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

@@ -11,10 +11,8 @@ import org.apache.commons.math3.optim.nonlinear.scalar.MultivariateOptimizer
 import org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunction
 import org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunctionGradient
 import org.apache.commons.math3.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer
-import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.AbstractSimplex
-import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.NelderMeadSimplex
-import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer
-import space.kscience.kmath.expressions.*
+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.*
@@ -24,107 +22,73 @@ public operator fun PointValuePair.component1(): DoubleArray = point
 public operator fun PointValuePair.component2(): Double = value
 
 public class CMOptimizerFactory(public val optimizerBuilder: () -> MultivariateOptimizer) : OptimizationFeature
-//public class CMOptimizerData(public val )
+public class CMOptimizerData(public val data: List<OptimizationData>) : OptimizationFeature {
+    public constructor(vararg data: OptimizationData) : this(data.toList())
+}
 
 @OptIn(UnstableKMathAPI::class)
 public class CMOptimization : Optimizer<FunctionOptimization<Double>> {
 
     override suspend fun process(
-        problem: FunctionOptimization<Double>
-    ): FunctionOptimization<Double> = withSymbols(problem.parameters){
-        val cmOptimizer: MultivariateOptimizer =
-            problem.getFeature<CMOptimizerFactory>()?.optimizerBuilder?.invoke() ?: SimplexOptimizer()
-
+        problem: FunctionOptimization<Double>,
+    ): FunctionOptimization<Double> = withSymbols(problem.parameters) {
         val convergenceChecker: ConvergenceChecker<PointValuePair> = SimpleValueChecker(
             DEFAULT_RELATIVE_TOLERANCE,
             DEFAULT_ABSOLUTE_TOLERANCE,
             DEFAULT_MAX_ITER
         )
 
+        val cmOptimizer: MultivariateOptimizer = problem.getFeature<CMOptimizerFactory>()?.optimizerBuilder?.invoke()
+            ?: NonLinearConjugateGradientOptimizer(
+                NonLinearConjugateGradientOptimizer.Formula.FLETCHER_REEVES,
+                convergenceChecker
+            )
+
         val optimizationData: HashMap<KClass<out OptimizationData>, OptimizationData> = HashMap()
 
         fun addOptimizationData(data: OptimizationData) {
             optimizationData[data::class] = data
         }
+
         addOptimizationData(MaxEval.unlimited())
         addOptimizationData(InitialGuess(problem.initialGuess.toDoubleArray()))
 
         fun exportOptimizationData(): List<OptimizationData> = optimizationData.values.toList()
 
-
-        /**
-         * Register no-deriv function instead of  differentiable function
-         */
-        /**
-         * Register no-deriv function instead of  differentiable function
-         */
-        fun noDerivFunction(expression: Expression<Double>): Unit {
         val objectiveFunction = ObjectiveFunction {
             val args = problem.initialGuess + it.toMap()
-                expression(args)
+            problem.expression(args)
         }
         addOptimizationData(objectiveFunction)
-        }
 
-        public override fun function(expression: DifferentiableExpression<Double, Expression<Double>>) {
-            noDerivFunction(expression)
         val gradientFunction = ObjectiveFunctionGradient {
-                val args = startingPoint + it.toMap()
+            val args = problem.initialGuess + it.toMap()
             DoubleArray(symbols.size) { index ->
-                    expression.derivative(symbols[index])(args)
+                problem.expression.derivative(symbols[index])(args)
             }
         }
         addOptimizationData(gradientFunction)
-            if (optimizerBuilder == null) {
-                optimizerBuilder = {
-                    NonLinearConjugateGradientOptimizer(
-                        NonLinearConjugateGradientOptimizer.Formula.FLETCHER_REEVES,
-                        convergenceChecker
-                    )
+
+        val logger = problem.getFeature<OptimizationLog>()
+
+        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" }
             }
         }
 
