forked from kscience/kmath
Remove unnecessary code from outer sift() function
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1c5113da29
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@ -76,17 +76,7 @@ public class EmpiricalModeDecomposition<BA, L: Number> (
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* @return [SiftingResult.NotEnoughExtrema] is returned if the signal has too few extrema to extract a mode.
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* Success of an appropriate type (See [SiftingResult.Success] class) is returned otherwise.
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*/
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private fun sift(signal: Series<Double>): SiftingResult = (seriesAlgebra) {
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val mean = findMean(signal) ?: return SiftingResult.NotEnoughExtrema
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val protoMode = signal.zip(mean) { s, m -> s - m }
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val sNumber = if (protoMode.sCondition()) 1 else 0
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return when {
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maxSiftIterations == 1 -> SiftingResult.MaxIterationsReached(protoMode)
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sNumber >= sConditionThreshold -> SiftingResult.SNumberReached(protoMode)
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relativeDifference(protoMode, signal) < siftingDelta * signal.size -> SiftingResult.DeltaReached(protoMode)
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else -> siftInner(protoMode, 2, sNumber)
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}
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}
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private fun sift(signal: Series<Double>): SiftingResult = siftInner(signal, 1, 0)
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/**
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* Compute a single iteration of the sifting process.
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@ -96,7 +86,9 @@ public class EmpiricalModeDecomposition<BA, L: Number> (
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iterationNumber: Int,
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sNumber: Int
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): SiftingResult = (seriesAlgebra) {
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val mean = findMean(prevMode) ?: return SiftingResult.SignalFlattened(prevMode)
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val mean = findMean(prevMode) ?:
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return if (iterationNumber == 1) SiftingResult.NotEnoughExtrema
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else SiftingResult.SignalFlattened(prevMode)
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val mode = prevMode.zip(mean) { p, m -> p - m }
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val newSNumber = if (mode.sCondition()) sNumber + 1 else sNumber
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return when {
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