Config:SequentialDesign

SampleSelector

empty

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

<[[Config:SampleSelector|SampleSelector]] id="empty" type="EmptySampleSelector" combineOutputs="false"/>

random

Each sampling iterations new samples are selected randomly

<[[Config:SampleSelector|SampleSelector]] id="random" type="RandomSampleSelector" combineOutputs="false"/>

combo

Allows you combine multiple sample selector algorithms

<[[Config:SampleSelector|SampleSelector]] id="combo" type="CombinedSampleSelector" combineOutputs="false">
   <[[Config:SampleSelector|SampleSelector]] type="RationalPoleSuppressionSampleSelector" combineOutputs="false"></[[Config:SampleSelector|SampleSelector]]>
   <[[Config:SampleSelector|SampleSelector]] type="DelaunaySampleSelector" combineOutputs="false">
      <Option key="sampleSelect" value="all"/>
      <Option key="nLastModels" value="2"/>
      <Option key="scoreFunction" value="weightedLinear"/>
      <Option key="lambda" value="0.5"/>
      <Option key="mu" value="0.5"/>
      <Option key="volumeScaling" value="max"/>
      <Option key="differenceScaling" value="capmax"/>
      <Option key="snapToEdge" value="enable"/>
      <Option key="snapThreshold" value=".05"/>
   </[[Config:SampleSelector|SampleSelector]]>
</[[Config:SampleSelector|SampleSelector]]>

delaunay

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

<[[Config:SampleSelector|SampleSelector]] id="delaunay" type="DelaunaySampleSelector" combineOutputs="false">
   <Option key="sampleSelect" value="all"/>
   <Option key="nLastModels" value="2"/>
   <Option key="scoreFunction" value="weightedLinear"/>
   <Option key="lambda" value="0.5"/>
   <Option key="mu" value="0.5"/>
   <Option key="volumeScaling" value="max"/>
   <Option key="differenceScaling" value="capmax"/>
   <Option key="snapToEdge" value="enable"/>
   <Option key="snapThreshold" value=".2"/>
</[[Config:SampleSelector|SampleSelector]]>

density

A simple density based sample selection algorithm

<[[Config:SampleSelector|SampleSelector]] id="density" type="DensitySampleSelector" combineOutputs="false"/>

error

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

<[[Config:SampleSelector|SampleSelector]] id="error" type="ErrorSampleSelector" combineOutputs="false">
   <Option key="nLastModels" value="4"/>
   <Option key="differenceScaling" value="none"/>
   <Option key="gridSize" value="50"/>
   <Option key="maxGridSize" value="100000"/>
   <Option key="closenessThreshold" value="0.2"/>
   <Option key="randomPercentage" value="20"/>
</[[Config:SampleSelector|SampleSelector]]>

gradient

A highly adaptive sampling algorithm, error and density based

<[[Config:SampleSelector|SampleSelector]] id="gradient" type="GradientSampleSelector" combineOutputs="false">
   <Option key="neighbourhoodSize" value="2"/>
</[[Config:SampleSelector|SampleSelector]]>

isc

A sampling algorithm aimed at optimization problems

<[[Config:SampleSelector|SampleSelector]] id="isc" type="InfillSamplingCriterion" combineOutputs="false">
   <Option key="criterion" value="gei"/>
   <Option key="g" value="1"/>
   <[[Config:Optimizer|Optimizer]] type="DirectOptimizer">
      <Option key="maxevals" value="1000"/>
      <Option key="maxits" value="300"/>
   </[[Config:Optimizer|Optimizer]]>
   <Option key="debug" value="off"/>
</[[Config:SampleSelector|SampleSelector]]>