levels = [8, 5]
box = ot.Box(levels)
inputSample = box.generate()
# Scale each direction
inputSample *= 10
# Define model
model = ot.Function(['x', 'y'], ['z'], ['cos(0.5*x) + sin(y)'])
outputSample = model(inputSample)
# 2) Definition of exponential model
covarianceModel = ot.SquaredExponential([1.988, 0.924], [3.153])
# 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
levels = [8, 5]
box = ot.Box(levels)
inputSample = box.generate()
# Scale each direction
inputSample *= 10
# Define model
model = ot.SymbolicFunction(['x', 'y'], ['cos(0.5*x) + sin(y)'])
outputSample = model(inputSample)
# 2) Definition of exponential model
covarianceModel = ot.SquaredExponential([1.988, 0.924], [3.153])
# 3) Basis definition
basisCollection = ot.BasisCollection(
1,
ot.ConstantBasisFactory(inputDimension).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