def getResiduals(self):
theGraph = ot.Graph('Residuals','varying dimension','residual',True,'')
theCurve = ot.Curve(list(range(len(self.__residuals__))),
self.__residuals__, 'residuals')
theGraph.add(theCurve)
ot.Show(theGraph)
gridLayout.setGraph(0, 0, graph1)
gridLayout.setGraph(0, 1, graph2)
view = viewer.View(gridLayout,
legend_kw={
"title": "infection rate",
"loc": "upper left"
})
view.ShowAll()
# %%
# We validate the pertinence of Karhunen-Loeve decomposition:
validationKL = ot.KarhunenLoeveValidation(outputFMUTestSample, resultKL)
graph = validationKL.computeResidualMean().draw()
ot.Show(graph)
# %%
# As the epidemiological model considers a population size of 700, the residual
# mean error on the field is acceptable.
# %%
# We validate the Kriging (using the Karhunen-Loeve coefficients of the test
# sample):
projectFunction = ot.KarhunenLoeveProjection(resultKL)
coefficientSample = projectFunction(outputFMUTestSample)
validationKriging = ot.MetaModelValidation(inputTestSample, coefficientSample,
metamodel)
Q2 = validationKriging.computePredictivityFactor()[0]
def getGraphs(self):
for graph in self.__graphs__ :
ot.Show(graph)