kmath-optimization/space.kscience.kmath.optimization/XYFit

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

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

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

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

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

Properties

val data: XYColumnarData<Double, Double, Double>

open override val features: FeatureSet<OptimizationFeature>

val model: DifferentiableExpression<Double>

val xSymbol: Symbol

Extensions

chiSquaredOrNull

val XYFit.chiSquaredOrNull: Double?

Compute chi squared value for completed fit. Return null for incomplete fit

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

Sources