build.gradle.kts

@@ -11,10 +11,7 @@ allprojects {
         jcenter()
         maven("https://clojars.org/repo")
         maven("https://dl.bintray.com/egor-bogomolov/astminer/")
-        maven("https://dl.bintray.com/kotlin/kotlin-eap")
+        maven("https://dl.bintray.com/hotkeytlt/maven")
-        maven("https://dl.bintray.com/kotlin/kotlinx")
-        maven("https://dl.bintray.com/mipt-npm/dev")
-        maven("https://dl.bintray.com/mipt-npm/kscience")
         maven("https://jitpack.io")
         maven("http://logicrunch.research.it.uu.se/maven/")
         mavenCentral()

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

@@ -15,10 +15,7 @@ import space.kscience.kmath.expressions.SymbolIndexer
 import space.kscience.kmath.expressions.derivative
 import space.kscience.kmath.misc.Symbol
 import space.kscience.kmath.misc.UnstableKMathAPI
-import space.kscience.kmath.optimization.FunctionOptimization
+import space.kscience.kmath.optimization.*
-import space.kscience.kmath.optimization.OptimizationFeature
-import space.kscience.kmath.optimization.OptimizationProblemFactory
-import space.kscience.kmath.optimization.OptimizationResult
 import kotlin.reflect.KClass
 
 public operator fun PointValuePair.component1(): DoubleArray = point
@@ -27,7 +24,8 @@ public operator fun PointValuePair.component2(): Double = value
 @OptIn(UnstableKMathAPI::class)
 public class CMOptimization(
     override val symbols: List<Symbol>,
-) : FunctionOptimization<Double>, SymbolIndexer, OptimizationFeature {
+) : FunctionOptimization<Double>, NoDerivFunctionOptimization<Double>, SymbolIndexer, OptimizationFeature {
+
     private val optimizationData: HashMap<KClass<out OptimizationData>, OptimizationData> = HashMap()
     private var optimizerBuilder: (() -> MultivariateOptimizer)? = null
     public var convergenceChecker: ConvergenceChecker<PointValuePair> = SimpleValueChecker(
@@ -36,6 +34,12 @@ public class CMOptimization(
         DEFAULT_MAX_ITER
     )
 
+    override var maximize: Boolean
+        get() = optimizationData[GoalType::class] == GoalType.MAXIMIZE
+        set(value) {
+            optimizationData[GoalType::class] = if (value) GoalType.MAXIMIZE else GoalType.MINIMIZE
+        }
+
     public fun addOptimizationData(data: OptimizationData) {
         optimizationData[data::class] = data
     }
@@ -50,7 +54,7 @@ public class CMOptimization(
         addOptimizationData(InitialGuess(map.toDoubleArray()))
     }
 
-    public override fun expression(expression: Expression<Double>): Unit {
+    public override fun function(expression: Expression<Double>): Unit {
         val objectiveFunction = ObjectiveFunction {
             val args = it.toMap()
             expression(args)
@@ -58,8 +62,8 @@ public class CMOptimization(
         addOptimizationData(objectiveFunction)
     }
 
-    public override fun diffExpression(expression: DifferentiableExpression<Double, Expression<Double>>) {
+    public override fun diffFunction(expression: DifferentiableExpression<Double, Expression<Double>>) {
-        expression(expression)
+        function(expression)
         val gradientFunction = ObjectiveFunctionGradient {
             val args = it.toMap()
             DoubleArray(symbols.size) { index ->

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

@@ -1,13 +1,13 @@
 package space.kscience.kmath.commons.optimization
 
 import org.apache.commons.math3.analysis.differentiation.DerivativeStructure
-import org.apache.commons.math3.optim.nonlinear.scalar.GoalType
 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.optimization.FunctionOptimization
 import space.kscience.kmath.optimization.OptimizationResult
+import space.kscience.kmath.optimization.noDerivOptimizeWith
 import space.kscience.kmath.optimization.optimizeWith
 import space.kscience.kmath.structures.Buffer
 import space.kscience.kmath.structures.asBuffer
@@ -44,7 +44,7 @@ public fun FunctionOptimization.Companion.chiSquared(
 public fun Expression<Double>.optimize(
     vararg symbols: Symbol,
     configuration: CMOptimization.() -> Unit,
-): OptimizationResult<Double> = optimizeWith(CMOptimization, symbols = symbols, configuration)
+): OptimizationResult<Double> = noDerivOptimizeWith(CMOptimization, symbols = symbols, configuration)
 
