Refactor structure features. Basic curve fitting

build.gradle.kts

@@ -1,5 +1,3 @@
-import ru.mipt.npm.gradle.KSciencePublishingPlugin
-
 plugins {
     id("ru.mipt.npm.gradle.project")
 }
@@ -20,11 +18,11 @@ allprojects {
     }
 
     group = "space.kscience"
-    version = "0.3.0-dev-3"
+    version = "0.3.0-dev-4"
 }
 
 subprojects {
-    if (name.startsWith("kmath")) apply<KSciencePublishingPlugin>()
+    if (name.startsWith("kmath")) apply(plugin = "maven-publish")
 }
 
 readme {

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

@@ -8,10 +8,14 @@ import kscience.plotly.models.TraceValues
 import space.kscience.kmath.commons.optimization.chiSquared
 import space.kscience.kmath.commons.optimization.minimize
 import space.kscience.kmath.expressions.symbol
+import space.kscience.kmath.optimization.FunctionOptimization
+import space.kscience.kmath.optimization.OptimizationResult
 import space.kscience.kmath.real.DoubleVector
 import space.kscience.kmath.real.map
 import space.kscience.kmath.real.step
-import space.kscience.kmath.stat.*
+import space.kscience.kmath.stat.Distribution
+import space.kscience.kmath.stat.RandomGenerator
+import space.kscience.kmath.stat.normal
 import space.kscience.kmath.structures.asIterable
 import space.kscience.kmath.structures.toList
 import kotlin.math.pow
@@ -58,7 +62,7 @@ fun main() {
     val yErr = y.map { sqrt(it) }//RealVector.same(x.size, sigma)
 
     // compute differentiable chi^2 sum for given model ax^2 + bx + c
-    val chi2 = Fitting.chiSquared(x, y, yErr) { x1 ->
+    val chi2 = FunctionOptimization.chiSquared(x, y, yErr) { x1 ->
         //bind variables to autodiff context
         val a = bind(a)
         val b = bind(b)

kmath-commons/src/main/kotlin/space/kscience/kmath/commons/linear/CMMatrix.kt

@@ -3,6 +3,7 @@ package space.kscience.kmath.commons.linear
 import org.apache.commons.math3.linear.*
 import space.kscience.kmath.linear.*
 import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.nd.StructureFeature
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.structures.DoubleBuffer
 import kotlin.reflect.KClass
@@ -89,7 +90,7 @@ public object CMLinearSpace : LinearSpace<Double, DoubleField> {
         v * this
 
     @UnstableKMathAPI
-    override fun <F : Any> getFeature(structure: Matrix<Double>, type: KClass<F>): F? {
+    override fun <F : StructureFeature> getFeature(structure: Matrix<Double>, type: KClass<out F>): F? {
         //Return the feature if it is intrinsic to the structure
         structure.getFeature(type)?.let { return it }
 

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

@@ -10,21 +10,25 @@ 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.stat.OptimizationFeature
+import space.kscience.kmath.optimization.FunctionOptimization
-import space.kscience.kmath.stat.OptimizationProblem
+import space.kscience.kmath.optimization.OptimizationFeature
-import space.kscience.kmath.stat.OptimizationProblemFactory
+import space.kscience.kmath.optimization.OptimizationProblemFactory
-import space.kscience.kmath.stat.OptimizationResult
+import space.kscience.kmath.optimization.OptimizationResult
 import kotlin.reflect.KClass
 
 public operator fun PointValuePair.component1(): DoubleArray = point
 public operator fun PointValuePair.component2(): Double = value
 
