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Merge pull request #203 from mipt-npm/refactor/histograms

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Refactor/histograms
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Alexander Nozik 2021-02-18 18:05:03 +03:00 committed by GitHub
commit ee4c348294
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23 changed files with 533 additions and 422 deletions
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1
CHANGELOG.md
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@ -41,6 +41,7 @@
- Refactor histograms. They are marked as prototype
- `Complex` and related features moved to a separate module `kmath-complex`
- Refactor AlgebraElement
- Add `out` projection to `Buffer` generic
### Deprecated

8
examples/build.gradle.kts
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@ -76,6 +76,14 @@ benchmark {
iterationTimeUnit = "ms" // time unity for iterationTime, default is seconds
include("DotBenchmark")
}
configurations.register("expressions") {
warmups = 1 // number of warmup iterations
iterations = 3 // number of iterations
iterationTime = 500 // time in seconds per iteration
iterationTimeUnit = "ms" // time unity for iterationTime, default is seconds
include("ExpressionsInterpretersBenchmark")
}
}
kotlin.sourceSets.all {

5
examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/LinearAlgebraBenchmark.kt
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@ -10,7 +10,6 @@ import kscience.kmath.linear.Matrix
import kscience.kmath.linear.MatrixContext
import kscience.kmath.linear.inverseWithLup
import kscience.kmath.linear.real
import kscience.kmath.operations.invoke
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import kotlin.random.Random
@ -34,14 +33,14 @@ internal class LinearAlgebraBenchmark {
@Benchmark
fun cmLUPInversion() {
CMMatrixContext {
with(CMMatrixContext) {
inverse(matrix)
}
}
@Benchmark
fun ejmlInverse() {
EjmlMatrixContext {
with(EjmlMatrixContext) {
inverse(matrix)
}
}

7
examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/NDFieldBenchmark.kt
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@ -2,7 +2,6 @@ package kscience.kmath.benchmarks
import kscience.kmath.nd.*
import kscience.kmath.operations.RealField
import kscience.kmath.operations.invoke
import kscience.kmath.structures.Buffer
import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
@ -12,7 +11,7 @@ import org.openjdk.jmh.annotations.State
internal class NDFieldBenchmark {
@Benchmark
fun autoFieldAdd() {
autoField {
with(autoField) {
var res: NDStructure<Double> = one
repeat(n) { res += one }
}
@ -20,7 +19,7 @@ internal class NDFieldBenchmark {
@Benchmark
fun specializedFieldAdd() {
specializedField {
with(specializedField) {
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
@ -29,7 +28,7 @@ internal class NDFieldBenchmark {
@Benchmark
fun boxingFieldAdd() {
genericField {
with(genericField) {
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}

7
examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/ViktorBenchmark.kt
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@ -2,7 +2,6 @@ package kscience.kmath.benchmarks
import kscience.kmath.nd.*
import kscience.kmath.operations.RealField
import kscience.kmath.operations.invoke
import kscience.kmath.viktor.ViktorNDField
import org.jetbrains.bio.viktor.F64Array
import org.openjdk.jmh.annotations.Benchmark
@ -21,7 +20,7 @@ internal class ViktorBenchmark {
@Benchmark
fun automaticFieldAddition() {
autoField {
with(autoField) {
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
@ -29,7 +28,7 @@ internal class ViktorBenchmark {
@Benchmark
fun realFieldAddition() {
realField {
with(realField) {
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
@ -37,7 +36,7 @@ internal class ViktorBenchmark {
@Benchmark
fun viktorFieldAddition() {
viktorField {
with(viktorField) {
var res = one
repeat(n) { res += 1.0 }
}

5
examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/ViktorLogBenchmark.kt
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@ -2,7 +2,6 @@ package kscience.kmath.benchmarks
import kscience.kmath.nd.*
import kscience.kmath.operations.RealField
import kscience.kmath.operations.invoke
import kscience.kmath.viktor.ViktorNDField
import org.jetbrains.bio.viktor.F64Array
import org.openjdk.jmh.annotations.Benchmark
@ -22,7 +21,7 @@ internal class ViktorLogBenchmark {
@Benchmark
fun realFieldLog() {
realField {
with(realField) {
val fortyTwo = produce { 42.0 }
var res = one
repeat(n) { res = ln(fortyTwo) }
@ -31,7 +30,7 @@ internal class ViktorLogBenchmark {
@Benchmark
fun viktorFieldLog() {
viktorField {
with(viktorField) {
val fortyTwo = produce { 42.0 }
var res = one
repeat(n) { res = ln(fortyTwo) }

13
kmath-ast/src/commonMain/kotlin/kscience/kmath/ast/MstExpression.kt
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@ -21,8 +21,17 @@ public class MstExpression<T, out A : Algebra<T>>(public val algebra: A, public
null
} ?: arguments.getValue(StringSymbol(value))
override fun unaryOperationFunction(operation: String): (arg: T) -> T = algebra.unaryOperationFunction(operation)
override fun binaryOperationFunction(operation: String): (left: T, right: T) -> T = algebra.binaryOperationFunction(operation)
override fun unaryOperation(operation: String, arg: T): T =
algebra.unaryOperation(operation, arg)
override fun binaryOperation(operation: String, left: T, right: T): T =
algebra.binaryOperation(operation, left, right)
override fun unaryOperationFunction(operation: String): (arg: T) -> T =
algebra.unaryOperationFunction(operation)
override fun binaryOperationFunction(operation: String): (left: T, right: T) -> T =
algebra.binaryOperationFunction(operation)
@Suppress("UNCHECKED_CAST")
override fun number(value: Number): T = if (algebra is NumericAlgebra<*>)