 /**
  * Optimize differentiable expression
@@ -58,10 +58,11 @@ public fun DifferentiableExpression<Double, Expression<Double>>.minimize(
     vararg startPoint: Pair<Symbol, Double>,
     configuration: CMOptimization.() -> Unit = {},
 ): OptimizationResult<Double> {
-    require(startPoint.isNotEmpty()) { "Must provide a list of symbols for optimization" }
+    val symbols = startPoint.map { it.first }.toTypedArray()
-    val problem = CMOptimization(startPoint.map { it.first }).apply(configuration)
+    return optimize(*symbols){
-    problem.diffExpression(this)
+        maximize = false
-    problem.initialGuess(startPoint.toMap())
+        initialGuess(startPoint.toMap())
-    problem.goal(GoalType.MINIMIZE)
+        diffFunction(this@minimize)
-    return problem.optimize()
+        configuration()
+    }
 }

kmath-core/api/kmath-core.api

@@ -1,3 +1,18 @@
+public final class space/kscience/kmath/data/ColumnarDataKt {
+}
+
+public final class space/kscience/kmath/data/XYColumnarData$DefaultImpls {
+	public static fun get (Lspace/kscience/kmath/data/XYColumnarData;Lspace/kscience/kmath/misc/Symbol;)Lspace/kscience/kmath/structures/Buffer;
+}
+
+public final class space/kscience/kmath/data/XYColumnarDataKt {
+	public static synthetic fun asXYData$default (Lspace/kscience/kmath/nd/Structure2D;IIILjava/lang/Object;)Lspace/kscience/kmath/data/XYColumnarData;
+}
+
+public final class space/kscience/kmath/data/XYZColumnarData$DefaultImpls {
+	public static fun get (Lspace/kscience/kmath/data/XYZColumnarData;Lspace/kscience/kmath/misc/Symbol;)Lspace/kscience/kmath/structures/Buffer;
+}
+
 public abstract interface class space/kscience/kmath/domains/Domain {
 	public abstract fun contains (Lspace/kscience/kmath/structures/Buffer;)Z
 	public abstract fun getDimension ()I
@@ -603,15 +618,6 @@ public final class space/kscience/kmath/misc/CumulativeKt {
 	public static final fun cumulativeSumOfLong (Lkotlin/sequences/Sequence;)Lkotlin/sequences/Sequence;
 }
 
-public final class space/kscience/kmath/misc/NDStructureColumn : space/kscience/kmath/structures/Buffer {
-	public fun <init> (Lspace/kscience/kmath/nd/Structure2D;I)V
-	public fun get (I)Ljava/lang/Object;
-	public final fun getColumn ()I
-	public fun getSize ()I
-	public final fun getStructure ()Lspace/kscience/kmath/nd/Structure2D;
-	public fun iterator ()Ljava/util/Iterator;
-}
-
 public final class space/kscience/kmath/misc/StringSymbol : space/kscience/kmath/misc/Symbol {
 	public static final synthetic fun box-impl (Ljava/lang/String;)Lspace/kscience/kmath/misc/StringSymbol;
 	public static fun constructor-impl (Ljava/lang/String;)Ljava/lang/String;
@@ -644,17 +650,6 @@ public final class space/kscience/kmath/misc/SymbolKt {
 public abstract interface annotation class space/kscience/kmath/misc/UnstableKMathAPI : java/lang/annotation/Annotation {
 }
 
