Fitting refactor
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package kscience.kmath.commons.optimization
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import kscience.kmath.commons.expressions.DerivativeStructureExpression
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import kscience.kmath.commons.expressions.DerivativeStructureField
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import kscience.kmath.expressions.DifferentiableExpression
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import kscience.kmath.expressions.Expression
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import kscience.kmath.expressions.StringSymbol
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import kscience.kmath.expressions.Symbol
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import kscience.kmath.structures.Buffer
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import kscience.kmath.structures.indices
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import org.apache.commons.math3.analysis.differentiation.DerivativeStructure
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import org.apache.commons.math3.optim.nonlinear.scalar.GoalType
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import kotlin.math.pow
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public object CMFit {
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/**
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* Generate a chi squared expression from given x-y-sigma model represented by an expression. Does not provide derivatives
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* TODO move to prob/stat
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*/
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public fun chiSquared(
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x: Buffer<Double>,
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y: Buffer<Double>,
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yErr: Buffer<Double>,
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model: Expression<Double>,
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xSymbol: Symbol = StringSymbol("x"),
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): Expression<Double> {
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require(x.size == y.size) { "X and y buffers should be of the same size" }
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require(y.size == yErr.size) { "Y and yErr buffer should of the same size" }
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return Expression { arguments ->
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x.indices.sumByDouble {
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val xValue = x[it]
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val yValue = y[it]
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val yErrValue = yErr[it]
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val modifiedArgs = arguments + (xSymbol to xValue)
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val modelValue = model(modifiedArgs)
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((yValue - modelValue) / yErrValue).pow(2)
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}
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}
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}
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/**
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* Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
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*/
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public fun chiSquared(
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x: Buffer<Double>,
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y: Buffer<Double>,
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yErr: Buffer<Double>,
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model: DerivativeStructureField.(x: DerivativeStructure) -> DerivativeStructure,
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): DerivativeStructureExpression {
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require(x.size == y.size) { "X and y buffers should be of the same size" }
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require(y.size == yErr.size) { "Y and yErr buffer should of the same size" }
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return DerivativeStructureExpression {
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var sum = zero
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x.indices.forEach {
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val xValue = x[it]
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val yValue = y[it]
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val yErrValue = yErr[it]
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val modelValue = model(const(xValue))
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sum += ((yValue - modelValue) / yErrValue).pow(2)
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}
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sum
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}
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}
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}
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/**
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* Optimize expression without derivatives
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*/
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public fun Expression<Double>.optimize(
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vararg symbols: Symbol,
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configuration: CMOptimizationProblem.() -> Unit,
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): OptimizationResult<Double> = optimizeWith(CMOptimizationProblem, symbols = symbols, configuration)
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/**
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* Optimize differentiable expression
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*/
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public fun DifferentiableExpression<Double>.optimize(
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vararg symbols: Symbol,
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configuration: CMOptimizationProblem.() -> Unit,
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): OptimizationResult<Double> = optimizeWith(CMOptimizationProblem, symbols = symbols, configuration)
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public fun DifferentiableExpression<Double>.minimize(
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vararg startPoint: Pair<Symbol, Double>,
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configuration: CMOptimizationProblem.() -> Unit = {},
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): OptimizationResult<Double> {
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require(startPoint.isNotEmpty()) { "Must provide a list of symbols for optimization" }
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val problem = CMOptimizationProblem(startPoint.map { it.first }).apply(configuration)
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problem.diffExpression(this)
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problem.initialGuess(startPoint.toMap())
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problem.goal(GoalType.MINIMIZE)
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return problem.optimize()
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}
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@ -0,0 +1,68 @@
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package kscience.kmath.commons.optimization
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import kscience.kmath.commons.expressions.DerivativeStructureField
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import kscience.kmath.expressions.DifferentiableExpression
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import kscience.kmath.expressions.Expression
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import kscience.kmath.expressions.Symbol
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import kscience.kmath.prob.Fitting
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import kscience.kmath.structures.Buffer
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import kscience.kmath.structures.asBuffer
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import org.apache.commons.math3.analysis.differentiation.DerivativeStructure
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import org.apache.commons.math3.optim.nonlinear.scalar.GoalType
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/**
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* Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
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*/
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public fun Fitting.chiSquared(
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x: Buffer<Double>,
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y: Buffer<Double>,
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yErr: Buffer<Double>,
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model: DerivativeStructureField.(x: DerivativeStructure) -> DerivativeStructure,
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): DifferentiableExpression<Double> = chiSquared(DerivativeStructureField, x, y, yErr, model)
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/**
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* Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