2
kmath-core/api/kmath-core.api
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@ -4,7 +4,7 @@ public abstract interface class kscience/kmath/domains/Domain {
}
public final class kscience/kmath/domains/HyperSquareDomain : kscience/kmath/domains/RealDomain {
public synthetic fun <init> ([D[DLkotlin/jvm/internal/DefaultConstructorMarker;)V
public fun <init> (Lkscience/kmath/structures/Buffer;Lkscience/kmath/structures/Buffer;)V
public fun contains (Lkscience/kmath/structures/Buffer;)Z
public fun getDimension ()I
public fun getLowerBound (I)Ljava/lang/Double;

11
kmath-core/src/commonMain/kotlin/kscience/kmath/domains/HyperSquareDomain.kt
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@ -16,6 +16,7 @@
package kscience.kmath.domains
import kscience.kmath.linear.Point
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.RealBuffer
import kscience.kmath.structures.indices
@ -25,20 +26,20 @@ import kscience.kmath.structures.indices
*
* @author Alexander Nozik
*/
public class HyperSquareDomain(private val lower: RealBuffer, private val upper: RealBuffer) : RealDomain {
public class HyperSquareDomain(private val lower: Buffer<Double>, private val upper: Buffer<Double>) : RealDomain {
public override val dimension: Int get() = lower.size
public override operator fun contains(point: Point<Double>): Boolean = point.indices.all { i ->
point[i] in lower[i]..upper[i]
}
public override fun getLowerBound(num: Int, point: Point<Double>): Double? = lower[num]
public override fun getLowerBound(num: Int, point: Point<Double>): Double = lower[num]
public override fun getLowerBound(num: Int): Double? = lower[num]
public override fun getLowerBound(num: Int): Double = lower[num]
public override fun getUpperBound(num: Int, point: Point<Double>): Double? = upper[num]
public override fun getUpperBound(num: Int, point: Point<Double>): Double = upper[num]
public override fun getUpperBound(num: Int): Double? = upper[num]
public override fun getUpperBound(num: Int): Double = upper[num]
public override fun nearestInDomain(point: Point<Double>): Point<Double> {
val res = DoubleArray(point.size) { i ->

15
kmath-core/src/commonMain/kotlin/kscience/kmath/nd/NDStructure.kt
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@ -78,8 +78,7 @@ public interface NDStructure<T> {
strides: Strides,
bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
initializer: (IntArray) -> T,
): NDBuffer<T> =
NDBuffer(strides, bufferFactory(strides.linearSize) { i -> initializer(strides.index(i)) })
): NDBuffer<T> = NDBuffer(strides, bufferFactory(strides.linearSize) { i -> initializer(strides.index(i)) })
/**
* Inline create NDStructure with non-boxing buffer implementation if it is possible
@ -87,15 +86,13 @@ public interface NDStructure<T> {
public inline fun <reified T : Any> auto(
strides: Strides,
crossinline initializer: (IntArray) -> T,
): NDBuffer<T> =
NDBuffer(strides, Buffer.auto(strides.linearSize) { i -> initializer(strides.index(i)) })
): NDBuffer<T> = NDBuffer(strides, Buffer.auto(strides.linearSize) { i -> initializer(strides.index(i)) })
public inline fun <T : Any> auto(
type: KClass<T>,
strides: Strides,
crossinline initializer: (IntArray) -> T,
): NDBuffer<T> =
NDBuffer(strides, Buffer.auto(type, strides.linearSize) { i -> initializer(strides.index(i)) })
): NDBuffer<T> = NDBuffer(strides, Buffer.auto(type, strides.linearSize) { i -> initializer(strides.index(i)) })
public fun <T> build(
shape: IntArray,
@ -106,8 +103,7 @@ public interface NDStructure<T> {
public inline fun <reified T : Any> auto(
shape: IntArray,
crossinline initializer: (IntArray) -> T,
): NDBuffer<T> =
auto(DefaultStrides(shape), initializer)
): NDBuffer<T> = auto(DefaultStrides(shape), initializer)
@JvmName("autoVarArg")
public inline fun <reified T : Any> auto(
@ -120,8 +116,7 @@ public interface NDStructure<T> {
type: KClass<T>,
vararg shape: Int,
crossinline initializer: (IntArray) -> T,
): NDBuffer<T> =
auto(type, DefaultStrides(shape), initializer)
): NDBuffer<T> = auto(type, DefaultStrides(shape), initializer)
}
}

2
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/Buffer.kt
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@ -21,7 +21,7 @@ public typealias MutableBufferFactory<T> = (Int, (Int) -> T) -> MutableBuffer<T>
*
* @param T the type of elements contained in the buffer.
*/
public interface Buffer<T> {
public interface Buffer<out T> {
/**
* The size of this buffer.
*/

8
kmath-histograms/build.gradle.kts
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@ -1,4 +1,10 @@
plugins { id("ru.mipt.npm.mpp") }
plugins {
id("ru.mipt.npm.mpp")
}
kscience {
useAtomic()
}
kotlin.sourceSets {
commonMain {

