MonteCarloExperiment

(Source code, png)

../../_images/MonteCarloExperiment.png
class MonteCarloExperiment(*args)

MonteCarlo experiment.

Available constructors:

MonteCarloExperiment(distribution, size)

MonteCarloExperiment(size)

Parameters:
distributionDistribution

Distribution \mu with an independent copula used to generate the set of input data.

sizepositive int

Number \sampleSize of points that will be generated in the experiment.

Notes

MonteCarloExperiment is a random weighted design of experiments (see [hammersley1961] page 51, [lemieux2009] page 3). The generate() method computes the nodes (\inputReal_i)_{i = 1, ..., \sampleSize} by generating independent observations from the distribution \mu. The weights associated to the points are all equal to w_i = \frac{1}{\sampleSize} where \sampleSize is the sample size. When the generate() method is called a second time, the generated sample changes.

Examples

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> experiment = ot.MonteCarloExperiment(ot.Normal(2), 5)
>>> print(experiment.generate())
    [ X0        X1        ]
0 : [  0.608202 -1.26617  ]
1 : [ -0.438266  1.20548  ]
2 : [ -2.18139   0.350042 ]
3 : [ -0.355007  1.43725  ]
4 : [  0.810668  0.793156 ]

Methods

generate()

Generate points according to the type of the experiment.

generateWithWeights()

Generate points and their associated weight according to the type of the experiment.

getClassName()

Accessor to the object's name.

getDistribution()

Accessor to the distribution.

getName()

Accessor to the object's name.

getSize()

Accessor to the size of the generated sample.

hasName()

Test if the object is named.

hasUniformWeights()

Ask whether the experiment has uniform weights.

isRandom()

Accessor to the randomness of quadrature.

setDistribution(distribution)

Accessor to the distribution.

setName(name)

Accessor to the object's name.

setSize(size)

Accessor to the size of the generated sample.

__init__(*args)
generate()

Generate points according to the type of the experiment.

Returns:
sampleSample

Points (\inputReal_i)_{i = 1, ..., \sampleSize} of the design of experiments. The sampling method is defined by the type of the weighted experiment.

Examples

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> myExperiment = ot.MonteCarloExperiment(ot.Normal(2), 5)
>>> sample = myExperiment.generate()
>>> print(sample)
    [ X0        X1        ]
0 : [  0.608202 -1.26617  ]
1 : [ -0.438266  1.20548  ]
2 : [ -2.18139   0.350042 ]
3 : [ -0.355007  1.43725  ]
4 : [  0.810668  0.793156 ]
generateWithWeights()

Generate points and their associated weight according to the type of the experiment.

Returns:
sampleSample

The points of the design of experiments. The sampling method is defined by the nature of the experiment.

weightsPoint of size \sampleSize

Weights (w_i)_{i = 1, ..., \sampleSize} associated with the points. By default, all the weights are equal to \frac{1}{\sampleSize}.

Examples

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> myExperiment = ot.MonteCarloExperiment(ot.Normal(2), 5)
>>> sample, weights = myExperiment.generateWithWeights()
>>> print(sample)
    [ X0        X1        ]
0 : [  0.608202 -1.26617  ]
1 : [ -0.438266  1.20548  ]
2 : [ -2.18139   0.350042 ]
3 : [ -0.355007  1.43725  ]
4 : [  0.810668  0.793156 ]
>>> print(weights)
[0.2,0.2,0.2,0.2,0.2]
getClassName()

Accessor to the object’s name.

Returns:
class_namestr

The object class name (object.__class__.__name__).

getDistribution()

Accessor to the distribution.

Returns:
distributionDistribution

Distribution of the input random vector.

getName()

Accessor to the object’s name.

Returns:
namestr

The name of the object.

getSize()

Accessor to the size of the generated sample.

Returns:
sizepositive int

Number \sampleSize of points constituting the design of experiments.

hasName()

Test if the object is named.

Returns:
hasNamebool

True if the name is not empty.

hasUniformWeights()

Ask whether the experiment has uniform weights.

Returns:
hasUniformWeightsbool

Whether the experiment has uniform weights.

isRandom()

Accessor to the randomness of quadrature.

Parameters:
isRandombool

Is true if the design of experiments is random. Otherwise, the design of experiment is assumed to be deterministic.

setDistribution(distribution)

Accessor to the distribution.

Parameters:
distributionDistribution

Distribution of the input random vector.

setName(name)

Accessor to the object’s name.

Parameters:
namestr

The name of the object.

setSize(size)

Accessor to the size of the generated sample.

Parameters:
sizepositive int

Number \sampleSize of points constituting the design of experiments.

Examples using the class

Advanced polynomial chaos construction

Advanced polynomial chaos construction

Estimate a probability with Monte Carlo

Estimate a probability with Monte Carlo

Specify a simulation algorithm

Specify a simulation algorithm

Estimate a probability with Monte-Carlo on axial stressed beam: a quick start guide to reliability

Estimate a probability with Monte-Carlo on axial stressed beam: a quick start guide to reliability

Exploitation of simulation algorithm results

Exploitation of simulation algorithm results

Time variant system reliability problem

Time variant system reliability problem

Create unions or intersections of events

Create unions or intersections of events

Axial stressed beam : comparing different methods to estimate a probability

Axial stressed beam : comparing different methods to estimate a probability

Using the FORM - SORM algorithms on a nonlinear function

Using the FORM - SORM algorithms on a nonlinear function

Use the ANCOVA indices

Use the ANCOVA indices

Create a Monte Carlo design of experiments

Create a Monte Carlo design of experiments

Probabilistic design of experiments

Probabilistic design of experiments

Compute the L2 error between two functions

Compute the L2 error between two functions

Create a random design of experiments

Create a random design of experiments

Create a design of experiments with discrete and continuous variables

Create a design of experiments with discrete and continuous variables

Control algorithm termination

Control algorithm termination