-public class CMOptimizationProblem(override val symbols: List<Symbol>) :
+public class CMOptimization(
-    OptimizationProblem<Double>, SymbolIndexer, OptimizationFeature {
+    override val symbols: List<Symbol>,
+) : FunctionOptimization<Double>, SymbolIndexer, OptimizationFeature {
     private val optimizationData: HashMap<KClass<out OptimizationData>, OptimizationData> = HashMap()
-    private var optimizatorBuilder: (() -> MultivariateOptimizer)? = null
+    private var optimizerBuilder: (() -> MultivariateOptimizer)? = null
-    public var convergenceChecker: ConvergenceChecker<PointValuePair> = SimpleValueChecker(DEFAULT_RELATIVE_TOLERANCE,
+    public var convergenceChecker: ConvergenceChecker<PointValuePair> = SimpleValueChecker(
-        DEFAULT_ABSOLUTE_TOLERANCE, DEFAULT_MAX_ITER)
+        DEFAULT_RELATIVE_TOLERANCE,
+        DEFAULT_ABSOLUTE_TOLERANCE,
+        DEFAULT_MAX_ITER
+    )
 
     public fun addOptimizationData(data: OptimizationData) {
         optimizationData[data::class] = data
@@ -57,8 +61,8 @@ public class CMOptimizationProblem(override val symbols: List<Symbol>) :
             }
         }
         addOptimizationData(gradientFunction)
-        if (optimizatorBuilder == null) {
+        if (optimizerBuilder == null) {
-            optimizatorBuilder = {
+            optimizerBuilder = {
                 NonLinearConjugateGradientOptimizer(
                     NonLinearConjugateGradientOptimizer.Formula.FLETCHER_REEVES,
                     convergenceChecker
@@ -70,8 +74,8 @@ public class CMOptimizationProblem(override val symbols: List<Symbol>) :
     public fun simplex(simplex: AbstractSimplex) {
         addOptimizationData(simplex)
         //Set optimization builder to simplex if it is not present
-        if (optimizatorBuilder == null) {
+        if (optimizerBuilder == null) {
-            optimizatorBuilder = { SimplexOptimizer(convergenceChecker) }
+            optimizerBuilder = { SimplexOptimizer(convergenceChecker) }
         }
     }
 
@@ -84,7 +88,7 @@ public class CMOptimizationProblem(override val symbols: List<Symbol>) :
     }
 
     public fun optimizer(block: () -> MultivariateOptimizer) {
-        optimizatorBuilder = block
+        optimizerBuilder = block
     }
 
     override fun update(result: OptimizationResult<Double>) {
@@ -92,19 +96,19 @@ public class CMOptimizationProblem(override val symbols: List<Symbol>) :
     }
 
     override fun optimize(): OptimizationResult<Double> {
-        val optimizer = optimizatorBuilder?.invoke() ?: error("Optimizer not defined")
+        val optimizer = optimizerBuilder?.invoke() ?: error("Optimizer not defined")
         val (point, value) = optimizer.optimize(*optimizationData.values.toTypedArray())
         return OptimizationResult(point.toMap(), value, setOf(this))
     }
 
-    public companion object : OptimizationProblemFactory<Double, CMOptimizationProblem> {
+    public companion object : OptimizationProblemFactory<Double, CMOptimization> {
         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>): CMOptimizationProblem = CMOptimizationProblem(symbols)
+        override fun build(symbols: List<Symbol>): CMOptimization = CMOptimization(symbols)
     }
 }
 
-public fun CMOptimizationProblem.initialGuess(vararg pairs: Pair<Symbol, Double>): Unit = initialGuess(pairs.toMap())
+public fun CMOptimization.initialGuess(vararg pairs: Pair<Symbol, Double>): Unit = initialGuess(pairs.toMap())
-public fun CMOptimizationProblem.simplexSteps(vararg pairs: Pair<Symbol, Double>): Unit = simplexSteps(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

