forked from kscience/kmath
[WIP] Another histogram refactor
This commit is contained in:
parent
ce82d2d076
commit
73f72f12bc
@ -48,6 +48,7 @@
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- Operations -> Ops
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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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### Deprecated
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- Specialized `DoubleBufferAlgebra`
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@ -35,6 +35,23 @@ public class DoubleDomain1D(
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}
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override fun volume(): Double = range.endInclusive - range.start
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override fun equals(other: Any?): Boolean {
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if (this === other) return true
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if (other == null || this::class != other::class) return false
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other as DoubleDomain1D
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if (doubleRange != other.doubleRange) return false
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return true
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}
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override fun hashCode(): Int = doubleRange.hashCode()
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override fun toString(): String = doubleRange.toString()
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}
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@UnstableKMathAPI
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@ -3,43 +3,71 @@
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* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
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*/
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package space.kscience.kmath.misc
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import kotlin.comparisons.*
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import space.kscience.kmath.structures.Buffer
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import space.kscience.kmath.structures.VirtualBuffer
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/**
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* Return a new list filled with buffer indices. Indice order is defined by sorting associated buffer value.
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* This feature allows to sort buffer values without reordering its content.
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* Return a new array filled with buffer indices. Indices order is defined by sorting associated buffer value.
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* This feature allows sorting buffer values without reordering its content.
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*
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* @return List of buffer indices, sorted by associated value.
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* @return Buffer indices, sorted by associated value.
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*/
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@PerformancePitfall
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@UnstableKMathAPI
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public fun <V: Comparable<V>> Buffer<V>.permSort() : IntArray = _permSortWith(compareBy<Int> { get(it) })
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public fun <V : Comparable<V>> Buffer<V>.indicesSorted(): IntArray = permSortIndicesWith(compareBy { get(it) })
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/**
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* Create a zero-copy virtual buffer that contains the same elements but in ascending order
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*/
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@OptIn(UnstableKMathAPI::class)
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public fun <V : Comparable<V>> Buffer<V>.sorted(): Buffer<V> {
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val permutations = indicesSorted()
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return VirtualBuffer(size) { this[permutations[it]] }
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}
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@PerformancePitfall
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@UnstableKMathAPI
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public fun <V: Comparable<V>> Buffer<V>.permSortDescending() : IntArray = _permSortWith(compareByDescending<Int> { get(it) })
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public fun <V : Comparable<V>> Buffer<V>.indicesSortedDescending(): IntArray =
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permSortIndicesWith(compareByDescending { get(it) })
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/**
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* Create a zero-copy virtual buffer that contains the same elements but in descending order
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*/
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@OptIn(UnstableKMathAPI::class)
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public fun <V : Comparable<V>> Buffer<V>.sortedDescending(): Buffer<V> {
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val permutations = indicesSortedDescending()
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return VirtualBuffer(size) { this[permutations[it]] }
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}
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@PerformancePitfall
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@UnstableKMathAPI
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public fun <V, C: Comparable<C>> Buffer<V>.permSortBy(selector: (V) -> C) : IntArray = _permSortWith(compareBy<Int> { selector(get(it)) })
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public fun <V, C : Comparable<C>> Buffer<V>.indicesSortedBy(selector: (V) -> C): IntArray =
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permSortIndicesWith(compareBy { selector(get(it)) })
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@OptIn(UnstableKMathAPI::class)
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public fun <V, C : Comparable<C>> Buffer<V>.sortedBy(selector: (V) -> C): Buffer<V> {
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val permutations = indicesSortedBy(selector)
