Package space.kscience.kmath.optimization

Types

FunctionOptimization

class FunctionOptimization<T>(features: FeatureSet<OptimizationFeature>, expression: DifferentiableExpression<T>) : OptimizationProblem<T>

FunctionOptimizationBuilder

class FunctionOptimizationBuilder<T>(expression: DifferentiableExpression<T>) : OptimizationBuilder<T, FunctionOptimization<T>>

FunctionOptimizationTarget

enum FunctionOptimizationTarget : Enum<FunctionOptimizationTarget> , OptimizationFeature

OptimizationBuilder

abstract class OptimizationBuilder<T, R : OptimizationProblem<T>>

OptimizationCovariance

class OptimizationCovariance<T>(covariance: Matrix<T>) : OptimizationFeature

OptimizationFeature

interface OptimizationFeature : Feature<OptimizationFeature>

OptimizationLog

class OptimizationLog(loggable: Loggable) : Loggable, OptimizationFeature

OptimizationParameters

class OptimizationParameters(symbols: List<Symbol>) : OptimizationFeature

OptimizationPrior

interface OptimizationPrior<T> : OptimizationFeature, DifferentiableExpression<T>

OptimizationProblem

interface OptimizationProblem<T> : Featured<OptimizationFeature>

OptimizationResult

open class OptimizationResult<T>(point: Map<Symbol, T>) : OptimizationFeature

OptimizationStartPoint

open class OptimizationStartPoint<T>(point: Map<Symbol, T>) : OptimizationFeature

OptimizationValue

class OptimizationValue<T>(value: T) : OptimizationFeature

Optimizer

interface Optimizer<T, P : OptimizationProblem<T>>

PointToCurveDistance

interface PointToCurveDistance : OptimizationFeature

Specify the way to compute distance from point to the curve as DifferentiableExpression

PointWeight

interface PointWeight : OptimizationFeature

Compute a wight of the point.

QowOptimizer

@UnstableKMathAPI()

object QowOptimizer : Optimizer<Double, XYFit>

An optimizer based onf Fyodor Tkachev's quasi-optimal weights method.

QowRuns

class QowRuns(runs: Int) : OptimizationFeature

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.

XYOptimizationBuilder

class XYOptimizationBuilder(data: XYColumnarData<Double, Double, Double>, model: DifferentiableExpression<Double>) : OptimizationBuilder<Double, XYFit>

Functions

fitWith

suspend fun <I : Any, A : ExtendedField<I>, ExpressionAlgebra<Double, I>> XYColumnarData<Double, Double, Double>.fitWith(optimizer: Optimizer<Double, XYFit>, processor: AutoDiffProcessor<Double, I, A>, startingPoint: Map<Symbol, Double>, vararg features: OptimizationFeature = emptyArray(), xSymbol: Symbol = Symbol.x, pointToCurveDistance: PointToCurveDistance = PointToCurveDistance.byY, pointWeight: PointWeight = PointWeight.byYSigma, model: A.(I) -> I): XYFit

Fit given dta with

FunctionOptimization

fun <T> FunctionOptimization(expression: DifferentiableExpression<T>, builder: FunctionOptimizationBuilder<T>.() -> Unit): FunctionOptimization<T>

getFeature

inline fun <F : OptimizationFeature> OptimizationProblem<*>.getFeature(): F?

maximumLogLikelihood

@UnstableKMathAPI()

suspend fun Optimizer<Double, FunctionOptimization<Double>>.maximumLogLikelihood(problem: XYFit): XYFit

Optimize given XY (least squares) problem using this function Optimizer.

@UnstableKMathAPI()

suspend fun Optimizer<Double, FunctionOptimization<Double>>.maximumLogLikelihood(data: XYColumnarData<Double, Double, Double>, model: DifferentiableExpression<Double>, builder: XYOptimizationBuilder.() -> Unit): XYFit

optimizeWith

suspend fun <T> DifferentiableExpression<T>.optimizeWith(optimizer: Optimizer<T, FunctionOptimization<T>>, vararg startingPoint: Pair<Symbol, T>, builder: FunctionOptimizationBuilder<T>.() -> Unit = {}): FunctionOptimization<T>

suspend fun <T> DifferentiableExpression<T>.optimizeWith(optimizer: Optimizer<T, FunctionOptimization<T>>, startingPoint: Map<Symbol, T>, builder: FunctionOptimizationBuilder<T>.() -> Unit = {}): FunctionOptimization<T>

suspend fun <T : Any> DifferentiableExpression<T>.optimizeWith(optimizer: Optimizer<T, FunctionOptimization<T>>, startingPoint: Map<Symbol, T>, vararg features: OptimizationFeature): FunctionOptimization<T>

Optimizes differentiable expression using specific optimizer form given startingPoint.

startAt

fun <T> OptimizationBuilder<T, *>.startAt(startingPoint: Map<Symbol, T>)

withFeature

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

withFeatures

fun <T> FunctionOptimization<T>.withFeatures(vararg newFeature: OptimizationFeature): FunctionOptimization<T>

XYOptimization

fun XYOptimization(data: XYColumnarData<Double, Double, Double>, model: DifferentiableExpression<Double>, builder: XYOptimizationBuilder.() -> Unit): XYFit

Properties

chiSquaredOrNull

val XYFit.chiSquaredOrNull: Double?

Compute chi squared value for completed fit.

resultPoint

val <T> OptimizationProblem<T>.resultPoint: Map<Symbol, T>

resultPointOrNull

val <T> OptimizationProblem<T>.resultPointOrNull: Map<Symbol, T>?

resultValue

val <T> FunctionOptimization<T>.resultValue: T

resultValueOrNull

val <T> FunctionOptimization<T>.resultValueOrNull: T?

startPoint

val <T> OptimizationProblem<T>.startPoint: Map<Symbol, T>

Get the starting point for optimization.