from __future__ import print_function
import openturns as ot
import otrobopt
full_fun = ot.SymbolicFunction(['x', 'theta'], ['theta*x'])
param_fun = ot.ParametricFunction(full_fun, [1], [1.0])
# Normal distribution associated to parameter theta
dist_theta = ot.Normal(2.0, 1.0)
# Definition of "analytical" mean measure associated to the parametric function
mean_measure = otrobopt.MeanMeasure(param_fun, dist_theta)
mean_measure_f = otrobopt.MeasureFunction(mean_measure)
print(mean_measure_f)
print(mean_measure_f.getInputDimension())
print(mean_measure_f.getOutputDimension())
x = [2.0]
print(mean_measure_f(x))
print(mean_measure_f([x] * 10))
print(mean_measure_f.gradient(x))
## Graphical comparison of both "analytical" and "discretized" mean measures
#x_min = -2.
#x_max = 2.
#graph = ot.Graph()
#graph.setGrid(True)
#graph.setLegendPosition("topright")
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)
continuous_measure = otrobopt.MeasureFunction(measure)
discretized_measure = otrobopt.MeasureFunction(discretizedMeasure)
x_min = -2.0
x_max = 2.0
n_points = 128
parametric_graph = f.draw(x_min, x_max, n_points)
continuous_graph = continuous_measure.draw(x_min, x_max, n_points)
discretized_graph = discretized_measure.draw(x_min, x_max, n_points)
parametric_curve = parametric_graph.getDrawable(0)
discretized_curve = discretized_graph.getDrawable(0)
left_graph = ot.Graph(continuous_graph)
left_graph.add(parametric_curve)
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)
calJ = ot.SymbolicFunction(
['x1', 'x2'],
['15.0 * (x1^2 + x2^2) - 100.0 * exp(-5. * ((x1 + 1.6)^2+(x2 + 1.6)^2))'])
# This is calligraphic G, the non-robust inequality constraints function
calG = ot.SymbolicFunction(
['x1', 'x2'], ['(x1 - 0.5)^2 + x2^2 - 4.0', '(x1 + 0.5)^2 + x2^2 - 4.0'])
# This is the perturbation function
noise = ot.SymbolicFunction(['x1', 'x2', 'xi1', 'xi2'],
['x1 + xi1', 'x2 + xi2'])
# This is capital J: J(x,xi) = calJ(x+xi), the parametric objective function
JFull = ot.ComposedFunction(calJ, noise)
J = ot.ParametricFunction(JFull, [2, 3], [0.0] * 2)
# This is g, the parametric constraints
