forked from kscience/kmath
Refactor multivariate histograms
This commit is contained in:
parent
27a252b637
commit
6247e79884
@ -49,6 +49,7 @@
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- Default Buffer and ND algebras are now Ops and lack neutral elements (0, 1) as well as algebra-level shapes.
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- Tensor algebra takes read-only structures as input and inherits AlgebraND
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- `UnivariateDistribution` renamed to `Distribution1D`
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- Rework of histograms.
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### Deprecated
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- Specialized `DoubleBufferAlgebra`
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@ -6,6 +6,8 @@ kotlin.code.style=official
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kotlin.jupyter.add.scanner=false
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kotlin.mpp.stability.nowarn=true
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kotlin.native.ignoreDisabledTargets=true
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kotlin.incremental.js.ir=true
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org.gradle.configureondemand=true
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org.gradle.jvmargs=-XX:MaxMetaspaceSize=1G
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org.gradle.parallel=true
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@ -194,7 +194,7 @@ public interface RingOpsND<T, out A : RingOps<T>> : RingOps<StructureND<T>>, Gro
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override fun multiply(left: StructureND<T>, right: StructureND<T>): StructureND<T> =
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zip(left, right) { aValue, bValue -> multiply(aValue, bValue) }
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//TODO move to extensions after KEEP-176
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//TODO move to extensions with context receivers
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/**
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* Multiplies an ND structure by an element of it.
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@ -32,18 +32,23 @@ public open class BufferND<out T>(
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/**
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* Transform structure to a new structure using provided [BufferFactory] and optimizing if argument is [BufferND]
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*/
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public inline fun <T, reified R : Any> StructureND<T>.mapToBuffer(
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factory: BufferFactory<R> = Buffer.Companion::auto,
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public inline fun <T, R : Any> StructureND<T>.mapToBuffer(
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factory: BufferFactory<R>,
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crossinline transform: (T) -> R,
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): BufferND<R> {
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return if (this is BufferND<T>)
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BufferND(this.indices, factory.invoke(indices.linearSize) { transform(buffer[it]) })
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else {
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val strides = DefaultStrides(shape)
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BufferND(strides, factory.invoke(strides.linearSize) { transform(get(strides.index(it))) })
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}
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): BufferND<R> = if (this is BufferND<T>)
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BufferND(this.indices, factory.invoke(indices.linearSize) { transform(buffer[it]) })
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else {
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val strides = DefaultStrides(shape)
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BufferND(strides, factory.invoke(strides.linearSize) { transform(get(strides.index(it))) })
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}
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/**
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* Transform structure to a new structure using inferred [BufferFactory]
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*/
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public inline fun <T, reified R : Any> StructureND<T>.mapToBuffer(
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crossinline transform: (T) -> R,
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): BufferND<R> = mapToBuffer(Buffer.Companion::auto, transform)
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/**
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* Represents [MutableStructureND] over [MutableBuffer].
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*
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@ -18,7 +18,8 @@ public interface Counter<T : Any> {
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public val value: T
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public companion object {
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public fun double(): ObjectCounter<Double> = ObjectCounter(DoubleField)
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public fun ofDouble(): ObjectCounter<Double> = ObjectCounter(DoubleField)
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public fun <T: Any> of(group: Group<T>): ObjectCounter<T> = ObjectCounter(group)
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}
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}
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@ -1,140 +0,0 @@
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/*
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* Copyright 2018-2021 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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package space.kscience.kmath.histogram
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import space.kscience.kmath.domains.HyperSquareDomain
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import space.kscience.kmath.linear.Point
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import space.kscience.kmath.misc.UnstableKMathAPI
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import space.kscience.kmath.nd.*
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import space.kscience.kmath.operations.DoubleField
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import space.kscience.kmath.structures.*
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import kotlin.math.floor
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/**
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* Multivariate histogram space for hyper-square real-field bins.
