Difference between revisions of "Config:ModelBuilder"
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Each configuration section consists of two parts. The first part is the <ModelFactory> tag, which defines all the settings and parameters for a particular [[Add_Model_Type|model type]] as well as their boundaries if those parameters need to be optimized. | Each configuration section consists of two parts. The first part is the <ModelFactory> tag, which defines all the settings and parameters for a particular [[Add_Model_Type|model type]] as well as their boundaries if those parameters need to be optimized. | ||
| − | The second part, the <AdaptiveModelBuilder> itself defines settings specific to the [[Add_Modeling_Algorithm|optimization algorithm]]. You can find out more about the available modeling algorithms and how to add your own [[Add_Modeling_Algorithm|here]]. | + | The second part, the <AdaptiveModelBuilder> itself defines settings specific to the [[Add_Modeling_Algorithm|optimization algorithm]]. You can find out '''more about the available modeling algorithms''' and how to add your own [[Add_Modeling_Algorithm|here]]. |
By combining these two elements you can create your own <AdaptiveModelBuiler>. If you want to add your own model type, please refer to this [[Add_Model_Type|page]]. To learn more about the optimization algorithms used or to add your own search algorithm, consult this [[Add_Modeling_Algorithm|page]]. | By combining these two elements you can create your own <AdaptiveModelBuiler>. If you want to add your own model type, please refer to this [[Add_Model_Type|page]]. To learn more about the optimization algorithms used or to add your own search algorithm, consult this [[Add_Modeling_Algorithm|page]]. | ||
Revision as of 17:30, 30 January 2012
The adaptive model builder component defines which model type (e.g. polynomial model, ANN, SVMs, etc...) is used to model your data as well as the algorithm used to optimize its hyperparameters (e.g. the order of a polynomial function, the number of hidden nodes of an ANN, the kernel parameters of an SVM, etc...).
This page lists all pre-defined <AdaptiveModelBuilder> configuration sections included in the default.xml. These configuration sections should be declared after the <Plan>...</Plan> section in your config.xml if you are using a custom configuration XML file.
In the default.xml each configuration section is also given an id, which can be referred to in the <Plan> section. These id's are not included in the configuration sections listed here. For example in the default.xml file, the following adaptive model builder configuration section can be referred to using <AdaptiveModelBuilder>rational</AdaptiveModelBuilder> in the <Plan> section.
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="rational" type="[[AdaptiveModelBuilder#SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="true">
...
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
Each configuration section consists of two parts. The first part is the <ModelFactory> tag, which defines all the settings and parameters for a particular model type as well as their boundaries if those parameters need to be optimized.
The second part, the <AdaptiveModelBuilder> itself defines settings specific to the optimization algorithm. You can find out more about the available modeling algorithms and how to add your own here.
By combining these two elements you can create your own <AdaptiveModelBuiler>. If you want to add your own model type, please refer to this page. To learn more about the optimization algorithms used or to add your own search algorithm, consult this page.
Generated for SUMO toolbox version 7.0.
We are well aware that documentation is not always complete 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. We are are a university research group after all. 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. If something is unclear please don't hesitate to ask.
Rational and polynomial models
rational
Build rational models using a custom stochastic hillclimber to select the model parameters
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="true">
<!-- Maximum number of models built before selecting new samples -->
<Option key="maximumRunLength" value="30"/>
<!-- Degeneration of score if a model gets older -->
<Option key="decay" value=".99"/>
<!-- Size of the best model history -->
<Option key="historySize" value="15"/>
<!-- One of best, last. When set to best the best `historySize' models are kept,
- - when set to last, the last `historySize' models are kept -->
<Option key="strategy" value="best"/>
<!-- <Option key="strategy" value="window"/> -->
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#RationalFactory|RationalFactory]]">
<!-- Bounds for the weights of the rational modeller -->
<Option key="weightBounds" value="1,40"/>
<!-- Bounds for the percentage of degrees of freedom wrt number of samples -->
<Option key="percentBounds" value="1,100"/>
<!-- Regardless of the percentage bounds, never use more than this many degrees of freedom -->
<Option key="maxDegrees" value="80"/>
<!-- When randomizing rational flags, what percentage should be set -->
<Option key="percentRational" value="70"/>
<!-- If a variable is named "f" of "frequency" it will be modelled differently, if this is set to auto,
- - If this field is set to a variable name, that variable will be considered to be the frequency -->
<Option key="frequencyVariable" value="auto"/>
<!-- Base function for interpolation, one of chebyshev, power, legendre -->
<Option key="basis" value="power"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
rationalgenetic
Build rational models using a genetic algorithm
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
<!--See that matlab gads toolbox documentation for more information on the options-->
<Option key="restartStrategy" value="continue"/>
<Option key="populationType" value="custom"/>
<Option key="populationSize" value="30"/>
<Option key="crossoverFraction" value="0.7"/>
<Option key="maxGenerations" value="20"/>
<Option key="eliteCount" value="1"/>
<Option key="stallGenLimit" value="5"/>
<Option key="stallTimeLimit" value="Inf"/>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#RationalFactory|RationalFactory]]">
<Option key="crossoverFcn" value="crossover"/>
