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SPOT (version 2.4.2)

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

2.4.2

License

GPL (>= 2)

Maintainer

Thomas Bartz-Beielstein

Last Published

April 18th, 2021

Functions in SPOT (2.4.2)

buildEnsembleStack

Ensemble: Stacking

Lasso Model Interface

Build Response Surface Model

Prepare DACE fit

calculationMothes

Cyclone Simulation: Mothes

Transform coded values to natural values

Correlation: Cubic

Sequential Parameter Optimization Toolbox

expectedImprovement

Expected Improvement

Correlation: Lin

Correlation: Exp

Correlation: Noisy Gauss

daceStartParameters

Start parameter setup DACE

Correlation: Kriging

Correlation: Expg

Correlation: Gauss

Wrapper for Maximum Likelihood Estimation

funOptimLecture

funOptimLecture

Parallel execution of code, dependent on the operating system

DACE objective function

duplicateAndReplicateHandling

duplicateAndReplicateHandling

normalizeMatrix

Normalize design

initialInputCheck

initialInputCheck. Initial Input Check of Spot Configuration

Expected Improvement Infill Criterion

getNatDesignFromCoded

Get natural parameter values from coded +-1 representation

Minimization by Latin Hypercube Sampling

Minimization by L-BFGS-B

Build Kriging Model

buildKrigingDACE

Build DACE model

Plot Best Objective Value

plotSingleDimFunction

plotSingleDimFunction

normalizeMatrix2

Normalize design 2

Interpolated plot

plot.spotTreeModel

Plot rpart tree model

Latin Hypercube Design Generator

funBaBSimHospital

Optimization of the BaBSim.Hospital Simulator

Normalized LHD Design

predict.spotTreeModel

Prediction method for rpart tree models

generateMCPrediction

generateMCPrediction

infillExpectedImprovement

infillExpectedImprovement

predictKrigingReinterpolation

Predict Kriging Model (Re-interpolating)

buildRandomForest

Random Forest Interface

ranger Interface

Surface plot of a function

checkLowerNotEqualsUpper

Check That Lower and Upper are not Equal

infillGetFullPrediction

infillGetFullPrediction

checkLowerSmallerThanUpper

Check That Lower is smaller than Upper

Correlation: Spline

maxNearestNeighbourDistance

maxNearestNeighbourDistance

Evaluate DACE fit

Surface plot of a model

modelMarkovChain

modelMarkovChain

evalMarkovChain

evalMarkovChain

funGoldsteinPrice

Goldstein-Price Test Function

Ishigami Test Function

predict.ensembleStack

Predict Stacked Ensemble

predict.kriging

Predict Kriging Model

predict.spotBOModel

Prediction method for bayesian optimization model

prepareBestObjectiveVal

Preprocess y Values to Plot Best Objective Value

preprocessCdeInputData

preprocessCdeInputData

print.spotLinearModel

Print method for linear model

Print method for RSM model

print.spotBOModel

Print method for BO model

predict.spotGaussianProcessModel

Prediction method for Gaussian Process Model

print.spotGaussianProcessModel

Print method for Gaussian Process Model

print.spotRandomForest

Print method for random forest

Linear Model Interface

S-Ring Simulation Data Obtained With SPOT

print.spotRanger

Print method for random forest

optimNLOPTR. Minimization by NLOPT

selectN. Select n Design Points

S-Ring Simulation Data Obtained With SPOT

parseTunedRegionModel

parseTunedRegionModel

Check for NAs in x lower and upper

Build LOESS Model

Evolution Strategy Implementation

checkFunEvalsDesignSize

Check funEvals Setting against designSize

spotAlgEsSelection

spotAlgEsSelection

spotAlgEsObjMutation

spotAlgEsObjMutation

checkTypesOfInput

Check Input Types

spotAlgEsDominantReco

spotAlgEsDominantReco

predict.cvModel

predict.cvModel

checkVerbosityLevels

Check correct verbosity levels

Correlation: Spherical

designUniformRandom

Uniform Design Generator

corrnoisykriging

Correlation: Noisy Kriging

predict.spotRanger

Predictor for spotExtraTrees

Objective function - Cyclone Simulation: Barth/Muschelknautz

predict.spotRandomForest

Prediction method for random forest

getCorrelationMatrix

getCorrelationMatrix

spotFillControlList

spotFillControlList

Satterthwaite Function

print.spotTreeModel

Print method for rpart tree models

regionPopulation

Region Population Data

Interface SANN to SPOT

preprocessCdeTestData

preprocessCdeTestData

preprocessInputData

preprocessInputData

Backslash operator.

simulate.kriging

Kriging Simulation

sequentialBifurcation

Sequential Bifurcation

thetaNuggetGradient

thetaNuggetGradient

spotAlgEsStratMutation

spotAlgEsStratMutation

spotAlgEsTermination

objectiveFunctionEvaluation

objectiveFunctionEvaluation Objective Function Evaluation

Minimization by Differential Evolution

spotAlgEsInterReco

spotAlgEsInterReco

spotAlgEsInterRecoBeSw02

spotAlgEsInterRecoBeSw02

Region Test Data

spotPlotSeverity

spotPlotSeverity

Optimal Computing Budget Allocation

Region Train Data

tuneRegionModel

tuneRegionModel

wrapSystemCommand

wrapSystemCommand

Function Evaluation Wrapper

wrapFunctionParallel

Parallelized Function Evaluation Wrapper

simulateFunction

simulateFunction

simulationDecompose

Kriging Simulation: Decomposition

wrapSystem_parseMatrixToString

wrapSystem_parseMatrixToString

spotAlgEsIndividualInitial

Individual Initialization

spotAlgEsInitParentPop

Initialize Parent Population

plotRegionByName

plotRegionByName

predict.spotLOESS

Predict loess model

Bayesian Optimization Model Interface

predict.spotLassoModel

Prediction method for lasso model

preprocessTestData

preprocessTestData

Print Function DACE Kriging

Tree Regression Interface

Regression: Regpoly0

Regression: Regpoly1

resTuneRegionModel

Tuned Region Model Data

calculationBarthMuschelknautz

Cyclone Simulation: Barth/Muschelknautz

checkInputDimensionsionalityCorrect

Check Dimensions of spotInputs

simulationSpectral

simulationSpectral

spotAlgEsMarriage

checkInputTypesInControl

Check input types in the spotControl list.

Fix model parameters DACE

spotAlgEsMarriageWithReplace

Marriage with replace

Descent RSM model

Sequential Parameter Optimization Main Loop

Gas Sensor Data

Sobol and Levitan Test Function

linearAdaptedSE

linearAdaptedSE

krigingLikelihood

Calculate negative log-likelihood

S-Ring Simulation Data

Evolution Strategy

Minimization by GENetic Optimization Using Derivatives

Return effects for each subgroup

predict.spotLinearModel

Prediction method for linear model

predict.spotRSM

Predict RSM model

print.spotLOESS

Print method for loess model

print.spotLassoModel

Print method for lasso model

Regression: Regpoly2

spotAlgEsGetSuccessRate

get Success Rate

repairNonNumeric

Repair Non-numeric Values

Termination hps

S-Ring Simulation Data

S-Ring Simulation Data

buildGaussianProcess

Gaussian Process Model Interface