Config:SequentialDesign

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Revision as of 11:23, 8 February 2008 by Icouckuy (talk | contribs)

SampleSelector

empty

Dont select any new samples, useful when modeling multiple outputs in paralel

<SampleSelector type="EmptySampleSelector" combineOutputs="false"/>

random

Each sampling iterations new samples are selected randomly

<SampleSelector type="RandomSampleSelector" combineOutputs="false"/>

combo

Allows you combine multiple sample selector algorithms

<SampleSelector type="CombinedSampleSelector" combineOutputs="false">
   <SampleSelector type="RationalPoleSuppressionSampleSelector" combineOutputs="false"><!-- Currently no options are available, if the model is a rational model, and a
 - - pole is found (using a simple search strategy), the pole is returned --></SampleSelector>
   <SampleSelector type="DelaunaySampleSelector" combineOutputs="false"><!-- One of all, data -->
      <Option key="sampleSelect" value="all"/><!-- Integer between 2 and 20 -->
      <Option key="nLastModels" value="2"/><!-- One of densityBased, differenceBased, weightedLinear, weightedGeometric -->
      <Option key="scoreFunction" value="weightedLinear"/><!-- Weighting for weightedLinear -->
      <Option key="lambda" value="0.5"/><!-- Weighting for weightedGeometric -->
      <Option key="mu" value="0.5"/><!-- One of none, max, cap, capmax -->
      <Option key="volumeScaling" value="max"/>
      <Option key="differenceScaling" value="capmax"/><!-- Boolean flag, if set all points closer than snapThreshold to the boundary of 
 - - the domain are clipped to the boundary -->
      <Option key="snapToEdge" value="enable"/>
      <Option key="snapThreshold" value=".05"/>
   </SampleSelector>
</SampleSelector>

delaunay

An adaptive sample selection algorithm that does a trade-off between error and density

<SampleSelector type="DelaunaySampleSelector" combineOutputs="false"><!-- One of all, data -->
   <Option key="sampleSelect" value="all"/><!-- Integer between 2 and 20 -->
   <Option key="nLastModels" value="2"/><!-- One of densityBased, differenceBased, weightedLinear, weightedGeometric -->
   <Option key="scoreFunction" value="weightedLinear"/>
   <Option key="lambda" value="0.5"/>
   <Option key="mu" value="0.5"/><!-- One of none, max, cap, capmax -->
   <Option key="volumeScaling" value="max"/>
   <Option key="differenceScaling" value="capmax"/><!-- Boolean flag -->
   <Option key="snapToEdge" value="enable"/>
   <Option key="snapThreshold" value=".2"/>
</SampleSelector>

density

A simple density based sample selection algorithm

<SampleSelector type="DensitySampleSelector" combineOutputs="false"/>

error

An adaptive sample selection algorithm (error based), driven by the evaluation of your model on a dense grid

<SampleSelector type="ErrorSampleSelector" combineOutputs="false"><!-- Integer between 2 and 20 -->
   <Option key="nLastModels" value="4"/><!-- One of none, max, cap, capmax -->
   <Option key="differenceScaling" value="none"/><!-- Gridsize to evaluate on, one of int or array of dimension length -->
   <Option key="gridSize" value="50"/><!-- Maximum total points to evaluate, distributed over dimensions -->
   <Option key="maxGridSize" value="100000"/><!-- Closeness threshold, Double -->
   <Option key="closenessThreshold" value="0.2"/><!-- Set a % of the maximumSamples to randomly chosen -->
   <Option key="randomPercentage" value="20"/>
</SampleSelector>

gradient

A highly adaptive sampling algorithm, error and density based

<SampleSelector type="GradientSampleSelector" combineOutputs="false"><!-- Integer between 2 and 20 -->
   <Option key="neighbourhoodSize" value="2"/>
</SampleSelector>

isc

A sampling algorithm aimed at optimization problems

<SampleSelector type="InfillSamplingCriterion" combineOutputs="false"><!-- A criterion determines the interesting regions to sample --><!-- Choose 1 from the following: -->
   <Option key="criterion" value="gei"/><!-- generalized expected improvement -->
   <Option key="g" value="1"/><!-- balanced local-global search --><!--<Option key="criterion" value="wei" />
--><!-- weighted expected improvement --><!--<Option key="w" value="0.5" />
--><!-- weight, 0 is global search, 1 is local search --><!--<Option key="criterion" value="ei" />
--><!-- expected improvement --><!--<Option key="criterion" value="kushner" />
--><!-- kushner --><!--<Option key="eps" value="0.001" />
--><!--<Option key="criterion" value="lcb" />
--><!-- lower confidence bound function --><!--<Option key="lcb" value="2.0" />
--><!--<Option key="criterion" value="maxvar" />
--><!-- maximizes variation --><!-- Watson and Barnes criterions --><!--<Option key="criterion" value="wb1" />
--><!-- threshold-bounded extreme --><!--<Option key="criterion" value="wb2" />
--><!-- regional extreme --><!--<Option key="criterion" value="crowdedness" />
--><!-- crowdedness function --><!-- This criterion has to be solved to choose new samples, one can choose the optimizer used here -->
   <Optimizer type="DirectOptimizer">
      <Option key="maxevals" value="1000"/>
      <Option key="maxits" value="300"/>
   </Optimizer><!--
		<Optimizer type="MatlabGA">

     </Optimizer>
		--><!-- when debug is 'on' a contour plot of the ISC function is drawn every iteration. --><!-- Together with the current samples and the chosen samples -->
   <Option key="debug" value="off"/>
</SampleSelector>