Create a gaussian process from a cov. model using HMatrix¶
In this basic example we are going to build a gaussian process from its covariance model and using the
HMatrix as sampling method.
from __future__ import print_function import openturns as ot
Define the covariance model :
dimension = 1 amplitude = [1.0] * dimension scale =  * dimension covarianceModel = ot.AbsoluteExponential(scale, amplitude)
Define the time grid on which we want to sample the gaussian process :
# define a mesh tmin = 0.0 step = 0.01 n = 10001 timeGrid = ot.RegularGrid(tmin, step, n)
Finally define the gaussian process :
# create the process process = ot.GaussianProcess(covarianceModel, timeGrid) print(process)
GaussianProcess(trend=[x0]->[0.0], covariance=AbsoluteExponential(scale=, amplitude=))
We set the sampling method to
We sample the process :
# draw a sample sample = process.getSample(6) sample.drawMarginal(0)
We notice here that we are able to sample the covariance model over a mesh of size
10000, which is usually tricky on laptop. This is mainly due to the compression.