MetaModelResult

class MetaModelResult(*args)

Data structure containing a metamodel.

Parameters:

sampleX, sampleY2-d sequence of float

Input/output samples

metaModelFunction

Definition of the response surface(s) of the model’s output(s).

Methods

getClassName()	Accessor to the object's name.
getInputSample()	Accessor to the input sample.
getMetaModel()	Accessor to the metamodel.
getName()	Accessor to the object's name.
getOutputSample()	Accessor to the output sample.
hasName()	Test if the object is named.
setInputSample(sampleX)	Accessor to the input sample.
setMetaModel(metaModel)	Accessor to the metamodel.
setName(name)	Accessor to the object's name.
setOutputSample(sampleY)	Accessor to the output sample.

See also

openturns.LinearModelResult, openturns.FunctionalChaosResult, openturns.experimental.GaussianProcessRegressionResult

Notes

Structure created by the method run() of GaussianProcessRegression or FunctionalChaosAlgorithm and obtained thanks to the method getResult() of these classes.

__init__(*args)

getClassName()

Accessor to the object’s name.

Returns:

class_namestr

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

getInputSample()

Accessor to the input sample.

Returns:

inputSampleSample

The input sample.

getMetaModel()

Accessor to the metamodel.

Returns:

metaModelFunction

Metamodel.

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

getOutputSample()

Accessor to the output sample.

Returns:

outputSampleSample

The output sample.

hasName()

Test if the object is named.

Returns:

hasNamebool

True if the name is not empty.

setInputSample(sampleX)

Accessor to the input sample.

Parameters:

inputSampleSample

The input sample.

setMetaModel(metaModel)

Accessor to the metamodel.

Parameters:

metaModelFunction

Metamodel.

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.

setOutputSample(sampleY)

Accessor to the output sample.

Parameters:

outputSampleSample

The output sample.