A minor change to XYfit arguments
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@ -30,7 +30,7 @@ public interface PointToCurveDistance : OptimizationFeature {
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return object : DifferentiableExpression<Double> {
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return object : DifferentiableExpression<Double> {
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override fun derivativeOrNull(
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override fun derivativeOrNull(
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symbols: List<Symbol>
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symbols: List<Symbol>,
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): Expression<Double>? = problem.model.derivativeOrNull(symbols)?.let { derivExpression ->
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): Expression<Double>? = problem.model.derivativeOrNull(symbols)?.let { derivExpression ->
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Expression { arguments ->
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Expression { arguments ->
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derivExpression.invoke(arguments + (Symbol.x to x))
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derivExpression.invoke(arguments + (Symbol.x to x))
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@ -93,24 +93,15 @@ public fun XYFit.withFeature(vararg features: OptimizationFeature): XYFit {
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return XYFit(data, model, this.features.with(*features), pointToCurveDistance, pointWeight)
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return XYFit(data, model, this.features.with(*features), pointToCurveDistance, pointWeight)
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}
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}
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/**
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public suspend fun XYColumnarData<Double, Double, Double>.fitWith(
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* Fit given dta with
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*/
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public suspend fun <I : Any, A> XYColumnarData<Double, Double, Double>.fitWith(
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optimizer: Optimizer<Double, XYFit>,
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optimizer: Optimizer<Double, XYFit>,
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processor: AutoDiffProcessor<Double, I, A>,
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modelExpression: DifferentiableExpression<Double>,
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startingPoint: Map<Symbol, Double>,
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startingPoint: Map<Symbol, Double>,
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vararg features: OptimizationFeature = emptyArray(),
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vararg features: OptimizationFeature = emptyArray(),
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xSymbol: Symbol = Symbol.x,
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xSymbol: Symbol = Symbol.x,
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pointToCurveDistance: PointToCurveDistance = PointToCurveDistance.byY,
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pointToCurveDistance: PointToCurveDistance = PointToCurveDistance.byY,
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pointWeight: PointWeight = PointWeight.byYSigma,
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pointWeight: PointWeight = PointWeight.byYSigma,
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model: A.(I) -> I
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): XYFit {
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): XYFit where A : ExtendedField<I>, A : ExpressionAlgebra<Double, I> {
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val modelExpression = processor.differentiate {
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val x = bindSymbol(xSymbol)
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model(x)
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}
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var actualFeatures = FeatureSet.of(*features, OptimizationStartPoint(startingPoint))
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var actualFeatures = FeatureSet.of(*features, OptimizationStartPoint(startingPoint))
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if (actualFeatures.getFeature<OptimizationLog>() == null) {
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if (actualFeatures.getFeature<OptimizationLog>() == null) {
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@ -127,20 +118,50 @@ public suspend fun <I : Any, A> XYColumnarData<Double, Double, Double>.fitWith(
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return optimizer.optimize(problem)
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return optimizer.optimize(problem)
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}
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}
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/**
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* Fit given data with a model provided as an expression
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*/
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public suspend fun <I : Any, A> XYColumnarData<Double, Double, Double>.fitWith(
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optimizer: Optimizer<Double, XYFit>,
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processor: AutoDiffProcessor<Double, I, A>,
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startingPoint: Map<Symbol, Double>,
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vararg features: OptimizationFeature = emptyArray(),
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xSymbol: Symbol = Symbol.x,
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pointToCurveDistance: PointToCurveDistance = PointToCurveDistance.byY,
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pointWeight: PointWeight = PointWeight.byYSigma,
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model: A.(I) -> I,
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): XYFit where A : ExtendedField<I>, A : ExpressionAlgebra<Double, I> {
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val modelExpression: DifferentiableExpression<Double> = processor.differentiate {
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val x = bindSymbol(xSymbol)
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model(x)
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}
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return fitWith(
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optimizer = optimizer,
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modelExpression = modelExpression,
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startingPoint = startingPoint,
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features = features,
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xSymbol = xSymbol,
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pointToCurveDistance = pointToCurveDistance,
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pointWeight = pointWeight
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)
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}
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/**
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/**
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* Compute chi squared value for completed fit. Return null for incomplete fit
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* Compute chi squared value for completed fit. Return null for incomplete fit
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*/
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*/
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public val XYFit.chiSquaredOrNull: Double? get() {
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public val XYFit.chiSquaredOrNull: Double?
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val result = resultPointOrNull ?: return null
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get() {
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val result = resultPointOrNull ?: return null
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return data.indices.sumOf { index->
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return data.indices.sumOf { index ->
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val x = data.x[index]
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val x = data.x[index]
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val y = data.y[index]
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val y = data.y[index]
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val yErr = data[Symbol.yError]?.get(index) ?: 1.0
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val yErr = data[Symbol.yError]?.get(index) ?: 1.0
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val mu = model.invoke(result + (xSymbol to x) )
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val mu = model.invoke(result + (xSymbol to x))
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((y - mu)/yErr).pow(2)
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((y - mu) / yErr).pow(2)
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}
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}
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}
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}
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