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SPOT (version 2.1.10)

Sequential Parameter Optimization Toolbox

Description

A set of tools for model-based optimization and tuning of algorithms. It includes surrogate models, optimizers, and design of experiment approaches. The main interface is spot, which uses sequentially updated surrogate models for the purpose of efficient optimization. The main goal is to ease the burden of objective function evaluations, when a single evaluation requires a significant amount of resources.

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Version

Install

install.packages('SPOT')

Monthly Downloads

201

Version

2.1.10

License

GPL (>= 2)

Maintainer

Thomas Bartz-Beielstein

Last Published

December 10th, 2020

Functions in SPOT (2.1.10)

Sequential Parameter Optimization Toolbox

Build Response Surface Model

Build LOESS Model

buildKrigingDACE

Build DACE model

Build Kriging Model

Linear Model Interface

Lasso Model Interface

buildEnsembleStack

Ensemble: Stacking

checkInputDimensionsionalityCorrect

Check Dimensions of spotInputs

buildRandomForest

Random Forest Interface

Correlation: Lin

calculationMothes

Cyclone Simulation: Mothes

Correlation: Kriging

ranger Interface

corrnoisykriging

Correlation: Noisy Kriging

Correlation: Noisy Gauss

Wrapper for Maximum Likelihood Estimation

Check for NAs in x lower and upper

Descent RSM model

checkFunEvalsDesignSize

Check funEvals Setting against designSize

checkLowerNotEqualsUpper

Check That Lower and Upper are not Equal

Correlation: Expg

Correlation: Gauss

Tree Regression Interface

Evaluate DACE fit

checkInputTypesInControl

Check input types in the spotControl list.

Fix model parameters DACE

duplicateAndReplicateHandling

duplicateAndReplicateHandling

Objective function - Cyclone Simulation: Barth/Muschelknautz

Correlation: Cubic

Correlation: Exp

krigingLikelihood

Calculate negative log-likelihood

Minimization by L-BFGS-B

Normalized LHD Design

linearAdaptedSE

linearAdaptedSE

Latin Hypercube Design Generator

calculationBarthMuschelknautz

Cyclone Simulation: Barth/Muschelknautz

checkTypesOfInput

Check Input Types

expectedImprovement

Expected Improvement

evalMarkovChain

evalMarkovChain

checkLowerSmallerThanUpper

Check That Lower is smaller than Upper

Evolution Strategy

Minimization by GENetic Optimization Using Derivatives

Minimization by Latin Hypercube Sampling

optimNLOPTR. Minimization by NLOPT

plot.spotTreeModel

Plot rpart tree model

initialInputCheck

initialInputCheck. Initial Input Check of Spot Configuration

Prepare DACE fit

DACE objective function

checkVerbosityLevels

Check correct verbosity levels

parseTunedRegionModel

parseTunedRegionModel

getCorrelationMatrix

getCorrelationMatrix

daceStartParameters

Start parameter setup DACE

Interpolated plot

predict.ensembleStack

Predict Stacked Ensemble

Correlation: Spherical

Correlation: Spline

Gas Sensor Data

print.spotTreeModel

Print method for rpart tree models

maxNearestNeighbourDistance

maxNearestNeighbourDistance

generateMCPrediction

generateMCPrediction

objectiveFunctionEvaluation

objectiveFunctionEvaluation Objective Function Evaluation

modelMarkovChain

modelMarkovChain

plotSingleDimFunction

plotSingleDimFunction

plotRegionByName

plotRegionByName

preprocessCdeTestData

preprocessCdeTestData

funOptimLecture

funOptimLecture

designUniformRandom

Uniform Design Generator

predict.cvModel

predict.cvModel

predict.spotRanger

Predictor for spotExtraTrees

Minimization by Differential Evolution

predict.spotTreeModel

Prediction method for rpart tree models

repairNonNumeric

Repair Non-numeric Values

preprocessTestData

preprocessTestData

preprocessInputData

preprocessInputData

Regression: Regpoly0

print.spotLinearModel

Print method for linear model

selectN. Select n Design Points

predict.kriging

Predict Kriging Model

Print Function DACE Kriging

simulateFunction

simulateFunction

S-Ring Simulation Data Obtained With SPOT

infillExpectedImprovement

infillExpectedImprovement

Print method for RSM model

infillGetFullPrediction

infillGetFullPrediction

predictKrigingReinterpolation

Predict Kriging Model (Re-interpolating)

predict.spotLOESS

Predict loess model

simulationDecompose

Kriging Simulation: Decomposition

Optimal Computing Budget Allocation

simulate.kriging

Kriging Simulation

spotAlgEsMarriage

print.spotLassoModel

Print method for lasso model

print.spotLOESS

Print method for loess model

normalizeMatrix

Normalize design

S-Ring Simulation Data

S-Ring Simulation Data

Surface plot of a function

spotAlgEsMarriageWithReplace

Marriage with replace

normalizeMatrix2

Normalize design 2

preprocessCdeInputData

preprocessCdeInputData

Evolution Strategy Implementation

print.spotRandomForest

Print method for random forest

simulationSpectral

simulationSpectral

S-Ring Simulation Data Obtained With SPOT

spotAlgEsDominantReco

spotAlgEsDominantReco

spotAlgEsTermination

spotAlgEsStratMutation

spotAlgEsStratMutation

spotAlgEsInterReco

spotAlgEsInterReco

print.spotRanger

Print method for random forest

S-Ring Simulation Data

Backslash operator.

Function Evaluation Wrapper

spotAlgEsInterRecoBeSw02

spotAlgEsInterRecoBeSw02

spotAlgEsIndividualInitial

Individual Initialization

Surface plot of a model

predict.spotLassoModel

Prediction method for lasso model

spotAlgEsInitParentPop

Initialize Parent Population

tuneRegionModel

tuneRegionModel

spotAlgEsSelection

spotAlgEsSelection

predict.spotRSM

Predict RSM model

spotAlgEsObjMutation

spotAlgEsObjMutation

predict.spotLinearModel

Prediction method for linear model

wrapFunctionParallel

Parallelized Function Evaluation Wrapper

wrapSystem_parseMatrixToString

wrapSystem_parseMatrixToString

wrapSystemCommand

wrapSystemCommand

predict.spotRandomForest

Prediction method for random forest

Regression: Regpoly1

Satterthwaite Function

spotAlgEsGetSuccessRate

get Success Rate

Termination hps

Regression: Regpoly2

spotFillControlList

spotFillControlList