<Option key="mutationFcn" value="mutation"/>
<Option key="creationFcn" value="createInitialPopulation"/>
<!-- Use the next three functions instead of the previous three if you set the
population type to doubleVector -->
<!--<Option key="creationFcn" value="@gacreationuniform"/>
<Option key="crossoverFcn" value="@crossoverheuristic"/>
<Option key="mutationFcn" value="@mutationadaptfeasible"/>-->
<!-- Bounds for the weights of the rational modeller -->
<Option key="weightBounds" value="1,40"/>
<!-- Bounds for the percentage of degrees of freedom wrt number of samples -->
<Option key="percentBounds" value="1,100"/>
<!-- Regardless of the percentage bounds, never use more than this many degrees of freedom -->
<Option key="maxDegrees" value="80"/>
<!-- When randomizing rational flags, what percentage should be set -->
<Option key="percentRational" value="70"/>
<!-- If a variable is named "f" of "frequency"
it will be modelled differently, if this is set to auto -->
<!-- If this field is set to a variable name, that variable will be considered to be the frequency -->
<Option key="frequencyVariable" value="auto"/>
<!-- Base function for interpolation, one of chebyshev, power, legendre -->
<Option key="basis" value="power"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
rationalpso
Generate Rational models using PSO
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#PSOtOptimizer|PSOtOptimizer]]">
<Option key="typePSO" value="0"/>
<Option key="seedPSO" value="1"/>
<Option key="popSize" value="10"/>
<Option key="maxiters" value="10"/>
<Option key="epochInertia" value="8"/>
<Option key="gradientTermination" value="8"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#RationalFactory|RationalFactory]]">
<!-- Bounds for the weights of the rational modeller -->
<Option key="weightBounds" value="1,40"/>
<!-- Bounds for the percentage of degrees of freedom wrt number of samples -->
<Option key="percentBounds" value="1,100"/>
<!-- Regardless of the percentage bounds, never use more than this many degrees of freedom -->
<Option key="maxDegrees" value="80"/>
<!-- When randomizing rational flags, what percentage should be set -->
<Option key="percentRational" value="70"/>
<!-- If a variable is named "f" of "frequency"
it will be modelled differently, if this is set to auto -->
<!-- If this field is set to a variable name, that variable will be considered to be the frequency -->
<Option key="frequencyVariable" value="auto"/>
<!-- Base function for interpolation, one of chebyshev, power, legendre -->
<Option key="basis" value="power"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
polynomialfixed
Build polynomial models with a fixed order
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#AdaptiveModelBuilder|AdaptiveModelBuilder]]" combineOutputs="false">
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#PolynomialFactory|PolynomialFactory]]">
<!-- Specifies the structure of the polynomial -->
<!-- Expects matlab matrix: Element (i,j) is the exponent of variable i for term j. -->
<Option key="degrees" value="[0 0;1 0;0 1;1 1;2 0;0 2;2 2]"/> <!-- Equals to 1+x+y+xy+xx+yy+xxyy -->
<!-- Base function for interpolation, one of chebyshev, power, legendre -->
<Option key="basis" value="power"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
Gaussian processes
rbf
Build Radial Basis Function models
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="false">
<!-- Maximum number of models built before selecting new samples -->
<Option key="maximumRunLength" value="20"/>
<!-- Degeneration of score if a model gets older -->
<Option key="decay" value=".9"/>
<!-- Size of the best model history -->
<Option key="historySize" value="15"/>
<!-- One of best, last. When set to best the best `historySize' models are kept,
- - when set to last, the last `historySize' models are kept -->
<Option key="strategy" value="best"/>
<!-- <Option key="strategy" value="window"/> -->
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#BFFactory|BFFactory]]">
<Option key="type" value="RBF"/>
<[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="ln"/>
<[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="ln"/>
<!--<[[Config:BasisFunction|BasisFunction]] name="biharmonic" min=".1" max="5" scale="ln"/> -->
<[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="ln,lin"/>
<Option key="regression" value="-1,0,1,2"/>
<Option key="backend" value="AP"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
rbfgenetic
Build Radial Basis Function models using a genetic algorithm
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
<Option key="restartStrategy" value="continue"/>
<!--See that matlab gads toolbox documentation for more information on the options-->
<Option key="populationType" value="custom"/>
<Option key="populationSize" value="15"/>
<Option key="crossoverFraction" value="0.7"/>
<Option key="maxGenerations" value="10"/>
<Option key="eliteCount" value="1"/>
<Option key="stallGenLimit" value="4"/>
<Option key="stallTimeLimit" value="Inf"/>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#BFFactory|BFFactory]]">
<Option key="type" value="RBF"/>
<Option key="crossoverFcn" value="crossover"/>
<Option key="mutationFcn" value="mutation"/>
<Option key="creationFcn" value="createInitialPopulation"/>
<!-- Bounds for the shape parameter -->
<[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="ln"/>
<[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="ln"/>
<!-- <[[Config:BasisFunction|BasisFunction]] name="biharmonic" min=".1" max="5" scale="ln"/> -->
<[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="ln,lin"/>
<Option key="regression" value="-1,0,1,2"/>
<!-- Basisfunction, one of 'multiquadric', 'triharmonic', 'biharmonic' -->
<!-- Specify which implementation to use, currently, 'Direct', 'AP', 'Greedy' and
'FastRBF' are supported.
'Direct' solves the direct problem by inverting the interpolation
matrix
'AP' uses an alternating projections method when the system gets
too large. This is *MUCH* slower than 'Direct', and doesn't
guarantee convergence, use with caution
'Greedy' uses a one point greedy algorithm for selecting the
interpolation centers. Same remark applies as with 'AP'
'FastRBF' interfaces the FastRBF library. When using FastRBF,
make sure your copy of the software is installed under
the src/matlab/contrib directory and that the software
is licensed properly.
