he blindDACE toolbox is a versatile Matlab toolbox that implements the popular Gaussian Process based kriging surrogate models. Kriging is in particular popular for approximating (and optimizing) deterministic computer experiments. Given a dataset the toolbox automatically fits a kriging surrogate model to it. Afterwards the kriging surrogate can be fully exploited instead of the (probably more expensive) simulation code.
The toolbox is aimed for solving complex applications (expensive simulation codes, physical experiments, ...) and for researching new kriging extensions and techniques.
See: download page
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.
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 wrapper scripts for more information).
... % Generate kriging options structure % hyperparameter optimization bounds % configure the optimization algorithm (only one optimizer is included) % the Matlab Optimization toolbox is REQUIRED % build and fit Kriging object % k represents the approximation and can now be used, e.g.,
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
The blindDACE toolbox provides two scripts dacefit.m and predictor.m that simulate the behavior of the DACE toolbox (). Note, that full compatibility between blindDACE and the DACE toolbox is not provided. The scripts merely aim to ease the transition from the DACE toolbox to blindDACE.
Obviously, a lot less code is used to copy the setup described above. However, less code means less flexibility (e.g., blind kriging and regression kriging is not available using the wrapper scripts). Hence, it is suggested to learn the object oriented interface of blindDACE and use that instead.
These bindings are very basic but they work for me. Please improve, extend, provide binaries, and of course contribute back.