55
kmath-histograms/src/commonMain/kotlin/kscience/kmath/histogram/Counters.kt
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@ -1,20 +1,49 @@
package kscience.kmath.histogram
/*
import kotlinx.atomicfu.atomic
import kotlinx.atomicfu.getAndUpdate
import kscience.kmath.operations.RealField
import kscience.kmath.operations.Space
/**
* Common representation for atomic counters
* TODO replace with atomics
*/
public expect class LongCounter() {
public fun decrement()
public fun increment()
public fun reset()
public fun sum(): Long
public fun add(l: Long)
public interface Counter<T : Any> {
public fun add(delta: T)
public val value: T
public companion object{
public fun real(): ObjectCounter<Double> = ObjectCounter(RealField)
}
}
public expect class DoubleCounter() {
public fun reset()
public fun sum(): Double
public fun add(d: Double)
public class IntCounter : Counter<Int> {
private val innerValue = atomic(0)
override fun add(delta: Int) {
innerValue += delta
}
override val value: Int get() = innerValue.value
}
public class LongCounter : Counter<Long> {
private val innerValue = atomic(0L)
override fun add(delta: Long) {
innerValue += delta
}
override val value: Long get() = innerValue.value
}
public class ObjectCounter<T : Any>(public val space: Space<T>) : Counter<T> {
private val innerValue = atomic(space.zero)
override fun add(delta: T) {
innerValue.getAndUpdate { space.run { it + delta } }
}
override val value: T get() = innerValue.value
}

25
kmath-histograms/src/commonMain/kotlin/kscience/kmath/histogram/Histogram.kt
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@ -6,18 +6,16 @@ import kscience.kmath.structures.ArrayBuffer
import kscience.kmath.structures.RealBuffer
/**
* The bin in the histogram. The histogram is by definition always done in the real space
* The binned data element. Could be a histogram bin with a number of counts or an artificial construct
*/
public interface Bin<T : Any> : Domain<T> {
/**
* The value of this bin.
*/
public val value: Number
public val center: Point<T>
}
public interface Histogram<T : Any, out B : Bin<T>> : Iterable<B> {
public interface Histogram<T : Any, out B : Bin<T>> {
/**
* Find existing bin, corresponding to given coordinates
*/
@ -27,28 +25,31 @@ public interface Histogram<T : Any, out B : Bin<T>> : Iterable<B> {
* Dimension of the histogram
*/
public val dimension: Int
public val bins: Iterable<B>
}
public interface MutableHistogram<T : Any, out B : Bin<T>> : Histogram<T, B> {
public fun interface HistogramBuilder<T : Any> {
/**
* Increment appropriate bin
*/
public fun putWithWeight(point: Point<out T>, weight: Double)
public fun putValue(point: Point<out T>, value: Number)
public fun put(point: Point<out T>): Unit = putWithWeight(point, 1.0)
}
public fun <T : Any> MutableHistogram<T, *>.put(vararg point: T): Unit = put(ArrayBuffer(point))
public fun <T : Any, B : Bin<T>> HistogramBuilder<T>.put(point: Point<out T>): Unit = putValue(point, 1.0)
public fun MutableHistogram<Double, *>.put(vararg point: Number): Unit =
public fun <T : Any> HistogramBuilder<T>.put(vararg point: T): Unit = put(ArrayBuffer(point))
public fun HistogramBuilder<Double>.put(vararg point: Number): Unit =
put(RealBuffer(point.map { it.toDouble() }.toDoubleArray()))
public fun MutableHistogram<Double, *>.put(vararg point: Double): Unit = put(RealBuffer(point))
public fun <T : Any> MutableHistogram<T, *>.fill(sequence: Iterable<Point<T>>): Unit = sequence.forEach { put(it) }
public fun HistogramBuilder<Double>.put(vararg point: Double): Unit = put(RealBuffer(point))
public fun <T : Any> HistogramBuilder<T>.fill(sequence: Iterable<Point<T>>): Unit = sequence.forEach { put(it) }
/**
* Pass a sequence builder into histogram
*/
public fun <T : Any> MutableHistogram<T, *>.fill(block: suspend SequenceScope<Point<T>>.() -> Unit): Unit =
public fun <T : Any> HistogramBuilder<T>.fill(block: suspend SequenceScope<Point<T>>.() -> Unit): Unit =
fill(sequence(block).asIterable())