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return VirtualBuffer(size) { this[permutations[it]] }
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}
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@PerformancePitfall
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@UnstableKMathAPI
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public fun <V, C: Comparable<C>> Buffer<V>.permSortByDescending(selector: (V) -> C) : IntArray = _permSortWith(compareByDescending<Int> { selector(get(it)) })
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public fun <V, C : Comparable<C>> Buffer<V>.indicesSortedByDescending(selector: (V) -> C): IntArray =
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permSortIndicesWith(compareByDescending { selector(get(it)) })
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@OptIn(UnstableKMathAPI::class)
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public fun <V, C : Comparable<C>> Buffer<V>.sortedByDescending(selector: (V) -> C): Buffer<V> {
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val permutations = indicesSortedByDescending(selector)
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return VirtualBuffer(size) { this[permutations[it]] }
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}
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@PerformancePitfall
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@UnstableKMathAPI
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public fun <V> Buffer<V>.permSortWith(comparator : Comparator<V>) : IntArray = _permSortWith { i1, i2 -> comparator.compare(get(i1), get(i2)) }
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public fun <V> Buffer<V>.indicesSortedWith(comparator: Comparator<V>): IntArray =
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permSortIndicesWith { i1, i2 -> comparator.compare(get(i1), get(i2)) }
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@PerformancePitfall
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@UnstableKMathAPI
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private fun <V> Buffer<V>._permSortWith(comparator : Comparator<Int>) : IntArray {
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if (size < 2) return IntArray(size)
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private fun <V> Buffer<V>.permSortIndicesWith(comparator: Comparator<Int>): IntArray {
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if (size < 2) return IntArray(size) { 0 }
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/* TODO: optimisation : keep a constant big array of indices (Ex: from 0 to 4096), then create indice
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/* TODO: optimisation : keep a constant big array of indices (Ex: from 0 to 4096), then create indices
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* arrays more efficiently by copying subpart of cached one. For bigger needs, we could copy entire
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* cached array, then fill remaining indices manually. Not done for now, because:
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* 1. doing it right would require some statistics about common used buffer sizes.
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@ -53,3 +81,12 @@ private fun <V> Buffer<V>._permSortWith(comparator : Comparator<Int>) : IntArray
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*/
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return packedIndices.sortedWith(comparator).toIntArray()
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}
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/**
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* Checks that the [Buffer] is sorted (ascending) and throws [IllegalArgumentException] if it is not.
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*/
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public fun <T : Comparable<T>> Buffer<T>.requireSorted() {
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for (i in 0..(size - 2)) {
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require(get(i + 1) >= get(i)) { "The buffer is not sorted at index $i" }
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}
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}
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@ -6,14 +6,13 @@
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package space.kscience.kmath.misc
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import space.kscience.kmath.misc.PermSortTest.Platform.*
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import kotlin.random.Random
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import kotlin.test.Test
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import kotlin.test.assertEquals
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import kotlin.test.assertTrue
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import space.kscience.kmath.structures.IntBuffer
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import space.kscience.kmath.structures.asBuffer
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import kotlin.random.Random
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import kotlin.test.Test
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import kotlin.test.assertContentEquals
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import kotlin.test.assertEquals
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import kotlin.test.assertTrue
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class PermSortTest {
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@ -29,9 +28,9 @@ class PermSortTest {
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@Test
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fun testOnEmptyBuffer() {
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val emptyBuffer = IntBuffer(0) {it}
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var permutations = emptyBuffer.permSort()
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var permutations = emptyBuffer.indicesSorted()
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assertTrue(permutations.isEmpty(), "permutation on an empty buffer should return an empty result")
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permutations = emptyBuffer.permSortDescending()
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permutations = emptyBuffer.indicesSortedDescending()
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assertTrue(permutations.isEmpty(), "permutation on an empty buffer should return an empty result")
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}
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@ -47,25 +46,25 @@ class PermSortTest {