-public final class space/kscience/kmath/misc/XYPointSet$DefaultImpls {
-	public static fun get (Lspace/kscience/kmath/misc/XYPointSet;Lspace/kscience/kmath/misc/Symbol;)Lspace/kscience/kmath/structures/Buffer;
-}
-
-public final class space/kscience/kmath/misc/XYPointSetKt {
-}
-
-public final class space/kscience/kmath/misc/XYZPointSet$DefaultImpls {
-	public static fun get (Lspace/kscience/kmath/misc/XYZPointSet;Lspace/kscience/kmath/misc/Symbol;)Lspace/kscience/kmath/structures/Buffer;
-}
-
 public abstract interface class space/kscience/kmath/nd/AlgebraND {
 	public static final field Companion Lspace/kscience/kmath/nd/AlgebraND$Companion;
 	public abstract fun combine (Lspace/kscience/kmath/nd/StructureND;Lspace/kscience/kmath/nd/StructureND;Lkotlin/jvm/functions/Function3;)Lspace/kscience/kmath/nd/StructureND;

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

@@ -0,0 +1,34 @@
+package space.kscience.kmath.data
+
+import space.kscience.kmath.misc.Symbol
+import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.nd.Structure2D
+import space.kscience.kmath.structures.Buffer
+
+/**
+ * A column-based data set with all columns of the same size (not necessary fixed in time).
+ * The column could be retrieved by a [get] operation.
+ */
+@UnstableKMathAPI
+public interface ColumnarData<out T> {
+    public val size: Int
+
+    public operator fun get(symbol: Symbol): Buffer<T>
+}
+
+/**
+ * A zero-copy method to represent a [Structure2D] as a two-column x-y data.
+ * There could more than two columns in the structure.
+ */
+@UnstableKMathAPI
+public fun <T> Structure2D<T>.asColumnarData(mapping: Map<Symbol, Int>): ColumnarData<T> {
+    require(shape[1] >= mapping.maxOf { it.value }) { "Column index out of bounds" }
+    return object : ColumnarData<T> {
+        override val size: Int get() = shape[0]
+        override fun get(symbol: Symbol): Buffer<T> {
+            val index = mapping[symbol] ?: error("No column mapping for symbol $symbol")
+            return columns[index]
+        }
+    }
+}
+

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

@@ -0,0 +1,55 @@
+package space.kscience.kmath.data
+
+import space.kscience.kmath.misc.Symbol
+import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.nd.Structure2D
+import space.kscience.kmath.structures.Buffer
+import kotlin.math.max
+
+/**
+ * The buffer of X values.
+ */
+@UnstableKMathAPI
+public interface XYColumnarData<T, out X : T, out Y : T> : ColumnarData<T> {
+    /**
+     * The buffer of X values
+     */
+    public val x: Buffer<X>
+
+    /**
+     * The buffer of Y values.
+     */
+    public val y: Buffer<Y>
+
+    override fun get(symbol: Symbol): Buffer<T> = when (symbol) {
+        Symbol.x -> x
+        Symbol.y -> y
+        else -> error("A column for symbol $symbol not found")
+    }
+}
+
+@Suppress("FunctionName")
+@UnstableKMathAPI
+public fun <T, X : T, Y : T> XYColumnarData(x: Buffer<X>, y: Buffer<Y>): XYColumnarData<T, X, Y> {
+    require(x.size == y.size) { "Buffer size mismatch. x buffer size is ${x.size}, y buffer size is ${y.size}" }
+    return object : XYColumnarData<T, X, Y> {
+        override val size: Int = x.size
+        override val x: Buffer<X> = x
+        override val y: Buffer<Y> = y
+    }
+}
+
+
+/**
+ * A zero-copy method to represent a [Structure2D] as a two-column x-y data.
+ * There could more than two columns in the structure.
+ */
+@UnstableKMathAPI
+public fun <T> Structure2D<T>.asXYData(xIndex: Int = 0, yIndex: Int = 1): XYColumnarData<T, T, T> {
+    require(shape[1] >= max(xIndex, yIndex)) { "Column index out of bounds" }
+    return object : XYColumnarData<T, T, T> {
+        override val size: Int get() = this@asXYData.shape[0]
+        override val x: Buffer<T> get() = columns[xIndex]
+        override val y: Buffer<T> get() = columns[yIndex]
+    }
+}