76
kmath-histograms/src/commonMain/kotlin/kscience/kmath/histogram/IndexedHistogramSpace.kt Normal file
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@ -0,0 +1,76 @@
package kscience.kmath.histogram
import kscience.kmath.domains.Domain
import kscience.kmath.linear.Point
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.nd.NDSpace
import kscience.kmath.nd.NDStructure
import kscience.kmath.nd.Strides
import kscience.kmath.operations.Space
import kscience.kmath.operations.SpaceElement
import kscience.kmath.operations.invoke
/**
* A simple histogram bin based on domain
*/
public data class DomainBin<T : Comparable<T>>(
public val domain: Domain<T>,
public override val value: Number,
) : Bin<T>, Domain<T> by domain
@OptIn(UnstableKMathAPI::class)
public class IndexedHistogram<T : Comparable<T>, V : Any>(
override val context: IndexedHistogramSpace<T, V>,
public val values: NDStructure<V>,
) : Histogram<T, Bin<T>>, SpaceElement<IndexedHistogram<T, V>, IndexedHistogramSpace<T, V>> {
override fun get(point: Point<T>): Bin<T>? {
val index = context.getIndex(point) ?: return null
return context.produceBin(index, values[index])
}
override val dimension: Int get() = context.strides.shape.size
override val bins: Iterable<Bin<T>>
get() = context.strides.indices().map {
context.produceBin(it, values[it])
}.asIterable()
}
/**
* A space for producing histograms with values in a NDStructure
*/
public interface IndexedHistogramSpace<T : Comparable<T>, V : Any> : Space<IndexedHistogram<T, V>> {
//public val valueSpace: Space<V>
public val strides: Strides
public val histogramValueSpace: NDSpace<V, *> //= NDAlgebra.space(valueSpace, Buffer.Companion::boxing, *shape),
/**
* Resolve index of the bin including given [point]
*/
public fun getIndex(point: Point<T>): IntArray?
/**
* Get a bin domain represented by given index
*/
public fun getDomain(index: IntArray): Domain<T>?
public fun produceBin(index: IntArray, value: V): Bin<T>
public fun produce(builder: HistogramBuilder<T>.() -> Unit): IndexedHistogram<T, V>
override fun add(a: IndexedHistogram<T, V>, b: IndexedHistogram<T, V>): IndexedHistogram<T, V> {
require(a.context == this) { "Can't operate on a histogram produced by external space" }
require(b.context == this) { "Can't operate on a histogram produced by external space" }
return IndexedHistogram(this, histogramValueSpace.invoke { a.values + b.values })
}
override fun multiply(a: IndexedHistogram<T, V>, k: Number): IndexedHistogram<T, V> {
require(a.context == this) { "Can't operate on a histogram produced by external space" }
return IndexedHistogram(this, histogramValueSpace.invoke { a.values * k })
}
override val zero: IndexedHistogram<T, V> get() = produce { }
}

165
kmath-histograms/src/commonMain/kotlin/kscience/kmath/histogram/RealHistogram.kt
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@ -1,165 +0,0 @@
package kscience.kmath.histogram
import kscience.kmath.linear.Point
import kscience.kmath.nd.DefaultStrides
import kscience.kmath.nd.NDStructure
import kscience.kmath.operations.SpaceOperations
import kscience.kmath.operations.invoke
import kscience.kmath.structures.*
import kotlin.math.floor
public data class MultivariateBinDefinition<T : Comparable<T>>(
public val space: SpaceOperations<Point<T>>,
public val center: Point<T>,
public val sizes: Point<T>,
) {
public fun contains(vector: Point<out T>): Boolean {
require(vector.size == center.size) { "Dimension mismatch for input vector. Expected ${center.size}, but found ${vector.size}" }
val upper = space { center + sizes / 2.0 }
val lower = space { center - sizes / 2.0 }
return vector.asSequence().mapIndexed { i, value -> value in lower[i]..upper[i] }.all { it }
}
}
public class MultivariateBin<T : Comparable<T>>(
public val definition: MultivariateBinDefinition<T>,
public val count: Long,
public override val value: Double,
) : Bin<T> {
public override val dimension: Int
get() = definition.center.size
public override val center: Point<T>
get() = definition.center
public override operator fun contains(point: Point<T>): Boolean = definition.contains(point)
}
/**
* Uniform multivariate histogram with fixed borders. Based on NDStructure implementation with complexity of m for bin search, where m is the number of dimensions.
*/
public class RealHistogram(
private val lower: Buffer<Double>,
private val upper: Buffer<Double>,
private val binNums: IntArray = IntArray(lower.size) { 20 },
) : MutableHistogram<Double, MultivariateBin<Double>> {
private val strides = DefaultStrides(IntArray(binNums.size) { binNums[it] + 2 })
private val counts: NDStructure<LongCounter> = NDStructure.auto(strides) { LongCounter() }
private val values: NDStructure<DoubleCounter> = NDStructure.auto(strides) { DoubleCounter() }
public override val dimension: Int get() = lower.size
private val binSize = RealBuffer(dimension) { (upper[it] - lower[it]) / binNums[it] }
init {
// argument checks
require(lower.size == upper.size) { "Dimension mismatch in histogram lower and upper limits." }
require(lower.size == binNums.size) { "Dimension mismatch in bin count." }
require(!(0 until dimension).any { upper[it] - lower[it] < 0 }) { "Range for one of axis is not strictly positive" }
}
/**
* Get internal [NDStructure] bin index for given axis
*/
private fun getIndex(axis: Int, value: Double): Int = when {
value >= upper[axis] -> binNums[axis] + 1 // overflow
value < lower[axis] -> 0 // underflow
else -> floor((value - lower[axis]) / binSize[axis]).toInt() + 1
}
private fun getIndex(point: Buffer<out Double>): IntArray = IntArray(dimension) { getIndex(it, point[it]) }
private fun getCount(index: IntArray): Long = counts[index].sum()
public fun getCount(point: Buffer<out Double>): Long = getCount(getIndex(point))
private fun getValue(index: IntArray): Double = values[index].sum()
public fun getValue(point: Buffer<out Double>): Double = getValue(getIndex(point))
private fun getBinDefinition(index: IntArray): MultivariateBinDefinition<Double> {
val center = index.mapIndexed { axis, i ->
when (i) {
0 -> Double.NEGATIVE_INFINITY
strides.shape[axis] - 1 -> Double.POSITIVE_INFINITY
else -> lower[axis] + (i.toDouble() - 0.5) * binSize[axis]
}
}.asBuffer()
return MultivariateBinDefinition(RealBufferFieldOperations, center, binSize)
}
public fun getBinDefinition(point: Buffer<out Double>): MultivariateBinDefinition<Double> = getBinDefinition(getIndex(point))
public override operator fun get(point: Buffer<out Double>): MultivariateBin<Double>? {
val index = getIndex(point)
return MultivariateBin(getBinDefinition(index), getCount(index),getValue(index))
}
// fun put(point: Point<out Double>){
// val index = getIndex(point)
// values[index].increment()
// }
public override fun putWithWeight(point: Buffer<out Double>, weight: Double) {
val index = getIndex(point)
counts[index].increment()
values[index].add(weight)
}
public override operator fun iterator(): Iterator<MultivariateBin<Double>> =
strides.indices().map { index->
MultivariateBin(getBinDefinition(index), counts[index].sum(), values[index].sum())
}.iterator()
/**
* NDStructure containing number of events in bins without weights
*/
public fun counts(): NDStructure<Long> = NDStructure.auto(counts.shape) { counts[it].sum() }
/**
* NDStructure containing values of bins including weights
*/
public fun values(): NDStructure<Double> = NDStructure.auto(values.shape) { values[it].sum() }
public companion object {
/**
* Use it like
* ```
*FastHistogram.fromRanges(
* (-1.0..1.0),
* (-1.0..1.0)
*)
*```
*/
public fun fromRanges(vararg ranges: ClosedFloatingPointRange<Double>): RealHistogram = RealHistogram(
ranges.map(ClosedFloatingPointRange<Double>::start).asBuffer(),
ranges.map(ClosedFloatingPointRange<Double>::endInclusive).asBuffer()
)
/**
* Use it like
* ```
*FastHistogram.fromRanges(
* (-1.0..1.0) to 50,
* (-1.0..1.0) to 32
*)
*```
*/
public fun fromRanges(vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>): RealHistogram =
RealHistogram(
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()
)
}
}

