[WIP] Optimization

examples/src/main/kotlin/space/kscience/kmath/commons/fit/fitWithAutoDiff.kt

@@ -8,7 +8,6 @@ package space.kscience.kmath.commons.fit
 import kotlinx.html.br
 import kotlinx.html.h3
 import space.kscience.kmath.commons.optimization.chiSquared
-import space.kscience.kmath.commons.optimization.minimize
 import space.kscience.kmath.distributions.NormalDistribution
 import space.kscience.kmath.expressions.symbol
 import space.kscience.kmath.optimization.FunctionOptimization

kmath-commons/src/main/kotlin/space/kscience/kmath/commons/optimization/CMOptimization.kt

@@ -13,7 +13,6 @@ import org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunctionGradient
 import org.apache.commons.math3.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer
 import space.kscience.kmath.expressions.derivative
 import space.kscience.kmath.expressions.withSymbols
-import space.kscience.kmath.misc.Symbol
 import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.optimization.*
 import kotlin.reflect.KClass
@@ -21,9 +20,15 @@ import kotlin.reflect.KClass
 public operator fun PointValuePair.component1(): DoubleArray = point
 public operator fun PointValuePair.component2(): Double = value
 
-public class CMOptimizerFactory(public val optimizerBuilder: () -> MultivariateOptimizer) : OptimizationFeature
+public class CMOptimizer(public val optimizerBuilder: () -> MultivariateOptimizer): OptimizationFeature{
+    override fun toString(): String = "CMOptimizer($optimizerBuilder)"
+}
+
 public class CMOptimizerData(public val data: List<OptimizationData>) : OptimizationFeature {
     public constructor(vararg data: OptimizationData) : this(data.toList())
+
+    override fun toString(): String = "CMOptimizerData($data)"
+
 }
 
 @OptIn(UnstableKMathAPI::class)
@@ -31,59 +36,69 @@ public class CMOptimization : Optimizer<FunctionOptimization<Double>> {
 
     override suspend fun process(
         problem: FunctionOptimization<Double>,
-    ): FunctionOptimization<Double> = withSymbols(problem.parameters) {
+    ): FunctionOptimization<Double> {
-        val convergenceChecker: ConvergenceChecker<PointValuePair> = SimpleValueChecker(
+        val startPoint = problem.getFeature<OptimizationStartPoint<Double>>()?.point
-            DEFAULT_RELATIVE_TOLERANCE,
+            ?: error("Starting point not defined in $problem")
-            DEFAULT_ABSOLUTE_TOLERANCE,
-            DEFAULT_MAX_ITER
-        )
 
-        val cmOptimizer: MultivariateOptimizer = problem.getFeature<CMOptimizerFactory>()?.optimizerBuilder?.invoke()
+        val parameters = problem.getFeature<OptimizationParameters>()?.symbols
-            ?: NonLinearConjugateGradientOptimizer(
+            ?: problem.getFeature<OptimizationStartPoint<Double>>()?.point?.keys
-                NonLinearConjugateGradientOptimizer.Formula.FLETCHER_REEVES,
+            ?:startPoint.keys
-                convergenceChecker
+
+
+        withSymbols(parameters) {
+            val convergenceChecker: ConvergenceChecker<PointValuePair> = SimpleValueChecker(
+                DEFAULT_RELATIVE_TOLERANCE,
+                DEFAULT_ABSOLUTE_TOLERANCE,
+                DEFAULT_MAX_ITER
             )
 
-        val optimizationData: HashMap<KClass<out OptimizationData>, OptimizationData> = HashMap()
+            val cmOptimizer: MultivariateOptimizer = problem.getFeature<CMOptimizer>()?.optimizerBuilder?.invoke()
+                ?: NonLinearConjugateGradientOptimizer(
+                    NonLinearConjugateGradientOptimizer.Formula.FLETCHER_REEVES,
+                    convergenceChecker
+                )
 
-        fun addOptimizationData(data: OptimizationData) {
+            val optimizationData: HashMap<KClass<out OptimizationData>, OptimizationData> = HashMap()
-            optimizationData[data::class] = data
-        }
 