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*/
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public class DoubleHistogramGroup(
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private val lower: Buffer<Double>,
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private val upper: Buffer<Double>,
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private val binNums: IntArray = IntArray(lower.size) { 20 },
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) : IndexedHistogramGroup<Double, Double> {
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init {
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// argument checks
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require(lower.size == upper.size) { "Dimension mismatch in histogram lower and upper limits." }
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require(lower.size == binNums.size) { "Dimension mismatch in bin count." }
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require(!lower.indices.any { upper[it] - lower[it] < 0 }) { "Range for one of axis is not strictly positive" }
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}
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public val dimension: Int get() = lower.size
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override val shape: IntArray = IntArray(binNums.size) { binNums[it] + 2 }
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override val histogramValueAlgebra: DoubleFieldND = DoubleField.ndAlgebra(*shape)
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private val binSize = DoubleBuffer(dimension) { (upper[it] - lower[it]) / binNums[it] }
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/**
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* Get internal [StructureND] bin index for given axis
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*/
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private fun getIndex(axis: Int, value: Double): Int = when {
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value >= upper[axis] -> binNums[axis] + 1 // overflow
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value < lower[axis] -> 0 // underflow
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else -> floor((value - lower[axis]) / binSize[axis]).toInt()
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}
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override fun getIndexOrNull(point: Buffer<Double>): IntArray = IntArray(dimension) {
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getIndex(it, point[it])
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}
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@OptIn(UnstableKMathAPI::class)
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override fun getDomain(index: IntArray): HyperSquareDomain {
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val lowerBoundary = index.mapIndexed { axis, i ->
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when (i) {
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0 -> Double.NEGATIVE_INFINITY
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shape[axis] - 1 -> upper[axis]
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else -> lower[axis] + (i.toDouble()) * binSize[axis]
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}
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}.asBuffer()
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val upperBoundary = index.mapIndexed { axis, i ->
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when (i) {
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0 -> lower[axis]
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shape[axis] - 1 -> Double.POSITIVE_INFINITY
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else -> lower[axis] + (i.toDouble() + 1) * binSize[axis]
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}
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}.asBuffer()
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return HyperSquareDomain(lowerBoundary, upperBoundary)
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}
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@OptIn(UnstableKMathAPI::class)
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override fun produceBin(index: IntArray, value: Double): DomainBin<Double, Double> {
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val domain = getDomain(index)
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return DomainBin(domain, value)
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}
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override fun produce(builder: HistogramBuilder<Double, Double>.() -> Unit): IndexedHistogram<Double, Double> {
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val ndCounter = StructureND.auto(shape) { Counter.double() }
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val hBuilder = object : HistogramBuilder<Double, Double> {
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override val defaultValue: Double get() = 1.0
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override fun putValue(point: Point<out Double>, value: Double) {
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val index = getIndexOrNull(point)
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ndCounter[index].add(value)
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}
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}
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hBuilder.apply(builder)
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val values: BufferND<Double> = ndCounter.mapToBuffer { it.value }
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return IndexedHistogram(this, values)
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}
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override fun IndexedHistogram<Double, Double>.unaryMinus(): IndexedHistogram<Double, Double> = this * (-1)
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public companion object {
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/**
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* Use it like
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* ```
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*FastHistogram.fromRanges(
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* (-1.0..1.0),
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* (-1.0..1.0)
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*)
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*```
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*/
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public fun fromRanges(
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vararg ranges: ClosedFloatingPointRange<Double>,
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): DoubleHistogramGroup = DoubleHistogramGroup(
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ranges.map(ClosedFloatingPointRange<Double>::start).asBuffer(),
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ranges.map(ClosedFloatingPointRange<Double>::endInclusive).asBuffer()
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)
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/**
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* Use it like
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* ```
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*FastHistogram.fromRanges(
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* (-1.0..1.0) to 50,
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* (-1.0..1.0) to 32
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*)
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*```
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*/
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public fun fromRanges(
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vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>,
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): DoubleHistogramGroup = DoubleHistogramGroup(