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@Test
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fun testPermSortBy() {
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val permutations = platforms.permSortBy { it.name }
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val permutations = platforms.indicesSortedBy { it.name }
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val expected = listOf(ANDROID, JS, JVM, NATIVE, WASM)
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assertContentEquals(expected, permutations.map { platforms[it] }, "Ascending PermSort by name")
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}
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@Test
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fun testPermSortByDescending() {
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val permutations = platforms.permSortByDescending { it.name }
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val permutations = platforms.indicesSortedByDescending { it.name }
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val expected = listOf(WASM, NATIVE, JVM, JS, ANDROID)
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assertContentEquals(expected, permutations.map { platforms[it] }, "Descending PermSort by name")
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}
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@Test
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fun testPermSortWith() {
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var permutations = platforms.permSortWith { p1, p2 -> p1.name.length.compareTo(p2.name.length) }
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var permutations = platforms.indicesSortedWith { p1, p2 -> p1.name.length.compareTo(p2.name.length) }
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val expected = listOf(JS, JVM, WASM, NATIVE, ANDROID)
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assertContentEquals(expected, permutations.map { platforms[it] }, "PermSort using custom ascending comparator")
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permutations = platforms.permSortWith(compareByDescending { it.name.length })
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permutations = platforms.indicesSortedWith(compareByDescending { it.name.length })
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assertContentEquals(expected.reversed(), permutations.map { platforms[it] }, "PermSort using custom descending comparator")
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}
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@ -75,7 +74,7 @@ class PermSortTest {
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println("Test randomization seed: $seed")
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val buffer = Random(seed).buffer(bufferSize)
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val indices = buffer.permSort()
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val indices = buffer.indicesSorted()
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assertEquals(bufferSize, indices.size)
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// Ensure no doublon is present in indices
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@ -87,7 +86,7 @@ class PermSortTest {
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assertTrue(current <= next, "Permutation indices not properly sorted")
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}
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val descIndices = buffer.permSortDescending()
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val descIndices = buffer.indicesSortedDescending()
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assertEquals(bufferSize, descIndices.size)
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// Ensure no doublon is present in indices
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assertEquals(descIndices.toSet().size, descIndices.size)
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@ -21,19 +21,22 @@ readme {
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feature(
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id = "DoubleVector",
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description = "Numpy-like operations for Buffers/Points",
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ref = "src/commonMain/kotlin/space/kscience/kmath/real/DoubleVector.kt"
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)
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){
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"Numpy-like operations for Buffers/Points"
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}
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feature(
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id = "DoubleMatrix",
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description = "Numpy-like operations for 2d real structures",
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ref = "src/commonMain/kotlin/space/kscience/kmath/real/DoubleMatrix.kt"
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)
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){
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"Numpy-like operations for 2d real structures"
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}
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feature(
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id = "grids",
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description = "Uniform grid generators",
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ref = "src/commonMain/kotlin/space/kscience/kmath/structures/grids.kt"
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)
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){
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"Uniform grid generators"
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}
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}
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@ -17,6 +17,8 @@ kotlin.sourceSets {
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commonTest {
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dependencies {
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implementation(project(":kmath-for-real"))
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implementation(projects.kmath.kmathStat)
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implementation("org.jetbrains.kotlinx:kotlinx-coroutines-test:1.6.0")
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}
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}