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

@@ -0,0 +1,21 @@
+package space.kscience.kmath.data
+
+import space.kscience.kmath.misc.Symbol
+import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.structures.Buffer
+
+/**
+ * A [XYColumnarData] with guaranteed [x], [y] and [z] columns designated by corresponding symbols.
+ * Inherits [XYColumnarData].
+ */
+@UnstableKMathAPI
+public interface XYZColumnarData<T, out X : T, out Y : T, out Z : T> : XYColumnarData<T, X, Y> {
+    public val z: Buffer<Z>
+
+    override fun get(symbol: Symbol): Buffer<T> = when (symbol) {
+        Symbol.x -> x
+        Symbol.y -> y
+        Symbol.z -> z
+        else -> error("A column for symbol $symbol not found")
+    }
+}

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

@@ -1,15 +0,0 @@
-package space.kscience.kmath.misc
-
-import space.kscience.kmath.structures.Buffer
-
-/**
- * A column-based data set with all columns of the same size (not necessary fixed in time).
- * The column could be retrieved by a [get] operation.
- */
-@UnstableKMathAPI
-public interface ColumnarData<out T> {
-    public val size: Int
-
-    public operator fun get(symbol: Symbol): Buffer<T>
-}
-

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

@@ -1,98 +0,0 @@
-package space.kscience.kmath.misc
-
-import space.kscience.kmath.nd.Structure2D
-import space.kscience.kmath.structures.Buffer
-
-/**
- * Pair of associated buffers for X and Y axes values.
- *
- * @param X the type of X values.
- * @param Y the type of Y values.
- */
-public interface XYPointSet<X, Y> {
-    /**
-     * The size of all the involved buffers.
-     */
-    public val size: Int
-
-    /**
-     * The buffer of X values.
-     */
-@UnstableKMathAPI
-public interface XYPointSet<T, X : T, Y : T> : ColumnarData<T> {
-    public val x: Buffer<X>
-
-    /**
-     * The buffer of Y values.
-     */
-    public val y: Buffer<Y>
-
-    override fun get(symbol: Symbol): Buffer<T> = when (symbol) {
-        Symbol.x -> x
-        Symbol.y -> y
-        else -> error("A column for symbol $symbol not found")
-    }
-}
-
-/**
- * Triple of associated buffers for X, Y, and Z axes values.
- *
- * @param X the type of X values.
- * @param Y the type of Y values.
- * @param Z the type of Z values.
- */
-public interface XYZPointSet<X, Y, Z> : XYPointSet<X, Y> {
-    /**
-     * The buffer of Z values.
-     */
-@UnstableKMathAPI
-public interface XYZPointSet<T, X : T, Y : T, Z : T> : XYPointSet<T, X, Y> {
-    public val z: Buffer<Z>
-
-    override fun get(symbol: Symbol): Buffer<T> = when (symbol) {
-        Symbol.x -> x
-        Symbol.y -> y
-        Symbol.z -> z
-        else -> error("A column for symbol $symbol not found")
-    }
-}
-
-internal fun <T : Comparable<T>> insureSorted(points: XYPointSet<T, *>) {
-    for (i in 0 until points.size - 1)
-        require(points.x[i + 1] > points.x[i]) { "Input data is not sorted at index $i" }
-}
-
-public class NDStructureColumn<T>(public val structure: Structure2D<T>, public val column: Int) : Buffer<T> {
-    public override val size: Int
-        get() = structure.rowNum
-
-    init {
-        require(column < structure.colNum) { "Column index is outside of structure column range" }
-    }
-
-    public override operator fun get(index: Int): T = structure[index, column]
-    public override operator fun iterator(): Iterator<T> = sequence { repeat(size) { yield(get(it)) } }.iterator()
-}
-
-@UnstableKMathAPI
-public class BufferXYPointSet<T, X : T, Y : T>(
-    public override val x: Buffer<X>,
-    public override val y: Buffer<Y>,
-) : XYPointSet<T, X, Y> {
-    public override val size: Int get() = x.size
-
-    init {
-        require(x.size == y.size) { "Sizes of x and y buffers should be the same" }
-    }
-}
-
-@UnstableKMathAPI
-public fun <T> Structure2D<T>.asXYPointSet(): XYPointSet<T, T, T> {
-    require(shape[1] == 2) { "Structure second dimension should be of size 2" }
-
-    return object : XYPointSet<T, T, T> {
-        override val size: Int get() = this@asXYPointSet.shape[0]
-        override val x: Buffer<T> get() = NDStructureColumn(this@asXYPointSet, 0)
-        override val y: Buffer<T> get() = NDStructureColumn(this@asXYPointSet, 1)
-    }
-}

