Difference between revisions of "OoDACE:ooDACE toolbox"
| Line 34: | Line 34: | ||
opts.hpOptimizer = MatlabOptimizer( dim, 1, optimopts ); | opts.hpOptimizer = MatlabOptimizer( dim, 1, optimopts ); | ||
| − | % | + | % build and fit Kriging object |
| − | + | k = Kriging( opts, theta0, 'regpoly0', @corrgauss ); | |
| − | + | k = k.fit( samples, values ); | |
| − | + | ||
| − | + | % k represents the approximation and can now be used, e.g., | |
| − | + | [y mse] = k.predict( [1 2] ) | |
| − | + | ... | |
| − | |||
| − | |||
| − | |||
</source> | </source> | ||
| − | See the included demo.m script for more example code on how to use the blindDACE toolbox. | + | See the included demo.m script for more example code on how to use the blindDACE toolbox (including more advances features such as using blind kriging or how to use regression instead of interpolation). |
== DACE toolbox interface == | == DACE toolbox interface == | ||
Revision as of 11:02, 9 February 2010
blindDACE: A versatile kriging Matlab Toolbox
TODO description
Download
See: [[1]]
Quick start guide
IMPORTANT: Before the toolbox can be used you have to include the toolbox in Matlab's path. You can do this manually by running startup or if Matlab is started in the root toolbox directory then startup will be run automatically.
startup
Now the toolbox is ready to be used. The blindDACE toolbox is designed in an object oriented (OO) fashion.
It is strongly recommended to exploit the OO design directly, i.e., use the Kriging and Optimizer matlab classes.
However, for convenience wrapper scripts (dacefit, predictor) are provided that simulate the DACE toolbox interface (see section TODO for more information).
...
% Generate kriging options structure
opts = getDefaultOptions();
opts.hpBounds = [lb ; ub]; % hyperparameter optimization bounds
% configure the optimization algorithm (only one optimizer is included)
% the Matlab Optimization toolbox is REQUIRED
optimopts.GradObj = 'on';
optimopts.DerivativeCheck = 'off';
optimopts.Diagnostics = 'off';
optimopts.Algorithm = 'active-set';
opts.hpOptimizer = MatlabOptimizer( dim, 1, optimopts );
% build and fit Kriging object
k = Kriging( opts, theta0, 'regpoly0', @corrgauss );
k = k.fit( samples, values );
% k represents the approximation and can now be used, e.g.,
[y mse] = k.predict( [1 2] )
...
See the included demo.m script for more example code on how to use the blindDACE toolbox (including more advances features such as using blind kriging or how to use regression instead of interpolation).
DACE toolbox interface
dacefit()
predictor()
Contribute
These bindings are very basic but they work for me. Please improve, extend, provide binaries, and of course contribute back.