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ListBuffer(
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ranges
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.map(Pair<ClosedFloatingPointRange<Double>, Int>::first)
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.map(ClosedFloatingPointRange<Double>::start)
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),
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ListBuffer(
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ranges
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.map(Pair<ClosedFloatingPointRange<Double>, Int>::first)
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.map(ClosedFloatingPointRange<Double>::endInclusive)
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),
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ranges.map(Pair<ClosedFloatingPointRange<Double>, Int>::second).toIntArray()
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)
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}
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}
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@ -20,6 +20,15 @@ public interface Bin<in T : Any, out V> : Domain<T> {
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public val binValue: V
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}
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/**
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* A simple histogram bin based on domain
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*/
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public data class DomainBin<in T : Comparable<T>, D : Domain<T>, out V>(
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public val domain: D,
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override val binValue: V,
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) : Bin<T, V>, Domain<T> by domain
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public interface Histogram<in T : Any, out V, out B : Bin<T, V>> {
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/**
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* Find existing bin, corresponding to given coordinates
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@ -0,0 +1,76 @@
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/*
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* Copyright 2018-2021 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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package space.kscience.kmath.histogram
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import space.kscience.kmath.domains.Domain
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import space.kscience.kmath.linear.Point
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import space.kscience.kmath.nd.DefaultStrides
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import space.kscience.kmath.nd.FieldOpsND
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import space.kscience.kmath.nd.Shape
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import space.kscience.kmath.nd.StructureND
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import space.kscience.kmath.operations.Group
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import space.kscience.kmath.operations.ScaleOperations
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import space.kscience.kmath.operations.invoke
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/**
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* @param T the type of the argument space
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* @param V the type of bin value
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*/
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public class HistogramND<T : Comparable<T>, D : Domain<T>, V : Any>(
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public val group: HistogramGroupND<T, D, V>,
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internal val values: StructureND<V>,
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) : Histogram<T, V, DomainBin<T, D, V>> {
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override fun get(point: Point<T>): DomainBin<T, D, V>? {
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val index = group.getIndexOrNull(point) ?: return null
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return group.produceBin(index, values[index])
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}
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override val dimension: Int get() = group.shape.size
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override val bins: Iterable<DomainBin<T, D, V>>
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get() = DefaultStrides(group.shape).asSequence().map {
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group.produceBin(it, values[it])
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}.asIterable()
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}
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/**
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* A space for producing histograms with values in a NDStructure
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*/
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public interface HistogramGroupND<T : Comparable<T>, D : Domain<T>, V : Any> :
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Group<HistogramND<T, D, V>>, ScaleOperations<HistogramND<T, D, V>> {
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public val shape: Shape
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public val valueAlgebra: FieldOpsND<V, *> //= NDAlgebra.space(valueSpace, Buffer.Companion::boxing, *shape),
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/**
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* Resolve index of the bin including given [point]. Return null if point is outside histogram area
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*/
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public fun getIndexOrNull(point: Point<T>): IntArray?
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/**
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* Get a bin domain represented by given index
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*/
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public fun getDomain(index: IntArray): Domain<T>?
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public fun produceBin(index: IntArray, value: V): DomainBin<T, D, V>
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public fun produce(builder: HistogramBuilder<T, V>.() -> Unit): HistogramND<T, D, V>
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override fun add(left: HistogramND<T, D, V>, right: HistogramND<T, D, V>): HistogramND<T, D, V> {
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require(left.group == this && right.group == this) {
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"A histogram belonging to a different group cannot be operated."
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}
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return HistogramND(this, valueAlgebra { left.values + right.values })
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}
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override fun scale(a: HistogramND<T, D, V>, value: Double): HistogramND<T, D, V> {
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require(a.group == this) { "A histogram belonging to a different group cannot be operated." }
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return HistogramND(this, valueAlgebra { a.values * value })
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}
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override val zero: HistogramND<T, D, V> get() = produce { }
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}
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@ -1,84 +0,0 @@
