forked from kscience/kmath
Interpolation API
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@ -8,5 +8,6 @@ dependencies {
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api(project(":kmath-core"))
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api(project(":kmath-coroutines"))
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api(project(":kmath-prob"))
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api(project(":kmath-functions"))
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api("org.apache.commons:commons-math3:3.6.1")
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}
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@ -73,6 +73,8 @@ fun <T> Buffer<T>.asSequence(): Sequence<T> = Sequence(::iterator)
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fun <T> Buffer<T>.asIterable(): Iterable<T> = asSequence().asIterable()
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val Buffer<*>.indices: IntRange get() = IntRange(0, size - 1)
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interface MutableBuffer<T> : Buffer<T> {
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operator fun set(index: Int, value: T)
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@ -6,14 +6,16 @@ interface Piecewise<T, R> {
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fun findPiece(arg: T): R?
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}
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interface PiecewisePolynomial<T : Any> : Piecewise<T, Polynomial<T>>
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interface PiecewisePolynomial<T : Any> :
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Piecewise<T, Polynomial<T>>
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/**
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* Ordered list of pieces in piecewise function
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*/
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class OrderedPiecewisePolynomial<T : Comparable<T>>(left: T) : PiecewisePolynomial<T> {
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class OrderedPiecewisePolynomial<T : Comparable<T>>(delimeter: T) :
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PiecewisePolynomial<T> {
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private val delimiters: ArrayList<T> = arrayListOf(left)
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private val delimiters: ArrayList<T> = arrayListOf(delimeter)
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private val pieces: ArrayList<Polynomial<T>> = ArrayList()
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/**
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@ -32,7 +32,8 @@ fun <T : Any, C : Ring<T>> Polynomial<T>.value(ring: C, arg: T): T = ring.run {
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/**
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* Represent a polynomial as a context-dependent function
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*/
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fun <T : Any, C : Ring<T>> Polynomial<T>.asMathFunction(): MathFunction<T, out C, T> = object : MathFunction<T, C, T> {
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fun <T : Any, C : Ring<T>> Polynomial<T>.asMathFunction(): MathFunction<T, out C, T> = object :
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MathFunction<T, C, T> {
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override fun C.invoke(arg: T): T = value(this, arg)
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}
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@ -61,7 +62,8 @@ class PolynomialSpace<T : Any, C : Ring<T>>(val ring: C) : Space<Polynomial<T>>
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}
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}
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override val zero: Polynomial<T> = Polynomial(emptyList())
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override val zero: Polynomial<T> =
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Polynomial(emptyList())
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operator fun Polynomial<T>.invoke(arg: T): T = value(ring, arg)
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}
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@ -5,7 +5,7 @@ import scientifik.kmath.functions.value
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import scientifik.kmath.operations.Ring
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interface Interpolator<X, Y> {
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fun interpolate(points: Collection<Pair<X, Y>>): (X) -> Y
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fun interpolate(points: XYPointSet<X, Y>): (X) -> Y
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}
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interface PolynomialInterpolator<T : Comparable<T>> : Interpolator<T, T> {
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@ -13,9 +13,9 @@ interface PolynomialInterpolator<T : Comparable<T>> : Interpolator<T, T> {
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fun getDefaultValue(): T = error("Out of bounds")
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fun interpolatePolynomials(points: Collection<Pair<T, T>>): PiecewisePolynomial<T>
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fun interpolatePolynomials(points: XYPointSet<T, T>): PiecewisePolynomial<T>
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override fun interpolate(points: Collection<Pair<T, T>>): (T) -> T = { x ->
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override fun interpolate(points: XYPointSet<T, T>): (T) -> T = { x ->
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interpolatePolynomials(points).value(algebra, x) ?: getDefaultValue()
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}
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}
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@ -10,18 +10,16 @@ import scientifik.kmath.operations.Field
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*/
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class LinearInterpolator<T : Comparable<T>>(override val algebra: Field<T>) : PolynomialInterpolator<T> {
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override fun interpolatePolynomials(points: Collection<Pair<T, T>>): PiecewisePolynomial<T> = algebra.run {
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require(points.isNotEmpty()) { "Point array should not be empty" }
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override fun interpolatePolynomials(points: XYPointSet<T, T>): PiecewisePolynomial<T> = algebra.run {
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require(points.size > 0) { "Point array should not be empty" }
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insureSorted(points)
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//sorting points
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val sorted = points.sortedBy { it.first }
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return@run OrderedPiecewisePolynomial(points.first().first).apply {
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OrderedPiecewisePolynomial(points.x[0]).apply {
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for (i in 0 until points.size - 1) {
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val slope = (sorted[i + 1].second - sorted[i].second) / (sorted[i + 1].first - sorted[i].first)
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val const = sorted[i].second - slope * sorted[i].first
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val slope = (points.y[i + 1] - points.y[i]) / (points.x[i + 1] - points.x[i])
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val const = points.x[i] - slope * points.x[i]
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val polynomial = Polynomial(const, slope)
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putRight(sorted[i + 1].first, polynomial)
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putRight(points.x[i + 1], polynomial)
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}
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}
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}
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@ -0,0 +1,296 @@
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//package scientifik.kmath.interpolation
