XYFit

class XYFit(data: XYColumnarData<Double, Double, Double>, model: DifferentiableExpression<Double>, features: FeatureSet<OptimizationFeature>, pointToCurveDistance: PointToCurveDistance, pointWeight: PointWeight, xSymbol: Symbol) : OptimizationProblem<Double>

A fit problem for X-Y-Yerr data. Also known as "least-squares" problem.

Constructors

XYFit

fun XYFit(data: XYColumnarData<Double, Double, Double>, model: DifferentiableExpression<Double>, features: FeatureSet<OptimizationFeature>, pointToCurveDistance: PointToCurveDistance = PointToCurveDistance.byY, pointWeight: PointWeight = PointWeight.byYSigma, xSymbol: Symbol = Symbol.x)

Functions

distance

fun distance(index: Int): DifferentiableExpression<Double>

getFeature

open override fun <F : OptimizationFeature> getFeature(type: KClass<out F>): F?

weight

fun weight(index: Int): DifferentiableExpression<Double>

Properties

data

val data: XYColumnarData<Double, Double, Double>

features

open override val features: FeatureSet<OptimizationFeature>

model

val model: DifferentiableExpression<Double>

xSymbol

val xSymbol: Symbol

Extensions

chiSquaredOrNull

val XYFit.chiSquaredOrNull: Double?

Compute chi squared value for completed fit.

withFeature

fun XYFit.withFeature(vararg features: OptimizationFeature): XYFit

Sources

common source