OptimalLHSExperiment¶

class
OptimalLHSExperiment
(*args)¶ OptimalLHS base class.
See also
Notes
Perform the generation of optimal LHS designs.
See a complementary bibiliographic reference: [mckay1979]
Methods
generate
()Generate points according to the type of the experiment.
generateWithWeights
(weights)Generate points and their associated weight according to the type of the experiment.
Accessor to the object’s name.
Accessor to the distribution.
getId
()Accessor to the object’s id.
getLHS
()Return the LHS design.
getName
()Accessor to the object’s name.
Result accessor.
Accessor to the object’s shadowed id.
getSize
()Accessor to the size of the generated sample.
Return the spacefilling criterion to be optimized.
Accessor to the object’s visibility state.
hasName
()Test if the object is named.
Ask whether the experiment has uniform weights.
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.

__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.
 sample
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
(weights)¶ 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_namestr
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
 idint
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
 namestr
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
 idint
Internal unique identifier.

getSize
()¶ Accessor to the size of the generated sample.
 Returns
 sizepositive 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
 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
 distribution
Distribution
Distribution used to generate the set of input data.
 distribution

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 of points constituting the design of experiments.

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