LHSResult

class LHSResult(*args)

Summarize the results of an LHS optimization.

Available constructor:

LHSResult(bounds, spaceFilling, nRestart)

Parameters
spaceFillingSpaceFilling

The space filling criteria used by optimization algorithm

nRestartint

The number of restarts performed by optimization algorithm

Notes

This class is not intendeted to be built by hand, but returned by optimization algorithms.

Examples

>>> import openturns as ot
>>> lhs = ot.LHSExperiment(ot.ComposedDistribution([ot.Uniform(0.0, 1.0)]*3), 100)
>>> lhs.setAlwaysShuffle(True) # randomized
>>> profile = ot.GeometricProfile()
>>> spaceFilling = ot.SpaceFillingC2()
>>> # Optim algo
>>> algo = ot.SimulatedAnnealingLHS(lhs, profile, spaceFilling)
>>> # first, generate a design
>>> design = algo.generate()
>>> # then, get the result
>>> result = algo.getResult()

Methods

drawHistoryCriterion(self, \*args)

Draw criterion history.

drawHistoryProbability(self, \*args)

Draw probability history.

drawHistoryTemperature(self, \*args)

Draw temperature history.

getAlgoHistory(self, \*args)

Accessor to the internal values computed during optimization algorithm.

getC2(self, \*args)

Accessor to the C2 criterion evaluated on the optimal design.

getClassName(self)

Accessor to the object’s name.

getId(self)

Accessor to the object’s id.

getMinDist(self, \*args)

Minimum distance accessor.

getName(self)

Accessor to the object’s name.

getNumberOfRestarts(self)

Restart number accessor.

getOptimalDesign(self, \*args)

Accessor to the optimal design.

getOptimalValue(self, \*args)

Optimal value accessor.

getPhiP(self, \*args)

Accessor the PhiP criterion evaluated on the optimal design.

getShadowedId(self)

Accessor to the object’s shadowed id.

getVisibility(self)

Accessor to the object’s visibility state.

hasName(self)

Test if the object is named.

hasVisibleName(self)

Test if the object has a distinguishable name.

setName(self, name)

Accessor to the object’s name.

setShadowedId(self, id)

Accessor to the object’s shadowed id.

setVisibility(self, visible)

Accessor to the object’s visibility state.

add

__init__(self, *args)

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

drawHistoryCriterion(self, *args)

Draw criterion history.

Parameters
restartint (optional)

The restart index.

titlestr (optional)

Graph title.

Returns
graphGraph

The resulting graph.

drawHistoryProbability(self, *args)

Draw probability history.

Parameters
restartint (optional)

The restart index.

titlestr (optional)

Graph title.

Returns
graphGraph

The resulting graph.

drawHistoryTemperature(self, *args)

Draw temperature history.

Parameters
restartint (optional)

The restart index.

titlestr (optional)

Graph title.

Returns
graphGraph

The resulting graph.

getAlgoHistory(self, *args)

Accessor to the internal values computed during optimization algorithm.

Returns
historyPoint

Some internal values computed during optimization algorithm. SimulatedAnnealingLHS stores criterion value, temperature and probability at each iteration.

Examples

>>> import openturns as ot
>>> lhs = ot.LHSExperiment(ot.ComposedDistribution([ot.Uniform(0.0, 1.0)]*3), 100)
>>> lhs.setAlwaysShuffle(True) # randomized
>>> profile = ot.GeometricProfile()
>>> spaceFilling = ot.SpaceFillingPhiP(50)
>>> algoSA = ot.SimulatedAnnealingLHS(lhs, profile, spaceFilling)
>>> # Get LHSResult
>>> design = algoSA.generateWithRestart(5)
>>> resultSA = algoSA.getResult()
>>> criterionHistory = resultSA.getAlgoHistory()
getC2(self, *args)

Accessor to the C2 criterion evaluated on the optimal design.

Returns
c2float

The C2 criterion.

Examples

>>> import openturns as ot
>>> lhs = ot.LHSExperiment(ot.ComposedDistribution([ot.Uniform(0.0, 1.0)]*3), 100)
>>> lhs.setAlwaysShuffle(True) # randomized
>>> profile = ot.GeometricProfile()
>>> spaceFilling = ot.SpaceFillingPhiP(50)
>>> algoSA = ot.SimulatedAnnealingLHS(lhs, profile, spaceFilling)
>>> # Get LHSResult
>>> design = algoSA.generate()
>>> resultSA = algoSA.getResult()
>>> c2 = resultSA.getC2()
getClassName(self)

Accessor to the object’s name.

Returns
class_namestr

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

getId(self)

Accessor to the object’s id.

Returns
idint

Internal unique identifier.

getMinDist(self, *args)

Minimum distance accessor.

Parameters
restartint (optional)

The restart index.

Returns
minDistfloat

The minimum distance of sample points.

getName(self)

Accessor to the object’s name.

Returns
namestr

The name of the object.

getNumberOfRestarts(self)

Restart number accessor.

Returns
restartint (optional)

The number of restart.

getOptimalDesign(self, *args)

Accessor to the optimal design.

Returns
designSample

The design that optimizes the criterion.

Examples

>>> import openturns as ot
>>> lhs = ot.LHSExperiment(ot.ComposedDistribution([ot.Uniform(0.0, 1.0)]*3), 100)
>>> lhs.setAlwaysShuffle(True) # randomized
>>> spaceFilling = ot.SpaceFillingPhiP(10)
>>> # By Monte Carlo
>>> algoMC = ot.MonteCarloLHS(lhs, 1000, spaceFilling)
>>> # Get LHSResult
>>> optimalDesignMC = algoMC.generate()
>>> resultMC = algoMC.getResult()
>>> # By simulated annealing, with restart
>>> profile = ot.GeometricProfile()
>>> algoSA = ot.SimulatedAnnealingLHS(lhs, profile, spaceFilling)
>>> # Get LHSResult
>>> optimalDesignSA = algoSA.generateWithRestart(5)
>>> resultSA = algoSA.getResult()
>>> # Get optimal results for all restarts
>>> optimRestart = [resultSA.getOptimalDesign(i) for i in range(resultSA.getNumberOfRestarts())]
getOptimalValue(self, *args)

Optimal value accessor.

Returns
valuefloat (optional)

The optimal value.

getPhiP(self, *args)

Accessor the PhiP criterion evaluated on the optimal design.

Returns
phiPfloat

The PhiP criterion.

Examples

>>> import openturns as ot
>>> lhs = ot.LHSExperiment(ot.ComposedDistribution([ot.Uniform(0.0, 1.0)]*3), 100)
>>> lhs.setAlwaysShuffle(True) # randomized
>>> profile = ot.GeometricProfile()
>>> spaceFilling = ot.SpaceFillingPhiP(50)
>>> algoSA = ot.SimulatedAnnealingLHS(lhs, profile, spaceFilling)
>>> design = algoSA.generate()
>>> # Get LHSResult
>>> resultSA = algoSA.getResult()
>>> phip = resultSA.getPhiP()
getShadowedId(self)

Accessor to the object’s shadowed id.

Returns
idint

Internal unique identifier.

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.

hasVisibleName(self)

Test if the object has a distinguishable name.

Returns
hasVisibleNamebool

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

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.

setVisibility(self, visible)

Accessor to the object’s visibility state.

Parameters
visiblebool

Visibility flag.