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Part 3

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This commit is contained in:
Igor Dunaev 2024-05-04 23:34:49 +03:00
parent d2c8423b6f
commit 5efd5e8304
2 changed files with 31 additions and 24 deletions
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10
examples/src/main/kotlin/space/kscience/kmath/series/emdDemo.kt
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@ -5,20 +5,24 @@
package space.kscience.kmath.series
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.operations.bufferAlgebra
import space.kscience.kmath.structures.*
import space.kscience.kmath.operations.invoke
import space.kscience.plotly.*
import space.kscience.plotly.models.Scatter
import kotlin.math.sin
fun main(): Unit = (Double.seriesAlgebra()) {
private val customAlgebra = (Double.algebra.bufferAlgebra) { SeriesAlgebra(this) { it.toDouble() } }
fun main(): Unit = (customAlgebra) {
val signal = DoubleArray(800) {
sin(it.toDouble() / 10.0) + 3.5 * sin(it.toDouble() / 60.0)
}.asBuffer().moveTo(0)
val emd = empiricalModeDecomposition(
sConditionThreshold = 1,
maxSiftIterations = 15,
siftingDelta = 1e-2,
nModes = 4
).decompose(signal)
println("EMD: ${emd.modes.size} modes extracted, terminated because ${emd.terminatedBecause}")
@ -26,7 +30,7 @@ fun main(): Unit = (Double.seriesAlgebra()) {
fun Plot.series(name: String, buffer: Buffer<Double>, block: Scatter.() -> Unit = {}) {
this.scatter {
this.name = name
this.x.numbers = buffer.offsetIndices
this.x.numbers = buffer.labels
this.y.doubles = buffer.toDoubleArray()
block()
}

45
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/series/EmpiricalModeDecomposition.kt
View File

