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#! /usr/bin/env python
import openturns as ot
import openturns.testing as ott
ot.TESTPREAMBLE()
ot.PlatformInfo.SetNumericalPrecision(2)
m = 10
x = [[0.5 + i] for i in range(m)]
inVars = ["a", "b", "c", "x"]
formulas = ["a + b * exp(c * x)", "(a * x^2 + b) / (c + x^2)"]
g = ot.SymbolicFunction(inVars, formulas)
trueParameter = [2.8, 1.2, 0.5]
params = [0, 1, 2]
model = ot.ParametricFunction(g, params, trueParameter)
y = model(x)
y += ot.Normal([0.0] * 2, [0.05] * 2, ot.IdentityMatrix(2)).getSample(m)
candidate = [1.0] * 3
bootstrapSizes = [0, 100]
for bootstrapSize in bootstrapSizes:
algo = ot.NonLinearLeastSquaresCalibration(model, x, y, candidate)
algo.setBootstrapSize(bootstrapSize)
algo.run()
result = algo.getResult()
# To avoid discrepance between the platforms with or without CMinpack
print("result (Auto)=", result.getParameterMAP())
ott.assert_almost_equal(
result.getObservationsError().getMean(), [0.0051, -0.0028], 1e-1, 1e-3
)
multiStartSize = 10
algo.setOptimizationAlgorithm(
ot.MultiStart(
ot.TNC(),
ot.LowDiscrepancyExperiment(
ot.SobolSequence(),
ot.Normal(
candidate, ot.CovarianceMatrix(ot.Point(candidate).getDimension())
),
multiStartSize,
).generate(),
)
)
algo.run()
result = algo.getResult()
# To avoid discrepance between the platforms with or without CMinpack
print("result (TNC)=", result.getParameterMAP())
ott.assert_almost_equal(
result.getObservationsError().getMean(), [0.0051, -0.0028], 1e-1, 1e-3
)
# Draw result
graph = result.drawParameterDistributions()
graph = result.drawResiduals()
graph = result.drawObservationsVsInputs()
graph = result.drawObservationsVsPredictions()
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