InferenceAnalysis

class persalys.InferenceAnalysis(*args)

Perform a Kolmogorov goodness-of-fit test for 1-D continuous distributions.

Parameters

namestr

Name

designDesignOfExperiment

Design of experiments

Examples

>>> import openturns as ot
>>> import persalys
>>> ot.RandomGenerator.SetSeed(0)

Create the model:

>>> filename = 'data.csv'
>>> sample = ot.Normal(3).getSample(100)
>>> sample.exportToCSVFile(filename)
>>> model = persalys.DataModel('myDataModel', 'data.csv', [0, 1, 2])

Create the Inference Analysis:

>>> analysis = persalys.InferenceAnalysis('analysis', model)
>>> analysis.run()

Get the result:

>>> result = analysis.getResult()
>>> resultX0 = result.getFittingTestResultForVariable('X0')

Methods

getClassName()	Accessor to the object's name.
getDesignOfExperiment()	Design of experiments accessor.
getDistributionsFactories(variableName)	Get the sequence of distributions to test for a variable.
getErrorMessage()	Error message accessor.
getInterestVariables()	Get the variables to analyse.
getLevel()	Level accessor.
getName()	Accessor to the object's name.
getPythonScript()	Physical model accessor.
getResult()	Result accessor.
getWarningMessage()	Warning message accessor.
hasName()	Test if the object is named.
hasValidResult()	Whether the analysis has been run.
isReliabilityAnalysis()	Whether the analysis involves reliability.
isRunning()	Whether the analysis is running.
run()	Launch the analysis.
setDistributionsFactories(variableName, ...)	Set the sequence of distributions to test for a variable.
setInterestVariables(variablesNames)	Set the variables to analyse.
setLevel(level)	Level accessor.
setName(name)	Accessor to the object's name.

canBeLaunched
getElapsedTime
getEstimateParametersConfidenceInterval
getLillieforsMaximumSamplingSize
getLillieforsMinimumSamplingSize
getLillieforsPrecision
getParametersConfidenceIntervalLevel
getParentObserver
getTestType
setEstimateParametersConfidenceInterval
setLillieforsMaximumSamplingSize
setLillieforsMinimumSamplingSize
setLillieforsPrecision
setParametersConfidenceIntervalLevel
setTestType

__init__(*args)

getClassName()

Accessor to the object’s name.

Returns

class_namestr

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

getDesignOfExperiment()

Design of experiments accessor.

Returns

modelDesignOfExperiment

Design of experiments

getDistributionsFactories(variableName)

Get the sequence of distributions to test for a variable.

Parameters

variablestr

Name of the variable

Returns

factoriessequence of openturns.DistributionFactory

Distributions to test

getErrorMessage()

Error message accessor.

Returns

messagestr

Error message if the analysis failed

getInterestVariables()

Get the variables to analyse.

Returns

variablesNamessequence of str

Names of the variables to analyse

getLevel()

Level accessor.

Returns

levelfloat, , optional

The risk of committing a Type I error, that is an incorrect rejection of a true null hypothesis

getName()

Accessor to the object’s name.

Returns

namestr

The name of the object.

getPythonScript()

Physical model accessor.

Returns

scriptstr

Python script to replay the analysis

getResult()

Result accessor.

Returns

resultInferenceResult

Result.

getWarningMessage()

Warning message accessor.

Returns

messagestr

Warning message which can appear during the analysis computation

hasName()

Test if the object is named.

Returns

hasNamebool

True if the name is not empty.

hasValidResult()

Whether the analysis has been run.

Returns

hasValidResultbool

Whether the analysis has already been run

isReliabilityAnalysis()

Whether the analysis involves reliability.

Returns

isReliabilityAnalysisbool

Whether the analysis involves a reliability analysis

isRunning()

Whether the analysis is running.

Returns

isRunningbool

Whether the analysis is running

run()

Launch the analysis.

setDistributionsFactories(variableName, distributionsFactories)

Set the sequence of distributions to test for a variable.

Parameters

variablestr

Name of the variable

factoriessequence of openturns.DistributionFactory

Distributions to test

setInterestVariables(variablesNames)

Set the variables to analyse.

Parameters

variablesNamessequence of str

Names of the variables to analyse

setLevel(level)

Level accessor.

Parameters

levelfloat, , optional

The risk of committing a Type I error, that is an incorrect rejection of a true null hypothesis

setName(name)

Accessor to the object’s name.

Parameters

namestr

The name of the object.