@ -26,13 +26,13 @@ import space.kscience.kmath.structures.last
* @param nModes how many modes should be extracted at most. The algorithm may return fewer modes if it was not
* possible to extract more modes from the signal.
*/
public class EmpiricalModeDecomposition<A: Field<Double>, BA, L: Number> (
private val seriesAlgebra: SeriesAlgebra<Double, A, BA, L>,
public class EmpiricalModeDecomposition<T: Comparable<T>, A: Field<T>, BA, L: T> (
private val seriesAlgebra: SeriesAlgebra<T, A, BA, L>,
private val sConditionThreshold: Int = 15,
private val maxSiftIterations: Int = 20,
private val siftingDelta: Double = 1e-2,
private val siftingDelta: T,
private val nModes: Int = 6
) where BA: BufferAlgebra<Double, A>, BA: FieldOps<Buffer<Double>> {
) where BA: BufferAlgebra<T, A>, BA: FieldOps<Buffer<T>> {
/**
* Take a signal, construct an upper and a lower envelopes, find the mean value of two,
@ -42,18 +42,18 @@ public class EmpiricalModeDecomposition<A: Field<Double>, BA, L: Number> (
* @return mean [Series] or `null`. `null` is returned in case
* the signal does not have enough extrema to construct envelopes.
*/
private fun findMean(signal: Series<Double>): Series<Double>? = (seriesAlgebra) {
private fun findMean(signal: Series<T>): Series<T>? = (seriesAlgebra) {
val interpolator = SplineInterpolator(elementAlgebra)
val makeBuffer = elementAlgebra.bufferFactory
fun generateEnvelope(extrema: List<Int>, paddedExtremeValues: Buffer<Double>): Series<Double> {
fun generateEnvelope(extrema: List<Int>, paddedExtremeValues: Buffer<T>): Series<T> {
val envelopeFunction = interpolator.interpolate(
Buffer(extrema.size) { signal.labels[extrema[it]].toDouble() },
makeBuffer(extrema.size) { signal.labels[extrema[it]] },
paddedExtremeValues
)
return signal.mapWithLabel { _, label ->
// For some reason PolynomialInterpolator is exclusive and the right boundary
// TODO Notify interpolator authors
envelopeFunction(label.toDouble()) ?: paddedExtremeValues.last()
envelopeFunction(label) ?: paddedExtremeValues.last()
// need to make the interpolator yield values outside boundaries?
}
}
@ -90,13 +90,13 @@ public class EmpiricalModeDecomposition<A: Field<Double>, BA, L: Number> (
* @return [SiftingResult.NotEnoughExtrema] is returned if the signal has too few extrema to extract a mode.
* Success of an appropriate type (See [SiftingResult.Success] class) is returned otherwise.
*/
private fun sift(signal: Series<Double>): SiftingResult = siftInner(signal, 1, 0)
private fun sift(signal: Series<T>): SiftingResult = siftInner(signal, 1, 0)
/**
* Compute a single iteration of the sifting process.
*/
private tailrec fun siftInner(
prevMode: Series<Double>,
prevMode: Series<T>,
iterationNumber: Int,
sNumber: Int
): SiftingResult = (seriesAlgebra) {
@ -108,7 +108,8 @@ public class EmpiricalModeDecomposition<A: Field<Double>, BA, L: Number> (
return when {
iterationNumber >= maxSiftIterations -> SiftingResult.MaxIterationsReached(mode)
sNumber >= sConditionThreshold -> SiftingResult.SNumberReached(mode)
relativeDifference(mode, prevMode) < siftingDelta * mode.size -> SiftingResult.DeltaReached(mode)
relativeDifference(mode, prevMode) < (elementAlgebra) { siftingDelta * mode.size } ->
SiftingResult.DeltaReached(mode)
else -> siftInner(mode, iterationNumber + 1, newSNumber)
}
}
@ -121,8 +122,8 @@ public class EmpiricalModeDecomposition<A: Field<Double>, BA, L: Number> (
* Modes returned in a list which contains as many modes as it was possible
* to extract before triggering one of the termination conditions.
*/
public fun decompose(signal: Series<Double>): EMDecompositionResult<Double> = (seriesAlgebra) {
val modes = mutableListOf<Series<Double>>()
public fun decompose(signal: Series<T>): EMDecompositionResult<T> = (seriesAlgebra) {
val modes = mutableListOf<Series<T>>()
var residual = signal
repeat(nModes) {
val nextMode = when(val r = sift(residual)) {
@ -130,14 +131,15 @@ public class EmpiricalModeDecomposition<A: Field<Double>, BA, L: Number> (
return EMDecompositionResult(
if (it == 0) EMDTerminationReason.SIGNAL_TOO_FLAT
else EMDTerminationReason.ALL_POSSIBLE_MODES_EXTRACTED,
modes
modes,
residual
)
is SiftingResult.Success<*> -> r.result
}
modes.add(nextMode as Series<Double>) // TODO remove unchecked cast
modes.add(nextMode as Series<T>) // TODO remove unchecked cast
residual = residual.zip(nextMode) { l, r -> l - r }
}
return EMDecompositionResult(EMDTerminationReason.MAX_MODES_REACHED, modes)
return EMDecompositionResult(EMDTerminationReason.MAX_MODES_REACHED, modes, residual)
}
}
@ -155,13 +157,13 @@ public class EmpiricalModeDecomposition<A: Field<Double>, BA, L: Number> (
* @param nModes how many modes should be extracted at most. The algorithm may return fewer modes if it was not
* possible to extract more modes from the signal.
*/
public fun <L: Number, A: Field<Double>, BA> SeriesAlgebra<Double, A, BA, L>.empiricalModeDecomposition(
public fun <T: Comparable<T>, L: T, A: Field<T>, BA> SeriesAlgebra<T, A, BA, L>.empiricalModeDecomposition(
sConditionThreshold: Int = 15,
maxSiftIterations: Int = 20,
siftingDelta: Double = 1e-2,
siftingDelta: T,
nModes: Int = 3
): EmpiricalModeDecomposition<A, BA, L>
where BA: BufferAlgebra<Double, A>, BA: FieldOps<Buffer<Double>> = EmpiricalModeDecomposition(
): EmpiricalModeDecomposition<T, A, BA, L>
where BA: BufferAlgebra<T, A>, BA: FieldOps<Buffer<T>> = EmpiricalModeDecomposition(
seriesAlgebra = this,
sConditionThreshold = sConditionThreshold,
maxSiftIterations = maxSiftIterations,
@ -281,5 +283,6 @@ public enum class EMDTerminationReason {
public data class EMDecompositionResult<T>(
val terminatedBecause: EMDTerminationReason,
val modes: List<Series<T>>
val modes: List<Series<T>>,
val residual: Series<T>
)