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, profile)

SimulatedAnnealingLHS(lhsDesign, profile, spaceFilling)

SimulatedAnnealingLHS(initialDesign, distribution)

SimulatedAnnealingLHS(initialDesign, distribution, profile)

SimulatedAnnealingLHS(initialDesign, distribution, profile, spaceFilling)

Parameters
lhsDesignLHSExperiment

Factory that generate designs

initialDesign2d-array sequence

Initial design to be optimized

distributionDistribution

Distribution of designs

profileTemperatureProfile

Temperature profile used by the simulated annealing algorithm Default one is GeometricProfile

spaceFillingSpaceFilling

Criterion to be optimized Default one is SpaceFillingMinDist

Notes

With the first constructor, the initial design is generated thanks to lhsDesign. With the second usage, we fix it. Starting from this design, a new design is obtained by permuting 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, geomProfile, spaceFilling)

Methods

generate(self)

Generate points according to the type of the experiment.

generateWithWeights(self)

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

getClassName(self)

Accessor to the object’s name.

getDistribution(self)

Accessor to the distribution.

getId(self)

Accessor to the object’s id.

getLHS(self)

Return the LHS design.

getName(self)

Accessor to the object’s name.

getResult(self)

Result accessor.

getShadowedId(self)

Accessor to the object’s shadowed id.

getSize(self)

Accessor to the size of the generated sample.

getSpaceFilling(self)

Return the space-filling criterion to be optimized.

getVisibility(self)

Accessor to the object’s visibility state.

hasName(self)

Test if the object is named.

hasUniformWeights(self)

Ask whether the experiment has uniform weights.

hasVisibleName(self)

Test if the object has a distinguishable name.

setDistribution(self, distribution)

Accessor to the distribution.

setName(self, name)

Accessor to the object’s name.

setShadowedId(self, id)

Accessor to the object’s shadowed id.

setSize(self, size)

Accessor to the size of the generated sample.

setVisibility(self, visible)

Accessor to the object’s visibility state.

generateWithRestart

__init__(self, *args)

Initialize self. See help(type(self)) for accurate signature.

generate(self)

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(self)

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(self)

Accessor to the object’s name.

Returns
class_namestr

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

getDistribution(self)

Accessor to the distribution.

Returns
distributionDistribution

Distribution used to generate the set of input data.

getId(self)

Accessor to the object’s id.

Returns
idint

Internal unique identifier.

getLHS(self)

Return the LHS design.

Returns
valueLHSExperiment

Result the factory that builds initial design to be optimized

getName(self)

Accessor to the object’s name.

Returns
namestr

The name of the object.

getResult(self)

Result accessor.

Returns
valueLHSResult

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

getShadowedId(self)

Accessor to the object’s shadowed id.

Returns
idint

Internal unique identifier.

getSize(self)

Accessor to the size of the generated sample.

Returns
sizepositive int

Number cardI of points constituting the design of experiments.

getSpaceFilling(self)

Return the space-filling criterion to be optimized.

Returns
valueSpaceFilling

Criterion function to be optimized

getVisibility(self)

Accessor to the object’s visibility state.

Returns
visiblebool

Visibility flag.

hasName(self)

Test if the object is named.

Returns
hasNamebool

True if the name is not empty.

hasUniformWeights(self)

Ask whether the experiment has uniform weights.

Returns
hasUniformWeightsbool

Whether the experiment has uniform weights.

hasVisibleName(self)

Test if the object has a distinguishable name.

Returns
hasVisibleNamebool

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

setDistribution(self, distribution)

Accessor to the distribution.

Parameters
distributionDistribution

Distribution used to generate the set of input data.

setName(self, name)

Accessor to the object’s name.

Parameters
namestr

The name of the object.

setShadowedId(self, id)

Accessor to the object’s shadowed id.

Parameters
idint

Internal unique identifier.

setSize(self, size)

Accessor to the size of the generated sample.

Parameters
sizepositive int

Number cardI of points constituting the design of experiments.

setVisibility(self, visible)

Accessor to the object’s visibility state.

Parameters
visiblebool

Visibility flag.