Difference between revisions of "Config:SequentialDesign"
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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 lang="xml"> | ||
| − | <[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector | + | <[[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 lang="xml"> | ||
| − | <[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector | + | <[[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 lang="xml"> | ||
| − | <[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector | + | <[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#CombinedSampleSelector|CombinedSampleSelector]]" combineOutputs="false"> |
| − | <[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector | + | <[[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]]> | ||
| − | <[[Config:SampleSelector|SampleSelector]] type="[[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="sampleSelect" value="all"/><!-- Integer between 2 and 20 --> | ||
<Option key="nLastModels" value="2"/><!-- One of densityBased, differenceBased, weightedLinear, weightedGeometric --> | <Option key="nLastModels" value="2"/><!-- One of densityBased, differenceBased, weightedLinear, weightedGeometric --> | ||
| Line 34: | Line 34: | ||
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 lang="xml"> | ||
| − | <[[Config:SampleSelector|SampleSelector]] type="[[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="sampleSelect" value="all"/><!-- Integer between 2 and 20 --> | ||
<Option key="nLastModels" value="2"/><!-- One of densityBased, differenceBased, weightedLinear, weightedGeometric --> | <Option key="nLastModels" value="2"/><!-- One of densityBased, differenceBased, weightedLinear, weightedGeometric --> | ||
| Line 49: | Line 49: | ||
A simple density based sample selection algorithm | A simple density based sample selection algorithm | ||
<source lang="xml"> | <source lang="xml"> | ||
| − | <[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector | + | <[[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 lang="xml"> | ||
| − | <[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector | + | <[[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="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="differenceScaling" value="none"/><!-- Gridsize to evaluate on, one of int or array of dimension length --> | ||
| Line 66: | Line 66: | ||
A highly adaptive sampling algorithm, error and density based | A highly adaptive sampling algorithm, error and density based | ||
<source lang="xml"> | <source lang="xml"> | ||
| − | <[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector | + | <[[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 73: | Line 73: | ||
A sampling algorithm aimed at optimization problems | A sampling algorithm aimed at optimization problems | ||
<source lang="xml"> | <source lang="xml"> | ||
| − | <[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector | + | <[[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="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 --> | <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 | + | <[[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 | + | <[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabGA|MatlabGA]]"> |
</[[Config:Optimizer|Optimizer]]> | </[[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 --> | --><!-- when debug is 'on' a contour plot of the ISC function is drawn every iteration. --><!-- Together with the current samples and the chosen samples --> | ||
Revision as of 10:23, 8 February 2008
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="off"/>
</[[Config:SampleSelector|SampleSelector]]>
