forked from kscience/kmath
[WIP] optimization refactoring
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@ -35,7 +35,7 @@ public class CMOptimizerData(public val data: List<OptimizationData>) : Optimiza
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@OptIn(UnstableKMathAPI::class)
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public class CMOptimization : Optimizer<FunctionOptimization<Double>> {
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override suspend fun process(
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override suspend fun optimize(
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problem: FunctionOptimization<Double>,
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): FunctionOptimization<Double> {
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val startPoint = problem.getFeature<OptimizationStartPoint<Double>>()?.point
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@ -67,12 +67,12 @@ public fun <T> FunctionOptimization<T>.withFeatures(
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/**
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* Optimize differentiable expression using specific [optimizer] form given [startingPoint]
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*/
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public suspend fun <T : Any> DifferentiableExpression<T, Expression<T>>.optimizeWith(
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public suspend fun <T : Any> DifferentiableExpression<T>.optimizeWith(
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optimizer: Optimizer<FunctionOptimization<T>>,
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startingPoint: Map<Symbol, T>,
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vararg features: OptimizationFeature,
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): FunctionOptimization<T> {
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val problem = FunctionOptimization<T>(FeatureSet.of(OptimizationStartPoint(startingPoint), *features), this)
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return optimizer.process(problem)
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return optimizer.optimize(problem)
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}
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@ -40,7 +40,3 @@ public class OptimizationParameters(public val symbols: List<Symbol>): Optimizat
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}
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public interface Optimizer<P : OptimizationProblem> {
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public suspend fun process(problem: P): P
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}
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@ -0,0 +1,10 @@
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/*
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* Copyright 2018-2021 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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package space.kscience.kmath.optimization
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public interface Optimizer<P : OptimizationProblem> {
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public suspend fun optimize(problem: P): P
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}
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@ -12,39 +12,63 @@ import space.kscience.kmath.expressions.Expression
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import space.kscience.kmath.expressions.Symbol
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import space.kscience.kmath.misc.FeatureSet
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import space.kscience.kmath.misc.UnstableKMathAPI
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import space.kscience.kmath.operations.Field
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import space.kscience.kmath.operations.invoke
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import kotlin.math.pow
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public fun interface PointToCurveDistance<T> {
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public fun distance(problem: XYOptimization<T>, index: Int): DifferentiableExpression<T, Expression<T>>
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public interface PointToCurveDistance : OptimizationFeature {
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public fun distance(problem: XYOptimization, index: Int): DifferentiableExpression<Double>
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public companion object {
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public fun <T> byY(
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algebra: Field<T>,
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): PointToCurveDistance<T> = PointToCurveDistance { problem, index ->
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algebra {
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public val byY: PointToCurveDistance = object : PointToCurveDistance {
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override fun distance(problem: XYOptimization, index: Int): DifferentiableExpression<Double> {
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val x = problem.data.x[index]
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val y = problem.data.y[index]
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val model = problem.model(args + (Symbol.x to x))
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model - y
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return object : DifferentiableExpression<Double> {
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override fun derivativeOrNull(symbols: List<Symbol>): Expression<Double>? =
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problem.model.derivativeOrNull(symbols)
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override fun invoke(arguments: Map<Symbol, Double>): Double =
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problem.model(arguments + (Symbol.x to x)) - y
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}
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}
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override fun toString(): String = "PointToCurveDistanceByY"
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}
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// val default = PointToCurveDistance<Double>{args, data, index ->
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//
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// }
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}
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}
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public class XYOptimization<T>(
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public class XYOptimization(
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override val features: FeatureSet<OptimizationFeature>,
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public val data: XYColumnarData<T, T, T>,
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public val model: DifferentiableExpression<T, Expression<T>>,
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public val data: XYColumnarData<Double, Double, Double>,
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public val model: DifferentiableExpression<Double>,
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) : OptimizationProblem
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public suspend fun Optimizer<FunctionOptimization<Double>>.maximumLogLikelihood(problem: XYOptimization): XYOptimization {
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val distanceBuilder = problem.getFeature() ?: PointToCurveDistance.byY
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val likelihood: DifferentiableExpression<Double> = object : DifferentiableExpression<Double> {
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override fun derivativeOrNull(symbols: List<Symbol>): Expression<Double>? {
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TODO("Not yet implemented")
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}
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override fun invoke(arguments: Map<Symbol, Double>): Double {
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var res = 0.0
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for (index in 0 until problem.data.size) {
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val d = distanceBuilder.distance(problem, index).invoke(arguments)
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val sigma: Double = TODO()
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res -= (d / sigma).pow(2)
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}
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return res
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}
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}
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val functionOptimization = FunctionOptimization(problem.features, likelihood)
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val result = optimize(functionOptimization)
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return XYOptimization(result.features, problem.data, problem.model)
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}
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//
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//@UnstableKMathAPI
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//public interface XYFit<T> : OptimizationProblem {
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