Difference between revisions of "OoDACE:ooDACE toolbox"
(→Quick start guide) 
(→Quick start guide) 

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However, for convenience wrapper scripts (dacefit, predictor) are provided that emulate the DACE toolbox interface (see [[#DACE toolbox interfacewrapper scripts]] for more information). 
However, for convenience wrapper scripts (dacefit, predictor) are provided that emulate the DACE toolbox interface (see [[#DACE toolbox interfacewrapper scripts]] for more information). 

−  +  Assume you want to fit a dataset of n samples in d dimensions. 

+  
−  <b>samples</b> holds the input parameters nXd array (each row is one observation) and <b>values</b> is the corresponding nX1 array containing the output values. 

+  <b>samples</b> holds the input parameters nbyd array (each row is one observation) and <b>values</b> is the corresponding nby1 array containing the output values. 

−  <b>lb</b> and <b>ub</b> are 1Xd arrays defining the lower bounds and upper bounds, respectively, needed to optimize the hyperparameters. In addition, a start values has to be specified (e.g., <b>theta0</b> is also an 1Xd array) 

+  <b>lb</b> and <b>ub</b> are 1byd arrays defining the lower bounds and upper bounds, respectively, needed to optimize the hyperparameters. In addition, a set of starting values has to be specified (e.g., <b>theta0</b> is also an 1byd array) 

The example code to fit the dataset is then: 
The example code to fit the dataset is then: 
Revision as of 16:25, 19 March 2010
Introduction
The 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.
Download
See: download page
Quick start guide
IMPORTANT: Before the toolbox can be used you have to include the toolbox in Matlab's search 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 emulate the DACE toolbox interface (see wrapper scripts for more information).
Assume you want to fit a dataset of n samples in d dimensions.
samples holds the input parameters nbyd array (each row is one observation) and values is the corresponding nby1 array containing the output values. lb and ub are 1byd arrays defining the lower bounds and upper bounds, respectively, needed to optimize the hyperparameters. In addition, a set of starting values has to be specified (e.g., theta0 is also an 1byd array)
The example code to fit the dataset is then:
... % 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 advanced features such as using blind kriging or how to use regression instead of interpolation). For more information on the classes and their methods please refer to the source files.
DACE toolbox interface
The blindDACE toolbox provides two scripts dacefit.m and predictor.m that simulate the behavior of the DACE toolbox ([1]). 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.
Example code:
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 are not available using the wrapper scripts). Hence, it is suggested to learn the object oriented interface of blindDACE and use that instead.
Contribute
Suggestions on how to improve the blindDACE toolbox are always welcome. For more information please see the feedback page.