-        addOptimizationData(MaxEval.unlimited())
+            fun addOptimizationData(data: OptimizationData) {
-        addOptimizationData(InitialGuess(problem.initialGuess.toDoubleArray()))
+                optimizationData[data::class] = data
-
-        fun exportOptimizationData(): List<OptimizationData> = optimizationData.values.toList()
-
-        val objectiveFunction = ObjectiveFunction {
-            val args = problem.initialGuess + it.toMap()
-            problem.expression(args)
-        }
-        addOptimizationData(objectiveFunction)
-
-        val gradientFunction = ObjectiveFunctionGradient {
-            val args = problem.initialGuess + it.toMap()
-            DoubleArray(symbols.size) { index ->
-                problem.expression.derivative(symbols[index])(args)
             }
-        }
-        addOptimizationData(gradientFunction)
 
-        val logger = problem.getFeature<OptimizationLog>()
+            addOptimizationData(MaxEval.unlimited())
+            addOptimizationData(InitialGuess(startPoint.toDoubleArray()))
 
-        for (feature in problem.features) {
+            fun exportOptimizationData(): List<OptimizationData> = optimizationData.values.toList()
-            when (feature) {
+
-                is CMOptimizerData -> feature.data.forEach { addOptimizationData(it) }
+            val objectiveFunction = ObjectiveFunction {
-                is FunctionOptimizationTarget -> when(feature){
+                val args = startPoint + it.toMap()
-                    FunctionOptimizationTarget.MAXIMIZE -> addOptimizationData(GoalType.MAXIMIZE)
+                problem.expression(args)
-                    FunctionOptimizationTarget.MINIMIZE -> addOptimizationData(GoalType.MINIMIZE)
+            }
+            addOptimizationData(objectiveFunction)
+
+            val gradientFunction = ObjectiveFunctionGradient {
+                val args = startPoint + it.toMap()
+                DoubleArray(symbols.size) { index ->
+                    problem.expression.derivative(symbols[index])(args)
                 }
-                else -> logger?.log { "The feature $feature is unused in optimization" }
             }
-        }
+            addOptimizationData(gradientFunction)
 
-        val (point, value) = cmOptimizer.optimize(*optimizationData.values.toTypedArray())
+            val logger = problem.getFeature<OptimizationLog>()
-        return problem.withFeatures(FunctionOptimizationResult(point.toMap(), value))
+
+            for (feature in problem.features) {
+                when (feature) {
+                    is CMOptimizerData -> feature.data.forEach { addOptimizationData(it) }
+                    is FunctionOptimizationTarget -> when (feature) {
+                        FunctionOptimizationTarget.MAXIMIZE -> addOptimizationData(GoalType.MAXIMIZE)
+                        FunctionOptimizationTarget.MINIMIZE -> addOptimizationData(GoalType.MINIMIZE)
+                    }
+                    else -> logger?.log { "The feature $feature is unused in optimization" }
+                }
+            }
+
+            val (point, value) = cmOptimizer.optimize(*optimizationData.values.toTypedArray())
+            return problem.withFeatures(OptimizationResult(point.toMap()), OptimizationValue(value))
+        }
     }
 
     public companion object {

kmath-commons/src/main/kotlin/space/kscience/kmath/commons/optimization/cmFit.kt

@@ -9,7 +9,7 @@ import org.apache.commons.math3.analysis.differentiation.DerivativeStructure
 import space.kscience.kmath.commons.expressions.DerivativeStructureField
 import space.kscience.kmath.expressions.DifferentiableExpression
 import space.kscience.kmath.expressions.Expression
-import space.kscience.kmath.misc.Symbol
+import space.kscience.kmath.expressions.Symbol
 import space.kscience.kmath.optimization.*
 import space.kscience.kmath.structures.Buffer
 import space.kscience.kmath.structures.asBuffer

kmath-core/src/commonMain/kotlin/space/kscience/kmath/data/XYErrorColumnarData.kt

@@ -5,10 +5,10 @@
 
 package space.kscience.kmath.data
 
-import space.kscience.kmath.misc.Symbol
+import space.kscience.kmath.data.XYErrorColumnarData.Companion
-import space.kscience.kmath.misc.Symbol.Companion.z
+import space.kscience.kmath.expressions.Symbol
+import space.kscience.kmath.expressions.symbol
 import space.kscience.kmath.misc.UnstableKMathAPI
-import space.kscience.kmath.misc.symbol
 import space.kscience.kmath.structures.Buffer
 