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//
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//import scientifik.kmath.functions.PiecewisePolynomial
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//import scientifik.kmath.operations.Ring
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//import scientifik.kmath.structures.Buffer
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//import kotlin.math.abs
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//import kotlin.math.sqrt
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//
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//
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///**
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// * Original code: https://github.com/apache/commons-math/blob/eb57d6d457002a0bb5336d789a3381a24599affe/src/main/java/org/apache/commons/math4/analysis/interpolation/LoessInterpolator.java
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// */
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//class LoessInterpolator<T : Comparable<T>>(override val algebra: Ring<T>) : PolynomialInterpolator<T> {
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// /**
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// * The bandwidth parameter: when computing the loess fit at
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// * a particular point, this fraction of source points closest
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// * to the current point is taken into account for computing
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// * a least-squares regression.
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// *
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// *
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// * A sensible value is usually 0.25 to 0.5.
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// */
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// private var bandwidth = 0.0
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//
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// /**
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// * The number of robustness iterations parameter: this many
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// * robustness iterations are done.
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// *
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// *
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// * A sensible value is usually 0 (just the initial fit without any
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// * robustness iterations) to 4.
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// */
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// private var robustnessIters = 0
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//
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// /**
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// * If the median residual at a certain robustness iteration
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// * is less than this amount, no more iterations are done.
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// */
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// private var accuracy = 0.0
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//
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// /**
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// * Constructs a new [LoessInterpolator]
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// * with a bandwidth of [.DEFAULT_BANDWIDTH],
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// * [.DEFAULT_ROBUSTNESS_ITERS] robustness iterations
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// * and an accuracy of {#link #DEFAULT_ACCURACY}.
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// * See [.LoessInterpolator] for an explanation of
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// * the parameters.
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// */
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// fun LoessInterpolator() {
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// bandwidth = DEFAULT_BANDWIDTH
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// robustnessIters = DEFAULT_ROBUSTNESS_ITERS
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// accuracy = DEFAULT_ACCURACY
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// }
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//
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// fun LoessInterpolator(bandwidth: Double, robustnessIters: Int) {
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// this(bandwidth, robustnessIters, DEFAULT_ACCURACY)
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// }
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//
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// fun LoessInterpolator(bandwidth: Double, robustnessIters: Int, accuracy: Double) {
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// if (bandwidth < 0 ||
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// bandwidth > 1
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// ) {
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// throw OutOfRangeException(LocalizedFormats.BANDWIDTH, bandwidth, 0, 1)
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// }
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// this.bandwidth = bandwidth
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// if (robustnessIters < 0) {
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// throw NotPositiveException(LocalizedFormats.ROBUSTNESS_ITERATIONS, robustnessIters)
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// }
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// this.robustnessIters = robustnessIters
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// this.accuracy = accuracy
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// }
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//
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// fun interpolate(
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// xval: DoubleArray,
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// yval: DoubleArray
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// ): PolynomialSplineFunction {
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// return SplineInterpolator().interpolate(xval, smooth(xval, yval))
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// }
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//
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// fun XYZPointSet<Double, Double, Double>.smooth(): XYPointSet<Double, Double> {
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// checkAllFiniteReal(x)
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// checkAllFiniteReal(y)
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// checkAllFiniteReal(z)
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// MathArrays.checkOrder(xval)
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// if (size == 1) {
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// return doubleArrayOf(y[0])
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// }
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// if (size == 2) {
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// return doubleArrayOf(y[0], y[1])
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// }
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// val bandwidthInPoints = (bandwidth * size).toInt()
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// if (bandwidthInPoints < 2) {
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// throw NumberIsTooSmallException(
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// LocalizedFormats.BANDWIDTH,
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// bandwidthInPoints, 2, true
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// )
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// }
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// val res = DoubleArray(size)
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// val residuals = DoubleArray(size)
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// val sortedResiduals = DoubleArray(size)
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// val robustnessWeights = DoubleArray(size)
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// // Do an initial fit and 'robustnessIters' robustness iterations.
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// // This is equivalent to doing 'robustnessIters+1' robustness iterations
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// // starting with all robustness weights set to 1.
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// Arrays.fill(robustnessWeights, 1.0)
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// for (iter in 0..robustnessIters) {
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// val bandwidthInterval = intArrayOf(0, bandwidthInPoints - 1)
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// // At each x, compute a local weighted linear regression
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// for (i in 0 until size) {
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//// val x = x[i]
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// // Find out the interval of source points on which
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// // a regression is to be made.
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// if (i > 0) {
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// updateBandwidthInterval(x, z, i, bandwidthInterval)
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// }
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// val ileft = bandwidthInterval[0]
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// val iright = bandwidthInterval[1]
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// // Compute the point of the bandwidth interval that is
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// // farthest from x
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// val edge: Int
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// edge = if (x[i] - x[ileft] > x[iright] - x[i]) {
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// ileft
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// } else {
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// iright
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// }
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// // Compute a least-squares linear fit weighted by
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// // the product of robustness weights and the tricube
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// // weight function.
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// // See http://en.wikipedia.org/wiki/Linear_regression
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// // (section "Univariate linear case")
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// // and http://en.wikipedia.org/wiki/Weighted_least_squares
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// // (section "Weighted least squares")
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// var sumWeights = 0.0
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// var sumX = 0.0
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// var sumXSquared = 0.0
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// var sumY = 0.0
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// var sumXY = 0.0
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// val denom: Double = abs(1.0 / (x[edge] - x[i]))
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// for (k in ileft..iright) {
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// val xk = x[k]
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// val yk = y[k]
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// val dist = if (k < i) x - xk else xk - x[i]
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// val w = tricube(dist * denom) * robustnessWeights[k] * z[k]
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// val xkw = xk * w
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// sumWeights += w
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// sumX += xkw
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// sumXSquared += xk * xkw
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// sumY += yk * w
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// sumXY += yk * xkw
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// }
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// val meanX = sumX / sumWeights
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// val meanY = sumY / sumWeights
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// val meanXY = sumXY / sumWeights
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// val meanXSquared = sumXSquared / sumWeights
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// val beta: Double
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// beta = if (sqrt(abs(meanXSquared - meanX * meanX)) < accuracy) {
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// 0.0
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// } else {
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// (meanXY - meanX * meanY) / (meanXSquared - meanX * meanX)
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// }
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// val alpha = meanY - beta * meanX
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// res[i] = beta * x[i] + alpha
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// residuals[i] = abs(y[i] - res[i])
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// }
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// // No need to recompute the robustness weights at the last
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// // iteration, they won't be needed anymore
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// if (iter == robustnessIters) {
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// break
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// }
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// // Recompute the robustness weights.
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// // Find the median residual.
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// // An arraycopy and a sort are completely tractable here,
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// // because the preceding loop is a lot more expensive
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// java.lang.System.arraycopy(residuals, 0, sortedResiduals, 0, size)
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// Arrays.sort(sortedResiduals)
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// val medianResidual = sortedResiduals[size / 2]
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// if (abs(medianResidual) < accuracy) {
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// break
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// }
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// for (i in 0 until size) {
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// val arg = residuals[i] / (6 * medianResidual)
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// if (arg >= 1) {
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// robustnessWeights[i] = 0.0
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// } else {
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// val w = 1 - arg * arg
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// robustnessWeights[i] = w * w
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// }
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// }
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// }
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// return res
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// }
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//
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// fun smooth(xval: DoubleArray, yval: DoubleArray): DoubleArray {
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// if (xval.size != yval.size) {
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// throw DimensionMismatchException(xval.size, yval.size)
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// }
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// val unitWeights = DoubleArray(xval.size)
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// Arrays.fill(unitWeights, 1.0)
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// return smooth(xval, yval, unitWeights)
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// }
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//
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// /**
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// * Given an index interval into xval that embraces a certain number of
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// * points closest to `xval[i-1]`, update the interval so that it
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// * embraces the same number of points closest to `xval[i]`,
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// * ignoring zero weights.
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// *
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// * @param xval Arguments array.
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// * @param weights Weights array.
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// * @param i Index around which the new interval should be computed.
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// * @param bandwidthInterval a two-element array {left, right} such that:
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// * `(left==0 or xval[i] - xval[left-1] > xval[right] - xval[i])`
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// * and
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// * `(right==xval.length-1 or xval[right+1] - xval[i] > xval[i] - xval[left])`.
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// * The array will be updated.
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// */
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// private fun updateBandwidthInterval(
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// xval: Buffer<Double>, weights: Buffer<Double>,
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// i: Int,
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// bandwidthInterval: IntArray
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// ) {
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// val left = bandwidthInterval[0]
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// val right = bandwidthInterval[1]
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// // The right edge should be adjusted if the next point to the right
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// // is closer to xval[i] than the leftmost point of the current interval
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// val nextRight = nextNonzero(weights, right)
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// if (nextRight < xval.size && xval[nextRight] - xval[i] < xval[i] - xval[left]) {
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// val nextLeft = nextNonzero(weights, bandwidthInterval[0])
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// bandwidthInterval[0] = nextLeft
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// bandwidthInterval[1] = nextRight
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// }
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// }
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//
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// /**
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// * Return the smallest index `j` such that
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// * `j > i && (j == weights.length || weights[j] != 0)`.
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// *
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// * @param weights Weights array.
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// * @param i Index from which to start search.
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// * @return the smallest compliant index.
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// */
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// private fun nextNonzero(weights: Buffer<Double>, i: Int): Int {
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// var j = i + 1
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// while (j < weights.size && weights[j] == 0.0) {
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// ++j
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// }
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// return j
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// }
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//
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// /**
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// * Compute the
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// * [tricube](http://en.wikipedia.org/wiki/Local_regression#Weight_function)
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// * weight function
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// *
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// * @param x Argument.
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// * @return `(1 - |x|<sup>3</sup>)<sup>3</sup>` for |x| < 1, 0 otherwise.
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// */
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// private fun tricube(x: Double): Double {
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// val absX: Double = FastMath.abs(x)
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// if (absX >= 1.0) {
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// return 0.0
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// }
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// val tmp = 1 - absX * absX * absX
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// return tmp * tmp * tmp
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// }
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//
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// /**
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// * Check that all elements of an array are finite real numbers.
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// *
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// * @param values Values array.
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// * @throws org.apache.commons.math4.exception.NotFiniteNumberException
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// * if one of the values is not a finite real number.
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// */
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// private fun checkAllFiniteReal(values: DoubleArray) {
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// for (i in values.indices) {
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// MathUtils.checkFinite(values[i])
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// }
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// }
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//
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// override fun interpolatePolynomials(points: Collection<Pair<T, T>>): PiecewisePolynomial<T> {
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// TODO("not implemented") //To change body of created functions use File | Settings | File Templates.
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// }
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//
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// companion object {
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// /** Default value of the bandwidth parameter. */
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// const val DEFAULT_BANDWIDTH = 0.3
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//
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// /** Default value of the number of robustness iterations. */
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// const val DEFAULT_ROBUSTNESS_ITERS = 2
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//
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// /**
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// * Default value for accuracy.
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// */
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// const val DEFAULT_ACCURACY = 1e-12
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// }
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//}
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@ -0,0 +1,58 @@
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package scientifik.kmath.interpolation
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import scientifik.kmath.functions.OrderedPiecewisePolynomial
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import scientifik.kmath.functions.PiecewisePolynomial
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import scientifik.kmath.functions.Polynomial
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import scientifik.kmath.operations.Field
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import scientifik.kmath.structures.MutableBufferFactory
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/**
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* Generic spline interpolator. Not recommended for performance critical places, use platform-specific and type specific ones.