@@ -6,16 +6,16 @@ import space.kscience.kmath.commons.expressions.DerivativeStructureField
 import space.kscience.kmath.expressions.DifferentiableExpression
 import space.kscience.kmath.expressions.Expression
 import space.kscience.kmath.expressions.Symbol
-import space.kscience.kmath.stat.Fitting
+import space.kscience.kmath.optimization.FunctionOptimization
-import space.kscience.kmath.stat.OptimizationResult
+import space.kscience.kmath.optimization.OptimizationResult
-import space.kscience.kmath.stat.optimizeWith
+import space.kscience.kmath.optimization.optimizeWith
 import space.kscience.kmath.structures.Buffer
 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 Fitting.chiSquared(
+public fun FunctionOptimization.Companion.chiSquared(
     x: Buffer<Double>,
     y: Buffer<Double>,
     yErr: Buffer<Double>,
@@ -25,7 +25,7 @@ public fun Fitting.chiSquared(
 /**
  * Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
  */
-public fun Fitting.chiSquared(
+public fun FunctionOptimization.Companion.chiSquared(
     x: Iterable<Double>,
     y: Iterable<Double>,
     yErr: Iterable<Double>,
@@ -43,23 +43,23 @@ public fun Fitting.chiSquared(
  */
 public fun Expression<Double>.optimize(
     vararg symbols: Symbol,
-    configuration: CMOptimizationProblem.() -> Unit,
+    configuration: CMOptimization.() -> Unit,
-): OptimizationResult<Double> = optimizeWith(CMOptimizationProblem, symbols = symbols, configuration)
+): OptimizationResult<Double> = optimizeWith(CMOptimization, symbols = symbols, configuration)
 
 /**
  * Optimize differentiable expression
  */
 public fun DifferentiableExpression<Double, Expression<Double>>.optimize(
     vararg symbols: Symbol,
-    configuration: CMOptimizationProblem.() -> Unit,
+    configuration: CMOptimization.() -> Unit,
-): OptimizationResult<Double> = optimizeWith(CMOptimizationProblem, symbols = symbols, configuration)
+): OptimizationResult<Double> = optimizeWith(CMOptimization, symbols = symbols, configuration)
 
 public fun DifferentiableExpression<Double, Expression<Double>>.minimize(
     vararg startPoint: Pair<Symbol, Double>,
-    configuration: CMOptimizationProblem.() -> Unit = {},
+    configuration: CMOptimization.() -> Unit = {},
 ): OptimizationResult<Double> {
     require(startPoint.isNotEmpty()) { "Must provide a list of symbols for optimization" }
-    val problem = CMOptimizationProblem(startPoint.map { it.first }).apply(configuration)
+    val problem = CMOptimization(startPoint.map { it.first }).apply(configuration)
     problem.diffExpression(this)
     problem.initialGuess(startPoint.toMap())
     problem.goal(GoalType.MINIMIZE)

kmath-commons/src/test/kotlin/space/kscience/kmath/commons/optimization/OptimizeTest.kt

@@ -3,8 +3,8 @@ package space.kscience.kmath.commons.optimization
 import org.junit.jupiter.api.Test
 import space.kscience.kmath.commons.expressions.DerivativeStructureExpression
 import space.kscience.kmath.expressions.symbol
+import space.kscience.kmath.optimization.FunctionOptimization
 import space.kscience.kmath.stat.Distribution
-import space.kscience.kmath.stat.Fitting
 import space.kscience.kmath.stat.RandomGenerator
 import space.kscience.kmath.stat.normal
 import kotlin.math.pow
@@ -55,7 +55,7 @@ internal class OptimizeTest {
 
         val yErr = List(x.size) { sigma }
 
-        val chi2 = Fitting.chiSquared(x, y, yErr) { x1 ->
+        val chi2 = FunctionOptimization.chiSquared(x, y, yErr) { x1 ->
             val cWithDefault = bindSymbolOrNull(c) ?: one
             bind(a) * x1.pow(2) + bind(b) * x1 + cWithDefault
         }

kmath-core/api/kmath-core.api

@@ -575,7 +575,7 @@ public final class space/kscience/kmath/linear/MatrixBuilderKt {
 	public static final fun row (Lspace/kscience/kmath/linear/LinearSpace;[Ljava/lang/Object;)Lspace/kscience/kmath/nd/Structure2D;
 }
 
