import openturns as ot
from openturns.viewer import View
# Factorial
d = ot.Factorial([1.5, 2.5, 3.5], [1, 2, 3])
s = d.generate()
s.setDescription(["X1", "X2", "X3"])
g = ot.Graph()
g.setTitle("Factorial experiment")
g.setGridColor("black")
p = ot.Pairs(s)
g.add(p)
View(g)
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
# Generate sample with the given plane
center = [0.5, 1.5]
levels = [4, 8, 16]
myPlane = ot.Factorial(center, levels)
sample = myPlane.generate()
# Create the graph
graph = ot.Graph("", "x1", "x2", True, "")
cloud = ot.Cloud(sample, "blue", "fsquare", "")
graph.add(cloud)
# Draw the graph
fig = plt.figure(figsize=(4, 4))
plt.suptitle(sample.getName())
axis = fig.add_subplot(111)
View(graph, figure=fig, axes=[axis], add_legend=False, square_axes=True)
axis.set_xlim(auto=True)
# print(X, f)
return [f]
model = ot.PythonFunction(dim, 1, ackley)
# problem
problem = ot.OptimizationProblem()
problem.setObjective(model)
bounds = ot.Interval([-15.0] * dim, [15.0] * dim)
problem.setBounds(bounds)
# design
center = [0.5] * dim
levels = [2.0, 4.0, 8.0, 14.0]
experiment = ot.Factorial(center, levels)
inputSample = experiment.generate()
outputSample = model(inputSample)
# first kriging model
covarianceModel = ot.SquaredExponential([2.50057] * dim, [0.1])
basis = ot.ConstantBasisFactory(dim).build()
kriging = ot.KrigingAlgorithm(inputSample, outputSample, covarianceModel,
basis)
kriging.run()
# algo
algo = ot.EfficientGlobalOptimization(problem, kriging.getResult())
# solver = ot.NLopt('GN_ESCH')
# solver = ot.NLopt('GN_MLSL')
algo.setMaximumIterationNumber(15)
# %% # Scale and to get desired location. # %% sample *= 2.0 sample += [5.0, 8.0] graph = drawBidimensionalSample(sample, "Axial") view = viewer.View(graph) # %% # Factorial design # ---------------- # # %% experiment = ot.Factorial(2, levels) sample = experiment.generate() sample *= 2.0 sample += [5.0, 8.0] graph = drawBidimensionalSample(sample, "Factorial") view = viewer.View(graph) # %% # Composite design # ---------------- # %% experiment = ot.Composite(2, levels) sample = experiment.generate() sample *= 2.0 sample += [5.0, 8.0]
