import openturns as ot
import otrobopt
from matplotlib import pyplot as plt
from openturns.viewer import View
thetaDist = ot.Normal(2.0, 0.1)
if 'VarianceMeasure' == 'WorstCaseMeasure':
thetaDist = ot.Uniform(-1.0, 4.0)
elif 'ChanceMeasure' in 'VarianceMeasure':
thetaDist = ot.Normal(1.0, 1.0)
f_base = ot.Function(['x', 'theta'], ['y'], ['x*theta'])
f = ot.Function(f_base, [1], thetaDist.getMean())
if 'VarianceMeasure' == 'JointChanceMeasure':
measure = otrobopt.JointChanceMeasure(f, thetaDist, ot.GreaterOrEqual(),
0.95)
elif 'VarianceMeasure' == 'IndividualChanceMeasure':
measure = otrobopt.IndividualChanceMeasure(f, thetaDist,
ot.GreaterOrEqual(), [0.95])
elif 'VarianceMeasure' == 'MeanStandardDeviationTradeoffMeasure':
measure = otrobopt.MeanStandardDeviationTradeoffMeasure(
f, thetaDist, [0.8])
elif 'VarianceMeasure' == 'QuantileMeasure':
measure = otrobopt.QuantileMeasure(f, thetaDist, 0.99)
else:
measure = otrobopt.VarianceMeasure(f, thetaDist)
N = 10
experiment = ot.LHSExperiment(N)
factory = otrobopt.MeasureFactory(experiment)
discretizedMeasure = factory.build(measure)
import otrobopt
#ot.Log.Show(ot.Log.Info)
calJ = ot.SymbolicFunction(['x', 'theta'], ['x^3 - 3*x + theta'])
calG = ot.SymbolicFunction(['x', 'theta'], ['-(x + theta - 2)'])
J = ot.ParametricFunction(calJ, [1], [0.5])
g = ot.ParametricFunction(calG, [1], [0.5])
dim = J.getInputDimension()
solver = ot.Cobyla()
solver.setMaximumIterationNumber(1000)
solver.setStartingPoint([0.0] * dim)
thetaDist = ot.Exponential(2.0)
robustnessMeasure = otrobopt.MeanMeasure(J, thetaDist)
reliabilityMeasure = otrobopt.JointChanceMeasure(g, thetaDist, ot.Greater(), 0.9)
problem = otrobopt.RobustOptimizationProblem(robustnessMeasure, reliabilityMeasure)
problem.setMinimization(False)
algo = otrobopt.SequentialMonteCarloRobustAlgorithm(problem, solver)
algo.setMaximumIterationNumber(10)
algo.setMaximumAbsoluteError(1e-3)
algo.setInitialSamplingSize(10)
algo.run()
result = algo.getResult()
print ('x*=', result.getOptimalPoint(), 'J(x*)=', result.getOptimalValue(), 'iteration=', result.getIterationNumber())
from __future__ import print_function
import openturns as ot
import otrobopt
# First test: theta of dimension 1
thetaDist = ot.Normal(2.0, 0.1)
f_base = ot.SymbolicFunction(['x', 'theta'], ['x*theta'])
f = ot.ParametricFunction(f_base, [1], [1.0])
x = [1.0]
measures = [otrobopt.MeanMeasure(f, thetaDist),
otrobopt.VarianceMeasure(f, thetaDist),
otrobopt.WorstCaseMeasure(f, ot.Uniform(-1.0, 4.0)),
otrobopt.WorstCaseMeasure(f, ot.Uniform(-1.0, 4.0), False),
otrobopt.JointChanceMeasure(
f, ot.Normal(1.0, 1.0), ot.GreaterOrEqual(), 0.95),
otrobopt.IndividualChanceMeasure(
f, ot.Normal(1.0, 1.0), ot.GreaterOrEqual(), [0.95]),
otrobopt.MeanStandardDeviationTradeoffMeasure(f, thetaDist, [0.8]),
otrobopt.QuantileMeasure(f, thetaDist, 0.99)
]
aggregated = otrobopt.AggregatedMeasure(measures)
measures.append(aggregated)
for measure in measures:
print(measure, '(continuous)', measure(x))
N = 10000
experiment = ot.LHSExperiment(N)
factory = otrobopt.MeasureFactory(experiment)
discretizedMeasure = factory.build(measure)
print(discretizedMeasure, '(discretized LHS)', discretizedMeasure(x))
fileName = 'myStudy.xml'
# Create a Study Object
myStudy = ot.Study()
myStudy.setStorageManager(ot.XMLStorageManager(fileName))
thetaDist = ot.Normal(2.0, 0.1)
f_base = ot.SymbolicFunction(['x', 'theta'], ['x*theta'])
f = ot.ParametricFunction(f_base, [1], [1.0])
measures = [
otrobopt.MeanMeasure(f, thetaDist),
otrobopt.VarianceMeasure(f, thetaDist),
otrobopt.WorstCaseMeasure(f, ot.Uniform(-1.0, 4.0), False),
otrobopt.JointChanceMeasure(f, ot.Normal(1.0, 1.0), ot.GreaterOrEqual(),
0.95),
otrobopt.IndividualChanceMeasure(f, ot.Normal(1.0, 1.0),
ot.GreaterOrEqual(), [0.95]),
otrobopt.MeanStandardDeviationTradeoffMeasure(f, thetaDist, [0.8]),
otrobopt.QuantileMeasure(f, thetaDist, 0.99)
]
aggregated = otrobopt.AggregatedMeasure(measures)
measures.append(aggregated)
for measure in measures:
myStudy.add('measure' + measure.__class__.__name__, measure)
measure2 = otrobopt.MeanMeasure(f, thetaDist)
measureFunction = otrobopt.MeasureFunction(measure2)
myStudy.add('measureFunction', measureFunction)