-public abstract interface class space/kscience/kmath/linear/MatrixFeature {
+public abstract interface class space/kscience/kmath/linear/MatrixFeature : space/kscience/kmath/nd/StructureFeature {
 }
 
 public final class space/kscience/kmath/linear/MatrixFeaturesKt {
@@ -1060,11 +1060,15 @@ public final class space/kscience/kmath/nd/Strides$DefaultImpls {
 }
 
 public abstract interface class space/kscience/kmath/nd/Structure1D : space/kscience/kmath/nd/StructureND, space/kscience/kmath/structures/Buffer {
+	public static final field Companion Lspace/kscience/kmath/nd/Structure1D$Companion;
 	public abstract fun get ([I)Ljava/lang/Object;
 	public abstract fun getDimension ()I
 	public abstract fun iterator ()Ljava/util/Iterator;
 }
 
+public final class space/kscience/kmath/nd/Structure1D$Companion {
+}
+
 public final class space/kscience/kmath/nd/Structure1D$DefaultImpls {
 	public static fun get (Lspace/kscience/kmath/nd/Structure1D;[I)Ljava/lang/Object;
 	public static fun getDimension (Lspace/kscience/kmath/nd/Structure1D;)I
@@ -1104,6 +1108,9 @@ public final class space/kscience/kmath/nd/Structure2DKt {
 	public static final fun as2D (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/nd/Structure2D;
 }
 
+public abstract interface class space/kscience/kmath/nd/StructureFeature {
+}
+
 public abstract interface class space/kscience/kmath/nd/StructureND {
 	public static final field Companion Lspace/kscience/kmath/nd/StructureND$Companion;
 	public abstract fun elements ()Lkotlin/sequences/Sequence;

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSpace.kt

@@ -164,7 +164,7 @@ public interface LinearSpace<T : Any, out A : Ring<T>> {
      * @return a feature object or `null` if it isn't present.
      */
     @UnstableKMathAPI
-    public fun <F : Any> getFeature(structure: Matrix<T>, type: KClass<F>): F? = structure.getFeature(type)
+    public fun <F : StructureFeature> getFeature(structure: Matrix<T>, type: KClass<out F>): F? = structure.getFeature(type)
 
     public companion object {
 
@@ -194,7 +194,7 @@ public interface LinearSpace<T : Any, out A : Ring<T>> {
  * @return a feature object or `null` if it isn't present.
  */
 @UnstableKMathAPI
-public inline fun <T : Any, reified F : Any> LinearSpace<T, *>.getFeature(structure: Matrix<T>): F? =
+public inline fun <T : Any, reified F : StructureFeature> LinearSpace<T, *>.getFeature(structure: Matrix<T>): F? =
     getFeature(structure, F::class)
 
 

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixFeatures.kt

@@ -1,10 +1,12 @@
 package space.kscience.kmath.linear
 
+import space.kscience.kmath.nd.StructureFeature
+
 /**
  * A marker interface representing some properties of matrices or additional transformations of them. Features are used
  * to optimize matrix operations performance in some cases or retrieve the APIs.
  */
-public interface MatrixFeature
+public interface MatrixFeature: StructureFeature
 
 /**
  * Matrices with this feature are considered to have only diagonal non-null elements.

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixWrapper.kt

@@ -1,6 +1,7 @@
 package space.kscience.kmath.linear
 
 import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.nd.StructureFeature
 import space.kscience.kmath.nd.getFeature
 import space.kscience.kmath.operations.Ring
 import kotlin.reflect.KClass
@@ -20,7 +21,7 @@ public class MatrixWrapper<T : Any> internal constructor(
      */
     @UnstableKMathAPI
     @Suppress("UNCHECKED_CAST")
-    override fun <T : Any> getFeature(type: KClass<T>): T? = features.singleOrNull { type.isInstance(it) } as? T
+    override fun <F : StructureFeature> getFeature(type: KClass<out F>): F? = features.singleOrNull { type.isInstance(it) } as? F
         ?: origin.getFeature(type)
 
