Config:SequentialDesign

SampleSelector

empty

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

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

random

Each sampling iterations new samples are selected randomly

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

combo

Allows you combine multiple sample selector algorithms

<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#CombinedSampleSelector|CombinedSampleSelector]]" combineOutputs="false">
   <[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#RationalPoleSuppressionSampleSelector|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 -->
   </[[Config:SampleSelector|SampleSelector]]>
   
   <[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#DelaunaySampleSelector|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"/>
   </[[Config:SampleSelector|SampleSelector]]>
</[[Config:SampleSelector|SampleSelector]]>

delaunay

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

<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#DelaunaySampleSelector|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"/>
</[[Config:SampleSelector|SampleSelector]]>

density

A simple density based sample selection algorithm

<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#DensitySampleSelector|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]] type="[[SampleSelector#ErrorSampleSelector|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"/>
</[[Config:SampleSelector|SampleSelector]]>

gradient

A highly adaptive sampling algorithm, error and density based

<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#GradientSampleSelector|GradientSampleSelector]]" combineOutputs="false">
   <!-- Integer between 2 and 20 -->
   <Option key="neighbourhoodSize" value="2"/>
</[[Config:SampleSelector|SampleSelector]]>

isc

A sampling algorithm aimed at optimization problems

<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#InfillSamplingCriterion|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 -->
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#DirectOptimizer|DirectOptimizer]]">
      <Option key="maxevals" value="1000"/>
      <Option key="maxits" value="300"/>
   </[[Config:Optimizer|Optimizer]]>

   <!--
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabGA|MatlabGA]]">
   </[[Config:Optimizer|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="on"/>
</[[Config:SampleSelector|SampleSelector]]>