#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
levelFunction = ot.NumericalMathFunction(["x1", "x2", "x3", "x4"], ["y1"],
["x1+2*x2-3*x3+4*x4"])
# Add a finite difference gradient to the function, as Abdo Rackwitz algorithm
# needs it
myGradient = ot.NonCenteredFiniteDifferenceGradient(
1e-7, levelFunction.getEvaluation())
print("myGradient = ", repr(myGradient))
# Substitute the gradient
levelFunction.setGradient(ot.NonCenteredFiniteDifferenceGradient(myGradient))
startingPoint = [0.0] * 4
algo = ot.AbdoRackwitz(ot.OptimizationProblem(levelFunction, 3.0))
algo.setStartingPoint(startingPoint)
algo.run()
print("result = ", algo.getResult())
levelFunction = ot.NumericalMathFunction(["x1", "x2", "x3", "x4"], ["y1"],
["x1*cos(x1)+2*x2*x3-3*x3+4*x3*x4"])
# Add a finite difference gradient to the function, as Abdo Rackwitz algorithm
# needs it
myGradient = ot.NonCenteredFiniteDifferenceGradient(
1e-7, levelFunction.getEvaluation())
print("myGradient = ", repr(myGradient))
# Substitute the gradient
levelFunction.setGradient(ot.NonCenteredFiniteDifferenceGradient(myGradient))
startingPoint = [0.0] * 4
algo = ot.AbdoRackwitz(ot.OptimizationProblem(levelFunction, -0.5))
f4.getCacheHits() # %% # Evaluate the gradient of the function at a particular point gradientMatrix = f.gradient(x) gradientMatrix # %% # Evaluate the hessian of the function at a particular point hessianMatrix = f.hessian(x) hessianMatrix # %% # Change the gradient method to a non centered finite difference method step = [1e-7] * f.getInputDimension() gradient = ot.NonCenteredFiniteDifferenceGradient(step, f.getEvaluation()) f.setGradient(gradient) gradient # %% # Change the hessian method to a centered finite difference method step = [1e-7] * f.getInputDimension() hessian = ot.CenteredFiniteDifferenceHessian(step, f.getEvaluation()) f.setHessian(hessian) hessian # %% # Get the number of times the function has been evaluated f.getEvaluationCallsNumber() # %%
par_grad = param_f.parameterGradient(x)
n_calls_1 = ot_f.getCallsNumber()
assert n_calls_1 - n_calls_0 == 4, "Expected n_calls_1 - n_calls_0 == 4, here n_calls_1 - n_calls_0 == " + \
str(n_calls_1 - n_calls_0)
assert n_calls == 4, "Expected n_calls == 4, here n_calls == " + str(n_calls)
n_calls = 0
n_calls_0 = ot_f.getCallsNumber()
f_grad = param_f.gradient(y)
n_calls_1 = ot_f.getCallsNumber()
assert n_calls_1 - n_calls_0 == 2, "Expected n_calls_1 - n_calls_0 == 2, here n_calls_1 - n_calls_0 == " + \
str(n_calls_1 - n_calls_0)
assert n_calls == 2, "Expected n_calls == 2, here n_calls == " + str(n_calls)
eps = 1e-7
gr_f = ot.NonCenteredFiniteDifferenceGradient(eps, ot_f.getEvaluation())
ot_f.setGradient(gr_f)
n_calls = 0
param_f = ot.ParametricFunction(ot_f, [0, 1], [1.0, 2.0])
x = [3.5]
y = [2.5]
n_calls_0 = ot_f.getCallsNumber()
par_grad = param_f.parameterGradient(x)
n_calls_1 = ot_f.getCallsNumber()
assert n_calls_1 - n_calls_0 == 3, "Expected n_calls_1 - n_calls_0 == 3, here n_calls_1 - n_calls_0 == " + \
str(n_calls_1 - n_calls_0)
assert n_calls == 3, "Expected n_calls == 3, here n_calls == " + str(n_calls)
n_calls = 0
n_calls_0 = ot_f.getCallsNumber()
#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
eps = 1e-4
# Instance creation
myFunc = ot.SymbolicFunction(['x1', 'x2'], [
'x1*sin(x2)', 'cos(x1+x2)', '(x2+1)*exp(x1-2*x2)'])
epsilon = ot.Point(myFunc.getInputDimension(), eps)
inPoint = ot.Point(epsilon.getDimension(), 1.0)
myGradient = ot.NonCenteredFiniteDifferenceGradient(
epsilon, myFunc.getEvaluation())
print("myGradient=", repr(myGradient))
print("myFunc.gradient(", repr(inPoint), ")=",
repr(myFunc.gradient(inPoint)))
print("myGradient.gradient(", repr(inPoint), ")=",
repr(myGradient.gradient(inPoint)))
# Substitute the gradient
# DEBUG ici
myFunc.setGradient(
ot.NonCenteredFiniteDifferenceGradient(myGradient))
print("myFunc.gradient(", repr(inPoint), ")=", repr(
myFunc.gradient(inPoint)), " (after substitution)")
