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.

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