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*/
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public fun Fitting.chiSquared(
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x: Iterable<Double>,
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y: Iterable<Double>,
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yErr: Iterable<Double>,
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model: DerivativeStructureField.(x: DerivativeStructure) -> DerivativeStructure,
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): DifferentiableExpression<Double> = chiSquared(
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DerivativeStructureField,
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x.toList().asBuffer(),
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y.toList().asBuffer(),
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yErr.toList().asBuffer(),
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model
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)
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/**
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* Optimize expression without derivatives
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*/
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public fun Expression<Double>.optimize(
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vararg symbols: Symbol,
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configuration: CMOptimizationProblem.() -> Unit,
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): OptimizationResult<Double> = optimizeWith(CMOptimizationProblem, symbols = symbols, configuration)
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/**
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* Optimize differentiable expression
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*/
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public fun DifferentiableExpression<Double>.optimize(
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vararg symbols: Symbol,
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configuration: CMOptimizationProblem.() -> Unit,
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): OptimizationResult<Double> = optimizeWith(CMOptimizationProblem, symbols = symbols, configuration)
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public fun DifferentiableExpression<Double>.minimize(
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vararg startPoint: Pair<Symbol, Double>,
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configuration: CMOptimizationProblem.() -> Unit = {},
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): OptimizationResult<Double> {
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require(startPoint.isNotEmpty()) { "Must provide a list of symbols for optimization" }
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val problem = CMOptimizationProblem(startPoint.map { it.first }).apply(configuration)
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problem.diffExpression(this)
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problem.initialGuess(startPoint.toMap())
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problem.goal(GoalType.MINIMIZE)
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return problem.optimize()
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}
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@ -3,6 +3,7 @@ package kscience.kmath.commons.optimization
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import kscience.kmath.commons.expressions.DerivativeStructureExpression
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import kscience.kmath.expressions.symbol
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import kscience.kmath.prob.Distribution
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import kscience.kmath.prob.Fitting
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import kscience.kmath.prob.RandomGenerator
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import kscience.kmath.prob.normal
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import kscience.kmath.structures.asBuffer
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@ -39,7 +40,7 @@ internal class OptimizeTest {
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}
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@Test
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fun testFit() {
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fun testCmFit() {
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val a by symbol
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val b by symbol
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val c by symbol
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@ -52,9 +53,8 @@ internal class OptimizeTest {
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it.pow(2) + it + 1 + chain.nextDouble()
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}
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val yErr = x.map { sigma }
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with(CMFit) {
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val chi2 = chiSquared(x.asBuffer(), y.asBuffer(), yErr.asBuffer()) { x ->
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val cWithDefault = bindOrNull(c)?: one
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val chi2 = Fitting.chiSquared(x.asBuffer(), y.asBuffer(), yErr.asBuffer()) { x ->
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val cWithDefault = bindOrNull(c) ?: one
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bind(a) * x.pow(2) + bind(b) * x + cWithDefault
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}
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@ -62,5 +62,5 @@ internal class OptimizeTest {
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println(result)
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println("Chi2/dof = ${result.value / (x.size - 3)}")
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}
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}
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}
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package kscience.kmath.prob
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import kscience.kmath.expressions.AutoDiffProcessor
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import kscience.kmath.expressions.DifferentiableExpression
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import kscience.kmath.expressions.ExpressionAlgebra
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import kscience.kmath.expressions.*
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import kscience.kmath.operations.ExtendedField
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import kscience.kmath.structures.Buffer
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import kscience.kmath.structures.indices
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import kotlin.math.pow
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public object Fit {
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public object Fitting {
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/**
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* Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
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@ -33,4 +32,28 @@ public object Fit {
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sum
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}
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}
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/**
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* Generate a chi squared expression from given x-y-sigma model represented by an expression. Does not provide derivatives
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*/
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public fun chiSquared(
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x: Buffer<Double>,
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y: Buffer<Double>,
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yErr: Buffer<Double>,
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model: Expression<Double>,
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xSymbol: Symbol = StringSymbol("x"),
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): Expression<Double> {
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require(x.size == y.size) { "X and y buffers should be of the same size" }
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require(y.size == yErr.size) { "Y and yErr buffer should of the same size" }
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return Expression { arguments ->
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x.indices.sumByDouble {
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val xValue = x[it]
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val yValue = y[it]
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val yErrValue = yErr[it]
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val modifiedArgs = arguments + (xSymbol to xValue)
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val modelValue = model(modifiedArgs)
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((yValue - modelValue) / yErrValue).pow(2)
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}
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}
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}
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}
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