Difference between revisions of "Outputs"
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There are three levels on which you can configure the way the different outputs in the simulator file are modeller. | There are three levels on which you can configure the way the different outputs in the simulator file are modeller. | ||
| − | = Default behaviour, all outputs modelled = | + | == Default behaviour, all outputs modelled == |
If no Outputs tag is defined in the configuration file, all outputs are modelled and evaluated with the default CrossValidation measure and target accuracy 0.001. Complex outputs are split into a real and imaginary part, and are modeller separately. | If no Outputs tag is defined in the configuration file, all outputs are modelled and evaluated with the default CrossValidation measure and target accuracy 0.001. Complex outputs are split into a real and imaginary part, and are modeller separately. | ||
| − | = Default behaviour, selected outputs modelled = | + | == Default behaviour, selected outputs modelled == |
To change this default behaviour, one must specify an Outputs tag in the Run configuration. If you just want to omit outputs and want to keep the default modelling behaviour of the toolbox (CrossValidation, 0.001 accuracy), you only have to add an Output subelement for each output that you want modelled. | To change this default behaviour, one must specify an Outputs tag in the Run configuration. If you just want to omit outputs and want to keep the default modelling behaviour of the toolbox (CrossValidation, 0.001 accuracy), you only have to add an Output subelement for each output that you want modelled. | ||
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</nowiki></pre> | </nowiki></pre> | ||
| − | = Custom behaviour for each output = | + | === d === |
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| + | == Custom behaviour for each output == | ||
If you also want to change or fine-tune the behaviour of the toolbox for each output separately, you can add subelements to each Output tag to customize the toolbox for that particular output. This allows you to use different sample selectors for each output, change the default measure, or combine multiple measures. | If you also want to change or fine-tune the behaviour of the toolbox for each output separately, you can add subelements to each Output tag to customize the toolbox for that particular output. This allows you to use different sample selectors for each output, change the default measure, or combine multiple measures. | ||
Revision as of 13:20, 27 June 2007
There are three levels on which you can configure the way the different outputs in the simulator file are modeller.
Default behaviour, all outputs modelled
If no Outputs tag is defined in the configuration file, all outputs are modelled and evaluated with the default CrossValidation measure and target accuracy 0.001. Complex outputs are split into a real and imaginary part, and are modeller separately.
Default behaviour, selected outputs modelled
To change this default behaviour, one must specify an Outputs tag in the Run configuration. If you just want to omit outputs and want to keep the default modelling behaviour of the toolbox (CrossValidation, 0.001 accuracy), you only have to add an Output subelement for each output that you want modelled.
An example of such a configuration for the Academic2DTwice test function is:
<Outputs> <Output name="out" /> <Output name="outinverse" /> </Outputs>
d
Custom behaviour for each output
If you also want to change or fine-tune the behaviour of the toolbox for each output separately, you can add subelements to each Output tag to customize the toolbox for that particular output. This allows you to use different sample selectors for each output, change the default measure, or combine multiple measures.
Several examples of valid Output configurations can be found commented in default.xml.
Here is an example of an output configuration for the Academic2DTwice test function:
<Outputs> <Output name="out"> <SampleSelector>gradient</SampleSelector> <Measure type="CrossValidation" target=".0001" use="on" /> </Output> <Output name="outinverse"> <SampleSelector>grid</SampleSelector> <Measure type="CrossValidation" target=".05" use="on" /> <Measure type="MinMax" /> </Output> </Outputs>
This configuration models the first Output named "out" using the CrossValidation measure, but with increased target accuracy, and uses the gradient sample selector to select new samples. The second output, named "outinverse", uses CrossValidation in combination with the MinMax measure, and uses the grid sample selector to select new samples for this output.
If an Output element does not contain any Measure elements, that output is modelled using the default configuration, which is the CrossValidation measure and target accuracy 0.001.
For more information on how multiple measures are handled and the configuration options of each measure, see Measures.