121
kmath-histograms/src/commonMain/kotlin/kscience/kmath/histogram/RealHistogramSpace.kt Normal file
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@ -0,0 +1,121 @@
package kscience.kmath.histogram
import kscience.kmath.domains.Domain
import kscience.kmath.domains.HyperSquareDomain
import kscience.kmath.nd.*
import kscience.kmath.structures.*
import kotlin.math.floor
public class RealHistogramSpace(
private val lower: Buffer<Double>,
private val upper: Buffer<Double>,
private val binNums: IntArray = IntArray(lower.size) { 20 },
) : IndexedHistogramSpace<Double, Double> {
init {
// argument checks
require(lower.size == upper.size) { "Dimension mismatch in histogram lower and upper limits." }
require(lower.size == binNums.size) { "Dimension mismatch in bin count." }
require(!lower.indices.any { upper[it] - lower[it] < 0 }) { "Range for one of axis is not strictly positive" }
}
public val dimension: Int get() = lower.size
private val shape = IntArray(binNums.size) { binNums[it] + 2 }
override val histogramValueSpace: RealNDField = NDAlgebra.real(*shape)
override val strides: Strides get() = histogramValueSpace.strides
private val binSize = RealBuffer(dimension) { (upper[it] - lower[it]) / binNums[it] }
/**
* Get internal [NDStructure] bin index for given axis
*/
private fun getIndex(axis: Int, value: Double): Int = when {
value >= upper[axis] -> binNums[axis] + 1 // overflow
value < lower[axis] -> 0 // underflow
else -> floor((value - lower[axis]) / binSize[axis]).toInt()
}
override fun getIndex(point: Buffer<Double>): IntArray = IntArray(dimension) {
getIndex(it, point[it])
}
override fun getDomain(index: IntArray): Domain<Double> {
val lowerBoundary = index.mapIndexed { axis, i ->
when (i) {
0 -> Double.NEGATIVE_INFINITY
strides.shape[axis] - 1 -> upper[axis]
else -> lower[axis] + (i.toDouble()) * binSize[axis]
}
}.asBuffer()
val upperBoundary = index.mapIndexed { axis, i ->
when (i) {
0 -> lower[axis]
strides.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: Double): Bin<Double> {
val domain = getDomain(index)
return DomainBin(domain, value)
}
override fun produce(builder: HistogramBuilder<Double>.() -> Unit): IndexedHistogram<Double, Double> {
val ndCounter = NDStructure.auto(strides) { Counter.real() }
val hBuilder = HistogramBuilder<Double> { point, value ->
val index = getIndex(point)
ndCounter[index].add(1.0)
}
hBuilder.apply(builder)
val values: NDBuffer<Double> = ndCounter.mapToBuffer { it.value }
return IndexedHistogram(this, values)
}
public companion object {
/**
* Use it like
* ```
*FastHistogram.fromRanges(
* (-1.0..1.0),
* (-1.0..1.0)
*)
*```
*/
public fun fromRanges(vararg ranges: ClosedFloatingPointRange<Double>): RealHistogramSpace = RealHistogramSpace(
ranges.map(ClosedFloatingPointRange<Double>::start).asBuffer(),
ranges.map(ClosedFloatingPointRange<Double>::endInclusive).asBuffer()
)
/**
* Use it like
* ```
*FastHistogram.fromRanges(
* (-1.0..1.0) to 50,
* (-1.0..1.0) to 32
*)
*```
*/
public fun fromRanges(vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>): RealHistogramSpace =
RealHistogramSpace(
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()
)
}
}

55
kmath-histograms/src/commonTest/kotlin/scietifik/kmath/histogram/MultivariateHistogramTest.kt
View File

