# Model
filename = 'data1.csv'
ot.RandomGenerator.SetSeed(0)
sample = ot.Normal(3).getSample(300)
sample.stack(ot.Gumbel().getSample(300))
sample.setDescription(['X0', 'X1', 'X2', 'X3'])
sample.exportToCSVFile(filename, ',')
columns = [0, 2, 3]
model = persalys.DataModel('myDataModel', "data1.csv", columns)
myStudy.add(model)
print(model)
# Inference analysis ##
analysis = persalys.InferenceAnalysis('analysis', model)
variables = ["X0", "X3"]
analysis.setInterestVariables(variables)
factories = [ot.NormalFactory(), ot.GumbelFactory()]
analysis.setDistributionsFactories("X3", factories)
analysis.setLevel(0.1)
myStudy.add(analysis)
print(analysis)
analysis.run()
result = analysis.getResult()
print("result=", result)
print(result.getFittingTestResultForVariable('X3'))
# script
sobol.setReplicationSize(3)
sobol.setBlockSize(1)
sobol.setSeed(2)
sobol.setInterestVariables(['y0', 'y1'])
myStudy.add(sobol)
# 4-b SRC ##
src = persalys.SRCAnalysis('SRC', model1)
src.setSimulationsNumber(20)
src.setSeed(2)
src.setInterestVariables(['y0', 'y1'])
myStudy.add(src)
# 7- data analysis ##
dataAnalysis = persalys.DataAnalysis('DataAnalysis', model3)
myStudy.add(dataAnalysis)
# 8- Marginals inference ##
inference = persalys.InferenceAnalysis('inference', model3)
inference.setInterestVariables(['x_0', 'x_3'])
factories = [ot.NormalFactory(), ot.GumbelFactory()]
inference.setDistributionsFactories('x_3', factories)
inference.setLevel(0.1)
myStudy.add(inference)
# 9- Copula inference ##
copulaInference = persalys.CopulaInferenceAnalysis('copulaInference', model3)
factories = [ot.NormalCopulaFactory(), ot.GumbelCopulaFactory()]
copulaInference.setDistributionsFactories(['x_0', 'x_3'], factories)
myStudy.add(copulaInference)