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/Interpolator.kt

@@ -1,17 +1,17 @@
 @file:OptIn(UnstableKMathAPI::class)
+
 package space.kscience.kmath.interpolation
 
+import space.kscience.kmath.data.XYColumnarData
 import space.kscience.kmath.functions.PiecewisePolynomial
 import space.kscience.kmath.functions.value
-import space.kscience.kmath.misc.BufferXYPointSet
 import space.kscience.kmath.misc.UnstableKMathAPI
-import space.kscience.kmath.misc.XYPointSet
 import space.kscience.kmath.operations.Ring
 import space.kscience.kmath.structures.Buffer
 import space.kscience.kmath.structures.asBuffer
 
 public fun interface Interpolator<T, X : T, Y : T> {
-    public fun interpolate(points: XYPointSet<T, X, Y>): (X) -> Y
+    public fun interpolate(points: XYColumnarData<T, X, Y>): (X) -> Y
 }
 
 public interface PolynomialInterpolator<T : Comparable<T>> : Interpolator<T, T, T> {
@@ -19,9 +19,9 @@ public interface PolynomialInterpolator<T : Comparable<T>> : Interpolator<T, T,
 
     public fun getDefaultValue(): T = error("Out of bounds")
 
-    public fun interpolatePolynomials(points: XYPointSet<T, T, T>): PiecewisePolynomial<T>
+    public fun interpolatePolynomials(points: XYColumnarData<T, T, T>): PiecewisePolynomial<T>
 
-    override fun interpolate(points: XYPointSet<T, T, T>): (T) -> T = { x ->
+    override fun interpolate(points: XYColumnarData<T, T, T>): (T) -> T = { x ->
         interpolatePolynomials(points).value(algebra, x) ?: getDefaultValue()
     }
 }
@@ -31,20 +31,20 @@ public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolatePolynomials(
     x: Buffer<T>,
     y: Buffer<T>,
 ): PiecewisePolynomial<T> {
-    val pointSet = BufferXYPointSet(x, y)
+    val pointSet = XYColumnarData(x, y)
     return interpolatePolynomials(pointSet)
 }
 
 public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolatePolynomials(
     data: Map<T, T>,
 ): PiecewisePolynomial<T> {
-    val pointSet = BufferXYPointSet(data.keys.toList().asBuffer(), data.values.toList().asBuffer())
+    val pointSet = XYColumnarData(data.keys.toList().asBuffer(), data.values.toList().asBuffer())
     return interpolatePolynomials(pointSet)
 }
 
 public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolatePolynomials(
     data: List<Pair<T, T>>,
 ): PiecewisePolynomial<T> {
-    val pointSet = BufferXYPointSet(data.map { it.first }.asBuffer(), data.map { it.second }.asBuffer())
+    val pointSet = XYColumnarData(data.map { it.first }.asBuffer(), data.map { it.second }.asBuffer())
     return interpolatePolynomials(pointSet)
 }

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/LinearInterpolator.kt

@@ -1,15 +1,15 @@
 package space.kscience.kmath.interpolation
 
+import space.kscience.kmath.data.XYColumnarData
 import space.kscience.kmath.functions.OrderedPiecewisePolynomial
 import space.kscience.kmath.functions.PiecewisePolynomial
 import space.kscience.kmath.functions.Polynomial
 import space.kscience.kmath.misc.UnstableKMathAPI
-import space.kscience.kmath.misc.XYPointSet
 import space.kscience.kmath.operations.Field
 import space.kscience.kmath.operations.invoke
 
