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:
- an "all purpose" release, which is suitable for use both with GNU Octave and with Matlab.
- an Octave package, for people who want to install and use STK as a regular Octave package.
Hint: if you're not sure about the version that you have...
- the "all purpose" release has this file (
README.md
) and thestk_init
function (stk_init.m
) in the top-level directory, - the Octave package has a
DESCRIPTION
file in the top-level directory and this file in thedoc/
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.
Citation pour cette source
Julien Bect, Emmanuel Vazquez and others (2022). STK: a Small (Matlab/Octave) Toolbox for Kriging. Release 2.7. URL https://github.com/stk-kriging/stk/
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arrays/@stk_dataframe
arrays/@stk_dataframe/private
arrays/@stk_factorialdesign
arrays/@stk_factorialdesign/private
arrays/@stk_hrect
arrays/generic
core
core/@stk_kreq_qr
core/@stk_kreq_qr/private
covfcs
covfcs/rbf
examples/01_kriging_basics
examples/02_design_of_experiments
examples/03_miscellaneous
examples/datasets
examples/test_functions
iago
iago/crit
iago/crit/private
iago/rep
iago/utils
lm
lm/@stk_lm_affine
lm/@stk_lm_constant
lm/@stk_lm_cubic
lm/@stk_lm_matrix
lm/@stk_lm_null
lm/@stk_lm_quadratic
misc/benchmarks
misc/design
misc/dist
misc/distrib
misc/error
misc/mole/graphics_toolkit
misc/mole/matlab
misc/mole/quantile
misc/optim
misc/optim/@stk_optim_fmincon
misc/optim/@stk_optim_fminsearch
misc/optim/@stk_optim_octavesqp
misc/optim/@stk_optim_octavesqp/private
misc/optim/@stk_optim_optimizer_
misc/options
misc/parallel
misc/parallel/@stk_parallel_engine_none
misc/parallel/@stk_parallel_engine_parfor
misc/pareto
misc/plot
misc/test
misc/text
model/@stk_model_
model/@stk_model_gpposterior
model/noise/@stk_gaussiannoise_
model/noise/@stk_gaussiannoise_het0
model/prior_struct
param/classes
param/estim
sampling
sampling/@stk_function
sampling/@stk_sampcrit_akg
sampling/@stk_sampcrit_ei
sampling/@stk_sampcrit_eqi
utils
Version | Publié le | Notes de version | |
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2.8.1.0 | See release notes for this release on GitHub: https://github.com/stk-kriging/stk/releases/tag/2.8.1 |
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2.7.0 | See release notes for this release on GitHub: https://github.com/stk-kriging/stk/releases/tag/2.7.0 |
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2.6.1.0 | See release notes for this release on GitHub: https://github.com/stk-kriging/stk/releases/tag/2.6.1 |
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2.6.0.0 | See release notes for this release on GitHub: https://github.com/stk-kriging/stk/releases/tag/2.6.0 |
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2.5.1.0 | See release notes for this release on GitHub: https://github.com/stk-kriging/stk/releases/tag/2.5.1 |
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2.5.0.0 | See release notes for this release on GitHub: https://github.com/stk-kriging/stk/releases/tag/2.5.0 |
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2.4.2.0 | See release notes for this release on GitHub: https://github.com/stk-kriging/stk/releases/tag/2.4.2 |
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2.4.1.0 | See release notes for this release on GitHub: https://github.com/stk-kriging/stk/releases/tag/2.4.1 |
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2.4.0.0 | See release notes for this release on GitHub: https://github.com/stk-kriging/stk/releases/tag/2.4.0 |
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2.3.4.0 | See release notes for this release on GitHub: https://github.com/stk-kriging/stk/releases/tag/2.3.4 |
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2.3.3.0 | See release notes for this release on GitHub: https://github.com/stk-kriging/stk/releases/tag/2.3.3 |
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2.3.2.0 | See release notes for this release on GitHub: https://github.com/stk-kriging/stk/releases/tag/2.3.2 |
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2.3.1.0 | See release notes for this release on GitHub: https://github.com/stk-kriging/stk/releases/tag/2.3.1 |
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2.3.0.0 | See release notes for this release on GitHub: https://github.com/stk-kriging/stk/releases/tag/2.3.0 |