
"Accessor to verbosity.

Parameters
----------
verbosity_enabled : bool
    If *True*, make the computation verbose. By default it is verbose."

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%feature("docstring") OT::Simulation::run
"Launch simulation.

Notes
-----
It launches the simulation and creates a :class:`~openturns.SimulationResult`,
structure containing all the results obtained after simulation.
It computes the probability of occurence of the given event by computing the
empirical mean of a sample of size at most *outerSampling * blockSize*,
this sample being built by blocks of size *blockSize*. It allows to use
efficiently the distribution of the computation as well as it allows to deal
with a sample size :math:`> 2^{32}` by a combination of *blockSize* and
*outerSampling*.

see also
--------
setBlockSize, setMaximumOuterSampling, ResourceMap, SimulationResult"

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%feature("docstring") OT::Simulation::drawProbabilityConvergence
"Draw the probability convergence at a given level.

Parameters
----------
level : float, optional
    The probability convergence is drawn at this given confidence length
    *level*. By default *level* is 0.95.

Returns
-------
graph : a :class:`~openturns.Graph`
    probability convergence graph"
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%feature("docstring") OT::Simulation::setProgressCallback
"Set up a progress callback.

Parameters
----------
callback : callable
    Takes a float as argument as percentage of progress."

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%feature("docstring") OT::Simulation::setStopCallback
"Set up a stop callback.

Parameters
----------
callback : callable
    Returns an int deciding whether to stop or continue."