The FastRBF matlab toolbox can be found at
http://www.farfieldtechnology.com
-->
<Option key="backend" value="AP"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
dace
Build DACE models (equivalent to Kriging but a custom implementation)
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="false">
<!-- Maximum number of models built before selecting new samples -->
<Option key="maximumRunLength" value="20"/>
<!-- Degeneration of score if a model gets older -->
<Option key="decay" value=".9"/>
<!-- Size of the best model history -->
<Option key="historySize" value="15"/>
<!-- One of best, last. When set to best the best `historySize' models are kept,
- - when set to last, the last `historySize' models are kept -->
<Option key="strategy" value="best"/>
<!-- <Option key="strategy" value="window"/> -->
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#BFFactory|BFFactory]]">
<Option key="type" value="DACE"/>
<[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="ln"/>
<[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="ln"/>
<!--<[[Config:BasisFunction|BasisFunction]] name="biharmonic" min=".1" max="5" scale="ln"/> -->
<[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="ln,lin"/>
<Option key="regression" value="-1,0,1,2"/>
<Option key="backend" value="AP"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
dacegenetic
Build DACE models using a genetic algorithm
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
<!--See that matlab gads toolbox documentation for more information on the options-->
<Option key="populationType" value="custom"/>
<Option key="populationSize" value="15"/>
<Option key="crossoverFraction" value="0.7"/>
<Option key="maxGenerations" value="10"/>
<Option key="eliteCount" value="1"/>
<Option key="stallGenLimit" value="4"/>
<Option key="stallTimeLimit" value="Inf"/>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#BFFactory|BFFactory]]">
<Option key="type" value="DACE"/>
<Option key="crossoverFcn" value="crossover"/>
<Option key="mutationFcn" value="mutation"/>
<Option key="creationFcn" value="createInitialPopulation"/>
<!-- Bounds for the shape parameter -->
<[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="ln"/>
<[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="ln"/>
<!-- <[[Config:BasisFunction|BasisFunction]] name="biharmonic" min=".1" max="5" scale="ln"/> -->
<[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="ln,lin"/>
<Option key="regression" value="-1,0,1,2"/>
<Option key="backend" value="AP"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
daceps
Build DACE using pattern search
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabPatternSearch|MatlabPatternSearch]]">
<Option key="maxIterations" value="500"/>
<Option key="maxFunEvals" value="100"/>
<Option key="searchMethod" value="GPSPositiveBasis2N"/>
<Option key="pollMethod" value="MADSPositiveBasis2N"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#BFFactory|BFFactory]]">
<Option key="type" value="DACE"/>
<!--Option key="multipleBasisFunctionsAllowed" value="false"/-->
<[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="ln"/>
<Option key="regression" value="-1,0,1,2"/>
<Option key="backend" value="AP"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
dacepso
Build DACE models using PSO
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#PSOtOptimizer|PSOtOptimizer]]">
<Option key="typePSO" value="0"/>
<Option key="seedPSO" value="1"/>
<Option key="popSize" value="10"/>
<Option key="maxiters" value="10"/>
<Option key="epochInertia" value="8"/>
<Option key="gradientTermination" value="8"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#BFFactory|BFFactory]]">
<Option key="type" value="DACE"/>
<!--Option key="multipleBasisFunctionsAllowed" value="false"/-->
<[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="ln"/>
<Option key="regression" value="-1,0,1,2"/>
<Option key="backend" value="AP"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
gpps
Build GP using pattern search
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabPatternSearch|MatlabPatternSearch]]">
<Option key="maxIterations" value="500"/>
<Option key="maxFunEvals" value="100"/>
<Option key="searchMethod" value="GPSPositiveBasis2N"/>
<Option key="pollMethod" value="MADSPositiveBasis2N"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#GaussianProcessFactory|GaussianProcessFactory]]">
<Option key="covFunction" value="covSEiso"/>
<Option key="lowerThetaBound" value="-5"/>
<Option key="upperThetaBound" value="3"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
gpgenetic
Build Gaussian Process models using a GA
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
<!--See that matlab gads toolbox documentation for more information on the options-->
<Option key="populationType" value="doubleVector"/>
<Option key="populationSize" value="15"/>
<Option key="crossoverFraction" value="0.7"/>
<Option key="maxGenerations" value="10"/>
<Option key="eliteCount" value="1"/>
<Option key="stallGenLimit" value="4"/>
<Option key="stallTimeLimit" value="Inf"/>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#GaussianProcessFactory|GaussianProcessFactory]]">
<Option key="creationFcn" value="@gacreationuniform"/>
<Option key="crossoverFcn" value="@crossoverheuristic"/>
<Option key="mutationFcn" value="@mutationadaptfeasible"/>
<!--
<Option key="crossoverFcn" value="crossover"/>
<Option key="mutationFcn" value="mutation"/>
<Option key="creationFcn" value="createInitialPopulation"/>
-->
<Option key="covFunction" value="covSEiso"/>
<Option key="lowerThetaBound" value="-5"/>
<Option key="upperThetaBound" value="3"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
krigingsim
Build kriging models using Simulated Annealing
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabSimAnnealing|MatlabSimAnnealing]]">
<Option key="maxIterations" value="100"/>
<Option key="maxFunEvals" value="20"/>
</[[Config:Optimizer|Optimizer]]>
<!-- See the documentaion for possible regression and correlation functions -->
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#KrigingFactory|KrigingFactory]]">
<Option key="regressionMetric" value=""/>
<Option key="regressionFunction" value="regpoly1"/>
<Option key="multipleBasisFunctionsAllowed" value="false"/>
<[[Config:BasisFunction|BasisFunction]]>corrgauss</[[Config:BasisFunction|BasisFunction]]>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
krigingps
Build kriging models using pattern search
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabPatternSearch|MatlabPatternSearch]]">
<Option key="maxIterations" value="100"/>
<Option key="maxFunEvals" value="20"/>
<Option key="searchMethod" value="GPSPositiveBasis2N"/>
<Option key="pollMethod" value="MADSPositiveBasis2N"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#KrigingFactory|KrigingFactory]]">
<Option key="regressionMetric" value=""/>
<Option key="regressionFunction" value="regpoly1"/>