@ -1,8 +1,8 @@
package scietifik.kmath.histogram
import kscience.kmath.histogram.RealHistogram
import kscience.kmath.histogram.fill
import kscience.kmath.histogram.RealHistogramSpace
import kscience.kmath.histogram.put
import kscience.kmath.operations.invoke
import kscience.kmath.real.RealVector
import kscience.kmath.real.invoke
import kotlin.random.Random
@ -11,12 +11,14 @@ import kotlin.test.*
internal class MultivariateHistogramTest {
@Test
fun testSinglePutHistogram() {
val histogram = RealHistogram.fromRanges(
val hSpace = RealHistogramSpace.fromRanges(
(-1.0..1.0),
(-1.0..1.0)
)
histogram.put(0.55, 0.55)
val bin = histogram.find { it.value.toInt() > 0 } ?: fail()
val histogram = hSpace.produce {
put(0.55, 0.55)
}
val bin = histogram.bins.find { it.value.toInt() > 0 } ?: fail()
assertTrue { bin.contains(RealVector(0.55, 0.55)) }
assertTrue { bin.contains(RealVector(0.6, 0.5)) }
assertFalse { bin.contains(RealVector(-0.55, 0.55)) }
@ -24,7 +26,7 @@ internal class MultivariateHistogramTest {
@Test
fun testSequentialPut() {
val histogram = RealHistogram.fromRanges(
val hSpace = RealHistogramSpace.fromRanges(
(-1.0..1.0),
(-1.0..1.0),
(-1.0..1.0)
@ -34,12 +36,45 @@ internal class MultivariateHistogramTest {
fun nextDouble() = random.nextDouble(-1.0, 1.0)
val n = 10000
histogram.fill {
val histogram = hSpace.produce {
repeat(n) {
yield(RealVector(nextDouble(), nextDouble(), nextDouble()))
put(nextDouble(), nextDouble(), nextDouble())
}
}
assertEquals(n, histogram.sumBy { it.value.toInt() })
assertEquals(n, histogram.bins.sumBy { it.value.toInt() })
}
@Test
fun testHistogramAlgebra() {
val hSpace = RealHistogramSpace.fromRanges(
(-1.0..1.0),
(-1.0..1.0),
(-1.0..1.0)
).invoke {
val random = Random(1234)
fun nextDouble() = random.nextDouble(-1.0, 1.0)
val n = 10000
val histogram1 = produce {
repeat(n) {
put(nextDouble(), nextDouble(), nextDouble())
}
}
val histogram2 = produce {
repeat(n) {
put(nextDouble(), nextDouble(), nextDouble())
}
}
val res = histogram1 - histogram2
assertTrue {
strides.indices().all { index ->
res.values[index] <= histogram1.values[index]
}
}
assertTrue {
res.bins.count() >= histogram1.bins.count()
}
assertEquals(0.0, res.bins.sumByDouble { it.value.toDouble() })
}
}
}

37
kmath-histograms/src/jsMain/kotlin/kscience/kmath/histogram/Counters.kt
View File

@ -1,37 +0,0 @@
package kscience.kmath.histogram
public actual class LongCounter {
private var sum: Long = 0L
public actual fun decrement() {
sum--
}
public actual fun increment() {
sum++
}
public actual fun reset() {
sum = 0
}
public actual fun sum(): Long = sum
public actual fun add(l: Long) {
sum += l
}
}
public actual class DoubleCounter {
private var sum: Double = 0.0
public actual fun reset() {
sum = 0.0
}
public actual fun sum(): Double = sum
public actual fun add(d: Double) {
sum += d
}
}

7
kmath-histograms/src/jvmMain/kotlin/kscience/kmath/histogram/Counters.kt
View File

@ -1,7 +0,0 @@
package kscience.kmath.histogram
import java.util.concurrent.atomic.DoubleAdder
import java.util.concurrent.atomic.LongAdder
public actual typealias LongCounter = LongAdder
public actual typealias DoubleCounter = DoubleAdder

155
kmath-histograms/src/jvmMain/kotlin/kscience/kmath/histogram/TreeHistogramSpace.kt Normal file
View File

