org.nield.kotlinstatistics.NumberStatistics.kt Maven / Gradle / Ivy

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Statistical and analytical extensions for Kotlin
There is a newer version: 1.2.1
package org.nield.kotlinstatistics

import org.apache.commons.math3.stat.StatUtils
import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics



//regression
inline fun  Iterable.simpleRegression(crossinline xSelector: (T) -> Number, crossinline ySelector: (T) -> Number) = asSequence().simpleRegression(xSelector, ySelector)

typealias ASR = org.apache.commons.math3.stat.regression.SimpleRegression

inline fun  Sequence.simpleRegression(crossinline xSelector: (T) -> Number, crossinline ySelector: (T) -> Number): SimpleRegression {
    val r = ASR()
    forEach { r.addData(xSelector(it).toDouble(), ySelector(it).toDouble()) }
    return ApacheSimpleRegression(r)
}

fun Sequence>.simpleRegression() = simpleRegression({it.first},{it.second})
fun Iterable>.simpleRegression() = simpleRegression({it.first},{it.second})


// Simple number vector ops
val  Iterable.descriptiveStatistics: Descriptives get() = DescriptiveStatistics().apply { forEach { addValue(it.toDouble()) } }.let(::ApacheDescriptives)
val  Sequence.descriptiveStatistics: Descriptives get() = DescriptiveStatistics().apply { forEach { addValue(it.toDouble()) } }.let(::ApacheDescriptives)
val  Array.descriptiveStatistics: Descriptives get() = DescriptiveStatistics().apply { forEach { addValue(it.toDouble()) } }.let(::ApacheDescriptives)




fun  Iterable.geometricMean() = asSequence().geometricMean()
fun  Sequence.geometricMean() = StatUtils.geometricMean(map { it.toDouble() }.toList().toDoubleArray())
fun  Array.geometricMean() = asSequence().geometricMean()


fun  Iterable.median() = asSequence().median()
fun  Sequence.median() = map { it.toDouble() }.percentile(50.0)
fun  Array.median() = asSequence().median()



fun  Iterable.percentile(percentile: Double) = asSequence().percentile(percentile)
fun  Sequence.percentile(percentile: Double) = StatUtils.percentile(map { it.toDouble() }.toList().toDoubleArray(), percentile)
fun  Array.percentile(percentile: Double) = asSequence().percentile(percentile)



fun  Iterable.variance() = asSequence().variance()
fun Sequence.variance() = StatUtils.variance(map { it.toDouble() }.toList().toDoubleArray())
fun  Array.variance() = asSequence().variance()




fun  Iterable.sumOfSquares() = asSequence().sumOfSquares()
fun  Sequence.sumOfSquares() = StatUtils.sumSq(map { it.toDouble() }.toList().toDoubleArray())
fun  Array.sumOfSquares() = asSequence().sumOfSquares()



fun  Iterable.standardDeviation() = descriptiveStatistics.standardDeviation
fun  Sequence.standardDeviation() = descriptiveStatistics.standardDeviation
fun  Array.standardDeviation() = descriptiveStatistics.standardDeviation



fun  Iterable.normalize() = asSequence().normalize()
fun  Sequence.normalize() = StatUtils.normalize(map { it.toDouble() }.toList().toDoubleArray())
fun  Array.normalize() = asSequence().normalize()



val  Iterable.kurtosis get() = descriptiveStatistics.kurtosis
val  Sequence.kurtosis get() = descriptiveStatistics.kurtosis
val  Array.kurtosis get() = descriptiveStatistics.kurtosis



val  Iterable.skewness get() = descriptiveStatistics.skewness
val  Sequence.skewness get() = descriptiveStatistics.skewness
val  Array.skewness get() = descriptiveStatistics.skewness



// slicing operations


inline fun  Sequence.descriptiveStatisticsBy(crossinline keySelector: (T) -> K, crossinline valueSelector: (T) -> Number) =
        groupApply(keySelector, valueSelector) { it.descriptiveStatistics }

inline fun  Iterable.descriptiveStatisticsBy(crossinline keySelector: (T) -> K, crossinline valueSelector: (T) -> Number) =
        asSequence().descriptiveStatisticsBy(keySelector, valueSelector)

fun  Sequence>.descriptiveStatisticsBy() =
        groupApply({it.first}, {it.second}) { it.descriptiveStatistics }

fun  Iterable>.descriptiveStatisticsBy() = asSequence().descriptiveStatisticsBy()





inline fun  Sequence.medianBy(crossinline keySelector: (T) -> K, crossinline valueSelector: (T) -> Number) =
        groupApply(keySelector, valueSelector) { it.median() }

fun  Sequence>.medianBy() =
        groupApply({it.first}, {it.second}) { it.median() }

inline fun  Iterable.medianBy(crossinline keySelector: (T) -> K, crossinline valueSelector: (T) -> Number) =
        asSequence().medianBy(keySelector, valueSelector)

fun  Iterable>.medianBy() = asSequence().medianBy()





inline fun  Sequence.percentileBy(percentile: Double, crossinline keySelector: (T) -> K, crossinline valueSelector: (T) -> Number) =
        groupApply(keySelector, valueSelector) { it.percentile(percentile) }

fun  Sequence>.percentileBy(percentile: Double) =
        groupApply({it.first}, {it.second}) { it.percentile(percentile) }

inline fun  Iterable.percentileBy(percentile: Double, crossinline keySelector: (T) -> K, crossinline valueSelector: (T) -> Number) =
        asSequence().percentileBy(percentile, keySelector, valueSelector)

fun  Iterable>.percentileBy(percentile: Double) = asSequence().percentileBy(percentile)






inline fun  Sequence.varianceBy(crossinline keySelector: (T) -> K, crossinline valueSelector: (T) -> Number) =
        groupApply(keySelector, valueSelector) { it.variance() }

fun  Sequence>.varianceBy() =
        groupApply({it.first}, {it.second}) { it.variance() }

inline fun  Iterable.varianceBy(crossinline keySelector: (T) -> K, crossinline valueSelector: (T) -> Number) =
        asSequence().varianceBy(keySelector, valueSelector)

fun  Iterable>.varianceBy() = asSequence().varianceBy()





inline fun  Sequence.standardDeviationBy(crossinline keySelector: (T) -> K, crossinline valueSelector: (T) -> Number) =
        groupApply(keySelector, valueSelector) { it.standardDeviation() }

fun  Sequence>.standardDeviationBy() =
        groupApply({it.first}, {it.second}) { it.standardDeviation() }


inline fun  Iterable.standardDeviationBy(crossinline keySelector: (T) -> K, crossinline valueSelector: (T) -> Number) =
        asSequence().standardDeviationBy(keySelector, valueSelector)

fun  Iterable>.standardDeviationBy() =  asSequence().standardDeviationBy()







inline fun  Sequence.geometricMeanBy(crossinline keySelector: (T) -> K, crossinline valueSelector: (T) -> Number) =
        groupApply(keySelector, valueSelector) { it.geometricMean() }

fun  Sequence>.geometricMeanBy() =
        groupApply({it.first}, {it.second}) { it.geometricMean() }


inline fun  Iterable.geometricMeanBy(crossinline keySelector: (T) -> K, crossinline valueSelector: (T) -> Number) =
        asSequence().standardDeviationBy(keySelector, valueSelector)

fun  Iterable>.geometricMeanBy() =  asSequence().standardDeviationBy()