 

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/GaussIntegrator.kt

@@ -51,7 +51,7 @@ public class GaussIntegrator<T : Any>(
         }
     }
 
-    override fun integrate(integrand: UnivariateIntegrand<T>): UnivariateIntegrand<T> = with(algebra) {
+    override fun process(integrand: UnivariateIntegrand<T>): UnivariateIntegrand<T> = with(algebra) {
         val f = integrand.function
         val (points, weights) = buildRule(integrand)
         var res = zero
@@ -95,7 +95,7 @@ public fun <T : Any> GaussIntegrator<T>.integrate(
     val ranges = UnivariateIntegrandRanges(
         (0 until intervals).map { i -> (range.start + rangeSize * i)..(range.start + rangeSize * (i + 1)) to order }
     )
-    return integrate(
+    return process(
         UnivariateIntegrand(
             function,
             IntegrationRange(range),

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/Integrand.kt

@@ -5,18 +5,20 @@
 
 package space.kscience.kmath.integration
 
+import space.kscience.kmath.misc.FeatureSet
+import space.kscience.kmath.misc.Featured
 import kotlin.reflect.KClass
 
 public interface IntegrandFeature {
     override fun toString(): String
 }
 
-public interface Integrand {
+public interface Integrand : Featured<IntegrandFeature> {
-    public val features: Set<IntegrandFeature>
+    public val features: FeatureSet<IntegrandFeature>
-    public fun <T : IntegrandFeature> getFeature(type: KClass<T>): T?
+    override fun <T : IntegrandFeature> getFeature(type: KClass<out T>): T? = features.getFeature(type)
 }
 
-public inline fun <reified T : IntegrandFeature> Integrand.getFeature(): T? = getFeature(T::class)
+public inline fun <reified T: IntegrandFeature> Integrand.getFeature(): T? = getFeature(T::class)
 
 public class IntegrandValue<T : Any>(public val value: T) : IntegrandFeature {
     override fun toString(): String = "Value($value)"

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/MultivariateIntegrand.kt

@@ -6,29 +6,21 @@
 package space.kscience.kmath.integration
 
 import space.kscience.kmath.linear.Point
-import kotlin.reflect.KClass
+import space.kscience.kmath.misc.FeatureSet
 
 public class MultivariateIntegrand<T : Any> internal constructor(
-    private val featureMap: Map<KClass<*>, IntegrandFeature>,
+    override val features: FeatureSet<IntegrandFeature>,
     public val function: (Point<T>) -> T,
 ) : Integrand {
 
-    override val features: Set<IntegrandFeature> get() = featureMap.values.toSet()
-
-    @Suppress("UNCHECKED_CAST")
-    override fun <T : IntegrandFeature> getFeature(type: KClass<T>): T? = featureMap[type] as? T
-
-    public operator fun <F : IntegrandFeature> plus(pair: Pair<KClass<out F>, F>): MultivariateIntegrand<T> =
-        MultivariateIntegrand(featureMap + pair, function)
-
     public operator fun <F : IntegrandFeature> plus(feature: F): MultivariateIntegrand<T> =
-        plus(feature::class to feature)
+        MultivariateIntegrand(features.with(feature), function)
 }
 
 @Suppress("FunctionName")
 public fun <T : Any> MultivariateIntegrand(
     vararg features: IntegrandFeature,
     function: (Point<T>) -> T,
-): MultivariateIntegrand<T> = MultivariateIntegrand(features.associateBy { it::class }, function)
+): MultivariateIntegrand<T> = MultivariateIntegrand(FeatureSet.of(*features), function)
 
 public val <T : Any> MultivariateIntegrand<T>.value: T? get() = getFeature<IntegrandValue<T>>()?.value

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/SimpsonIntegrator.kt

@@ -43,7 +43,7 @@ public class SimpsonIntegrator<T : Any>(
         return res
     }
 
