Difference between revisions of "OoDACE:ooDACE toolbox"
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IMPORTANT: Before the toolbox can be used you have to include the toolbox in Matlab's path. You can do this manually | IMPORTANT: Before the toolbox can be used you have to include the toolbox in Matlab's path. You can do this manually | ||
by running <b>startup</b> or if Matlab is started in the root toolbox directory then startup will be run automatically. | by running <b>startup</b> or if Matlab is started in the root toolbox directory then startup will be run automatically. | ||
| + | |||
| + | <code><pre>startup</pre></code> | ||
The blindDACE toolbox is designed in an object oriented (OO) fashion. | The blindDACE toolbox is designed in an object oriented (OO) fashion. | ||
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However, for convenience wrapper scripts (dacefit, predictor) are provided that simulate the DACE toolbox interface. | However, for convenience wrapper scripts (dacefit, predictor) are provided that simulate the DACE toolbox interface. | ||
| − | See demo.m for example code on how to use the blindDACE toolbox. | + | <code><pre>TODO</pre></code> |
| − | + | ||
| − | + | See the included demo.m script for more example code on how to use the blindDACE toolbox. | |
== Download == | == Download == | ||
Revision as of 10:53, 9 February 2010
blindDACE: A versatile kriging Matlab Toolbox
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
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.
TODO
See the included demo.m script for more example code on how to use the blindDACE toolbox.
Download
See: [[1]]
Usage
- Create a network
ann = createFann([2 5 3 1],connectivity)
- Train a network
ann = trainFann(ann,samples,values,[desired error],[max epochs])
- Use a network
values = testFann(ann,samples)
Where samples are the input data points and values the output values, both in column format (1 column per dimension).
Note: do not use a connectivity different from 1 for else you will get wrong results (I hope this can be fixed with the new fann library)
Contribute
These bindings are very basic but they work for me. Please improve, extend, provide binaries, and of course contribute back.