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/*
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* Copyright 2018-2021 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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package space.kscience.kmath.histogram
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import space.kscience.kmath.domains.Domain
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import space.kscience.kmath.linear.Point
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import space.kscience.kmath.nd.DefaultStrides
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import space.kscience.kmath.nd.FieldND
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import space.kscience.kmath.nd.Shape
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import space.kscience.kmath.nd.StructureND
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import space.kscience.kmath.operations.Group
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import space.kscience.kmath.operations.ScaleOperations
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import space.kscience.kmath.operations.invoke
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/**
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* A simple histogram bin based on domain
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*/
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public data class DomainBin<in T : Comparable<T>, out V>(
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public val domain: Domain<T>,
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override val binValue: V,
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) : Bin<T, V>, Domain<T> by domain
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/**
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* @param T the type of the argument space
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* @param V the type of bin value
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*/
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public class IndexedHistogram<T : Comparable<T>, V : Any>(
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public val histogramGroup: IndexedHistogramGroup<T, V>,
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public val values: StructureND<V>,
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) : Histogram<T, V, DomainBin<T, V>> {
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override fun get(point: Point<T>): DomainBin<T, V>? {
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val index = histogramGroup.getIndexOrNull(point) ?: return null
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return histogramGroup.produceBin(index, values[index])
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}
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override val dimension: Int get() = histogramGroup.shape.size
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override val bins: Iterable<DomainBin<T, V>>
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get() = DefaultStrides(histogramGroup.shape).asSequence().map {
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histogramGroup.produceBin(it, values[it])
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}.asIterable()
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}
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/**
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* A space for producing histograms with values in a NDStructure
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*/
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public interface IndexedHistogramGroup<T : Comparable<T>, V : Any> : Group<IndexedHistogram<T, V>>,
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ScaleOperations<IndexedHistogram<T, V>> {
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public val shape: Shape
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public val histogramValueAlgebra: FieldND<V, *> //= NDAlgebra.space(valueSpace, Buffer.Companion::boxing, *shape),
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/**
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* Resolve index of the bin including given [point]. Return null if point is outside histogram area
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*/
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public fun getIndexOrNull(point: Point<T>): IntArray?
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/**
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* Get a bin domain represented by given index
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*/
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public fun getDomain(index: IntArray): Domain<T>?
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public fun produceBin(index: IntArray, value: V): DomainBin<T, V>
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public fun produce(builder: HistogramBuilder<T, V>.() -> Unit): IndexedHistogram<T, V>
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override fun add(left: IndexedHistogram<T, V>, right: IndexedHistogram<T, V>): IndexedHistogram<T, V> {
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require(left.histogramGroup == this && right.histogramGroup == this) {
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"A histogram belonging to a different group cannot be operated."
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}
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return IndexedHistogram(this, histogramValueAlgebra { left.values + right.values })
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}
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override fun scale(a: IndexedHistogram<T, V>, value: Double): IndexedHistogram<T, V> {
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require(a.histogramGroup == this) { "A histogram belonging to a different group cannot be operated." }
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return IndexedHistogram(this, histogramValueAlgebra { a.values * value })
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}
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override val zero: IndexedHistogram<T, V> get() = produce { }
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}
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@ -0,0 +1,163 @@
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/*
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* Copyright 2018-2021 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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@file:OptIn(UnstableKMathAPI::class)
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package space.kscience.kmath.histogram
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import space.kscience.kmath.domains.HyperSquareDomain
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import space.kscience.kmath.linear.Point
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import space.kscience.kmath.misc.UnstableKMathAPI
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import space.kscience.kmath.nd.*
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import space.kscience.kmath.operations.DoubleField
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import space.kscience.kmath.operations.Field
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import space.kscience.kmath.operations.invoke
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import space.kscience.kmath.structures.*
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import kotlin.math.floor
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public typealias HyperSquareBin<V> = DomainBin<Double, HyperSquareDomain, V>
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/**
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* Multivariate histogram space for hyper-square real-field bins.
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* @param bufferFactory is an optional parameter used to optimize buffer production.