 @OptIn(UnstableKMathAPI::class)
-internal fun <T : Comparable<T>> insureSorted(points: XYPointSet<*, T, *>) {
+internal fun <T : Comparable<T>> insureSorted(points: XYColumnarData<*, T, *>) {
     for (i in 0 until points.size - 1)
         require(points.x[i + 1] > points.x[i]) { "Input data is not sorted at index $i" }
 }
@@ -19,7 +19,7 @@ internal fun <T : Comparable<T>> insureSorted(points: XYPointSet<*, T, *>) {
  */
 public class LinearInterpolator<T : Comparable<T>>(public override val algebra: Field<T>) : PolynomialInterpolator<T> {
     @OptIn(UnstableKMathAPI::class)
-    public override fun interpolatePolynomials(points: XYPointSet<T, T, T>): PiecewisePolynomial<T> = algebra {
+    public override fun interpolatePolynomials(points: XYColumnarData<T, T, T>): PiecewisePolynomial<T> = algebra {
         require(points.size > 0) { "Point array should not be empty" }
         insureSorted(points)
 

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/SplineInterpolator.kt

@@ -1,10 +1,10 @@
 package space.kscience.kmath.interpolation
 
+import space.kscience.kmath.data.XYColumnarData
 import space.kscience.kmath.functions.OrderedPiecewisePolynomial
 import space.kscience.kmath.functions.PiecewisePolynomial
 import space.kscience.kmath.functions.Polynomial
 import space.kscience.kmath.misc.UnstableKMathAPI
-import space.kscience.kmath.misc.XYPointSet
 import space.kscience.kmath.operations.Field
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.structures.MutableBufferFactory
@@ -23,7 +23,7 @@ public class SplineInterpolator<T : Comparable<T>>(
     //TODO possibly optimize zeroed buffers
 
     @OptIn(UnstableKMathAPI::class)
-    public override fun interpolatePolynomials(points: XYPointSet<T, T, T>): PiecewisePolynomial<T> = algebra {
+    public override fun interpolatePolynomials(points: XYColumnarData<T, T, T>): PiecewisePolynomial<T> = algebra {
         require(points.size >= 3) { "Can't use spline interpolator with less than 3 points" }
         insureSorted(points)
         // Number of intervals.  The number of data points is n + 1.

kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/DifferentiableMstExpression.kt

@@ -18,8 +18,10 @@ import space.kscience.kmath.operations.NumericAlgebra
  * @param A the [NumericAlgebra] of [T].
  * @property expr the underlying [MstExpression].
  */
-public inline class DifferentiableMstExpression<T, A>(public val expr: MstExpression<T, A>) :
+public inline class DifferentiableMstExpression<T: Number, A>(
-    DifferentiableExpression<T, MstExpression<T, A>> where A : NumericAlgebra<T>, T : Number {
+    public val expr: MstExpression<T, A>,
+) : DifferentiableExpression<T, MstExpression<T, A>> where A : NumericAlgebra<T> {
+
     public constructor(algebra: A, mst: MST) : this(MstExpression(algebra, mst))
 
     /**

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

@@ -1,17 +0,0 @@
-package space.kscience.kmath.optimization
-
-import space.kscience.kmath.expressions.DifferentiableExpression
-import space.kscience.kmath.misc.StringSymbol
-import space.kscience.kmath.misc.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

@@ -4,45 +4,31 @@ import space.kscience.kmath.expressions.AutoDiffProcessor
 import space.kscience.kmath.expressions.DifferentiableExpression
 import space.kscience.kmath.expressions.Expression
 import space.kscience.kmath.expressions.ExpressionAlgebra
-import space.kscience.kmath.misc.StringSymbol
 import space.kscience.kmath.misc.Symbol
 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
+ * A likelihood function optimization problem with provided derivatives
  */
-public interface FunctionOptimization<T: Any>: Optimization<T>, DataFit<T> {
+public interface FunctionOptimization<T : Any> : Optimization<T> {
+    /**
+     * 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 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>>)
+    public fun diffFunction(expression: DifferentiableExpression<T, Expression<T>>)
 
