# 3) Basis definition
basisCollection = ot.BasisCollection(
1,
ot.ConstantBasisFactory(spatialDimension).build())
# Kriring algorithm
algo = ot.KrigingAlgorithm(inputSample, outputSample, covarianceModel,
basisCollection)
algo.run()
result = algo.getResult()
vertices = [[1.0, 0.0], [2.0, 0.0], [2.0, 1.0], [1.0, 1.0], [1.5, 0.5]]
simplicies = [[0, 1, 4], [1, 2, 4], [2, 3, 4], [3, 0, 4]]
mesh2D = ot.Mesh(vertices, simplicies)
process = ot.ConditionedGaussianProcess(result, mesh2D)
# Get a realization of the process
realization = process.getRealization()
print("realization = ", repr(realization))
# Get a sample & compare it to expectation
sample = process.getSample(5000)
mean = sample.computeMean()
print("Mean over 5000 realizations = ", repr(mean))
except:
import sys
print("t_ConditionedGaussianProcess_std.py",
sys.exc_info()[0],
sys.exc_info()[1])
covarianceModel = ot.SquaredExponential([7.63, 2.11], [7.38])
# 3) Basis definition
basis = ot.ConstantBasisFactory(inputDimension).build()
# Kriging algorithm
algo = ot.KrigingAlgorithm(inputSample, outputSample, covarianceModel, basis)
algo.setOptimizeParameters(False) # do not optimize hyper-parameters
algo.run()
result = algo.getResult()
vertices = [[1.0, 0.0], [2.0, 0.0], [2.0, 1.0], [1.0, 1.0], [1.5, 0.5]]
simplicies = [[0, 1, 4], [1, 2, 4], [2, 3, 4], [3, 0, 4]]
mesh2D = ot.Mesh(vertices, simplicies)
process = ot.ConditionedGaussianProcess(result, mesh2D)
# Get a realization of the process
realization = process.getRealization()
print("realization = ", repr(realization))
# Get a sample & compare it to expectation
sample = process.getSample(5000)
mean = sample.computeMean()
print("Mean over 5000 realizations = ", repr(mean))
# Check if one can sample the process over a mesh containing conditioning points
# and 100 new points
vertices = ot.Sample(inputSample)
vertices.add(
ot.ComposedDistribution([ot.Uniform(0.0, 10.0)] * 2).getSample(100))
graph.setAxes(True)
graph.setXTitle("X")
graph.setYTitle("Y")
graph.setLegendPosition("topright")
view = viewer.View(graph)
# %%
# Simulate new trajectories
# -------------------------
#
# In order to generate new trajectories of the conditioned gaussian process, we couild technically use the `KrigingRandomVector` class, because it provides the `getSample` method that we need. However, the `KrigingRandomVector` class was more specifically designed to create a `RandomVector` so that it can feed, for example, a function which has a field as input argument.
#
# This is why we use the `ConditionedGaussianProcess`, which provides a `Process`.
# %%
process = ot.ConditionedGaussianProcess(krigingResult, myRegularGrid)
# %%
trajectories = process.getSample(10)
type(trajectories)
# %%
# The `getSample` method returns a `ProcessSample`. By comparison, the `getSample` method of a `KrigingRandomVector` would return a `Sample`.
# %%
# sphinx_gallery_thumbnail_number = 3
graph = trajectories.drawMarginal()
graph.add(plot_data_test(x_test, y_test))
graph.add(plot_data_train(x_train, y_train))
graph.setAxes(True)
graph.setXTitle("X")
print(" Delete %s" % (x_train_value))
x_test_filtered = np.delete(x_test_filtered, indices[0, 0])
else:
print(" OK")
return x_test_filtered
# %%
vertices_filtered = deleteCommonValues(np.array(x_train.asPoint()),
np.array(vertices.asPoint()))
# %%
evaluationMesh = ot.Mesh(ot.Sample([[vf] for vf in vertices_filtered]))
# %%
process = ot.ConditionedGaussianProcess(krigingResult, evaluationMesh)
# %%
trajectories = process.getSample(10)
type(trajectories)
# %%
# The `getSample` method returns a `ProcessSample`. By comparison, the `getSample` method of a `KrigingRandomVector` would return a `Sample`.
# %%
graph = trajectories.drawMarginal()
graph.add(plot_data_test(x_test, y_test))
graph.add(plot_data_train(x_train, y_train))
graph.setAxes(True)
graph.setXTitle("X")
graph.setYTitle("Y")