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*/
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public class UniformHistogramGroupND<V : Any, A : Field<V>>(
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override val valueAlgebra: FieldOpsND<V, A>,
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private val lower: Buffer<Double>,
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private val upper: Buffer<Double>,
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private val binNums: IntArray = IntArray(lower.size) { 20 },
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private val bufferFactory: BufferFactory<V> = Buffer.Companion::boxing,
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) : HistogramGroupND<Double, HyperSquareDomain, V> {
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init {
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// argument checks
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require(lower.size == upper.size) { "Dimension mismatch in histogram lower and upper limits." }
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require(lower.size == binNums.size) { "Dimension mismatch in bin count." }
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require(!lower.indices.any { upper[it] - lower[it] < 0 }) { "Range for one of axis is not strictly positive" }
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}
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public val dimension: Int get() = lower.size
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override val shape: IntArray = IntArray(binNums.size) { binNums[it] + 2 }
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private val binSize = DoubleBuffer(dimension) { (upper[it] - lower[it]) / binNums[it] }
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/**
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* Get internal [StructureND] bin index for given axis
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*/
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private fun getIndex(axis: Int, value: Double): Int = when {
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value >= upper[axis] -> binNums[axis] + 1 // overflow
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value < lower[axis] -> 0 // underflow
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else -> floor((value - lower[axis]) / binSize[axis]).toInt()
|
||||
}
|
||||
|
||||
override fun getIndexOrNull(point: Buffer<Double>): IntArray = IntArray(dimension) {
|
||||
getIndex(it, point[it])
|
||||
}
|
||||
|
||||
override fun getDomain(index: IntArray): HyperSquareDomain {
|
||||
val lowerBoundary = index.mapIndexed { axis, i ->
|
||||
when (i) {
|
||||
0 -> Double.NEGATIVE_INFINITY
|
||||
shape[axis] - 1 -> upper[axis]
|
||||
else -> lower[axis] + (i.toDouble()) * binSize[axis]
|
||||
}
|
||||
}.asBuffer()
|
||||
|
||||
val upperBoundary = index.mapIndexed { axis, i ->
|
||||
when (i) {
|
||||
0 -> lower[axis]
|
||||
shape[axis] - 1 -> Double.POSITIVE_INFINITY
|
||||
else -> lower[axis] + (i.toDouble() + 1) * binSize[axis]
|
||||
}
|
||||
}.asBuffer()
|
||||
|
||||
return HyperSquareDomain(lowerBoundary, upperBoundary)
|
||||
}
|
||||
|
||||
override fun produceBin(index: IntArray, value: V): HyperSquareBin<V> {
|
||||
val domain = getDomain(index)
|
||||
return DomainBin(domain, value)
|
||||
}
|
||||
|
||||
|
||||
override fun produce(builder: HistogramBuilder<Double, V>.() -> Unit): HistogramND<Double, HyperSquareDomain, V> {
|
||||
val ndCounter = StructureND.buffered(shape) { Counter.of(valueAlgebra.elementAlgebra) }
|
||||
val hBuilder = object : HistogramBuilder<Double, V> {
|
||||
override val defaultValue: V get() = valueAlgebra.elementAlgebra.one
|
||||
|
||||
override fun putValue(point: Point<out Double>, value: V) = with(valueAlgebra.elementAlgebra) {
|
||||
val index = getIndexOrNull(point)
|
||||
ndCounter[index].add(value)
|
||||
}
|
||||
}
|
||||
hBuilder.apply(builder)
|
||||
val values: BufferND<V> = ndCounter.mapToBuffer(bufferFactory) { it.value }
|
||||
return HistogramND(this, values)
|
||||
}
|
||||
|
||||
