The STK is a (not so) Small Toolbox for Kriging. Its primary focus is on the interpolation/regression technique known as kriging, which is very closely related to Splines and Radial Basis Functions, and can be interpreted as a non-parametric Bayesian method using a Gaussian Process (GP) prior.
This README file is part of
STK: a Small (Matlab/Octave) Toolbox for Kriging
https://github.com/stk-kriging/stk
STK is free software: you can redistribute it and/or modify it under
the terms of the GNU General Public License as published by the Free
Software Foundation, either version 3 of the License, or (at your
option) any later version.
STK is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public
License for more details.
You should have received a copy of the GNU General Public License
along with STK. If not, see http://www.gnu.org/licenses.
Version: See stk_version.m
Authors: See AUTHORS file
Maintainers: Julien Bect julien.bect@centralesupelec.fr
and Emmanuel Vazquez emmanuel.vazquez@centralesupelec.fr
Description: The STK is a (not so) Small Toolbox for Kriging. Its
primary focus is on the interpolation/regression
technique known as kriging, which is very closely related
to Splines and Radial Basis Functions, and can be
interpreted as a non-parametric Bayesian method using a
Gaussian Process (GP) prior. The STK also provides tools
for the sequential and non-sequential design of
experiments. Even though it is, currently, mostly geared
towards the Design and Analysis of Computer Experiments
(DACE), the STK can be useful for other applications
areas (such as Geostatistics, Machine Learning,
Non-parametric Regression, etc.).
Copyright: Large portions are Copyright (C) 2011-2014 SUPELEC
and Copyright (C) 2015-2023 CentraleSupelec.
See individual copyright notices for more details.
License: GNU General Public License, version 3 (GPLv3).
See COPYING for the full license.
URL: https://github.com/stk-kriging/stk
The STK toolbox comes in two flavours:
Hint: if you’re not sure about the version that you have…
README.md) and the stk_initstk_init.m) in the top-level directory,DESCRIPTION file in the top-level directorydoc/ subdirectory.Download and unpack an archive of the “all purpose”
release.
Run stk_init in either Octave or Matlab. One way to do so is to navigate
to the root directory of STK and then simply type:
stk_init
Alternatively, if you don’t want to change the current directory, you can use:
run /path/to/stk/stk_init.m
Note that this second approach is suitable for inclusion in your startup script.
After that, you should be able to run the examples located in the examples
directory. All of them are scripts, the file name of which starts with
the stk_example_ prefix.
For instance, type
stk_example_kb03
to run the third example in the “kriging basics” series.
Remark: when using STK with Mathworks’ Parallel Computing Toolbox, it is
important to run stk_init within each worker. This can be achieved using:
pctRunOnAll run /path/to/stk/stk_init.m
Assuming that you have a working Internet connection, typing
pkg install -forge stk
(from within Octave) will automatically download the latest STK package tarball from the
Octave Forge
file release system
on SourceForge and install it for you.
Alternatively, if you want to install an older (or beta) release, you can download
the tarball from either the STK project FRS or the Octave Forge FRS, and install it
with
pkg install FILENAME.tar.gz
After that, you can load STK using
pkg load stk
To check that STK is properly loaded, try for instance
stk_example_kb03
to run the third example in the “kriging basics” series.
Your installation must be able to compile C mex files.
The STK is tested to work with
GNU Octave 4.0.1 or newer.
The STK is tested to work with
Matlab R2014a or newer.
The Optimization Toolbox is recommended.
The Parallel Computing Toolbox is optional.
By publishing this toolbox, the idea is to provide a convenient and
flexible research tool for working with kriging-based methods. The
code of the toolbox is meant to be easily understandable, modular,
and reusable. By way of illustration, it is very easy to use this
toolbox for implementing the EGO algorithm [1].
Besides, this toolbox can serve as a basis for the implementation
of advanced algorithms such as Stepwise Uncertainty Reduction (SUR)
algorithms [2].
The toolbox consists of three parts:
The first part is the implementation of a number of covariance
functions, and tools to compute covariance vectors and matrices.
The structure of the STK makes it possible to use any kind of
covariances: stationary or non-stationary covariances, aniso-
tropic covariances, generalized covariances, etc.
The second part is the implementation of a REMAP procedure to
estimate the parameters of the covariance. This makes it possible
to deal with generalized covariances and to take into account
prior knowledge about the parameters of the covariance.
The third part consists of prediction procedures. In its current
form, the STK has been optimized to deal with moderately large
data sets.
[1] D. R. Jones, M. Schonlau, and William J. Welch. Efficient global
optimization of expensive black-box functions. Journal of Global
Optimization, 13(4):455-492, 1998.
[2] J. Bect, D. Ginsbourger, L. Li, V. Picheny, and E. Vazquez.
Sequential design of computer experiments for the estimation of a
probability of failure. Statistics and Computing, pages 1-21, 2011.
DOI: 10.1007/s11222-011-9241-4.
Use the “help” mailing-list:
kriging-help@lists.sourceforge.net
(register/browse the archives: here)
to ask for help on STK, and the ticket manager:
https://github.com/stk-kriging/stk/issues
to report bugs or ask for new features (do not hesitate to do so!).
If you use STK in Octave, you can also have a look there:
https://octave.sourceforge.io/support-help.php
The contribution process is explained in
CONTRIBUTING.md.