SequentialSamplingAlgorithm

class SequentialSamplingAlgorithm(*args)

Approximation algorithm.

Methods

generate([size])	Generate a new sample.
getClassName()	Accessor to the object's name.
getGenerationIndices()	Accessor to the generation indices.
getId()	Accessor to the object's id.
getImplementation()	Accessor to the underlying implementation.
getInputSample()	Accessor to the input sample.
getName()	Accessor to the object's name.
getOutputSample()	Accessor to the output sample.
setName(name)	Accessor to the object's name.
update(x, y)	Update the current sample.

Notes

This class is not usable because it has sense only within the FunctionalChaosAlgorithm.

__init__(*args)

generate(size=1)

Generate a new sample.

Parameters:

sizeint

Size of sample to generate

Returns:

xSample

New input sample.

getClassName()

Accessor to the object’s name.

Returns:

class_namestr

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

getGenerationIndices()

Accessor to the generation indices.

Returns:

generationIndicesIndices

Indices of last element for each generation. This is updated each time the update() method is evaluated.

getId()

Accessor to the object’s id.

Returns:

idint

Internal unique identifier.

getImplementation()

Accessor to the underlying implementation.

Returns:

implImplementation

A copy of the underlying implementation object.

getInputSample()

Accessor to the input sample.

Returns:

xSample

Input sample

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

getOutputSample()

Accessor to the output sample.

Returns:

ySample

Input sample

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.

update(x, y)

Update the current sample.

Adds a new incremental sample (input and output) to the existing sample.

Parameters:

x, ySample

New incremental sample.