SimulatedAnnealingLHS¶

class SimulatedAnnealingLHS(*args)¶

LHS optimization using simulated annealing.

Performs the optimization of an LHS using simulated annealing algorithm.

Available constructors:

SimulatedAnnealingLHS(lhsDesign)

SimulatedAnnealingLHS(lhsDesign, spaceFilling)

SimulatedAnnealingLHS(lhsDesign, spaceFilling, profile)

SimulatedAnnealingLHS(initialDesign, distribution)

SimulatedAnnealingLHS(initialDesign, distribution, spaceFilling)

SimulatedAnnealingLHS(initialDesign, distribution, spaceFilling, profile)

Parameters:

lhsDesignLHSExperiment: Factory that generate designs
initialDesign2d-array sequence: Initial design to be optimized
distributionDistribution: Distribution of designs
spaceFillingSpaceFilling: Criterion to be optimized Default is SpaceFillingPhiP
profileTemperatureProfile: Temperature profile used by the simulated annealing algorithm Default is GeometricProfile

Notes

With the first constructor, the initial design is generated thanks to lhsDesign. With the second usage (initialDesign), it must be already generated. Starting from this design, a new design is obtained by swapping one random coordinate of two randomly chosen elements; by construction, this design is also an LHS design. If the new design is better than the previous one, it is kept. If it is worse, it may anyway be kept with some probability, which depends on how these designs compare, but also on a temperature profile T which decreases over time. This means that jumping away from local extrema becomes less probable over time.

Examples

>>> import openturns as ot
>>> dimension = 3
>>> size = 100
>>> # Build standard randomized LHS algorithm
>>> distribution = ot.ComposedDistribution([ot.Uniform(0.0, 1.0)]*dimension)
>>> lhs = ot.LHSExperiment(distribution, size)
>>> lhs.setAlwaysShuffle(True) # randomized
>>> # Defining space fillings
>>> spaceFilling = ot.SpaceFillingC2()
>>> # Geometric profile
>>> geomProfile = ot.GeometricProfile(10.0, 0.95, 2000)
>>> # Simulated Annealing LHS with geometric temperature profile, C2 optimization
>>> optimalLHSAlgorithm = ot.SimulatedAnnealingLHS(lhs, spaceFilling, geomProfile)

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.
`getId`()	Accessor to the object's id.
`getLHS`()	Return the LHS design.
`getName`()	Accessor to the object's name.
`getResult`()	Result accessor.
`getShadowedId`()	Accessor to the object's shadowed id.
`getSize`()	Accessor to the size of the generated sample.
`getSpaceFilling`()	Return the space-filling criterion to be optimized.
`getVisibility`()	Accessor to the object's visibility state.
`hasName`()	Test if the object is named.
`hasUniformWeights`()	Ask whether the experiment has uniform weights.
`hasVisibleName`()	Test if the object has a distinguishable name.
`setDistribution`(distribution)	Accessor to the distribution.
`setName`(name)	Accessor to the object's name.
`setShadowedId`(id)	Accessor to the object's shadowed id.
`setSize`(size)	Accessor to the size of the generated sample.
`setVisibility`(visible)	Accessor to the object's visibility state.

generateWithRestart

__init__(*args)¶

generate()¶

Generate points according to the type of the experiment.

Returns:

sampleSample: Points $(\Xi_i)_{i \in I}$ which constitute the design of experiments with $card I = size$ . The sampling method is defined by the nature 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 which constitute the design of experiments. The sampling method is defined by the nature of the experiment.
weightsPoint of size $cardI$: Weights $(\omega_i)_{i \in I}$ associated with the points. By default, all the weights are equal to $1/cardI$ .

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 used to generate the set of input data.

getId()¶

Accessor to the object’s id.

Returns:

idint: Internal unique identifier.

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

getShadowedId()¶

Accessor to the object’s shadowed id.

Returns:

idint: Internal unique identifier.

getSize()¶

Accessor to the size of the generated sample.

Returns:

sizepositive int: Number $cardI$ of points constituting the design of experiments.

getSpaceFilling()¶

Return the space-filling criterion to be optimized.

Returns:

valueSpaceFilling: Criterion function to be optimized

getVisibility()¶

Accessor to the object’s visibility state.

Returns:

visiblebool: Visibility flag.

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.

hasVisibleName()¶

Test if the object has a distinguishable name.

Returns:

hasVisibleNamebool: True if the name is not empty and not the default one.

setDistribution(distribution)¶

Accessor to the distribution.

Parameters:

distributionDistribution: Distribution used to generate the set of input data.

setName(name)¶

Accessor to the object’s name.

Parameters:

namestr: The name of the object.

setShadowedId(id)¶

Accessor to the object’s shadowed id.

Parameters:

idint: Internal unique identifier.

setSize(size)¶

Accessor to the size of the generated sample.

Parameters:

sizepositive int: Number $cardI$ of points constituting the design of experiments.

setVisibility(visible)¶

Accessor to the object’s visibility state.

Parameters:

visiblebool: Visibility flag.

Examples using the class¶

Kriging :configure the optimization solver

Various design of experiments in OpenTURNS

Optimize an LHS design of experiments

OpenTURNS

An Open source initiative for the Treatment of Uncertainties, Risks'N Statistics

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SimulatedAnnealingLHS¶

Examples using the class¶