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:  lhsDesign :
LHSExperiment
Factory that generate designs
 initialDesign : 2darray sequence
Initial design to be optimized
 distribution :
Distribution
Distribution of designs
 profile :
TemperatureProfile
Temperature profile used by the simulated annealing algorithm Default one is GeometricProfile
 spaceFilling :
SpaceFilling
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)
Attributes: thisown
The membership flag
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 spacefilling 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)¶ Initialize self. See help(type(self)) for accurate signature.

generate
()¶ Generate points according to the type of the experiment.
Returns:  sample :
Sample
Points which constitute the design of experiments with . 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 ]
 sample :

generateWithWeights
()¶ Generate points and their associated weight according to the type of the experiment.
Returns: 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_name : str
The object class name (object.__class__.__name__).

getDistribution
()¶ Accessor to the distribution.
Returns:  distribution :
Distribution
Distribution used to generate the set of input data.
 distribution :

getId
()¶ Accessor to the object’s id.
Returns:  id : int
Internal unique identifier.

getLHS
()¶ Return the LHS design.
Returns:  value :
LHSExperiment
Result the factory that builds initial design to be optimized
 value :

getName
()¶ Accessor to the object’s name.
Returns:  name : str
The name of the object.

getResult
()¶ Result accessor.
Returns:  value :
LHSResult
Result of generation that contains the optimal design, some criteria and history
 value :

getShadowedId
()¶ Accessor to the object’s shadowed id.
Returns:  id : int
Internal unique identifier.

getSize
()¶ Accessor to the size of the generated sample.
Returns:  size : positive int
Number of points constituting the design of experiments.

getSpaceFilling
()¶ Return the spacefilling criterion to be optimized.
Returns:  value :
SpaceFilling
Criterion function to be optimized
 value :

getVisibility
()¶ Accessor to the object’s visibility state.
Returns:  visible : bool
Visibility flag.

hasName
()¶ Test if the object is named.
Returns:  hasName : bool
True if the name is not empty.

hasUniformWeights
()¶ Ask whether the experiment has uniform weights.
Returns:  hasUniformWeights : bool
Whether the experiment has uniform weights.

hasVisibleName
()¶ Test if the object has a distinguishable name.
Returns:  hasVisibleName : bool
True if the name is not empty and not the default one.

setDistribution
(distribution)¶ Accessor to the distribution.
Parameters:  distribution :
Distribution
Distribution used to generate the set of input data.
 distribution :

setName
(name)¶ Accessor to the object’s name.
Parameters:  name : str
The name of the object.

setShadowedId
(id)¶ Accessor to the object’s shadowed id.
Parameters:  id : int
Internal unique identifier.

setSize
(size)¶ Accessor to the size of the generated sample.
Parameters:  size : positive int
Number of points constituting the design of experiments.

setVisibility
(visible)¶ Accessor to the object’s visibility state.
Parameters:  visible : bool
Visibility flag.

thisown
¶ The membership flag
 lhsDesign :