kmath-commons/src/main/kotlin/kscience/kmath/commons/optimization/CMFit.kt

@@ -1,95 +0,0 @@
-package kscience.kmath.commons.optimization
-
-import kscience.kmath.commons.expressions.DerivativeStructureExpression
-import kscience.kmath.commons.expressions.DerivativeStructureField
-import kscience.kmath.expressions.DifferentiableExpression
-import kscience.kmath.expressions.Expression
-import kscience.kmath.expressions.StringSymbol
-import kscience.kmath.expressions.Symbol
-import kscience.kmath.structures.Buffer
-import kscience.kmath.structures.indices
-import org.apache.commons.math3.analysis.differentiation.DerivativeStructure
-import org.apache.commons.math3.optim.nonlinear.scalar.GoalType
-import kotlin.math.pow
-
-
-public object CMFit {
-
-    /**
-     * Generate a chi squared expression from given x-y-sigma model represented by an expression. Does not provide derivatives
-     * TODO move to prob/stat
-     */
-    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)
-            }
-        }
-    }
-
-    /**
-     * Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
-     */
-    public fun chiSquared(
-        x: Buffer<Double>,
-        y: Buffer<Double>,
-        yErr: Buffer<Double>,
-        model: DerivativeStructureField.(x: DerivativeStructure) -> DerivativeStructure,
-    ): DerivativeStructureExpression {
-        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 DerivativeStructureExpression {
-            var sum = zero
-            x.indices.forEach {
-                val xValue = x[it]
-                val yValue = y[it]
-                val yErrValue = yErr[it]
-                val modelValue = model(const(xValue))
-                sum += ((yValue - modelValue) / yErrValue).pow(2)
-            }
-            sum
-        }
-    }
-}
-
-/**
- * Optimize expression without derivatives
- */
-public fun Expression<Double>.optimize(
-    vararg symbols: Symbol,
-    configuration: CMOptimizationProblem.() -> Unit,
-): OptimizationResult<Double> = optimizeWith(CMOptimizationProblem, symbols = symbols, configuration)
-
-
-/**
- * Optimize differentiable expression
- */
-public fun DifferentiableExpression<Double>.optimize(
-    vararg symbols: Symbol,
-    configuration: CMOptimizationProblem.() -> Unit,
-): OptimizationResult<Double> = optimizeWith(CMOptimizationProblem, symbols = symbols, configuration)
-
-public fun DifferentiableExpression<Double>.minimize(
-    vararg startPoint: Pair<Symbol, Double>,
-    configuration: CMOptimizationProblem.() -> Unit = {},
-): OptimizationResult<Double> {
-    require(startPoint.isNotEmpty()) { "Must provide a list of symbols for optimization" }
-    val problem = CMOptimizationProblem(startPoint.map { it.first }).apply(configuration)
-    problem.diffExpression(this)
-    problem.initialGuess(startPoint.toMap())
-    problem.goal(GoalType.MINIMIZE)
-    return problem.optimize()
-}