<Option key="multipleBasisFunctionsAllowed" value="true"/>
<[[Config:BasisFunction|BasisFunction]]>corrgauss</[[Config:BasisFunction|BasisFunction]]>
<[[Config:BasisFunction|BasisFunction]]>correxp</[[Config:BasisFunction|BasisFunction]]>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
krigingoptim
Build kriging models using the matlab optimization toolbox
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabOptimizer|MatlabOptimizer]]">
<Option key="maxIterations" value="100"/>
<Option key="maxFunEvals" value="20"/>
</[[Config:Optimizer|Optimizer]]>
<!-- See the documentaion for possible regression and correlation functions -->
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#KrigingFactory|KrigingFactory]]">
<Option key="regressionMetric" value=""/>
<Option key="regressionFunction" value="regpoly1"/>
<Option key="multipleBasisFunctionsAllowed" value="false"/>
<[[Config:BasisFunction|BasisFunction]]>corrgauss</[[Config:BasisFunction|BasisFunction]]>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
kriginggenetic
Build kriging models using a genetic algorithm
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<!-- If you specify "custom" as the population type you will be evolving models
and will use the genetic operators defined in the KrigingFactory class -->
<Option key="populationType" value="doubleVector"/>
<Option key="populationSize" value="10"/>
<Option key="maxGenerations" value="10"/>
<Option key="eliteCount" value="1"/>
<Option key="crossoverFraction" value="0.7"/>
<Option key="stallGenLimit" value="4"/>
<Option key="stallTimeLimit" value="Inf"/>
<!-- See the documentaion for possible regression and correlation functions -->
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#KrigingFactory|KrigingFactory]]">
<Option key="creationFcn" value="@gacreationuniform"/>
<Option key="crossoverFcn" value="@crossoverheuristic"/>
<Option key="mutationFcn" value="@mutationadaptfeasible"/>
<!-- <Option key="creationFcn" value="createInitialPopulation"/>
<Option key="crossoverFcn" value="crossover"/>
<Option key="mutationFcn" value="mutation"/> -->
<!-- See the documentaion for possible regression and correlation functions --> <Option key="regressionMetric" value=""/>
<Option key="regressionFunction" value="regpoly1"/>
<Option key="multipleBasisFunctionsAllowed" value="false"/>
<[[Config:BasisFunction|BasisFunction]]>corrgauss</[[Config:BasisFunction|BasisFunction]]>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
krigingpso
Build kriging models using PSO
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#PSOtOptimizer|PSOtOptimizer]]">
<Option key="typePSO" value="0"/>
<Option key="seedPSO" value="1"/>
<Option key="popSize" value="10"/>
<Option key="maxiters" value="10"/>
<Option key="epochInertia" value="8"/>
<Option key="gradientTermination" value="8"/>
</[[Config:Optimizer|Optimizer]]>
<!-- See the documentaion for possible regression and correlation functions -->
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#KrigingFactory|KrigingFactory]]">
<Option key="regressionMetric" value=""/>
<Option key="regressionFunction" value="regpoly1"/>
<Option key="multipleBasisFunctionsAllowed" value="false"/>
<[[Config:BasisFunction|BasisFunction]]>corrgauss</[[Config:BasisFunction|BasisFunction]]>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
krigingnsga
Build kriging models using NSGA-II, requires a multi-output or multi-measure setup
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#ParetoModelBuilder|ParetoModelBuilder]]" combineOutputs="true">
<Option key="restartStrategy" value="model"/>
<Option key="populationSize" value="30"/>
<Option key="maxGenerations" value="30"/>
<Option key="plotParetoFront" value="false"/>
<Option key="paretoMode" value="true"/>
<!-- See the documentaion for possible regression and correlation functions -->
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#KrigingFactory|KrigingFactory]]">
<Option key="regressionMetric" value=""/>
<Option key="regressionFunction" value="regpoly1"/>
<Option key="multipleBasisFunctionsAllowed" value="false"/>
<[[Config:BasisFunction|BasisFunction]]>corrgauss</[[Config:BasisFunction|BasisFunction]]>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
krigingrandom
Build kriging models randomly, useful as a baseline comparison
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<!-- Build 100 random models before restarting -->
<Option key="runSize" value="100"/>
<!-- See the documentaion for possible regression and correlation functions -->
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#KrigingFactory|KrigingFactory]]">
<Option key="regressionMetric" value=""/>
<Option key="regressionFunction" value="regpoly1"/>
<Option key="multipleBasisFunctionsAllowed" value="false"/>
<[[Config:BasisFunction|BasisFunction]]>corrgauss</[[Config:BasisFunction|BasisFunction]]>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
blindkriging
Build blind kriging models
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#AdaptiveModelBuilder|AdaptiveModelBuilder]]" combineOutputs="false">
<Option key="nBestModels" value="1"/>
<!-- See the documentation for possible regression and correlation functions -->
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#KrigingFactory|KrigingFactory]]">
<Option key="regressionMetric" value="cvpe"/>
<Option key="regressionFunction" value="regpoly0"/>
<Option key="multipleBasisFunctionsAllowed" value="false"/>
<Option key="initialHp" value="0.5"/>
<[[Config:BasisFunction|BasisFunction]]>corrgauss</[[Config:BasisFunction|BasisFunction]]>
<[[Config:Optimizer|Optimizer]]>fminconWithDerivatives</[[Config:Optimizer|Optimizer]]>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
kriging
Build kriging models using the maximum likelihood to set the thetas
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#AdaptiveModelBuilder|AdaptiveModelBuilder]]" combineOutputs="false">
<Option key="nBestModels" value="1"/>
<!-- See the documentation for possible regression and correlation functions -->
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#KrigingFactory|KrigingFactory]]">
<Option key="regressionMetric" value=""/>
<Option key="regressionFunction" value="regpoly0"/>
<Option key="multipleBasisFunctionsAllowed" value="false"/>
<Option key="initialHp" value="0.5"/>
<[[Config:BasisFunction|BasisFunction]]>corrgauss</[[Config:BasisFunction|BasisFunction]]>
<[[Config:Optimizer|Optimizer]]>fminconWithDerivatives</[[Config:Optimizer|Optimizer]]>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
Splines
splinesgenetic
Build spline models using a GA
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<Option key="populationType" value="doubleVector"/>
<Option key="populationSize" value="10"/>
<Option key="maxGenerations" value="10"/>
<Option key="eliteCount" value="1"/>
<Option key="crossoverFraction" value="0.7"/>
<Option key="stallGenLimit" value="4"/>
<Option key="stallTimeLimit" value="Inf"/>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SplineFactory|SplineFactory]]">
