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

PDFScoringModel

abstract class PDFScoringModel(val name: String, val domain: Interval = Interval(0.0, Double.MAX_VALUE), val allowLowerLimitAdjustment: Boolean = true, val allowUpperLimitAdjustment: Boolean = true) : Metric

Computes a score to indicate the quality of fit for the proposed continuous distribution for the supplied data

Inheritors

AkaikeInfoCriterionScoringModel

AndersonDarlingScoringModel

BayesianInfoCriterionScoringModel

ChiSquaredScoringModel

CramerVonMisesScoringModel

MallowsL2ScoringModel

PPCorrelationScoringModel

PPSSEScoringModel

QQCorrelationScoringModel

QQSSEScoringModel

SquaredErrorScoringModel

Constructors

PDFScoringModel

constructor(name: String, domain: Interval = Interval(0.0, Double.MAX_VALUE), allowLowerLimitAdjustment: Boolean = true, allowUpperLimitAdjustment: Boolean = true)

Types

object Companion

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

open override var description: String?

open override var direction: MetricIfc.Direction

override val domain: Interval

open override val name: String

open override var unitsOfMeasure: String?

Functions

open fun badScore(): Score

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

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

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

abstract override fun newInstance(): PDFScoringModel

fun score(result: EstimationResult): Score

abstract fun score(data: DoubleArray, cdf: ContinuousDistributionIfc): Score

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

open override fun toString(): String