Config:SequentialDesign

Generated for SUMO toolbox version 6. We are well aware that the list below is incomplete and possibly even out of date in some cases. We try to document everything as best we can but much is limited by available time and manpower. The most up to date documentation can always be found (if not here) in the default.xml configuration file and, of course, in the source files.

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]]>

autoSampling

A wrapper around another sample selector that filters out the auto-sampled dimensions.

<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#AutoSamplingSampleSelector|AutoSamplingSampleSelector]]" combineOutputs="false">
   
   <!-- The filter function to use. Default = max. -->
   <Option key="function" value="max"/>
   
   <!-- The sample selector to use for non-auto sampled dimensions. -->
   <[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#GradientSampleSelector|GradientSampleSelector]]" combineOutputs="false">
      <Option key="neighbourhoodSize" value="2"/>
   </[[Config:SampleSelector|SampleSelector]]>
   
</[[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]]>