MonteCarloLHS¶
- class MonteCarloLHS(*args)¶
Monte Carlo LHS optimization.
Performs the optimization of an LHS using Monte Carlo simulations.
- Parameters:
- lhsDesign
LHSExperiment
Factory that generate designs
- Nint
Number of simulations
- spaceFilling
SpaceFilling
, optional Criterion to be optimized, default is
SpaceFillingMinDist
- lhsDesign
Notes
MonteCarloLHS generates LHS designs and returns the optimal one with respect to a space-filling criterion. Unlike
SimulatedAnnealingLHS
it does not apply cell swaps directly so the design may not change that much if the shuffle property of theLHSExperiment
is disabled.Examples
>>> import openturns as ot >>> dimension = 3 >>> size = 100 >>> # Build standard randomized LHS algorithm >>> distribution = ot.JointDistribution([ot.Uniform(0.0, 1.0)]*dimension) >>> lhs = ot.LHSExperiment(distribution, size) >>> lhs.setAlwaysShuffle(True) # randomized >>> # Defining space fillings >>> spaceFilling = ot.SpaceFillingC2() >>> # RandomBruteForce MonteCarlo with N designs (LHS with C2 optimization) >>> N = 10000 >>> optimalLHSAlgorithm = ot.MonteCarloLHS(lhs, N, spaceFilling)
Methods
generate
()Generate points according to the type of the experiment.
Generate points and their associated weight according to the type of the experiment.
Accessor to the object's name.
Accessor to the distribution.
getLHS
()Return the LHS design.
getName
()Accessor to the object's name.
Result accessor.
getSize
()Accessor to the size of the generated sample.
Return the space-filling criterion to be optimized.
hasName
()Test if the object is named.
Ask whether the experiment has uniform weights.
isRandom
()Accessor to the randomness of quadrature.
setDistribution
(distribution)Accessor to the distribution.
setName
(name)Accessor to the object's name.
setSize
(size)Accessor to the size of the generated sample.
- __init__(*args)¶
- generate()¶
Generate points according to the type of the experiment.
- Returns:
- sample
Sample
Points of the design of experiments. The sampling method is defined by the type 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()¶
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 of the input random vector.
- distribution
- 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
- getSize()¶
Accessor to the size of the generated sample.
- Returns:
- sizepositive int
Number of points constituting the design of experiments.
- getSpaceFilling()¶
Return the space-filling criterion to be optimized.
- Returns:
- value
SpaceFilling
Criterion function to be optimized
- value
- 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.
- isRandom()¶
Accessor to the randomness of quadrature.
- Parameters:
- isRandombool
Is true if the design of experiments is random. Otherwise, the design of experiment is assumed to be deterministic.
- setDistribution(distribution)¶
Accessor to the distribution.
- Parameters:
- distribution
Distribution
Distribution of the input random vector.
- distribution
- setName(name)¶
Accessor to the object’s name.
- Parameters:
- namestr
The name of the object.
- setSize(size)¶
Accessor to the size of the generated sample.
- Parameters:
- sizepositive int
Number of points constituting the design of experiments.