-    override fun integrate(integrand: UnivariateIntegrand<T>): UnivariateIntegrand<T> {
+    override fun process(integrand: UnivariateIntegrand<T>): UnivariateIntegrand<T> {
         val ranges = integrand.getFeature<UnivariateIntegrandRanges>()
         return if (ranges != null) {
             val res = algebra.sum(ranges.ranges.map { integrateRange(integrand, it.first, it.second) })
@@ -89,7 +89,7 @@ public object DoubleSimpsonIntegrator : UnivariateIntegrator<Double> {
         return res
     }
 
-    override fun integrate(integrand: UnivariateIntegrand<Double>): UnivariateIntegrand<Double> {
+    override fun process(integrand: UnivariateIntegrand<Double>): UnivariateIntegrand<Double> {
         val ranges = integrand.getFeature<UnivariateIntegrandRanges>()
         return if (ranges != null) {
             val res = ranges.ranges.sumOf { integrateRange(integrand, it.first, it.second) }

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/SplineIntegrator.kt

@@ -53,7 +53,7 @@ public class SplineIntegrator<T : Comparable<T>>(
     public val algebra: Field<T>,
     public val bufferFactory: MutableBufferFactory<T>,
 ) : UnivariateIntegrator<T> {
-    override fun integrate(integrand: UnivariateIntegrand<T>): UnivariateIntegrand<T> = algebra {
+    override fun process(integrand: UnivariateIntegrand<T>): UnivariateIntegrand<T> = algebra {
         val range = integrand.getFeature<IntegrationRange>()?.range ?: 0.0..1.0
 
         val interpolator: PolynomialInterpolator<T> = SplineInterpolator(algebra, bufferFactory)
@@ -80,7 +80,7 @@ public class SplineIntegrator<T : Comparable<T>>(
  */
 @UnstableKMathAPI
 public object DoubleSplineIntegrator : UnivariateIntegrator<Double> {
-    override fun integrate(integrand: UnivariateIntegrand<Double>): UnivariateIntegrand<Double> {
+    override fun process(integrand: UnivariateIntegrand<Double>): UnivariateIntegrand<Double> {
         val range = integrand.getFeature<IntegrationRange>()?.range ?: 0.0..1.0
 
         val interpolator: PolynomialInterpolator<Double> = SplineInterpolator(DoubleField, ::DoubleBuffer)

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/UnivariateIntegrand.kt

@@ -5,33 +5,24 @@
 
 package space.kscience.kmath.integration
 
+import space.kscience.kmath.misc.FeatureSet
 import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.structures.Buffer
 import space.kscience.kmath.structures.DoubleBuffer
-import kotlin.reflect.KClass
 
 public class UnivariateIntegrand<T> internal constructor(
-    private val featureMap: Map<KClass<*>, IntegrandFeature>,
+    override val features: FeatureSet<IntegrandFeature>,
     public val function: (Double) -> T,
 ) : Integrand {
-
-    override val features: Set<IntegrandFeature> get() = featureMap.values.toSet()
-
-    @Suppress("UNCHECKED_CAST")
-    override fun <T : IntegrandFeature> getFeature(type: KClass<T>): T? = featureMap[type] as? T
-
-    public operator fun <F : IntegrandFeature> plus(pair: Pair<KClass<out F>, F>): UnivariateIntegrand<T> =
-        UnivariateIntegrand(featureMap + pair, function)
-
     public operator fun <F : IntegrandFeature> plus(feature: F): UnivariateIntegrand<T> =
-        plus(feature::class to feature)
+        UnivariateIntegrand(features.with(feature), function)
 }
 
 @Suppress("FunctionName")
 public fun <T : Any> UnivariateIntegrand(
     function: (Double) -> T,
     vararg features: IntegrandFeature,
-): UnivariateIntegrand<T> = UnivariateIntegrand(features.associateBy { it::class }, function)
+): UnivariateIntegrand<T> = UnivariateIntegrand(FeatureSet.of(*features), function)
 
 public typealias UnivariateIntegrator<T> = Integrator<UnivariateIntegrand<T>>
 
@@ -79,7 +70,7 @@ public val <T : Any> UnivariateIntegrand<T>.value: T get() = valueOrNull ?: erro
 public fun <T : Any> UnivariateIntegrator<T>.integrate(
     vararg features: IntegrandFeature,
     function: (Double) -> T,
-): UnivariateIntegrand<T> = integrate(UnivariateIntegrand(function, *features))
+): UnivariateIntegrand<T> = process(UnivariateIntegrand(function, *features))
 