     override fun toString(): String {

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/AlgebraND.kt

@@ -67,7 +67,8 @@ public interface AlgebraND<T, C : Algebra<T>> {
      * @return a feature object or `null` if it isn't present.
      */
     @UnstableKMathAPI
-    public fun <F : Any> getFeature(structure: StructureND<T>, type: KClass<F>): F? = structure.getFeature(type)
+    public fun <F : StructureFeature> getFeature(structure: StructureND<T>, type: KClass<out F>): F? =
+        structure.getFeature(type)
 
     public companion object
 }
@@ -81,7 +82,7 @@ public interface AlgebraND<T, C : Algebra<T>> {
  * @return a feature object or `null` if it isn't present.
  */
 @UnstableKMathAPI
-public inline fun <T : Any, reified F : Any> AlgebraND<T, *>.getFeature(structure: StructureND<T>): F? =
+public inline fun <T : Any, reified F : StructureFeature> AlgebraND<T, *>.getFeature(structure: StructureND<T>): F? =
     getFeature(structure, F::class)
 
 /**

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt

@@ -15,6 +15,8 @@ public interface Structure1D<T> : StructureND<T>, Buffer<T> {
     }
 
     public override operator fun iterator(): Iterator<T> = (0 until size).asSequence().map(::get).iterator()
+
+    public companion object
 }
 
 /**

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt

@@ -69,7 +69,7 @@ private inline class Structure2DWrapper<T>(val structure: StructureND<T>) : Stru
     override operator fun get(i: Int, j: Int): T = structure[i, j]
 
     @UnstableKMathAPI
-    override fun <F : Any> getFeature(type: KClass<F>): F? = structure.getFeature(type)
+    override fun <F : StructureFeature> getFeature(type: KClass<out F>): F? = structure.getFeature(type)
 
     override fun elements(): Sequence<Pair<IntArray, T>> = structure.elements()
 }

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt

@@ -7,6 +7,8 @@ import kotlin.jvm.JvmName
 import kotlin.native.concurrent.ThreadLocal
 import kotlin.reflect.KClass
 
+public interface StructureFeature
+
 /**
  * Represents n-dimensional structure, i.e. multidimensional container of items of the same type and size. The number
  * of dimensions and items in an array is defined by its shape, which is a sequence of non-negative integers that
@@ -48,7 +50,7 @@ public interface StructureND<T> {
      * If the feature is not present, null is returned.
      */
     @UnstableKMathAPI
-    public fun <F : Any> getFeature(type: KClass<F>): F? = null
+    public fun <F : StructureFeature> getFeature(type: KClass<out F>): F? = null
 
     public companion object {
         /**
@@ -144,7 +146,7 @@ public interface StructureND<T> {
 public operator fun <T> StructureND<T>.get(vararg index: Int): T = get(index)
 
 @UnstableKMathAPI
-public inline fun <reified T : Any> StructureND<*>.getFeature(): T? = getFeature(T::class)
+public inline fun <reified T : StructureFeature> StructureND<*>.getFeature(): T? = getFeature(T::class)
 
 /**
  * Represents mutable [StructureND].

kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/BlockingDoubleChain.kt

@@ -8,5 +8,5 @@ public abstract class BlockingDoubleChain : Chain<Double> {
 
     override suspend fun next(): Double = nextDouble()
 
-    public fun nextBlock(size: Int): DoubleArray = DoubleArray(size) { nextDouble() }
+    public open fun nextBlock(size: Int): DoubleArray = DoubleArray(size) { nextDouble() }
 }

kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt

@@ -4,6 +4,7 @@ import org.ejml.dense.row.factory.DecompositionFactory_DDRM
 import org.ejml.simple.SimpleMatrix
 import space.kscience.kmath.linear.*
 import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.nd.StructureFeature
 import space.kscience.kmath.nd.getFeature
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.structures.DoubleBuffer
@@ -89,7 +90,7 @@ public object EjmlLinearSpace : LinearSpace<Double, DoubleField> {
         v.toEjml().origin.scale(this).wrapVector()
 
