KSLCore/ksl.utilities.distributions.fitting.scoring/MallowsL2ScoringModel

MallowsL2ScoringModel

class MallowsL2ScoringModel : PDFScoringModel

This scoring model represents the Mallows L2 distance between the theoretical probabilities and the observed probabilities based on a histogram of the data. The break points for the histogram are specified by PDFModeler.equalizedCDFBreakPoints()

The Mallows L2 distance is the square root of the mean squared error for the theoretical versus the observed probabilities.

See: http://luthuli.cs.uiuc.edu/~daf/courses/Opt-2017/Combinatorialpapers/EMD.pdf

Constructors

MallowsL2ScoringModel

constructor()

Properties

allowLowerLimitAdjustment

open override val allowLowerLimitAdjustment: Boolean = true

Indicates if the lower limit of the domain may be adjusted during scaling processes

allowUpperLimitAdjustment

open override val allowUpperLimitAdjustment: Boolean = true

Indicates if the upper limit of the domain may be adjusted during scaling processes

description

open override var description: String?

direction

open override var direction: MetricIfc.Direction

domain

override val domain: Interval

name

open override val name: String

unitsOfMeasure

open override var unitsOfMeasure: String?

Functions

badScore

open fun badScore(): Score

Returns a valid score that has the worst possible value according to the direction of the meaning of better.

metricData

open fun metricData(modaName: String, weight: Double): MetricData

Returns an instance of MetricData based on (modaName, weight)

newInstance

open override fun newInstance(): MallowsL2ScoringModel

score

open override fun score(data: DoubleArray, cdf: ContinuousDistributionIfc): Score

fun score(result: EstimationResult): Score

fun score(data: DoubleArray, parameters: RVParameters): Score

toString

open override fun toString(): String