 /**
  * A shortcut method to integrate a [function] in [range] with additional [features].
@@ -90,7 +81,7 @@ public fun <T : Any> UnivariateIntegrator<T>.integrate(
     range: ClosedRange<Double>,
     vararg features: IntegrandFeature,
     function: (Double) -> T,
-): UnivariateIntegrand<T> = integrate(UnivariateIntegrand(function, IntegrationRange(range), *features))
+): UnivariateIntegrand<T> = process(UnivariateIntegrand(function, IntegrationRange(range), *features))
 
 /**
  * A shortcut method to integrate a [function] in [range] with additional [features].
@@ -107,5 +98,5 @@ public fun <T : Any> UnivariateIntegrator<T>.integrate(
         featureBuilder()
         add(IntegrationRange(range))
     }
-    return integrate(UnivariateIntegrand(function, *features.toTypedArray()))
+    return process(UnivariateIntegrand(function, *features.toTypedArray()))
 }

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/optimization/FunctionOptimization.kt

@@ -10,12 +10,13 @@ import space.kscience.kmath.expressions.DifferentiableExpression
 import space.kscience.kmath.expressions.Expression
 import space.kscience.kmath.expressions.ExpressionAlgebra
 import space.kscience.kmath.misc.FeatureSet
-import space.kscience.kmath.misc.Symbol
 import space.kscience.kmath.operations.ExtendedField
 import space.kscience.kmath.structures.Buffer
 import space.kscience.kmath.structures.indices
 
-public class FunctionOptimizationResult<T>(point: Map<Symbol, T>, public val value: T) : OptimizationResult<T>(point)
+public class OptimizationValue<T>(public val value: T) : OptimizationFeature{
+    override fun toString(): String = "Value($value)"
+}
 
 public enum class FunctionOptimizationTarget : OptimizationFeature {
     MAXIMIZE,
@@ -25,9 +26,8 @@ public enum class FunctionOptimizationTarget : OptimizationFeature {
 public class FunctionOptimization<T>(
     override val features: FeatureSet<OptimizationFeature>,
     public val expression: DifferentiableExpression<T, Expression<T>>,
-    public val initialGuess: Map<Symbol, T>,
-    public val parameters: Collection<Symbol>,
 ) : OptimizationProblem{
+
     public companion object{
         /**
          * Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
@@ -65,7 +65,5 @@ public fun <T> FunctionOptimization<T>.withFeatures(
 ): FunctionOptimization<T> = FunctionOptimization(
     features.with(*newFeature),
     expression,
-    initialGuess,
-    parameters
 )
 

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/optimization/OptimizationProblem.kt

@@ -5,13 +5,15 @@
 
 package space.kscience.kmath.optimization
 
+import space.kscience.kmath.expressions.Symbol
 import space.kscience.kmath.misc.FeatureSet
 import space.kscience.kmath.misc.Featured
 import space.kscience.kmath.misc.Loggable
-import space.kscience.kmath.misc.Symbol
 import kotlin.reflect.KClass
 
-public interface OptimizationFeature
+public interface OptimizationFeature {
+    override fun toString(): String
+}
 
 public interface OptimizationProblem : Featured<OptimizationFeature> {
     public val features: FeatureSet<OptimizationFeature>
@@ -20,31 +22,22 @@ public interface OptimizationProblem : Featured<OptimizationFeature> {
 
 public inline fun <reified T : OptimizationFeature> OptimizationProblem.getFeature(): T? = getFeature(T::class)
 
-public open class OptimizationResult<T>(public val point: Map<Symbol, T>) : OptimizationFeature
+public open class OptimizationStartPoint<T>(public val point: Map<Symbol, T>) : OptimizationFeature {
+    override fun toString(): String = "StartPoint($point)"
+}
 