-        public fun simplex(simplex: AbstractSimplex) {
-            addOptimizationData(simplex)
-            //Set optimization builder to simplex if it is not present
-            if (optimizerBuilder == null) {
-                optimizerBuilder = { SimplexOptimizer(convergenceChecker) }
-            }
+        val (point, value) = cmOptimizer.optimize(*optimizationData.values.toTypedArray())
+        return problem.withFeatures(FunctionOptimizationResult(point.toMap(), value))
     }
 
-        public fun simplexSteps(steps: Map<Symbol, Double>) {
-            simplex(NelderMeadSimplex(steps.toDoubleArray()))
-        }
-
-        public fun goal(goalType: GoalType) {
-            addOptimizationData(goalType)
-        }
-
-        public fun optimizer(block: () -> MultivariateOptimizer) {
-            optimizerBuilder = block
-        }
-
-        override fun update(result: OptimizationResult<Double>) {
-            initialGuess(result.point)
-        }
-
-        override suspend fun optimize(): OptimizationResult<Double> {
-            val optimizer = optimizerBuilder?.invoke() ?: error("Optimizer not defined")
-            val (point, value) = optimizer.optimize(*optimizationData.values.toTypedArray())
-            return OptimizationResult(point.toMap(), value)
-        }
-        return@withSymbols TODO()
-    }
-
-    public companion object : OptimizationProblemFactory<Double, CMOptimization> {
+    public companion object {
         public const val DEFAULT_RELATIVE_TOLERANCE: Double = 1e-4
         public const val DEFAULT_ABSOLUTE_TOLERANCE: Double = 1e-4
         public const val DEFAULT_MAX_ITER: Int = 1000
-
-        override fun build(symbols: List<Symbol>): CMOptimization = CMOptimization(symbols)
     }
 }
-
-public fun CMOptimization.initialGuess(vararg pairs: Pair<Symbol, Double>): Unit = initialGuess(pairs.toMap())
-public fun CMOptimization.simplexSteps(vararg pairs: Pair<Symbol, Double>): Unit = simplexSteps(pairs.toMap())

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

@@ -17,22 +17,22 @@ import space.kscience.kmath.structures.asBuffer
 /**
  * Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
  */
-public fun FunctionOptimization.Companion.chiSquared(
+public fun FunctionOptimization.Companion.chiSquaredExpression(
     x: Buffer<Double>,
     y: Buffer<Double>,
     yErr: Buffer<Double>,
     model: DerivativeStructureField.(x: DerivativeStructure) -> DerivativeStructure,
-): DifferentiableExpression<Double, Expression<Double>> = chiSquared(DerivativeStructureField, x, y, yErr, model)
+): DifferentiableExpression<Double, Expression<Double>> = chiSquaredExpression(DerivativeStructureField, x, y, yErr, model)
 
 /**
  * Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
  */
-public fun FunctionOptimization.Companion.chiSquared(
+public fun FunctionOptimization.Companion.chiSquaredExpression(
     x: Iterable<Double>,
     y: Iterable<Double>,
     yErr: Iterable<Double>,
     model: DerivativeStructureField.(x: DerivativeStructure) -> DerivativeStructure,
-): DifferentiableExpression<Double, Expression<Double>> = chiSquared(
+): DifferentiableExpression<Double, Expression<Double>> = chiSquaredExpression(
     DerivativeStructureField,
     x.toList().asBuffer(),
     y.toList().asBuffer(),

kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/Featured.kt

@@ -17,7 +17,7 @@ public interface Featured<F : Any> {
 /**
  * A container for a set of features
  */
-public class FeatureSet<F : Any> private constructor(public val features: Map<KClass<out F>, Any>) : Featured<F> {
+public class FeatureSet<F : Any> private constructor(public val features: Map<KClass<out F>, F>) : Featured<F> {
     @Suppress("UNCHECKED_CAST")
     override fun <T : F> getFeature(type: KClass<out T>): T? = features[type] as? T
 