<Option key="creationFcn" value="@gacreationuniform"/>
<Option key="crossoverFcn" value="@crossoverheuristic"/>
<Option key="mutationFcn" value="@mutationadaptfeasible"/>
<!-- <Option key="creationFcn" value="createInitialPopulation"/>
<Option key="crossoverFcn" value="crossover"/>
<Option key="mutationFcn" value="mutation"/> -->
<Option key="smoothingBounds" value="0,1"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
splinessim
Build spline models using the Simulated Annealing modelbuilder
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabSimAnnealing|MatlabSimAnnealing]]">
<Option key="maxIterations" value="100"/>
<Option key="maxFunEvals" value="20"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SplineFactory|SplineFactory]]">
<Option key="smoothingBounds" value="0,1"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
splinesps
Build spline models using the Pattern Search modelbuilder
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabPatternSearch|MatlabPatternSearch]]">
<Option key="maxIterations" value="100"/>
<Option key="maxFunEvals" value="20"/>
<Option key="searchMethod" value="GPSPositiveBasis2N"/>
<Option key="pollMethod" value="MADSPositiveBasis2N"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SplineFactory|SplineFactory]]">
<Option key="smoothingBounds" value="0,1"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
splinesoptim
Use the Matlab optimization toolbox to build spline models
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabOptimizer|MatlabOptimizer]]">
<Option key="maxIterations" value="100"/>
<Option key="maxFunEvals" value="20"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SplineFactory|SplineFactory]]">
<Option key="smoothingBounds" value="0,1"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
Interpolation model
ipol
Simple linear/cubic/nearest neighbour interpolation models for scattered data. For one D uses interp1, for nD uses griddata(n)
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#AdaptiveModelBuilder|AdaptiveModelBuilder]]" combineOutputs="false">
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#InterpolationFactory|InterpolationFactory]]">
<!-- depending on the input dimension options are: linear, nearest, and cubic
if you are using Matlab r2009 or later you can also use 'natural' -->
<Option key="method" value="linear"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
Artificial neural networks
ann
Use a custom evolutionary-like strategy to generate ANN models, this is much faster than the GA approach but not necessarily better
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="false">
<Option key="maximumRunLength" value="15"/>
<!-- Degeneration of score if a model gets older -->
<Option key="decay" value=".99"/>
<!-- Size of the best model history -->
<Option key="historySize" value="6"/>
<!-- One of best, last. When set to best the best `historySize' models are kept,
- - when set to last, the last `historySize' models are kept -->
<Option key="strategy" value="best"/>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#ANNFactory|ANNFactory]]">
<!--initial hidden layer dimension-->
<Option key="initialSize" value="3,3"/>
<!--comma separated list of allowed learning rules-->
<Option key="allowedLearningRules" value="trainbr"/>
<!--performance function to use, empty means use the matlab default-->
<Option key="performFcn" value=""/>
<!--how many epochs to train for-->
<Option key="epochs" value="300"/>
<!--max time to train for-->
<Option key="trainingTime" value="Inf"/>
<!--range of initial random weights-->
<Option key="initWeightRange" value="-0.8,0.8"/>
<!--mutation changes x neurons at a time (in a random layer) with x in [lb ub]-->
<Option key="hiddenUnitDelta" value="-2,3"/>
<!--train until the error reaches this goal-->
<Option key="trainingGoal" value="0"/>
<!--show training progress every x epochs, set to NaN to disable-->
<Option key="trainingProgress" value="NaN"/>
<!--How to train the network, one of 'auto' or 'earlyStopping'
auto: train with early stopping unless regularization is employed
Set to any other value for simply training on all the data, doing nothing special -->
<Option key="trainMethod" value="auto"/>
<!--the training set - validation set - testset ratios-->
<Option key="earlyStoppingRatios" value="0.80,0.20,0"/>
<!-- Transfer function to use for all hidden layers and the output layer
So should be a list of max 2 items -->
<Option key="transferFunctionTemplate" value="tansig,purelin"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
anngenetic
Use the matlab gads toolbox to select ANN parameters using a GA
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="continue"/>
<!--See that matlab gads toolbox documentation for more information on the options-->
<Option key="populationType" value="custom"/>
<Option key="populationSize" value="10"/>
<Option key="crossoverFraction" value="0.7"/>
<Option key="maxGenerations" value="10"/>
<Option key="eliteCount" value="1"/>
<Option key="stallGenLimit" value="4"/>
<Option key="stallTimeLimit" value="Inf"/>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#ANNFactory|ANNFactory]]">
<Option key="crossoverFcn" value="crossover"/>
<Option key="mutationFcn" value="mutation"/>
<Option key="creationFcn" value="createInitialPopulation"/>
<!--initial hidden layer dimension-->
<Option key="initialSize" value="3,3"/>
<!--comma separated list of allowed learning rules-->
<Option key="allowedLearningRules" value="trainbr"/>
<!--performance function to use, empty means use the matlab default-->
<Option key="performFcn" value=""/>
<!--how many epochs to train for-->
<Option key="epochs" value="300"/>
<!--max time to train for-->
<Option key="trainingTime" value="Inf"/>
<!--range of initial random weights-->
<Option key="initWeightRange" value="-0.8,0.8"/>
<!--mutation changes x neurons at a time (in a random layer) with x in [lb ub]-->
<Option key="hiddenUnitDelta" value="-2,3"/>
<!--train until the error reaches this goal-->
<Option key="trainingGoal" value="0"/>
<!--show training progress every x epochs, set to NaN to disable-->
<Option key="trainingProgress" value="NaN"/>
<!--How to train the network, one of 'auto' or 'earlyStopping'
auto: train with early stopping unless regularization is employed
Set to any other value for simply training on all the data, doing nothing special -->
<Option key="trainMethod" value="auto"/>
<!--the training set - validation set - testset ratios-->
<Option key="earlyStoppingRatios" value="0.80,0.20,0"/>
<!-- Transfer function to use for all hidden layers and the output layer
So should be a list of max 2 items -->
<Option key="transferFunctionTemplate" value="tansig,purelin"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
annfixed
Fixed ANN model builder, allows you to choose the hidden layer structure manually Thus there is no optimization algorithm involved.