     @UnstableKMathAPI
-    override fun <F : Any> getFeature(structure: Matrix<Double>, type: KClass<F>): F? {
+    override fun <F : StructureFeature> getFeature(structure: Matrix<Double>, type: KClass<out F>): F? {
         //Return the feature if it is intrinsic to the structure
         structure.getFeature(type)?.let { return it }
 

kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/grids.kt

@@ -1,7 +1,43 @@
 package space.kscience.kmath.real
 
-import space.kscience.kmath.structures.asBuffer
+import space.kscience.kmath.misc.UnstableKMathAPI
-import kotlin.math.abs
+import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.DoubleBuffer
+import kotlin.math.floor
+
+public val ClosedFloatingPointRange<Double>.length: Double get() = endInclusive - start
+
+/**
+ * Create a Buffer-based grid with equally distributed [numberOfPoints] points. The range could be increasing or  decreasing.
+ * If range has a zero size, then the buffer consisting of [numberOfPoints] equal values is returned.
+ */
+@UnstableKMathAPI
+public fun Buffer.Companion.fromRange(range: ClosedFloatingPointRange<Double>, numberOfPoints: Int): DoubleBuffer {
+    require(numberOfPoints >= 2) { "Number of points in grid must be more than 1" }
+    val normalizedRange = when {
+        range.endInclusive > range.start -> range
+        range.endInclusive < range.start -> range.endInclusive..range.start
+        else -> return DoubleBuffer(numberOfPoints) { range.start }
+    }
+    val step = normalizedRange.length / (numberOfPoints - 1)
+    return DoubleBuffer(numberOfPoints) { normalizedRange.start + step * it / (numberOfPoints - 1) }
+}
+
+/**
+ * Create a Buffer-based grid with equally distributed points with a fixed [step]. The range could be increasing or  decreasing.
+ * If the step is larger than the range size, single point is returned.
+ */
+@UnstableKMathAPI
+public fun Buffer.Companion.fromRange(range: ClosedFloatingPointRange<Double>, step: Double): DoubleBuffer {
+    require(step > 0) { "The grid step must be positive" }
+    val normalizedRange = when {
+        range.endInclusive > range.start -> range
+        range.endInclusive < range.start -> range.endInclusive..range.start
+        else -> return DoubleBuffer(range.start)
+    }
+    val numberOfPoints = floor(normalizedRange.length / step).toInt()
+    return DoubleBuffer(numberOfPoints) { normalizedRange.start + step * it / (numberOfPoints - 1) }
+}
 
 /**
  * Convert double range to sequence.
@@ -11,35 +47,5 @@ import kotlin.math.abs
  *
  * If step is negative, the same goes from upper boundary downwards
  */
-public fun ClosedFloatingPointRange<Double>.toSequenceWithStep(step: Double): Sequence<Double> = when {
+@UnstableKMathAPI
-    step == 0.0 -> error("Zero step in double progression")
+public infix fun ClosedFloatingPointRange<Double>.step(step: Double): DoubleBuffer = Buffer.fromRange(this, step)
-
-    step > 0 -> sequence {
-        var current = start
-
-        while (current <= endInclusive) {
-            yield(current)
-            current += step
-        }
-    }
-
-    else -> sequence {
-        var current = endInclusive
-
-        while (current >= start) {
-            yield(current)
-            current += step
-        }
-    }
-}
-
-public infix fun ClosedFloatingPointRange<Double>.step(step: Double): DoubleVector =
-    toSequenceWithStep(step).toList().asBuffer()
-
-/**
- * Convert double range to sequence with the fixed number of points
- */
-public fun ClosedFloatingPointRange<Double>.toSequenceWithPoints(numPoints: Int): Sequence<Double> {
-    require(numPoints > 1) { "The number of points should be more than 2" }
-    return toSequenceWithStep(abs(endInclusive - start) / (numPoints - 1))
-}

kmath-stat/build.gradle.kts

@@ -3,14 +3,6 @@ plugins {
 }
 
 kotlin.sourceSets {
-    all {
-        languageSettings.apply {
-            useExperimentalAnnotation("kotlinx.coroutines.FlowPreview")
-            useExperimentalAnnotation("kotlinx.coroutines.ExperimentalCoroutinesApi")
-            useExperimentalAnnotation("kotlinx.coroutines.ObsoleteCoroutinesApi")
-        }
-    }
-
     commonMain {
         dependencies {
             api(project(":kmath-coroutines"))