-public class OptimizationLog(private val loggable: Loggable) : Loggable by loggable, OptimizationFeature
+public open class OptimizationResult<T>(public val point: Map<Symbol, T>) : OptimizationFeature {
+    override fun toString(): String = "Result($point)"
+}
+
+public class OptimizationLog(private val loggable: Loggable) : Loggable by loggable, OptimizationFeature {
+    override fun toString(): String = "Log($loggable)"
+}
+
+public class OptimizationParameters(public val symbols: List<Symbol>): OptimizationFeature{
+    override fun toString(): String = "Parameters($symbols)"
+}
 
-//public class OptimizationResult<T>(
-//    public val point: Map<Symbol, T>,
-//    public val value: T,
-//    public val features: Set<OptimizationFeature> = emptySet(),
-//) {
-//    override fun toString(): String {
-//        return "OptimizationResult(point=$point, value=$value)"
-//    }
-//}
-//
-//public operator fun <T> OptimizationResult<T>.plus(
-//    feature: OptimizationFeature,
-//): OptimizationResult<T> = OptimizationResult(point, value, features + feature)
-//public fun interface OptimizationProblemFactory<T : Any, out P : OptimizationProblem<T>> {
-//    public fun build(symbols: List<Symbol>): P
-//}
-//
-//public operator fun <T : Any, P : OptimizationProblem<T>> OptimizationProblemFactory<T, P>.invoke(
-//    symbols: List<Symbol>,
-//    block: P.() -> Unit,
-//): P = build(symbols).apply(block)
 
 public interface Optimizer<P : OptimizationProblem> {
     public suspend fun process(problem: P): P

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/optimization/XYFit.kt

@@ -10,8 +10,6 @@ import space.kscience.kmath.expressions.*
 import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.operations.ExtendedField
 import space.kscience.kmath.operations.Field
-import space.kscience.kmath.structures.Buffer
-import space.kscience.kmath.structures.indices
 
 @UnstableKMathAPI
 public interface XYFit<T> : OptimizationProblem {
@@ -59,15 +57,16 @@ public interface XYFit<T> : OptimizationProblem {
 //}
 
 /**
- * Optimize differentiable expression using specific [OptimizationProblemFactory]
+ * Optimize differentiable expression using specific [Optimizer]
  */
 public suspend fun <T : Any, F : FunctionOptimization<T>> DifferentiableExpression<T, Expression<T>>.optimizeWith(
-    factory: OptimizationProblemFactory<T, F>,
+    optimizer: Optimizer<F>,
-    vararg symbols: Symbol,
+    startingPoint: Map<Symbol,T>,
-    configuration: F.() -> Unit,
+    vararg features: OptimizationFeature
-): OptimizationResult<T> {
+): OptimizationProblem {
-    require(symbols.isNotEmpty()) { "Must provide a list of symbols for optimization" }
+//    require(startingPoint.isNotEmpty()) { "Must provide a list of symbols for optimization" }
-    val problem = factory(symbols.toList(), configuration)
+//    val problem = factory(symbols.toList(), configuration)
-    problem.function(this)
+//    problem.function(this)
-    return problem.optimize()
+//    return problem.optimize()
+    val problem = FunctionOptimization<T>()
 }

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/optimization/minuit/Range.kt

@@ -1,32 +0,0 @@
-/* 
- * Copyright 2015 Alexander Nozik.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package ru.inr.mass.minuit
-
-/**
- * A class representing a pair of double (x,y) or (lower,upper)
- *
- * @version $Id$
- * @author Darksnake
- */
-class Range
-/**
- *
- * Constructor for Range.
- *
- * @param k a double.
- * @param v a double.
- */
-    (k: Double, v: Double) : Pair<Double?, Double?>(k, v)

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/optimization/qow/QowFit.kt

@@ -7,12 +7,8 @@ package space.kscience.kmath.optimization.qow
 
 import space.kscience.kmath.data.ColumnarData
 import space.kscience.kmath.data.XYErrorColumnarData
-import space.kscience.kmath.expressions.DifferentiableExpression
+import space.kscience.kmath.expressions.*
-import space.kscience.kmath.expressions.Expression
-import space.kscience.kmath.expressions.SymbolIndexer
-import space.kscience.kmath.expressions.derivative
 import space.kscience.kmath.linear.*
-import space.kscience.kmath.misc.Symbol
 import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.operations.Field