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#AdaptiveModelBuilder|AdaptiveModelBuilder]]" combineOutputs="false">
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#ANNFactory|ANNFactory]]">
<Option key="allowedLearningRules" value="trainbr"/>
<Option key="initialSize" value="3,3"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
annrandom
Random ANN model builder, usefull as a baseline comparison
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
<!--This many iterations before allowing new samples-->
<Option key="runSize" value="10"/>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#ANNFactory|ANNFactory]]">
<Option key="allowedLearningRules" value="trainbr,trainlm,trainscg"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
fanngenetic
Use the matlab gads toolbox to select ANN parameters using a GA (based on the FANN library)
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="continue"/>
<!--See that matlab gads toolbox documentation for more information on the options-->
<Option key="populationType" value="custom"/>
<Option key="populationSize" value="10"/>
<Option key="crossoverFraction" value="0.7"/>
<Option key="maxGenerations" value="10"/>
<Option key="eliteCount" value="1"/>
<Option key="stallGenLimit" value="4"/>
<Option key="stallTimeLimit" value="Inf"/>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#FANNFactory|FANNFactory]]">
<Option key="crossoverFcn" value="crossover"/>
<Option key="mutationFcn" value="mutation"/>
<Option key="creationFcn" value="createInitialPopulation"/>
<!--initial hidden layer dimension-->
<Option key="initialSize" value="4,4"/>
<!--how many epochs to train for-->
<Option key="epochs" value="1500"/>
<!--range of initial random weights-->
<Option key="initWeightRange" value="-0.8,0.8"/>
<!--mutation changes x neurons at a time (in a random layer) with x in [lb ub]-->
<Option key="hiddenUnitDelta" value="-2,2"/>
<!--train until the error reaches this goal-->
<Option key="trainingGoal" value="0"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
nanngenetic
Use the matlab gads toolbox to select ANN parameters using a GA (based on the NNSYSID library)
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="continue"/>
<!--See that matlab gads toolbox documentation for more information on the options-->
<Option key="populationType" value="custom"/>
<Option key="populationSize" value="10"/>
<Option key="crossoverFraction" value="0.7"/>
<Option key="maxGenerations" value="10"/>
<Option key="eliteCount" value="1"/>
<Option key="stallGenLimit" value="4"/>
<Option key="stallTimeLimit" value="Inf"/>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#NANNFactory|NANNFactory]]">
<Option key="crossoverFcn" value="crossover"/>
<Option key="mutationFcn" value="mutation"/>
<Option key="creationFcn" value="createInitialPopulation"/>
<!--initial hidden layer dimension-->
<Option key="initialSize" value="10"/>
<!--how many epochs to train for-->
<Option key="epochs" value="500"/>
<!--range of initial random weights-->
<Option key="initWeightRange" value="-0.8,0.8"/>
<!--mutation changes x neurons at a time (in a random layer) with x in [lb ub]-->
<Option key="hiddenUnitDelta" value="-2,3"/>
<!-- pruning techniques used : 0: none, 1: Mag Threshold, 2: Iterative Mag, 3: OBD, 4: OBS -->
<Option key="allowedPruneTechniques" value="0,1,2,3,4"/>
<!-- threshold for magnitude based pruning -->
<Option key="threshold" value="0.2"/>
<!-- retrain epochs while pruning-->
<Option key="retrain" value="50"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
rbfnngenetic
Genetic model builder for Radial Basis Function Neural networks
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<Option key="populationType" value="doubleVector"/>
<Option key="populationSize" value="10"/>
<Option key="maxGenerations" value="10"/>
<Option key="eliteCount" value="1"/>
<Option key="crossoverFraction" value="0.7"/>
<Option key="stallGenLimit" value="4"/>
<Option key="stallTimeLimit" value="Inf"/>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#RBFNNFactory|RBFNNFactory]]">
<Option key="creationFcn" value="@gacreationuniform"/>
<Option key="crossoverFcn" value="@crossoverheuristic"/>
<Option key="mutationFcn" value="@mutationadaptfeasible"/>
<!-- <Option key="creationFcn" value="createInitialPopulation"/>
<Option key="crossoverFcn" value="crossover"/>
<Option key="mutationFcn" value="mutation"/> -->
<!--Error goal when constructing the network-->
<Option key="goal" value="0"/>
<!--Initial value for the spread -->
<Option key="spread" value="1"/>
<!--Spread bounds -->
<Option key="spreadBounds" value="0.0001,30"/>
<!--Maximum number of neurons to use per network-->
<Option key="maxNeurons" value="500"/>
<Option key="trainingProgress" value="Inf"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
rbfnnps
Build Radial Basis Function Neural networks using Pattern Search
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabPatternSearch|MatlabPatternSearch]]">
<Option key="maxIterations" value="100"/>
<Option key="maxFunEvals" value="20"/>
<Option key="searchMethod" value="GPSPositiveBasis2N"/>
<Option key="pollMethod" value="MADSPositiveBasis2N"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#RBFNNFactory|RBFNNFactory]]">
<!--Error goal when constructing the network-->
<Option key="goal" value="0"/>
<!--Initial value for the spread -->
<Option key="spread" value="1"/>
<!--Spread bounds -->
<Option key="spreadBounds" value="0.0001,30"/>
<!--Maximum number of neurons to use per network-->
<Option key="maxNeurons" value="500"/>
<Option key="trainingProgress" value="Inf"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
rbfnnsim
Build Radial Basis Function Neural networks using Simulated Annealing
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabSimAnnealing|MatlabSimAnnealing]]">
<Option key="maxIterations" value="100"/>
<Option key="maxFunEvals" value="20"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#RBFNNFactory|RBFNNFactory]]">
<!--Error goal when constructing the network-->
<Option key="goal" value="0"/>
<!--Initial value for the spread -->
<Option key="spread" value="1"/>
<!--Spread bounds -->
<Option key="spreadBounds" value="0.0001,30"/>
<!--Maximum number of neurons to use per network-->
<Option key="maxNeurons" value="500"/>
<Option key="trainingProgress" value="Inf"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
Support vector machines
lssvmgenetic
Use the matlab gads toolbox to select LSSVM parameters using a GA (based on LSSVM-lab)
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<Option key="populationType" value="doubleVector"/>
<Option key="populationSize" value="10"/>
<Option key="maxGenerations" value="10"/>
<Option key="eliteCount" value="1"/>
<Option key="crossoverFraction" value="0.7"/>
<Option key="stallGenLimit" value="4"/>
<Option key="stallTimeLimit" value="Inf"/>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
<Option key="creationFcn" value="@gacreationuniform"/>
<Option key="crossoverFcn" value="@crossoverheuristic"/>
<Option key="mutationFcn" value="@mutationadaptfeasible"/>
<!-- <Option key="creationFcn" value="createInitialPopulation"/>
<Option key="crossoverFcn" value="crossover"/>
<Option key="mutationFcn" value="mutation"/> -->
<Option key="backend" value="lssvm"/>
