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.