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expectedImprovementEuclidean Class Reference

Calculates the expected improvement statistical infill criterion. More...

Inheritance diagram for expectedImprovementEuclidean:
Inheritance graph
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Public Member Functions

function expectedImprovementEuclidean (var varargin)
 
function getDesignType ()
 
function initNewSamples (var state)
 
function scoreCandidates (var points, var state)
 
function score (var candidates, var state)
 Scores a set of candidate samples. More...
 
function scoreMinimize (var candidates, var state)
 Simply calls -CandidateRanker.score(candidates, state) More...
 
function setOrder (var order)
 
function getType ()
 
function getDimension ()
 
function instantiate (var inDim, var varargin)
 
function plotRanker (var state, var newsamples)
 

Public Attributes

var paretoFront
 pareto front updater object More...
 
var weights
 scaling of cost functions More...
 
var inDim
 
var scalingFunction
 
var sortOrder
 
var debug
 
var debugSave
 
var debugPlot
 

Detailed Description

Calculates the expected improvement statistical infill criterion.

Constructor & Destructor Documentation

function expectedImprovementEuclidean ( var  varargin)
inline

Member Function Documentation

function getDesignType ( )
inline
function getDimension ( )
inlineinherited
function getType ( )
inlineinherited
function initNewSamples ( var  state)
inline
function instantiate ( var  inDim,
var  varargin 
)
inlineinherited
function plotRanker ( var  state,
var  newsamples 
)
inlineinherited
function score ( var  candidates,
var  state 
)
inlineinherited

Scores a set of candidate samples.

Parameters
candidatesmatrix of candidate samples
statecurrent state
Return values
scoresvector of scores (priorites)
dscoresderivatives of scores w.r.t. candidates
function scoreCandidates ( var  points,
var  state 
)
inline
Todo:
let metamodel decide what it can handle ?
Todo:
doesnt work for model with multiple outputs (need for-loop)
Todo:
mosbo EI derivatives
function scoreMinimize ( var  candidates,
var  state 
)
inlineinherited

Simply calls -CandidateRanker.score(candidates, state)

This is needed primarily for OptimizeCriterion, as multiple output arguments don't work with: func = -this.score(...)

Parameters
candidatesmatrix of candidate samples
statecurrent state
Return values
scoresvector of -scores (lower is better)
dscoresderivatives of -scores w.r.t. candidates
function setOrder ( var  order)
inlineinherited

Member Data Documentation

var debug
inherited
var debugPlot
inherited
var debugSave
inherited
var inDim
inherited
var paretoFront

pareto front updater object

var scalingFunction
inherited
var sortOrder
inherited
var weights

scaling of cost functions


The documentation for this class was generated from the following file: