Package space.kscience.kmath.interpolation

Types

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fun interface Interpolator<T, in X : T, Y : T>

And interpolator for data with x column type X, y column type Y.

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class LinearInterpolator<T : Comparable<T>>(algebra: Field<T>) : PolynomialInterpolator<T>

Reference JVM implementation: https://github.com/apache/commons-math/blob/master/src/main/java/org/apache/commons/math4/analysis/interpolation/LinearInterpolator.java

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interface PolynomialInterpolator<T : Comparable<T>> : Interpolator<T, T, T>

And interpolator returning PiecewisePolynomial function

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class SplineInterpolator<T : Comparable<T>>(algebra: Field<T>, bufferFactory: MutableBufferFactory<T>) : PolynomialInterpolator<T>

Generic spline interpolator. Not recommended for performance critical places, use platform-specific and type specific ones.

Functions

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fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(data: List<Pair<T, T>>): (T) -> T?
fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(data: Map<T, T>): (T) -> T?
fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(data: List<Pair<T, T>>, defaultValue: T): (T) -> T
fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(data: Map<T, T>, defaultValue: T): (T) -> T
fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(x: Buffer<T>, y: Buffer<T>): (T) -> T?
fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(x: Buffer<T>, y: Buffer<T>, defaultValue: T): (T) -> T
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fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolatePolynomials(data: List<Pair<T, T>>): PiecewisePolynomial<T>
fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolatePolynomials(data: Map<T, T>): PiecewisePolynomial<T>
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fun <T : Comparable<T>> Field<T>.splineInterpolator(bufferFactory: MutableBufferFactory<T>): SplineInterpolator<T>

Properties

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val <T : Comparable<T>> Field<T>.linearInterpolator: LinearInterpolator<T>
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val DoubleField.splineInterpolator: SplineInterpolator<Double>