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}
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@ -16,11 +16,11 @@ 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 DoubleHistogramSpace(
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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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) : IndexedHistogramSpace<Double, Double> {
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) : IndexedHistogramGroup<Double, Double> {
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init {
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// argument checks
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@ -105,7 +105,7 @@ public class DoubleHistogramSpace(
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*/
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public fun fromRanges(
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vararg ranges: ClosedFloatingPointRange<Double>,
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): DoubleHistogramSpace = DoubleHistogramSpace(
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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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@ -121,7 +121,7 @@ public class DoubleHistogramSpace(
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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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): DoubleHistogramSpace = DoubleHistogramSpace(
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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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@ -6,6 +6,7 @@
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package space.kscience.kmath.histogram
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import space.kscience.kmath.domains.Domain1D
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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.operations.asSequence
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import space.kscience.kmath.structures.Buffer
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@ -18,7 +19,7 @@ import space.kscience.kmath.structures.Buffer
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* @property standardDeviation Standard deviation of the bin value. Zero or negative if not applicable
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*/
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@UnstableKMathAPI
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public class Bin1D<T : Comparable<T>, out V>(
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public data class Bin1D<T : Comparable<T>, out V>(
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public val domain: Domain1D<T>,
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override val binValue: V,
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) : Bin<T, V>, ClosedRange<T> by domain.range {
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@ -35,12 +36,15 @@ public interface Histogram1D<T : Comparable<T>, V> : Histogram<T, V, Bin1D<T, V>
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override operator fun get(point: Buffer<T>): Bin1D<T, V>? = get(point[0])
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}
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@UnstableKMathAPI
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public interface Histogram1DBuilder<in T : Any, V : Any> : HistogramBuilder<T, V> {
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/**
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* Thread safe put operation
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*/
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public fun putValue(at: T, value: V = defaultValue)
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override fun putValue(point: Point<out T>, value: V) {
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putValue(point[0], value)
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}
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}
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@UnstableKMathAPI
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@ -52,5 +56,5 @@ public fun Histogram1DBuilder<Double, *>.fill(array: DoubleArray): Unit =
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array.forEach(this::putValue)
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@UnstableKMathAPI
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public fun Histogram1DBuilder<Double, *>.fill(buffer: Buffer<Double>): Unit =
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public fun <T: Any> Histogram1DBuilder<T, *>.fill(buffer: Buffer<T>): Unit =
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buffer.asSequence().forEach(this::putValue)
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@ -28,7 +28,7 @@ public data class DomainBin<in T : Comparable<T>, out V>(
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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 histogramSpace: IndexedHistogramSpace<T, V>,
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public val histogramSpace: 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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@ -48,8 +48,8 @@ public class IndexedHistogram<T : Comparable<T>, V : Any>(
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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 IndexedHistogramSpace<T : Comparable<T>, V : Any>
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: Group<IndexedHistogram<T, V>>, ScaleOperations<IndexedHistogram<T, V>> {
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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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@ -5,8 +5,92 @@
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package space.kscience.kmath.histogram
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//class UniformHistogram1D(
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// public val borders: Buffer<Double>,
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// public val values: Buffer<Long>,
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//) : Histogram1D<Double, Long> {
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//}
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import space.kscience.kmath.domains.DoubleDomain1D
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import space.kscience.kmath.misc.UnstableKMathAPI
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import space.kscience.kmath.operations.Group
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import space.kscience.kmath.operations.Ring
