[WIP] optimization refactoring

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

@@ -45,6 +45,27 @@ public fun <T, X : T, Y : T> XYColumnarData(x: Buffer<X>, y: Buffer<Y>): XYColum
     }
 }
 
+/**
+ * Represent a [ColumnarData] as an [XYColumnarData]. The presence or respective columns is checked on creation.
+ */
+@UnstableKMathAPI
+public fun <T> ColumnarData<T>.asXYData(
+    xSymbol: Symbol,
+    ySymbol: Symbol,
+): XYColumnarData<T, T, T> = object : XYColumnarData<T, T, T> {
+    init {
+        requireNotNull(this@asXYData[xSymbol]){"The column with name $xSymbol is not present in $this"}
+        requireNotNull(this@asXYData[ySymbol]){"The column with name $ySymbol is not present in $this"}
+    }
+    override val size: Int get() = this@asXYData.size
+    override val x: Buffer<T> get() = this@asXYData[xSymbol]!!
+    override val y: Buffer<T> get() = this@asXYData[ySymbol]!!
+    override fun get(symbol: Symbol): Buffer<T>? = when (symbol) {
+        Symbol.x -> x
+        Symbol.y -> y
+        else -> this@asXYData.get(symbol)
+    }
+}
 
 /**
  * A zero-copy method to represent a [Structure2D] as a two-column x-y data.

kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/Symbol.kt

@@ -19,9 +19,12 @@ public interface Symbol : MST {
     public val identity: String
 
     public companion object {
-        public val x: StringSymbol = StringSymbol("x")
+        public val x: Symbol = Symbol("x")
-        public val y: StringSymbol = StringSymbol("y")
+        public val xError: Symbol = Symbol("x.error")
-        public val z: StringSymbol = StringSymbol("z")
+        public val y: Symbol = Symbol("y")
+        public val yError: Symbol = Symbol("y.error")
+        public val z: Symbol = Symbol("z")
+        public val zError: Symbol = Symbol("z.error")
     }
 }
 
@@ -29,10 +32,15 @@ public interface Symbol : MST {
  * A [Symbol] with a [String] identity
  */
 @JvmInline
-public value class StringSymbol(override val identity: String) : Symbol {
+internal value class StringSymbol(override val identity: String) : Symbol {
     override fun toString(): String = identity
 }
 
+/**
+ * Create s Symbols with a string identity
+ */
+public fun Symbol(identity: String): Symbol = StringSymbol(identity)
+
 /**
  * A delegate to create a symbol with a string identity in this scope
  */

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

@@ -5,10 +5,7 @@
 
 package space.kscience.kmath.optimization
 
-import space.kscience.kmath.expressions.AutoDiffProcessor
+import space.kscience.kmath.expressions.*
-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.operations.ExtendedField
 import space.kscience.kmath.structures.Buffer
@@ -67,3 +64,15 @@ public fun <T> FunctionOptimization<T>.withFeatures(
     expression,
 )
 
+/**
+ * Optimize differentiable expression using specific [optimizer] form given [startingPoint]
+ */
+public suspend fun <T : Any> DifferentiableExpression<T, Expression<T>>.optimizeWith(
+    optimizer: Optimizer<FunctionOptimization<T>>,
+    startingPoint: Map<Symbol, T>,
+    vararg features: OptimizationFeature,
+): FunctionOptimization<T> {
+    val problem = FunctionOptimization<T>(FeatureSet.of(OptimizationStartPoint(startingPoint), *features), this)
+    return optimizer.process(problem)
+}
+

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

@@ -35,6 +35,7 @@ public class OptimizationLog(private val loggable: Loggable) : Loggable by logga
 }
 
 public class OptimizationParameters(public val symbols: List<Symbol>): OptimizationFeature{
+    public constructor(vararg symbols: Symbol) : this(listOf(*symbols))
     override fun toString(): String = "Parameters($symbols)"
 }
 

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

@@ -1,72 +0,0 @@
-/*
- * 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.optimization
-
-import space.kscience.kmath.data.ColumnarData
-import space.kscience.kmath.expressions.*
-import space.kscience.kmath.misc.UnstableKMathAPI
-import space.kscience.kmath.operations.ExtendedField
-import space.kscience.kmath.operations.Field
-
-@UnstableKMathAPI
-public interface XYFit<T> : OptimizationProblem {
-
-    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)) }
-    }
-}
-
-//
-///**
-// * 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 [Optimizer]
- */
-public suspend fun <T : Any, F : FunctionOptimization<T>> DifferentiableExpression<T, Expression<T>>.optimizeWith(
-    optimizer: Optimizer<F>,
-    startingPoint: Map<Symbol,T>,
-    vararg features: OptimizationFeature
-): OptimizationProblem {
-//    require(startingPoint.isNotEmpty()) { "Must provide a list of symbols for optimization" }
-//    val problem = factory(symbols.toList(), configuration)
-//    problem.function(this)
-//    return problem.optimize()
-    val problem = FunctionOptimization<T>()
-}

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

@@ -0,0 +1,92 @@
+/*
+ * 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.
+ */
+@file:OptIn(UnstableKMathAPI::class)
+
+package space.kscience.kmath.optimization
+
+import space.kscience.kmath.data.XYColumnarData
+import space.kscience.kmath.expressions.DifferentiableExpression
+import space.kscience.kmath.expressions.Expression
+import space.kscience.kmath.expressions.Symbol
+import space.kscience.kmath.misc.FeatureSet
+import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.operations.Field
+import space.kscience.kmath.operations.invoke
+
+public fun interface PointToCurveDistance<T> {
+    public fun distance(problem: XYOptimization<T>, index: Int): DifferentiableExpression<T, Expression<T>>
+
+    public companion object {
+        public fun <T> byY(
+            algebra: Field<T>,
+        ): PointToCurveDistance<T> = PointToCurveDistance { problem, index ->
+            algebra {
+                val x = problem.data.x[index]
+                val y = problem.data.y[index]
+                
+                val model = problem.model(args + (Symbol.x to x))
+                model - y
+            }
+        }
+
+
+//        val default = PointToCurveDistance<Double>{args, data, index ->
+//
+//        }
+    }
+}
+
+
+public class XYOptimization<T>(
+    override val features: FeatureSet<OptimizationFeature>,
+    public val data: XYColumnarData<T, T, T>,
+    public val model: DifferentiableExpression<T, Expression<T>>,
+) : OptimizationProblem
+
+//
+//@UnstableKMathAPI
+//public interface XYFit<T> : OptimizationProblem {
+//
+//    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)) }
+//    }
+//}
+
+//
+///**
+// * 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
+//}

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

@@ -15,7 +15,7 @@ import space.kscience.kmath.operations.Field
 import space.kscience.kmath.optimization.OptimizationFeature
 import space.kscience.kmath.optimization.OptimizationProblemFactory
 import space.kscience.kmath.optimization.OptimizationResult
-import space.kscience.kmath.optimization.XYFit
+import space.kscience.kmath.optimization.XYOptimization
 import space.kscience.kmath.structures.DoubleBuffer
 import space.kscience.kmath.structures.DoubleL2Norm
 import kotlin.math.pow
@@ -28,7 +28,7 @@ public class QowFit(
     override val symbols: List<Symbol>,
     private val space: LinearSpace<Double, DoubleField>,
     private val solver: LinearSolver<Double>,
-) : XYFit<Double>, SymbolIndexer {
+) : XYOptimization<Double>, SymbolIndexer {
 
     private var logger: FitLogger? = null