<Option key="kernel" value="rbf"/>
<Option key="kernelParamBounds" value="-2,2"/>
<Option key="regParamBounds" value="-5,5"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
lssvmps
Use the matlab gads toolbox to select LSSVM parameters using Pattern Search
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<!--See that matlab gads toolbox documentation for more information on the options-->
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabPatternSearch|MatlabPatternSearch]]">
<Option key="maxIterations" value="100"/>
<Option key="maxFunEvals" value="20"/>
<Option key="searchMethod" value="GPSPositiveBasis2N"/>
<Option key="pollMethod" value="MADSPositiveBasis2N"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
<Option key="backend" value="lssvm"/>
<Option key="kernel" value="rbf"/>
<Option key="kernelParamBounds" value="-2,2"/>
<Option key="regParamBounds" value="-5,5"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
lssvmoptim
Use the matlab optimization toolbox to select LSSVM parameters
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<!--See the interface matlab file and the optimization toolbox documentation for more information on the options-->
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabOptimizer|MatlabOptimizer]]">
<Option key="maxIterations" value="100"/>
<Option key="maxFunEvals" value="20"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
<Option key="backend" value="lssvm"/>
<Option key="kernel" value="rbf"/>
<Option key="kernelParamBounds" value="-2,2"/>
<Option key="regParamBounds" value="-5,5"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
lssvmpso
Use the PSO toolbox to select LSSVM parameters using Particle Swarm Optimization
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#PSOtOptimizer|PSOtOptimizer]]">
<Option key="typePSO" value="0"/>
<Option key="seedPSO" value="1"/>
<Option key="popSize" value="10"/>
<Option key="maxiters" value="10"/>
<Option key="epochInertia" value="8"/>
<Option key="gradientTermination" value="8"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
<Option key="backend" value="lssvm"/>
<Option key="kernel" value="rbf"/>
<Option key="kernelParamBounds" value="-2,2"/>
<Option key="regParamBounds" value="-5,5"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
lssvmsim
Use the matlab gads toolbox to select LSSVM parameters using simulated annealing
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<!--See the interface matlab file and the gads toolbox documentation for more information on the options-->
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabSimAnnealing|MatlabSimAnnealing]]">
<Option key="maxIterations" value="100"/>
<Option key="maxFunEvals" value="20"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
<Option key="backend" value="lssvm"/>
<Option key="kernel" value="rbf"/>
<Option key="kernelParamBounds" value="-2,2"/>
<Option key="regParamBounds" value="-5,5"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
lssvmdirect
Use the DIviding RECtangles algorithm to optimize the LS-SVM hyperparameters
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#DirectOptimizer|DirectOptimizer]]">
<Option key="maxits" value="100"/>
<Option key="maxevals" value="20"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
<Option key="backend" value="lssvm"/>
<Option key="kernel" value="rbf"/>
<Option key="kernelParamBounds" value="-2,2"/>
<Option key="regParamBounds" value="-5,5"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
lssvmego
Generate LS-SVM models using Efficient Global Optimization (EGO). This means internally a kriging model is constructed to predict where one can expect to find good model parameters
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#EGOModelBuilder|EGOModelBuilder]]" combineOutputs="false">
<Option key="numIterations" value="10"/>
<Option key="initPopSize" value="5"/>
<Option key="restartStrategy" value="continue"/>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
<Option key="backend" value="lssvm"/>
<Option key="kernel" value="rbf"/>
<Option key="kernelParamBounds" value="-4,4"/>
<Option key="regParamBounds" value="-5,5"/>
</[[Config:ModelFactory|ModelFactory]]>
<!-- Optimizer for the internal kriging model -->
<[[Config:Optimizer|Optimizer]]>fminconWithDerivatives</[[Config:Optimizer|Optimizer]]>
<!-- Sampleselector to use -->
<[[Config:SampleSelector|SampleSelector]]>expectedImprovement</[[Config:SampleSelector|SampleSelector]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
lssvmrandom
Generate random LSSVM models, useful as a baseline comparison
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<Option key="runSize" value="20"/>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
<Option key="backend" value="lssvm"/>
<Option key="kernel" value="rbf"/>
<Option key="kernelParamBounds" value="-2,2"/>
<Option key="regParamBounds" value="-5,5"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
svmgenetic
Use the matlab gads toolbox to select SVM parameters using a GA (based on libsvm)
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<!--See the interface matlab file and the gads toolbox documentation for more information on the options-->
<Option key="populationType" value="doubleVector"/>
<Option key="populationSize" value="10"/>
<Option key="maxGenerations" value="10"/>
<Option key="eliteCount" value="1"/>
<Option key="crossoverFraction" value="0.7"/>
<Option key="stallGenLimit" value="4"/>
<Option key="stallTimeLimit" value="Inf"/>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
<Option key="creationFcn" value="@gacreationuniform"/>
<Option key="crossoverFcn" value="@crossoverheuristic"/>
<Option key="mutationFcn" value="@mutationadaptfeasible"/>
<!-- <Option key="creationFcn" value="createInitialPopulation"/>
<Option key="crossoverFcn" value="crossover"/>
<Option key="mutationFcn" value="mutation"/>-->
<Option key="backend" value="libSVM"/>
<Option key="type" value="epsilon-SVR"/>
<Option key="kernel" value="rbf"/>
<Option key="kernelParamBounds" value="-4,4"/>
<Option key="regParamBounds" value="-5,5"/>
<Option key="nu" value="0.01"/>
<Option key="epsilon" value="0"/>
<Option key="stoppingTolerance" value="1e-6"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
svmps
Use the matlab gads toolbox to select SVM parameters using Pattern Search
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<!--See the interface matlab file and the gads toolbox documentation for more information on the options-->
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabPatternSearch|MatlabPatternSearch]]">
<Option key="maxIterations" value="100"/>
<Option key="maxFunEvals" value="20"/>
<Option key="searchMethod" value="GPSPositiveBasis2N"/>
<Option key="pollMethod" value="MADSPositiveBasis2N"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
<Option key="backend" value="libSVM"/>
<Option key="type" value="epsilon-SVR"/>
<Option key="kernel" value="rbf"/>
<Option key="kernelParamBounds" value="-2,2"/>
<Option key="regParamBounds" value="-5,5"/>
<Option key="nu" value="0.01"/>
<Option key="epsilon" value="0"/>
<Option key="stoppingTolerance" value="1e-6"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
svmsim
Use the matlab gads toolbox to select SVM parameters using simulated annealing