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import space.kscience.kmath.operations.ScaleOperations
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import space.kscience.kmath.operations.invoke
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import space.kscience.kmath.structures.Buffer
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import kotlin.math.floor
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@OptIn(UnstableKMathAPI::class)
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public class UniformHistogram1D<V : Any>(
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public val group: UniformHistogram1DGroup<V, *>,
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public val values: Map<Int, V>,
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) : Histogram1D<Double, V> {
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private val startPoint get() = group.startPoint
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private val binSize get() = group.binSize
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private fun produceBin(index: Int, value: V): Bin1D<Double, V> {
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val domain = DoubleDomain1D((startPoint + index * binSize)..(startPoint + (index + 1) * binSize))
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return Bin1D(domain, value)
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}
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override val bins: Iterable<Bin1D<Double, V>> get() = values.map { produceBin(it.key, it.value) }
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override fun get(value: Double): Bin1D<Double, V>? {
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val index: Int = group.getIndex(value)
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val v = values[index]
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return v?.let { produceBin(index, it) }
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}
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}
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public class UniformHistogram1DGroup<V : Any, A>(
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public val valueAlgebra: A,
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public val binSize: Double,
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public val startPoint: Double = 0.0,
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) : Group<UniformHistogram1D<V>>, ScaleOperations<UniformHistogram1D<V>> where A : Ring<V>, A : ScaleOperations<V> {
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override val zero: UniformHistogram1D<V> by lazy { UniformHistogram1D(this, emptyMap()) }
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public fun getIndex(at: Double): Int = floor((at - startPoint) / binSize).toInt()
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override fun add(left: UniformHistogram1D<V>, right: UniformHistogram1D<V>): UniformHistogram1D<V> = valueAlgebra {
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require(left.group == this@UniformHistogram1DGroup)
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require(right.group == this@UniformHistogram1DGroup)
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val keys = left.values.keys + right.values.keys
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UniformHistogram1D(
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this@UniformHistogram1DGroup,
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keys.associateWith { (left.values[it] ?: valueAlgebra.zero) + (right.values[it] ?: valueAlgebra.zero) }
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)
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}
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override fun UniformHistogram1D<V>.unaryMinus(): UniformHistogram1D<V> = valueAlgebra {
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UniformHistogram1D(this@UniformHistogram1DGroup, values.mapValues { -it.value })
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}
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override fun scale(
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a: UniformHistogram1D<V>,
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value: Double,
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): UniformHistogram1D<V> = UniformHistogram1D(
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this@UniformHistogram1DGroup,
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a.values.mapValues { valueAlgebra.scale(it.value, value) }
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)
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public inline fun produce(block: Histogram1DBuilder<Double, V>.() -> Unit): UniformHistogram1D<V> {
|
||||
val map = HashMap<Int, V>()
|
||||
val builder = object : Histogram1DBuilder<Double, V> {
|
||||
override val defaultValue: V get() = valueAlgebra.zero
|
||||
|
||||
override fun putValue(at: Double, value: V) {
|
||||
val index = getIndex(at)
|
||||
map[index] = with(valueAlgebra) { (map[index] ?: zero) + one }
|
||||
}
|
||||
}
|
||||
builder.block()
|
||||
return UniformHistogram1D(this, map)
|
||||
}
|
||||
}
|
||||
|
||||
public fun <V : Any, A> Histogram.Companion.uniform1D(
|
||||
algebra: A,
|
||||
binSize: Double,
|
||||
startPoint: Double = 0.0,
|
||||
): UniformHistogram1DGroup<V, A> where A : Ring<V>, A : ScaleOperations<V> =
|
||||
UniformHistogram1DGroup(algebra, binSize, startPoint)
|
||||
|
||||
@UnstableKMathAPI
|
||||
public fun <V : Any> UniformHistogram1DGroup<V, *>.produce(
|
||||
buffer: Buffer<Double>,
|
||||
): UniformHistogram1D<V> = produce { fill(buffer) }
|
@ -14,7 +14,7 @@ import kotlin.test.*
|
||||
internal class MultivariateHistogramTest {
|
||||
@Test
|
||||
fun testSinglePutHistogram() {
|
||||
val hSpace = DoubleHistogramSpace.fromRanges(
|
||||
val hSpace = DoubleHistogramGroup.fromRanges(
|
||||
(-1.0..1.0),
|
||||
(-1.0..1.0)
|
||||
)
|
||||
@ -29,7 +29,7 @@ internal class MultivariateHistogramTest {
|
||||
|
||||
@Test
|
||||
fun testSequentialPut() {
|
||||
val hSpace = DoubleHistogramSpace.fromRanges(
|
||||
val hSpace = DoubleHistogramGroup.fromRanges(
|
||||
(-1.0..1.0),
|
||||
(-1.0..1.0),
|
||||
(-1.0..1.0)
|
||||
@ -49,7 +49,7 @@ internal class MultivariateHistogramTest {
|
||||
|
||||
@Test
|
||||
fun testHistogramAlgebra() {
|
||||
DoubleHistogramSpace.fromRanges(
|
||||
DoubleHistogramGroup.fromRanges(
|
||||
(-1.0..1.0),
|
||||
(-1.0..1.0),
|
||||
(-1.0..1.0)
|
||||
|
@ -0,0 +1,34 @@
|
||||
/*
|
||||
* Copyright 2018-2021 KMath contributors.