-    override fun modelAndData(
+    public companion object {
-        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
          */
@@ -70,46 +56,22 @@ public interface FunctionOptimization<T: Any>: Optimization<T>, DataFit<T> {
                 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]
+ * Define a chi-squared-based objective function
  */
-public fun <T : Any, F : FunctionOptimization<T>> Expression<T>.optimizeWith(
+public fun <T: Any, I : Any, A> FunctionOptimization<T>.chiSquared(
-    factory: OptimizationProblemFactory<T, F>,
+    autoDiff: AutoDiffProcessor<T, I, A, Expression<T>>,
-    vararg symbols: Symbol,
+    x: Buffer<T>,
-    configuration: F.() -> Unit,
+    y: Buffer<T>,
-): OptimizationResult<T> {
+    yErr: Buffer<T>,
-    require(symbols.isNotEmpty()) { "Must provide a list of symbols for optimization" }
+    model: A.(I) -> I,
-    val problem = factory(symbols.toList(), configuration)
+) where A : ExtendedField<I>, A : ExpressionAlgebra<T, I> {
-    problem.expression(this)
+    val chiSquared = FunctionOptimization.chiSquared(autoDiff, x, y, yErr, model)
-    return problem.optimize()
+    diffFunction(chiSquared)
+    maximize = false
 }
 
 /**
@@ -122,6 +84,6 @@ public fun <T : Any, F : FunctionOptimization<T>> DifferentiableExpression<T, Ex
 ): OptimizationResult<T> {
     require(symbols.isNotEmpty()) { "Must provide a list of symbols for optimization" }
     val problem = factory(symbols.toList(), configuration)
-    problem.diffExpression(this)
+    problem.diffFunction(this)
     return problem.optimize()
 }

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

@@ -0,0 +1,69 @@
+package space.kscience.kmath.optimization
+
+import space.kscience.kmath.expressions.Expression
+import space.kscience.kmath.misc.Symbol
+import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.indices
+import kotlin.math.pow
+
+/**
+ * A likelihood function optimization problem
+ */
+public interface NoDerivFunctionOptimization<T : Any> : Optimization<T> {
+    /**
+     * 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 an objective function expression
+     */
+    public fun function(expression: Expression<T>)
+
+    public companion object {
+        /**
+         * 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 = Symbol.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 : NoDerivFunctionOptimization<T>> Expression<T>.noDerivOptimizeWith(
+    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/XYFit.kt

@@ -0,0 +1,40 @@
+package space.kscience.kmath.optimization
+
+import space.kscience.kmath.data.ColumnarData
+import space.kscience.kmath.expressions.AutoDiffProcessor
+import space.kscience.kmath.expressions.DifferentiableExpression
+import space.kscience.kmath.expressions.Expression
+import space.kscience.kmath.expressions.ExpressionAlgebra
+import space.kscience.kmath.misc.Symbol
+import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.operations.ExtendedField
+import space.kscience.kmath.operations.Field
+
+@UnstableKMathAPI
+public interface XYFit<T : Any> : Optimization<T> {
+
+    public val algebra: Field<T>
+
+    /**
+     * Set X-Y data for this fit optionally including x and y errors
+     */
+    public fun data(
+        dataSet: ColumnarData<T>,
+        xSymbol: Symbol,
+        ySymbol: Symbol,
+        xErrSymbol: Symbol? = null,
+        yErrSymbol: Symbol? = null,
+    )
+
+    public fun model(model: (T) -> DifferentiableExpression<T, *>)
+
+    /**
+     * Set the differentiable model for this fit
+     */
+    public fun <I : Any, A> model(
+        autoDiff: AutoDiffProcessor<T, I, A, Expression<T>>,
+        modelFunction: A.(I) -> I,
+    ): Unit where A : ExtendedField<I>, A : ExpressionAlgebra<T, I> = model { arg ->
+        autoDiff.process { modelFunction(const(arg)) }
+    }
+}

settings.gradle.kts

@@ -4,12 +4,11 @@ pluginManagement {
         mavenLocal()
         gradlePluginPortal()
         jcenter()
-        maven("https://dl.bintray.com/kotlin/kotlin-eap")
         maven("https://dl.bintray.com/kotlin/kotlinx")
     }
 
-    val toolsVersion = "0.9.1"
+    val toolsVersion = "0.9.3"
-    val kotlinVersion = "1.4.31"
+    val kotlinVersion = "1.4.32"
 
     plugins {
         id("kotlinx.benchmark") version "0.2.0-dev-20"