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

@@ -0,0 +1,68 @@
+package kscience.kmath.commons.optimization
+
+import kscience.kmath.commons.expressions.DerivativeStructureField
+import kscience.kmath.expressions.DifferentiableExpression
+import kscience.kmath.expressions.Expression
+import kscience.kmath.expressions.Symbol
+import kscience.kmath.prob.Fitting
+import kscience.kmath.structures.Buffer
+import kscience.kmath.structures.asBuffer
+import org.apache.commons.math3.analysis.differentiation.DerivativeStructure
+import org.apache.commons.math3.optim.nonlinear.scalar.GoalType
+
+
+/**
+ * Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
+ */
+public fun Fitting.chiSquared(
+    x: Buffer<Double>,
+    y: Buffer<Double>,
+    yErr: Buffer<Double>,
+    model: DerivativeStructureField.(x: DerivativeStructure) -> DerivativeStructure,
+): DifferentiableExpression<Double> = chiSquared(DerivativeStructureField, x, y, yErr, model)
+
+/**
+ * Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
+ */
+public fun Fitting.chiSquared(
+    x: Iterable<Double>,
+    y: Iterable<Double>,
+    yErr: Iterable<Double>,
+    model: DerivativeStructureField.(x: DerivativeStructure) -> DerivativeStructure,
+): DifferentiableExpression<Double> = chiSquared(
+    DerivativeStructureField,
+    x.toList().asBuffer(),
+    y.toList().asBuffer(),
+    yErr.toList().asBuffer(),
+    model
+)
+
+
+/**
+ * Optimize expression without derivatives
+ */
+public fun Expression<Double>.optimize(
+    vararg symbols: Symbol,
+    configuration: CMOptimizationProblem.() -> Unit,
+): OptimizationResult<Double> = optimizeWith(CMOptimizationProblem, symbols = symbols, configuration)
+
+
+/**
+ * Optimize differentiable expression
+ */
+public fun DifferentiableExpression<Double>.optimize(
+    vararg symbols: Symbol,
+    configuration: CMOptimizationProblem.() -> Unit,
+): OptimizationResult<Double> = optimizeWith(CMOptimizationProblem, symbols = symbols, configuration)
+
+public fun DifferentiableExpression<Double>.minimize(
+    vararg startPoint: Pair<Symbol, Double>,
+    configuration: CMOptimizationProblem.() -> Unit = {},
+): OptimizationResult<Double> {
+    require(startPoint.isNotEmpty()) { "Must provide a list of symbols for optimization" }
+    val problem = CMOptimizationProblem(startPoint.map { it.first }).apply(configuration)
+    problem.diffExpression(this)
+    problem.initialGuess(startPoint.toMap())
+    problem.goal(GoalType.MINIMIZE)
+    return problem.optimize()
+}

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

@@ -3,6 +3,7 @@ package kscience.kmath.commons.optimization
 import kscience.kmath.commons.expressions.DerivativeStructureExpression
 import kscience.kmath.expressions.symbol
 import kscience.kmath.prob.Distribution
+import kscience.kmath.prob.Fitting
 import kscience.kmath.prob.RandomGenerator
 import kscience.kmath.prob.normal
 import kscience.kmath.structures.asBuffer
@@ -39,7 +40,7 @@ internal class OptimizeTest {
     }
 
     @Test
-    fun testFit() {
+    fun testCmFit() {
         val a by symbol
         val b by symbol
         val c by symbol
@@ -52,15 +53,14 @@ internal class OptimizeTest {
             it.pow(2) + it + 1 + chain.nextDouble()
         }
         val yErr = x.map { sigma }
-        with(CMFit) {
+        val chi2 = Fitting.chiSquared(x.asBuffer(), y.asBuffer(), yErr.asBuffer()) { x ->
-            val chi2 = chiSquared(x.asBuffer(), y.asBuffer(), yErr.asBuffer()) { x ->
+            val cWithDefault = bindOrNull(c) ?: one
-                val cWithDefault = bindOrNull(c)?: one
+            bind(a) * x.pow(2) + bind(b) * x + cWithDefault
-                bind(a) * x.pow(2) + bind(b) * x + cWithDefault
-            }
-
-            val result = chi2.minimize(a to 1.5, b to 0.9, c to 1.0)
-            println(result)
-            println("Chi2/dof = ${result.value / (x.size - 3)}")
         }
+
+        val result = chi2.minimize(a to 1.5, b to 0.9, c to 1.0)
+        println(result)
+        println("Chi2/dof = ${result.value / (x.size - 3)}")
     }
+
 }

kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/Fitting.kt

@@ -1,13 +1,12 @@
 package kscience.kmath.prob
 
-import kscience.kmath.expressions.AutoDiffProcessor
+import kscience.kmath.expressions.*
-import kscience.kmath.expressions.DifferentiableExpression
-import kscience.kmath.expressions.ExpressionAlgebra
 import kscience.kmath.operations.ExtendedField
 import kscience.kmath.structures.Buffer
 import kscience.kmath.structures.indices
+import kotlin.math.pow
 
-public object Fit {
+public object Fitting {
 
     /**
      * Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
@@ -33,4 +32,28 @@ public object Fit {
             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)
+            }
+        }
+    }
 }