@@ -31,6 +31,8 @@ public class FeatureSet<F : Any> private constructor(public val features: Map<KC
     public fun with(vararg otherFeatures: F): FeatureSet<F> =
         FeatureSet(features + otherFeatures.associateBy { it::class })
 
+    public operator fun iterator(): Iterator<F> = features.values.iterator()
+
     public companion object {
         public fun <F : Any> of(vararg features: F): FeatureSet<F> = FeatureSet(features.associateBy { it::class })
     }

kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/logging.kt

@@ -0,0 +1,14 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.misc
+
+public interface Loggable {
+    public fun log(tag: String = INFO, block: () -> String)
+
+    public companion object {
+        public const val INFO: String = "INFO"
+    }
+}

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

@@ -15,91 +15,57 @@ 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 FunctionOptimization<T : Any>(
+public enum class FunctionOptimizationTarget : OptimizationFeature {
+    MAXIMIZE,
+    MINIMIZE
+}
+
+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>,
-    public val maximize: Boolean,
-) : OptimizationProblem
+) : OptimizationProblem{
+    public companion object{
+        /**
+         * Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
+         */
+        public fun <T : Any, I : Any, A> chiSquaredExpression(
+            autoDiff: AutoDiffProcessor<T, I, A, Expression<T>>,
+            x: Buffer<T>,
+            y: Buffer<T>,
+            yErr: Buffer<T>,
+            model: A.(I) -> I,
+        ): DifferentiableExpression<T, Expression<T>> where A : ExtendedField<I>, A : ExpressionAlgebra<T, I> {
+            require(x.size == y.size) { "X and y buffers should be of the same size" }
+            require(y.size == yErr.size) { "Y and yErr buffer should of the same size" }
+
+            return autoDiff.process {
+                var sum = zero
+
+                x.indices.forEach {
+                    val xValue = const(x[it])
+                    val yValue = const(y[it])
+                    val yErrValue = const(yErr[it])
+                    val modelValue = model(xValue)
+                    sum += ((yValue - modelValue) / yErrValue).pow(2)
+                }
+
+                sum
+            }
+        }
+    }
+}
+
+
+public fun <T> FunctionOptimization<T>.withFeatures(
+    vararg newFeature: OptimizationFeature,
+): FunctionOptimization<T> = FunctionOptimization(
+    features.with(*newFeature),
+    expression,
+    initialGuess,
+    parameters
+)
 
