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# STK: a Small (Matlab/Octave) Toolbox for Kriging
[](COPYING)
[](https://github.com/stk-kriging/stk/commits/main)
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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/>.
## General information
Version: 2.8.0 (2023-01-06)
Authors: See [AUTHORS](./AUTHORS.md) 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/>
## One toolbox, two flavours
The STK toolbox comes in two flavours:
* an "all purpose" release, which is suitable for use both with
[GNU Octave](http://www.gnu.org/software/octave/)
and with [Matlab](https://www.mathworks.com/products/matlab/).
* an Octave package, for people who want to install and use STK as a
regular [Octave package](http://www.gnu.org/software/octave/doc/interpreter/Packages.html#Packages).
Hint: if you're not sure about the version that you have...
* the "all purpose" release has this file (`README.md`) and the `stk_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 the `doc/` subdirectory.
## Quick Start
### Quick start with the "all purpose" release (Matlab/Octave)
Download and unpack an archive of the "all purpose"
[release](https://github.com/stk-kriging/stk/releases).
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
```
### Quick start with the Octave package release (Octave only)
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](http://octave.sourceforge.net/)
[file release system](https://sourceforge.net/projects/octave/files/)
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.
## Requirements and recommendations
### Common requirement
Your installation must be able to compile C mex files.
### Requirements and recommendations for use with GNU Octave
The STK is tested to work with
[GNU Octave 4.0.1 or newer](https://wiki.octave.org/Release_History).
### Requirements and recommendations for use with Matlab
The STK is tested to work with
[Matlab R2014a or newer](https://en.wikipedia.org/wiki/MATLAB#Release_history).
The Optimization Toolbox is recommended.
The Parallel Computing Toolbox is optional.
## Content
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:
1. 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.
2. 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.
3. The third part consists of prediction procedures. In its current
form, the STK has been optimized to deal with moderately large
data sets.
### References
[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.
## Ways to get help, report bugs, ask for new features...
Use the "help" mailing-list:
<kriging-help@lists.sourceforge.net>
(register/browse the archives: [here](https://sourceforge.net/p/kriging/mailman))
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>
## How to contribute
The contribution process is explained in
[CONTRIBUTING.md](https://github.com/stk-kriging/stk/blob/master/.github/CONTRIBUTING.md).
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