Difference between revisions of "Config:SequentialDesign"

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== SampleSelector ==
 
== SampleSelector ==
  +
 
 
=== empty ===
 
=== empty ===
 
Dont select any new samples, useful when modeling multiple outputs in paralel
 
Dont select any new samples, useful when modeling multiple outputs in paralel
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
 
<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#EmptySampleSelector|EmptySampleSelector]]" combineOutputs="false"/>
 
<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#EmptySampleSelector|EmptySampleSelector]]" combineOutputs="false"/>
 
</source>
 
</source>
 
=== random ===
 
=== random ===
 
Each sampling iterations new samples are selected randomly
 
Each sampling iterations new samples are selected randomly
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
 
<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#RandomSampleSelector|RandomSampleSelector]]" combineOutputs="false"/>
 
<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#RandomSampleSelector|RandomSampleSelector]]" combineOutputs="false"/>
 
</source>
 
</source>
 
=== combo ===
 
=== combo ===
 
Allows you combine multiple sample selector algorithms
 
Allows you combine multiple sample selector algorithms
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
 
<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#CombinedSampleSelector|CombinedSampleSelector]]" combineOutputs="false">
 
<[[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
+
<[[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]]>
 
  +
- - pole is found (using a simple search strategy), the pole is returned -->
<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#DelaunaySampleSelector|DelaunaySampleSelector]]" combineOutputs="false"><!-- One of all, data -->
 
  +
</[[Config:SampleSelector|SampleSelector]]>
<Option key="sampleSelect" value="all"/><!-- Integer between 2 and 20 -->
 
  +
<Option key="nLastModels" value="2"/><!-- One of densityBased, differenceBased, weightedLinear, weightedGeometric -->
 
  +
<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#DelaunaySampleSelector|DelaunaySampleSelector]]" combineOutputs="false">
<Option key="scoreFunction" value="weightedLinear"/><!-- Weighting for weightedLinear -->
 
<Option key="lambda" value="0.5"/><!-- Weighting for weightedGeometric -->
+
<!-- One of all, data -->
<Option key="mu" value="0.5"/><!-- One of none, max, cap, capmax -->
+
<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="volumeScaling" value="max"/>
<Option key="differenceScaling" value="capmax"/><!-- Boolean flag, if set all points closer than snapThreshold to the boundary of
+
<Option key="differenceScaling" value="capmax"/>
- - the domain are clipped to the boundary -->
+
<!-- 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="snapToEdge" value="enable"/>
 
<Option key="snapThreshold" value=".05"/>
 
<Option key="snapThreshold" value=".05"/>
Line 33: Line 43:
 
=== delaunay ===
 
=== delaunay ===
 
An adaptive sample selection algorithm that does a trade-off between error and density
 
An adaptive sample selection algorithm that does a trade-off between error and density
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#DelaunaySampleSelector|DelaunaySampleSelector]]" combineOutputs="false"><!-- One of all, data -->
+
<[[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="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="scoreFunction" value="weightedLinear"/>
 
<Option key="lambda" value="0.5"/>
 
<Option key="lambda" value="0.5"/>
<Option key="mu" value="0.5"/><!-- One of none, max, cap, capmax -->
+
<Option key="mu" value="0.5"/>
  +
<!-- One of none, max, cap, capmax -->
 
<Option key="volumeScaling" value="max"/>
 
<Option key="volumeScaling" value="max"/>
<Option key="differenceScaling" value="capmax"/><!-- Boolean flag -->
+
<Option key="differenceScaling" value="capmax"/>
  +
<!-- Boolean flag -->
 
<Option key="snapToEdge" value="enable"/>
 
<Option key="snapToEdge" value="enable"/>
 
<Option key="snapThreshold" value=".2"/>
 
<Option key="snapThreshold" value=".2"/>
Line 48: Line 63:
 
=== density ===
 
=== density ===
 
A simple density based sample selection algorithm
 
A simple density based sample selection algorithm
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
 
<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#DensitySampleSelector|DensitySampleSelector]]" combineOutputs="false"/>
 
<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#DensitySampleSelector|DensitySampleSelector]]" combineOutputs="false"/>
 
</source>
 
</source>
 
=== error ===
 
=== error ===
 
An adaptive sample selection algorithm (error based), driven by the evaluation of your model on a dense grid
 
An adaptive sample selection algorithm (error based), driven by the evaluation of your model on a dense grid
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#ErrorSampleSelector|ErrorSampleSelector]]" combineOutputs="false"><!-- Integer between 2 and 20 -->
+
<[[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="nLastModels" value="4"/>
  +
<!-- One of none, max, cap, capmax -->
<Option key="gridSize" value="50"/><!-- Maximum total points to evaluate, distributed over dimensions -->
 
<Option key="maxGridSize" value="100000"/><!-- Closeness threshold, Double -->
+
<Option key="differenceScaling" value="none"/>
  +
<!-- Gridsize to evaluate on, one of int or array of dimension length -->
<Option key="closenessThreshold" value="0.2"/><!-- Set a % of the maximumSamples to randomly chosen -->
 
  +
<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"/>
 
<Option key="randomPercentage" value="20"/>
 
</[[Config:SampleSelector|SampleSelector]]>
 
</[[Config:SampleSelector|SampleSelector]]>
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=== gradient ===
 
=== gradient ===
 
A highly adaptive sampling algorithm, error and density based
 
A highly adaptive sampling algorithm, error and density based
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#GradientSampleSelector|GradientSampleSelector]]" combineOutputs="false"><!-- Integer between 2 and 20 -->
+
<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#GradientSampleSelector|GradientSampleSelector]]" combineOutputs="false">
  +
<!-- Integer between 2 and 20 -->
 
<Option key="neighbourhoodSize" value="2"/>
 
<Option key="neighbourhoodSize" value="2"/>
 
</[[Config:SampleSelector|SampleSelector]]>
 
</[[Config:SampleSelector|SampleSelector]]>
Line 72: Line 94:
 
=== isc ===
 
=== isc ===
 
A sampling algorithm aimed at optimization problems
 
A sampling algorithm aimed at optimization problems
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#InfillSamplingCriterion|InfillSamplingCriterion]]" combineOutputs="false"><!-- A criterion determines the interesting regions to sample --><!-- Choose 1 from the following: -->
+
<[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#InfillSamplingCriterion|InfillSamplingCriterion]]" combineOutputs="false">
  +
<!-- A criterion determines the interesting regions to sample -->
<Option key="criterion" value="gei"/><!-- generalized expected improvement -->
 
  +
<!-- Choose 1 from the following: -->
<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 -->
 
  +
<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]]">
 
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#DirectOptimizer|DirectOptimizer]]">
 
<Option key="maxevals" value="1000"/>
 
<Option key="maxevals" value="1000"/>
 
<Option key="maxits" value="300"/>
 
<Option key="maxits" value="300"/>
</[[Config:Optimizer|Optimizer]]><!--
+
</[[Config:Optimizer|Optimizer]]>
  +
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabGA|MatlabGA]]">
 
  +
<!--
</[[Config:Optimizer|Optimizer]]>
 
  +
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabGA|MatlabGA]]">
--><!-- when debug is 'on' a contour plot of the ISC function is drawn every iteration. --><!-- Together with the current samples and the chosen samples -->
 
  +
</[[Config:Optimizer|Optimizer]]>
<Option key="debug" value="off"/>
 
  +
-->
  +
  +
<!--
  +
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]]>
 
</[[Config:SampleSelector|SampleSelector]]>
 
</source>
 
</source>

Revision as of 14:56, 13 February 2008

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="on"/>
</SampleSelector>