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class=DataModel name=myDataModel fileName=data1.csv inputColumns=[0,2,3] outputColumns=[] inputNames=[X0,X2,X3] outputNames=[]
class=InferenceAnalysis name=analysis designOfExperiment=myDataModel interestVariables=[X0,X3] level=0.1 interestVariable X0 distributionFactories=[NormalFactory] interestVariable X3 distributionFactories=[NormalFactory,GumbelFactory]
result= class=InferenceResult FittingTestResultCollection=[class=FittingTestResult variableName=X0 testedDistributions=[Normal(mu = -0.0698719, sigma = 0.948364)] kolmogorovTestResults=[class=TestResult name=Unnamed type=Kolmogorov Normal binaryQualityMeasure=true p-value threshold=0.1 p-value=0.927898 statistic=0.0309239 description=[Normal(mu = -0.0698719, sigma = 0.948364) vs sample Unnamed]] bicResults=class=Point name=Unnamed dimension=1 values=[2.76653] error messages=[],class=FittingTestResult variableName=X3 testedDistributions=[Normal(mu = 0.651858, sigma = 1.37028),Gumbel(beta = 1.0684, gamma = 0.0351576)] kolmogorovTestResults=[class=TestResult name=Unnamed type=Kolmogorov Normal binaryQualityMeasure=false p-value threshold=0.1 p-value=0.00286352 statistic=0.103832 description=[Normal(mu = 0.651858, sigma = 1.37028) vs sample Unnamed],class=TestResult name=Unnamed type=Kolmogorov Gumbel binaryQualityMeasure=true p-value threshold=0.1 p-value=0.835684 statistic=0.0353023 description=[Gumbel(beta = 1.0684, gamma = 0.0351576) vs sample Unnamed]] bicResults=class=Point name=Unnamed dimension=2 values=[3.5026,3.29942] error messages=[,]]
class=FittingTestResult variableName=X3 testedDistributions=[Normal(mu = 0.651858, sigma = 1.37028),Gumbel(beta = 1.0684, gamma = 0.0351576)] kolmogorovTestResults=[class=TestResult name=Unnamed type=Kolmogorov Normal binaryQualityMeasure=false p-value threshold=0.1 p-value=0.00286352 statistic=0.103832 description=[Normal(mu = 0.651858, sigma = 1.37028) vs sample Unnamed],class=TestResult name=Unnamed type=Kolmogorov Gumbel binaryQualityMeasure=true p-value threshold=0.1 p-value=0.835684 statistic=0.0353023 description=[Gumbel(beta = 1.0684, gamma = 0.0351576) vs sample Unnamed]] bicResults=class=Point name=Unnamed dimension=2 values=[3.5026,3.29942] error messages=[,]
#!/usr/bin/env python
import openturns as ot
import persalys
myStudy = persalys.Study('myStudy')
persalys.Study.Add(myStudy)
inputColumns = [0, 2, 3]
outputColumns = []
inputNames = ['X0', 'X2', 'X3']
outputNames = []
myDataModel = persalys.DataModel('myDataModel', 'data1.csv', inputColumns, outputColumns, inputNames, outputNames)
myStudy.add(myDataModel)
analysis = persalys.InferenceAnalysis('analysis', myDataModel)
interestVariables = ['X0', 'X3']
analysis.setInterestVariables(interestVariables)
factories = [ot.NormalFactory()]
analysis.setDistributionsFactories('X0', factories)
factories = [ot.NormalFactory(), ot.GumbelFactory()]
analysis.setDistributionsFactories('X3', factories)
analysis.setLevel(0.1)
myStudy.add(analysis)
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