-//
-///**
-// * A likelihood function optimization problem with provided derivatives
-// */
-//public interface FunctionOptimizationBuilder<T : Any> {
-//    /**
-//     * The optimization direction. If true search for function maximum, if false, search for the minimum
-//     */
-//    public var maximize: Boolean
-//
-//    /**
-//     * Define the initial guess for the optimization problem
-//     */
-//    public fun initialGuess(map: Map<Symbol, T>)
-//
-//    /**
-//     * Set a differentiable expression as objective function as function and gradient provider
-//     */
-//    public fun function(expression: DifferentiableExpression<T, Expression<T>>)
-//
-//    public companion object {
-//        /**
-//         * Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
-//         */
-//        public fun <T : Any, I : Any, A> chiSquared(
-//            autoDiff: AutoDiffProcessor<T, I, A, Expression<T>>,
-//            x: Buffer<T>,
-//            y: Buffer<T>,
-//            yErr: Buffer<T>,
-//            model: A.(I) -> I,
-//        ): DifferentiableExpression<T, Expression<T>> where A : ExtendedField<I>, A : ExpressionAlgebra<T, I> {
-//            require(x.size == y.size) { "X and y buffers should be of the same size" }
-//            require(y.size == yErr.size) { "Y and yErr buffer should of the same size" }
-//
-//            return autoDiff.process {
-//                var sum = zero
-//
-//                x.indices.forEach {
-//                    val xValue = const(x[it])
-//                    val yValue = const(y[it])
-//                    val yErrValue = const(yErr[it])
-//                    val modelValue = model(xValue)
-//                    sum += ((yValue - modelValue) / yErrValue).pow(2)
-//                }
-//
-//                sum
-//            }
-//        }
-//    }
-//}
-//
-///**
-// * Define a chi-squared-based objective function
-// */
-//public fun <T : Any, I : Any, A> FunctionOptimization<T>.chiSquared(
-//    autoDiff: AutoDiffProcessor<T, I, A, Expression<T>>,
-//    x: Buffer<T>,
-//    y: Buffer<T>,
-//    yErr: Buffer<T>,
-//    model: A.(I) -> I,
-//) where A : ExtendedField<I>, A : ExpressionAlgebra<T, I> {
-//    val chiSquared = FunctionOptimization.chiSquared(autoDiff, x, y, yErr, model)
-//    function(chiSquared)
-//    maximize = false
-//}
-//
-///**
-// * Optimize differentiable expression using specific [OptimizationProblemFactory]
-// */
-//public suspend fun <T : Any, F : FunctionOptimization<T>> DifferentiableExpression<T, Expression<T>>.optimizeWith(
-//    factory: OptimizationProblemFactory<T, F>,
-//    vararg symbols: Symbol,
-//    configuration: F.() -> Unit,
-//): OptimizationResult<T> {
-//    require(symbols.isNotEmpty()) { "Must provide a list of symbols for optimization" }
-//    val problem = factory(symbols.toList(), configuration)
-//    problem.function(this)
-//    return problem.optimize()
-//}

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

@@ -7,6 +7,8 @@ package space.kscience.kmath.optimization
 
 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
@@ -18,6 +20,10 @@ 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 class OptimizationLog(private val loggable: Loggable) : Loggable by loggable, OptimizationFeature
+
 //public class OptimizationResult<T>(
 //    public val point: Map<Symbol, T>,
 //    public val value: T,

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

@@ -14,9 +14,11 @@ import space.kscience.kmath.misc.Symbol
 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<T> {
+public interface XYFit<T> : OptimizationProblem {
 
     public val algebra: Field<T>
 
@@ -43,3 +45,33 @@ public interface XYFit<T> : OptimizationProblem<T> {
         autoDiff.process { modelFunction(const(arg)) }
     }
 }
+
+//
+///**
+// * Define a chi-squared-based objective function
+// */
+//public fun <T : Any, I : Any, A> FunctionOptimization<T>.chiSquared(
+//    autoDiff: AutoDiffProcessor<T, I, A, Expression<T>>,
+//    x: Buffer<T>,
+//    y: Buffer<T>,
+//    yErr: Buffer<T>,
+//    model: A.(I) -> I,
+//) where A : ExtendedField<I>, A : ExpressionAlgebra<T, I> {
+//    val chiSquared = FunctionOptimization.chiSquared(autoDiff, x, y, yErr, model)
+//    function(chiSquared)
+//    maximize = false
+//}
+
+/**
+ * Optimize differentiable expression using specific [OptimizationProblemFactory]
+ */
+public suspend fun <T : Any, F : FunctionOptimization<T>> DifferentiableExpression<T, Expression<T>>.optimizeWith(
+    factory: OptimizationProblemFactory<T, F>,
+    vararg symbols: Symbol,
+    configuration: F.() -> Unit,
+): OptimizationResult<T> {
+    require(symbols.isNotEmpty()) { "Must provide a list of symbols for optimization" }
+    val problem = factory(symbols.toList(), configuration)
+    problem.function(this)
+    return problem.optimize()
+}

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

@@ -27,10 +27,6 @@ import kotlin.math.pow
 
 private typealias ParamSet = Map<Symbol, Double>
 
-public fun interface FitLogger {
-    public fun log(block: () -> String)
-}
-
 @OptIn(UnstableKMathAPI::class)
 public class QowFit(
     override val symbols: List<Symbol>,