@ -0,0 +1,155 @@
package kscience.kmath.histogram
import kscience.kmath.domains.UnivariateDomain
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.operations.Space
import kscience.kmath.structures.Buffer
import java.util.*
import kotlin.math.abs
import kotlin.math.sqrt
private fun <B : ClosedFloatingPointRange<Double>> TreeMap<Double, B>.getBin(value: Double): B? {
// check ceiling entry and return it if it is what needed
val ceil = ceilingEntry(value)?.value
if (ceil != null && value in ceil) return ceil
//check floor entry
val floor = floorEntry(value)?.value
if (floor != null && value in floor) return floor
//neither is valid, not found
return null
}
@UnstableKMathAPI
public class TreeHistogram(
override val context: TreeHistogramSpace,
private val binMap: TreeMap<Double, out UnivariateBin>,
) : UnivariateHistogram {
override fun get(value: Double): UnivariateBin? = binMap.getBin(value)
override val dimension: Int get() = 1
override val bins: Collection<UnivariateBin> get() = binMap.values
}
/**
* A space for univariate histograms with variable bin borders based on a tree map
*/
@UnstableKMathAPI
public class TreeHistogramSpace(
public val binFactory: (Double) -> UnivariateDomain,
) : Space<UnivariateHistogram> {
private class BinCounter(val domain: UnivariateDomain, val counter: Counter<Double> = Counter.real()) :
ClosedFloatingPointRange<Double> by domain.range
public fun produce(builder: UnivariateHistogramBuilder.() -> Unit): UnivariateHistogram {
val bins: TreeMap<Double, BinCounter> = TreeMap()
val hBuilder = object : UnivariateHistogramBuilder {
fun get(value: Double): BinCounter? = bins.getBin(value)
fun createBin(value: Double): BinCounter {
val binDefinition = binFactory(value)
val newBin = BinCounter(binDefinition)
synchronized(this) { bins[binDefinition.center] = newBin }
return newBin
}
/**
* Thread safe put operation
*/
override fun putValue(at: Double, value: Double) {
(get(at) ?: createBin(at)).apply {
counter.add(value)
}
}
override fun putValue(point: Buffer<Double>, value: Number) {
put(point[0], value.toDouble())
}
}
hBuilder.apply(builder)
val resBins = TreeMap<Double, UnivariateBin>()
bins.forEach { key, binCounter ->
val count = binCounter.counter.value
resBins[key] = UnivariateBin(binCounter.domain, count, sqrt(count))
}
return TreeHistogram(this, resBins)
}
override fun add(
a: UnivariateHistogram,
b: UnivariateHistogram,
): UnivariateHistogram {
require(a.context == this) { "Histogram $a does not belong to this context" }
require(b.context == this) { "Histogram $b does not belong to this context" }
val bins = TreeMap<Double, UnivariateBin>().apply {
(a.bins.map { it.domain } union b.bins.map { it.domain }).forEach { def ->
put(def.center,
UnivariateBin(
def,
value = (a[def.center]?.value ?: 0.0) + (b[def.center]?.value ?: 0.0),
standardDeviation = (a[def.center]?.standardDeviation
?: 0.0) + (b[def.center]?.standardDeviation ?: 0.0)
)
)
}
}
return TreeHistogram(this, bins)
}
override fun multiply(a: UnivariateHistogram, k: Number): UnivariateHistogram {
val bins = TreeMap<Double, UnivariateBin>().apply {
a.bins.forEach { bin ->
put(bin.domain.center,
UnivariateBin(
bin.domain,
value = bin.value * k.toDouble(),
standardDeviation = abs(bin.standardDeviation * k.toDouble())
)
)
}
}
return TreeHistogram(this, bins)
}
override val zero: UnivariateHistogram = produce { }
public companion object {
/**
* Build and fill a [UnivariateHistogram]. Returns a read-only histogram.
*/
public fun uniform(
binSize: Double,
start: Double = 0.0,
): TreeHistogramSpace = TreeHistogramSpace { value ->
val center = start + binSize * Math.floor((value - start) / binSize + 0.5)
UnivariateDomain((center - binSize / 2)..(center + binSize / 2))
}
/**
* Create a histogram with custom cell borders
*/
public fun custom(borders: DoubleArray): TreeHistogramSpace {
val sorted = borders.sortedArray()
return TreeHistogramSpace { value ->
when {
value < sorted.first() -> UnivariateDomain(
Double.NEGATIVE_INFINITY..sorted.first()
)
value > sorted.last() -> UnivariateDomain(
sorted.last()..Double.POSITIVE_INFINITY
)
else -> {
val index = sorted.indices.first { value > sorted[it] }
val left = sorted[index]
val right = sorted[index + 1]
UnivariateDomain(left..right)
}
}
}
}
}
}

144
kmath-histograms/src/jvmMain/kotlin/kscience/kmath/histogram/UnivariateHistogram.kt
View File