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

@@ -0,0 +1,17 @@
+package space.kscience.kmath.optimization
+
+import space.kscience.kmath.expressions.DifferentiableExpression
+import space.kscience.kmath.expressions.StringSymbol
+import space.kscience.kmath.expressions.Symbol
+import space.kscience.kmath.structures.Buffer
+
+public interface DataFit<T : Any> : Optimization<T> {
+
+    public fun modelAndData(
+        x: Buffer<T>,
+        y: Buffer<T>,
+        yErr: Buffer<T>,
+        model: DifferentiableExpression<T, *>,
+        xSymbol: Symbol = StringSymbol("x"),
+    )
+}

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

@@ -0,0 +1,122 @@
+package space.kscience.kmath.optimization
+
+import space.kscience.kmath.expressions.*
+import space.kscience.kmath.operations.ExtendedField
+import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.indices
+import kotlin.math.pow
+
+/**
+ * A likelihood function optimization problem
+ */
+public interface FunctionOptimization<T: Any>: Optimization<T>, DataFit<T> {
+    /**
+     * Define the initial guess for the optimization problem
+     */
+    public fun initialGuess(map: Map<Symbol, T>)
+
+    /**
+     * Set an objective function expression
+     */
+    public fun expression(expression: Expression<T>)
+
+    /**
+     * Set a differentiable expression as objective function as function and gradient provider
+     */
+    public fun diffExpression(expression: DifferentiableExpression<T, Expression<T>>)
+
+    override fun modelAndData(
+        x: Buffer<T>,
+        y: Buffer<T>,
+        yErr: Buffer<T>,
+        model: DifferentiableExpression<T, *>,
+        xSymbol: Symbol,
+    ) {
+        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" }
+
+    }
+
+    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
+            }
+        }
+
+        /**
+         * Generate a chi squared expression from given x-y-sigma model represented by an expression. Does not provide derivatives
+         */
+        public fun chiSquared(
+            x: Buffer<Double>,
+            y: Buffer<Double>,
+            yErr: Buffer<Double>,
+            model: Expression<Double>,
+            xSymbol: Symbol = StringSymbol("x"),
+        ): Expression<Double> {
+            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 Expression { arguments ->
+                x.indices.sumByDouble {
+                    val xValue = x[it]
+                    val yValue = y[it]
+                    val yErrValue = yErr[it]
+                    val modifiedArgs = arguments + (xSymbol to xValue)
+                    val modelValue = model(modifiedArgs)
+                    ((yValue - modelValue) / yErrValue).pow(2)
+                }
+            }
+        }
+    }
+}
+
+/**
+ * Optimize expression without derivatives using specific [OptimizationProblemFactory]
+ */
+public fun <T : Any, F : FunctionOptimization<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.expression(this)
+    return problem.optimize()
+}
+
+/**
+ * Optimize differentiable expression using specific [OptimizationProblemFactory]
+ */
+public 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.diffExpression(this)
+    return problem.optimize()
+}

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

@@ -0,0 +1,44 @@
+package space.kscience.kmath.optimization
+
+import space.kscience.kmath.expressions.Symbol
+
+public interface OptimizationFeature
+
+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)
+
+/**
+ * An optimization problem builder over [T] variables
+ */
+public interface Optimization<T : Any> {
+
+    /**
+     * Update the problem from previous optimization run
+     */
+    public fun update(result: OptimizationResult<T>)
+
+    /**
+     * Make an optimization run
+     */
+    public fun optimize(): OptimizationResult<T>
+}
+
+public fun interface OptimizationProblemFactory<T : Any, out P : Optimization<T>> {
+    public fun build(symbols: List<Symbol>): P
+}
+
+public operator fun <T : Any, P : Optimization<T>> OptimizationProblemFactory<T, P>.invoke(
+    symbols: List<Symbol>,
+    block: P.() -> Unit,
+): P = build(symbols).apply(block)

