MonteCarloLHS

class MonteCarloLHS(*args)

Monte Carlo LHS optimization.

Performs the optimization of an LHS using Monte Carlo simulations.

Parameters:

lhsDesignLHSExperiment

Factory that generate designs

Nint

Number of simulations

spaceFillingSpaceFilling, optional

Criterion to be optimized, default is SpaceFillingMinDist

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.
getLHS()	Return the LHS design.
getName()	Accessor to the object's name.
getResult()	Result accessor.
getSize()	Accessor to the size of the generated sample.
getSpaceFilling()	Return the space-filling criterion to be optimized.
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.

Notes

MonteCarloLHS generates LHS designs and returns the optimal one with respect to a space-filling criterion. Unlike SimulatedAnnealingLHS it does not apply cell swaps directly so the design may not change that much if the shuffle property of the LHSExperiment is disabled.

Examples

>>> import openturns as ot
>>> dimension = 3
>>> size = 100
>>> # Build standard randomized LHS algorithm
>>> distribution = ot.JointDistribution([ot.Uniform(0.0, 1.0)]*dimension)
>>> lhs = ot.LHSExperiment(distribution, size)
>>> lhs.setAlwaysShuffle(True) # randomized
>>> # Defining space fillings
>>> spaceFilling = ot.SpaceFillingC2()
>>> # RandomBruteForce MonteCarlo with N designs (LHS with C2 optimization)
>>> N = 10000
>>> optimalLHSAlgorithm = ot.MonteCarloLHS(lhs, N, spaceFilling)

__init__(*args)

generate()

Generate points according to the type of the experiment.

Returns:

sampleSample

Points 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

Weights associated with the points. By default, all the weights are equal to .

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.

getLHS()

Return the LHS design.

Returns:

valueLHSExperiment

Result the factory that builds initial design to be optimized

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

getResult()

Result accessor.

Returns:

valueLHSResult

Result of generation that contains the optimal design, some criteria and history

getSize()

Accessor to the size of the generated sample.

Returns:

sizepositive int

Number of points constituting the design of experiments.

getSpaceFilling()

Return the space-filling criterion to be optimized.

Returns:

valueSpaceFilling

Criterion function to be optimized

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 of points constituting the design of experiments.