@ -1,76 +1,38 @@
package kscience.kmath.histogram
import kscience.kmath.linear.Point
import kscience.kmath.domains.UnivariateDomain
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.operations.Space
import kscience.kmath.operations.SpaceElement
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.asBuffer
import kscience.kmath.structures.asSequence
import java.util.*
import kotlin.math.floor
//TODO move to common
public val UnivariateDomain.center: Double get() = (range.endInclusive - range.start) / 2
/**
* A univariate bin based an a range
* @param value The value of histogram including weighting
* @param standardDeviation Standard deviation of the bin value. Zero or negative if not applicable
*/
public class UnivariateBin(
public val position: Double,
public val size: Double,
) : Bin<Double> {
//internal mutation operations
internal val counter: LongCounter = LongCounter()
internal val weightCounter: DoubleCounter = DoubleCounter()
public val domain: UnivariateDomain,
override val value: Double,
public val standardDeviation: Double,
) : Bin<Double>, ClosedFloatingPointRange<Double> by domain.range {
/**
* The precise number of events ignoring weighting
*/
public val count: Long get() = counter.sum()
/**
* The value of histogram including weighting
*/
public override val value: Double get() = weightCounter.sum()
public override val center: Point<Double> get() = doubleArrayOf(position).asBuffer()
public override val dimension: Int get() = 1
public operator fun contains(value: Double): Boolean = value in (position - size / 2)..(position + size / 2)
public override fun contains(point: Buffer<Double>): Boolean = contains(point[0])
public override fun contains(point: Buffer<Double>): Boolean = point.size == 1 && contains(point[0])
}
/**
* Univariate histogram with log(n) bin search speed
*/
@OptIn(UnstableKMathAPI::class)
public abstract class UnivariateHistogram protected constructor(
protected val bins: TreeMap<Double, UnivariateBin> = TreeMap(),
) : Histogram<Double, UnivariateBin>, SpaceElement<UnivariateHistogram, UnivariateHistogramSpace> {
public operator fun get(value: Double): UnivariateBin? {
// check ceiling entry and return it if it is what needed
val ceil = bins.ceilingEntry(value)?.value
if (ceil != null && value in ceil) return ceil
//check floor entry
val floor = bins.floorEntry(value)?.value
if (floor != null && value in floor) return floor
//neither is valid, not found
return null
}
public override operator fun get(point: Buffer<out Double>): UnivariateBin? = get(point[0])
public override val dimension: Int get() = 1
public override operator fun iterator(): Iterator<UnivariateBin> = bins.values.iterator()
public interface UnivariateHistogram : Histogram<Double, UnivariateBin>,
SpaceElement<UnivariateHistogram, Space<UnivariateHistogram>> {
public operator fun get(value: Double): UnivariateBin?
public override operator fun get(point: Buffer<Double>): UnivariateBin? = get(point[0])
public companion object {
/**
* Build a histogram with a uniform binning with a start at [start] and a bin size of [binSize]
*/
public fun uniformBuilder(binSize: Double, start: Double = 0.0): UnivariateHistogramBuilder =
UnivariateHistogramSpace { value ->
val center = start + binSize * floor((value - start) / binSize + 0.5)
UnivariateBin(center, binSize)
}.builder()
/**
* Build and fill a [UnivariateHistogram]. Returns a read-only histogram.
*/
@ -78,35 +40,7 @@ public abstract class UnivariateHistogram protected constructor(
binSize: Double,
start: Double = 0.0,
builder: UnivariateHistogramBuilder.() -> Unit,
): UnivariateHistogram = uniformBuilder(binSize, start).apply(builder)
/**
* Create a histogram with custom cell borders
*/
public fun customBuilder(borders: DoubleArray): UnivariateHistogramBuilder {
val sorted = borders.sortedArray()
return UnivariateHistogramSpace { value ->
when {
value < sorted.first() -> UnivariateBin(
Double.NEGATIVE_INFINITY,
Double.MAX_VALUE
)
value > sorted.last() -> UnivariateBin(
Double.POSITIVE_INFINITY,
Double.MAX_VALUE
)
else -> {
val index = sorted.indices.first { value > sorted[it] }
val left = sorted[index]
val right = sorted[index + 1]
UnivariateBin((left + right) / 2, (right - left))
}
}
}.builder()
}
): UnivariateHistogram = TreeHistogramSpace.uniform(binSize, start).produce(builder)
/**
* Build and fill a histogram with custom borders. Returns a read-only histogram.
@ -114,48 +48,26 @@ public abstract class UnivariateHistogram protected constructor(
public fun custom(
borders: DoubleArray,
builder: UnivariateHistogramBuilder.() -> Unit,
): UnivariateHistogram = customBuilder(borders).apply(builder)
): UnivariateHistogram = TreeHistogramSpace.custom(borders).produce(builder)
}
}
public class UnivariateHistogramBuilder internal constructor(
override val context: UnivariateHistogramSpace,
) : UnivariateHistogram(), MutableHistogram<Double, UnivariateBin> {
private fun createBin(value: Double): UnivariateBin = context.binFactory(value).also {
synchronized(this) { bins[it.position] = it }
}
@UnstableKMathAPI
public interface UnivariateHistogramBuilder : HistogramBuilder<Double> {
/**
* Thread safe put operation
*/
public fun put(value: Double, weight: Double = 1.0) {
(get(value) ?: createBin(value)).apply {
counter.increment()
weightCounter.add(weight)
}
}
public fun putValue(at: Double, value: Double = 1.0)
override fun putWithWeight(point: Buffer<out Double>, weight: Double) {
put(point[0], weight)
}
/**
* Put several items into a single bin
*/
public fun putMany(value: Double, count: Int, weight: Double = count.toDouble()) {
(get(value) ?: createBin(value)).apply {
counter.add(count.toLong())
weightCounter.add(weight)
}
}
override fun putValue(point: Buffer<Double>, value: Number)
}
@UnstableKMathAPI
public fun UnivariateHistogramBuilder.fill(items: Iterable<Double>): Unit = items.forEach(::put)
public fun UnivariateHistogramBuilder.fill(items: Iterable<Double>): Unit = items.forEach(this::putValue)
@UnstableKMathAPI
public fun UnivariateHistogramBuilder.fill(array: DoubleArray): Unit = array.forEach(::put)
public fun UnivariateHistogramBuilder.fill(array: DoubleArray): Unit = array.forEach(this::putValue)
@UnstableKMathAPI
public fun UnivariateHistogramBuilder.fill(buffer: Buffer<Double>): Unit = buffer.asSequence().forEach(::put)
public fun UnivariateHistogramBuilder.fill(buffer: Buffer<Double>): Unit = buffer.asSequence().forEach(this::putValue)

25
kmath-histograms/src/jvmMain/kotlin/kscience/kmath/histogram/UnivariateHistogramSpace.kt
View File

@ -1,25 +0,0 @@
package kscience.kmath.histogram
import kscience.kmath.operations.Space
public class UnivariateHistogramSpace(public val binFactory: (Double) -> UnivariateBin) : Space<UnivariateHistogram> {
public fun builder(): UnivariateHistogramBuilder = UnivariateHistogramBuilder(this)
public fun produce(builder: UnivariateHistogramBuilder.() -> Unit): UnivariateHistogram = builder().apply(builder)
override fun add(
a: UnivariateHistogram,
b: UnivariateHistogram,
): UnivariateHistogram {
require(a.context == this){"Histogram $a does not belong to this context"}
require(b.context == this){"Histogram $b does not belong to this context"}
TODO()
}
override fun multiply(a: UnivariateHistogram, k: Number): UnivariateHistogram {
TODO("Not yet implemented")
}
override val zero: UnivariateHistogram = produce { }
}