override fun HistogramND<Double, HyperSquareDomain, V>.unaryMinus(): HistogramND<Double, HyperSquareDomain, V> =
|
||||
this * (-1)
|
||||
}
|
||||
|
||||
/**
|
||||
* Use it like
|
||||
* ```
|
||||
*FastHistogram.fromRanges(
|
||||
* (-1.0..1.0),
|
||||
* (-1.0..1.0)
|
||||
*)
|
||||
*```
|
||||
*/
|
||||
public fun <V : Any, A : Field<V>> Histogram.Companion.uniformNDFromRanges(
|
||||
valueAlgebra: FieldOpsND<V, A>,
|
||||
vararg ranges: ClosedFloatingPointRange<Double>,
|
||||
bufferFactory: BufferFactory<V> = Buffer.Companion::boxing,
|
||||
): UniformHistogramGroupND<V, A> = UniformHistogramGroupND(
|
||||
valueAlgebra,
|
||||
ranges.map(ClosedFloatingPointRange<Double>::start).asBuffer(),
|
||||
ranges.map(ClosedFloatingPointRange<Double>::endInclusive).asBuffer(),
|
||||
bufferFactory = bufferFactory
|
||||
)
|
||||
|
||||
public fun Histogram.Companion.uniformDoubleNDFromRanges(
|
||||
vararg ranges: ClosedFloatingPointRange<Double>,
|
||||
): UniformHistogramGroupND<Double, DoubleField> =
|
||||
uniformNDFromRanges(DoubleFieldOpsND, *ranges, bufferFactory = ::DoubleBuffer)
|
||||
|
||||
|
||||
/**
|
||||
* Use it like
|
||||
* ```
|
||||
*FastHistogram.fromRanges(
|
||||
* (-1.0..1.0) to 50,
|
||||
* (-1.0..1.0) to 32
|
||||
*)
|
||||
*```
|
||||
*/
|
||||
public fun <V : Any, A : Field<V>> Histogram.Companion.uniformNDFromRanges(
|
||||
valueAlgebra: FieldOpsND<V, A>,
|
||||
vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>,
|
||||
bufferFactory: BufferFactory<V> = Buffer.Companion::boxing,
|
||||
): UniformHistogramGroupND<V, A> = UniformHistogramGroupND(
|
||||
valueAlgebra,
|
||||
ListBuffer(
|
||||
ranges
|
||||
.map(Pair<ClosedFloatingPointRange<Double>, Int>::first)
|
||||
.map(ClosedFloatingPointRange<Double>::start)
|
||||
),
|
||||
ListBuffer(
|
||||
ranges
|
||||
.map(Pair<ClosedFloatingPointRange<Double>, Int>::first)
|
||||
.map(ClosedFloatingPointRange<Double>::endInclusive)
|
||||
),
|
||||
ranges.map(Pair<ClosedFloatingPointRange<Double>, Int>::second).toIntArray(),
|
||||
bufferFactory = bufferFactory
|
||||
)
|
||||
|
||||
public fun Histogram.Companion.uniformDoubleNDFromRanges(
|
||||
vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>,
|
||||
): UniformHistogramGroupND<Double, DoubleField> =
|
||||
uniformNDFromRanges(DoubleFieldOpsND, *ranges, bufferFactory = ::DoubleBuffer)
|
@ -3,8 +3,11 @@
|
||||
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
|
||||
*/
|
||||
|
||||
@file:OptIn(UnstableKMathAPI::class)
|
||||
|
||||
package space.kscience.kmath.histogram
|
||||
|
||||
import space.kscience.kmath.misc.UnstableKMathAPI
|
||||
import space.kscience.kmath.nd.DefaultStrides
|
||||
import space.kscience.kmath.operations.invoke
|
||||
import space.kscience.kmath.real.DoubleVector
|
||||
@ -14,7 +17,7 @@ import kotlin.test.*
|
||||
internal class MultivariateHistogramTest {
|
||||
@Test
|
||||
fun testSinglePutHistogram() {
|
||||
val hSpace = DoubleHistogramGroup.fromRanges(
|
||||
val hSpace = Histogram.uniformDoubleNDFromRanges(
|
||||
(-1.0..1.0),
|
||||
(-1.0..1.0)
|
||||
)
|
||||
@ -29,7 +32,7 @@ internal class MultivariateHistogramTest {
|
||||
|
||||
@Test
|
||||
fun testSequentialPut() {
|
||||
val hSpace = DoubleHistogramGroup.fromRanges(
|
||||
val hSpace = Histogram.uniformDoubleNDFromRanges(
|
||||
(-1.0..1.0),
|
||||
(-1.0..1.0),
|
||||
(-1.0..1.0)
|
||||
@ -49,7 +52,7 @@ internal class MultivariateHistogramTest {
|
||||
|
||||
@Test
|
||||
fun testHistogramAlgebra() {
|
||||
DoubleHistogramGroup.fromRanges(
|
||||
Histogram.uniformDoubleNDFromRanges(
|
||||
(-1.0..1.0),
|
||||
(-1.0..1.0),
|
||||
(-1.0..1.0)
|
||||
|
@ -45,7 +45,7 @@ internal class TreeHistogramBuilder(val binFactory: (Double) -> DoubleDomain1D)
|
||||
|
||||
override val defaultValue: Double get() = 1.0
|
||||
|
||||
internal class BinCounter(val domain: DoubleDomain1D, val counter: Counter<Double> = Counter.double()) :
|
||||
internal class BinCounter(val domain: DoubleDomain1D, val counter: Counter<Double> = Counter.ofDouble()) :
|
||||
ClosedRange<Double> by domain.range
|
||||
|
||||
private val bins: TreeMap<Double, BinCounter> = TreeMap()
|
||||
|
Loading…
Reference in New Issue
Block a user