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Fitting.kt

@@ -1,63 +0,0 @@
-package space.kscience.kmath.stat
-
-import space.kscience.kmath.expressions.*
-import space.kscience.kmath.operations.ExtendedField
-import space.kscience.kmath.structures.Buffer
-import space.kscience.kmath.structures.indices
-import kotlin.math.pow
-
-public object Fitting {
-
-    /**
-     * 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
-        }
-    }
-
-    /**
-     * Generate a chi squared expression from given x-y-sigma model represented by an expression. Does not provide derivatives
-     */
-    public fun chiSquared(
-        x: Buffer<Double>,
-        y: Buffer<Double>,
-        yErr: Buffer<Double>,
-        model: Expression<Double>,
-        xSymbol: Symbol = StringSymbol("x"),
-    ): Expression<Double> {
-        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 Expression { arguments ->
-            x.indices.sumByDouble {
-                val xValue = x[it]
-                val yValue = y[it]
-                val yErrValue = yErr[it]
-                val modifiedArgs = arguments + (xSymbol to xValue)
-                val modelValue = model(modifiedArgs)
-                ((yValue - modelValue) / yErrValue).pow(2)
-            }
-        }
-    }
-}

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

@@ -1,88 +0,0 @@
-package space.kscience.kmath.stat
-
-import space.kscience.kmath.expressions.DifferentiableExpression
-import space.kscience.kmath.expressions.Expression
-import space.kscience.kmath.expressions.Symbol
-
-public interface OptimizationFeature
-
-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)
-
-/**
- * A configuration builder for optimization problem
- */
-public interface OptimizationProblem<T : Any> {
-    /**
-     * Define the initial guess for the optimization problem
-     */
-    public fun initialGuess(map: Map<Symbol, T>)
-
-    /**
-     * Set an objective function expression
-     */
-    public fun expression(expression: Expression<T>)
-
-    /**
-     * Set a differentiable expression as objective function as function and gradient provider
-     */
-    public fun diffExpression(expression: DifferentiableExpression<T, Expression<T>>)
-
-    /**
-     * Update the problem from previous optimization run
-     */
-    public fun update(result: OptimizationResult<T>)
-
-    /**
-     * Make an optimization run
-     */
-    public fun optimize(): OptimizationResult<T>
-}
-
-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)
-
-/**
- * Optimize expression without derivatives using specific [OptimizationProblemFactory]
- */
-public fun <T : Any, F : OptimizationProblem<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.expression(this)
-    return problem.optimize()
-}
-
-/**
- * Optimize differentiable expression using specific [OptimizationProblemFactory]
- */
-public fun <T : Any, F : OptimizationProblem<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.diffExpression(this)
-    return problem.optimize()
-}

kmath-stat/src/jvmMain/kotlin/space/kscience/kmath/stat/distributions.kt

@@ -80,9 +80,10 @@ public fun Distribution.Companion.normal(
     override fun probability(arg: Double): Double = exp(-(arg - mean).pow(2) / 2 / sigma2) / norm
 }
 
-public fun Distribution.Companion.poisson(lambda: Double): DiscreteSamplerDistribution =
+public fun Distribution.Companion.poisson(
-    object : DiscreteSamplerDistribution() {
+    lambda: Double,
-        private val computedProb: MutableMap<Int, Double> = hashMapOf(0 to exp(-lambda))
+): DiscreteSamplerDistribution = object : DiscreteSamplerDistribution() {
+    private val computedProb: HashMap<Int, Double> = hashMapOf(0 to exp(-lambda))
 
     override fun buildSampler(generator: RandomGenerator): DiscreteSampler =
         PoissonSampler.of(generator.asUniformRandomProvider(), lambda)