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<!--See the interface matlab file and the gads toolbox documentation for more information on the options-->
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabSimAnnealing|MatlabSimAnnealing]]">
<Option key="maxIterations" value="100"/>
<Option key="maxFunEvals" value="20"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
<Option key="backend" value="libSVM"/>
<Option key="type" value="epsilon-SVR"/>
<Option key="kernel" value="rbf"/>
<Option key="kernelParamBounds" value="-2,2"/>
<Option key="regParamBounds" value="-5,5"/>
<Option key="nu" value="0.01"/>
<Option key="epsilon" value="0"/>
<Option key="stoppingTolerance" value="1e-6"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
svmoptim
Use the matlab optimization toolbox to select SVM parameters
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<!--See the interface matlab file and the optimization toolbox documentation for more
information on the options-->
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabOptimizer|MatlabOptimizer]]">
<Option key="maxIterations" value="100"/>
<Option key="maxFunEvals" value="20"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
<Option key="backend" value="libSVM"/>
<Option key="type" value="epsilon-SVR"/>
<Option key="kernel" value="rbf"/>
<Option key="kernelParamBounds" value="-2,2"/>
<Option key="regParamBounds" value="-5,5"/>
<Option key="nu" value="0.01"/>
<Option key="epsilon" value="0"/>
<Option key="stoppingTolerance" value="1e-6"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
svmpso
Use the PSO toolbox to select SVM parameters using Particle Swarm Optimization
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#PSOtOptimizer|PSOtOptimizer]]">
<Option key="typePSO" value="0"/>
<Option key="seedPSO" value="1"/>
<Option key="popSize" value="10"/>
<Option key="maxiters" value="10"/>
<Option key="epochInertia" value="8"/>
<Option key="gradientTermination" value="8"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
<Option key="backend" value="libSVM"/>
<Option key="type" value="epsilon-SVR"/>
<Option key="kernel" value="rbf"/>
<Option key="kernelParamBounds" value="-2,2"/>
<Option key="regParamBounds" value="-5,5"/>
<Option key="nu" value="0.01"/>
<Option key="epsilon" value="0"/>
<Option key="stoppingTolerance" value="1e-6"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
svmdirect
Use the DIviding RECtangles algorithm to optimize the SVM hyperparameters
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
<!-- Re-start strategy for resuming the optimization process between sampling iterations.
One of 'random','continue','model' and 'intelligent' (Default). See the docs for more information -->
<Option key="restartStrategy" value="intelligent"/>
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<[[Config:Optimizer|Optimizer]] type="[[Optimizer#DirectOptimizer|DirectOptimizer]]">
<Option key="maxits" value="100"/>
<Option key="maxevals" value="20"/>
</[[Config:Optimizer|Optimizer]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
<Option key="backend" value="libSVM"/>
<Option key="type" value="epsilon-SVR"/>
<Option key="kernel" value="rbf"/>
<Option key="kernelParamBounds" value="-2,2"/>
<Option key="regParamBounds" value="-5,5"/>
<Option key="nu" value="0.01"/>
<Option key="epsilon" value="0"/>
<Option key="stoppingTolerance" value="1e-6"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
svmrandom
Generate random SVMs, useful as a baseline comparison
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
<!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
<Option key="plotOptimSurface" value="false"/>
<Option key="runSize" value="20"/>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
<Option key="backend" value="libSVM"/>
<Option key="type" value="epsilon-SVR"/>
<Option key="kernel" value="rbf"/>
<Option key="kernelParamBounds" value="-2,2"/>
<Option key="regParamBounds" value="-5,5"/>
<Option key="nu" value="0.01"/>
<Option key="epsilon" value="0"/>
<Option key="stoppingTolerance" value="1e-6"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
Ensemble and heterogenetic models
heterogenetic
A heterogeneous genetic model builder. Uses a genetic algorithm with speciation (island model) to evolve different model types together. The models types compete against each other until the best model prevails. So this model builder is a way to automatically select the best model type.
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
<Option key="restartStrategy" value="continue"/>
<Option key="populationType" value="custom"/>
<!-- the population size must match the number of model interfaces minus 1 -->
<Option key="populationSize" value="10,10,10"/>
<Option key="maxGenerations" value="10"/>
<Option key="crossoverFraction" value="0.7"/>
<Option key="eliteCount" value="1"/>
<Option key="stallGenLimit" value="4"/>
<Option key="stallTimeLimit" value="Inf"/>
<Option key="migrationDirection" value="forward"/>
<Option key="migrationFraction" value="0.1"/>
<Option key="migrationInterval" value="4"/>
<!-- Do we want to prevent any model type going completely extinct -->
<Option key="extinctionPrevention" value="yes"/>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#HeterogeneousFactory|HeterogeneousFactory]]">
<Option key="creationFcn" value="createInitialPopulation"/>
<Option key="crossoverFcn" value="crossover"/>
<Option key="mutationFcn" value="mutation"/>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#EnsembleFactory|EnsembleFactory]]">
<Option key="crossoverFcn" value="crossover"/>
<Option key="mutationFcn" value="mutation"/>
<!-- the maximum ensemble size -->
<Option key="maxSize" value="4"/>
<!-- Ensemble members should differ this much percent -->
<Option key="equalityThreshold" value="0.05"/>
</[[Config:ModelFactory|ModelFactory]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
<Option key="creationFcn" value="createInitialPopulation"/>
<Option key="crossoverFcn" value="crossover"/>
<Option key="mutationFcn" value="mutation"/>
<Option key="backend" value="lssvm"/>
<Option key="kernel" value="rbf"/>
<Option key="kernelParamBounds" value="-2,2"/>
<Option key="regParamBounds" value="-5,5"/>
</[[Config:ModelFactory|ModelFactory]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#RationalFactory|RationalFactory]]">
<Option key="crossoverFcn" value="crossover"/>
<Option key="mutationFcn" value="mutation"/>
<Option key="creationFcn" value="createInitialPopulation"/>
<Option key="weightBounds" value="1,40"/>
<Option key="percentBounds" value="1,100"/>
<Option key="percentRational" value="70"/>
<Option key="frequencyVariable" value="off"/>
<Option key="basis" value="power"/>
</[[Config:ModelFactory|ModelFactory]]>
<[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#BFFactory|BFFactory]]">
<Option key="type" value="RBF"/>
<Option key="crossoverFcn" value="crossover"/>
<Option key="mutationFcn" value="mutation"/>
<Option key="creationFcn" value="createInitialPopulation"/>
<[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="ln"/>
<[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="ln"/>
<[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="ln,lin"/>
<Option key="regression" value="-1,0,1,2"/>
<Option key="backend" value="Direct"/>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