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* Based on https://github.com/apache/commons-math/blob/eb57d6d457002a0bb5336d789a3381a24599affe/src/main/java/org/apache/commons/math4/analysis/interpolation/SplineInterpolator.java
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*/
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class SplineInterpolator<T : Comparable<T>>(
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override val algebra: Field<T>,
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val bufferFactory: MutableBufferFactory<T>
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) : PolynomialInterpolator<T> {
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//TODO possibly optimize zeroed buffers
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override fun interpolatePolynomials(points: XYPointSet<T, T>): PiecewisePolynomial<T> = algebra.run {
|
||||
if (points.size < 3) {
|
||||
error("Can't use spline interpolator with less than 3 points")
|
||||
}
|
||||
insureSorted(points)
|
||||
|
||||
// Number of intervals. The number of data points is n + 1.
|
||||
val n = points.size - 1
|
||||
// Differences between knot points
|
||||
val h = bufferFactory(points.size) { i -> points.x[i + 1] - points.x[i] }
|
||||
val mu = bufferFactory(points.size - 1) { zero }
|
||||
val z = bufferFactory(points.size) { zero }
|
||||
|
||||
for (i in 1 until n) {
|
||||
val g = 2.0 * (points.x[i + 1] - points.x[i - 1]) - h[i - 1] * mu[i - 1]
|
||||
mu[i] = h[i] / g
|
||||
z[i] =
|
||||
(3.0 * (points.y[i + 1] * h[i - 1] - points.x[i] * (points.x[i + 1] - points.x[i - 1]) + points.y[i - 1] * h[i]) / (h[i - 1] * h[i])
|
||||
- h[i - 1] * z[i - 1]) / g
|
||||
}
|
||||
|
||||
// cubic spline coefficients -- b is linear, c quadratic, d is cubic (original y's are constants)
|
||||
|
||||
OrderedPiecewisePolynomial<T>(points.x[points.size - 1]).apply {
|
||||
var cOld = zero
|
||||
for (j in n - 1 downTo 0) {
|
||||
val c = z[j] - mu[j] * cOld
|
||||
val a = points.y[j]
|
||||
val b = (points.y[j + 1] - points.y[j]) / h[j] - h[j] * (cOld + 2.0 * c) / 3.0
|
||||
val d = (cOld - c) / (3.0 * h[j])
|
||||
val polynomial = Polynomial(a, b, c, d)
|
||||
cOld = c
|
||||
putLeft(points.x[j], polynomial)
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
}
|
||||
@ -0,0 +1,54 @@
|
||||
package scientifik.kmath.interpolation
|
||||
|
||||
import scientifik.kmath.structures.Buffer
|
||||
import scientifik.kmath.structures.Structure2D
|
||||
|
||||
interface XYPointSet<X, Y> {
|
||||
val size: Int
|
||||
val x: Buffer<X>
|
||||
val y: Buffer<Y>
|
||||
}
|
||||
|
||||
interface XYZPointSet<X, Y, Z> : XYPointSet<X, Y> {
|
||||
val z: Buffer<Z>
|
||||
}
|
||||
|
||||
internal fun <T : Comparable<T>> insureSorted(points: XYPointSet<T, *>) {
|
||||
for (i in 0 until points.size - 1) {
|
||||
if (points.x[i + 1] <= points.x[i]) error("Input data is not sorted at index $i")
|
||||
}
|
||||
}
|
||||
|
||||
class NDStructureColumn<T>(val structure: Structure2D<T>, val column: Int) : Buffer<T> {
|
||||
init {
|
||||
require(column < structure.colNum) { "Column index is outside of structure column range" }
|
||||
}
|
||||
|
||||
override val size: Int get() = structure.rowNum
|
||||
|
||||
override fun get(index: Int): T = structure[index, column]
|
||||
|
||||
override fun iterator(): Iterator<T> = sequence {
|
||||
repeat(size) {
|
||||
yield(get(it))
|
||||
}
|
||||
}.iterator()
|
||||
}
|
||||
|
||||
class BufferXYPointSet<X, Y>(override val x: Buffer<X>, override val y: Buffer<Y>) : XYPointSet<X, Y> {
|
||||
init {
|
||||
require(x.size == y.size) { "Sizes of x and y buffers should be the same" }
|
||||
}
|
||||
|
||||
override val size: Int
|
||||
get() = x.size
|
||||
}
|
||||
|
||||
fun <T> Structure2D<T>.asXYPointSet(): XYPointSet<T, T> {
|
||||
require(shape[1] == 2) { "Structure second dimension should be of size 2" }
|
||||
return object : XYPointSet<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)
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user