|
||||
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
|
||||
*/
|
||||
|
||||
package space.kscience.kmath.histogram
|
||||
|
||||
import kotlinx.coroutines.ExperimentalCoroutinesApi
|
||||
import kotlinx.coroutines.test.runTest
|
||||
import space.kscience.kmath.distributions.NormalDistribution
|
||||
import space.kscience.kmath.misc.UnstableKMathAPI
|
||||
import space.kscience.kmath.operations.DoubleField
|
||||
import space.kscience.kmath.stat.RandomGenerator
|
||||
import space.kscience.kmath.stat.nextBuffer
|
||||
import kotlin.test.Test
|
||||
import kotlin.test.assertEquals
|
||||
|
||||
@OptIn(ExperimentalCoroutinesApi::class, UnstableKMathAPI::class)
|
||||
internal class UniformHistogram1DTest {
|
||||
@Test
|
||||
fun normal() = runTest {
|
||||
val generator = RandomGenerator.default(123)
|
||||
val distribution = NormalDistribution(0.0, 1.0)
|
||||
with(Histogram.uniform1D(DoubleField, 0.1)) {
|
||||
val h1 = produce(distribution.nextBuffer(generator, 10000))
|
||||
|
||||
val h2 = produce(distribution.nextBuffer(generator, 50000))
|
||||
|
||||
val h3 = h1 + h2
|
||||
|
||||
assertEquals(60000, h3.bins.sumOf { it.binValue }.toInt())
|
||||
}
|
||||
}
|
||||
}
|
@ -27,7 +27,7 @@ public interface Distribution<T : Any> : Sampler<T> {
|
||||
public companion object
|
||||
}
|
||||
|
||||
public interface UnivariateDistribution<T : Comparable<T>> : Distribution<T> {
|
||||
public interface Distribution1D<T : Comparable<T>> : Distribution<T> {
|
||||
/**
|
||||
* Cumulative distribution for ordered parameter (CDF)
|
||||
*/
|
||||
@ -37,7 +37,7 @@ public interface UnivariateDistribution<T : Comparable<T>> : Distribution<T> {
|
||||
/**
|
||||
* Compute probability integral in an interval
|
||||
*/
|
||||
public fun <T : Comparable<T>> UnivariateDistribution<T>.integral(from: T, to: T): Double {
|
||||
public fun <T : Comparable<T>> Distribution1D<T>.integral(from: T, to: T): Double {
|
||||
require(to > from)
|
||||
return cumulative(to) - cumulative(from)
|
||||
}
|
||||
|
@ -14,14 +14,9 @@ import space.kscience.kmath.stat.RandomGenerator
|
||||
import kotlin.math.*
|
||||
|
||||
/**
|
||||
* Implements [UnivariateDistribution] for the normal (gaussian) distribution.
|
||||
* Implements [Distribution1D] for the normal (gaussian) distribution.
|
||||
*/
|
||||
public class NormalDistribution(public val sampler: GaussianSampler) : UnivariateDistribution<Double> {
|
||||
public constructor(
|
||||
mean: Double,
|
||||
standardDeviation: Double,
|
||||
normalized: NormalizedGaussianSampler = ZigguratNormalizedGaussianSampler,
|
||||
) : this(GaussianSampler(mean, standardDeviation, normalized))
|
||||
public class NormalDistribution(public val sampler: GaussianSampler) : Distribution1D<Double> {
|
||||
|
||||
override fun probability(arg: Double): Double {
|
||||
val x1 = (arg - sampler.mean) / sampler.standardDeviation
|
||||
@ -43,3 +38,9 @@ public class NormalDistribution(public val sampler: GaussianSampler) : Univariat
|
||||
private val SQRT2 = sqrt(2.0)
|
||||
}
|
||||
}
|
||||
|
||||
public fun NormalDistribution(
|
||||
mean: Double,
|
||||
standardDeviation: Double,
|
||||
normalized: NormalizedGaussianSampler = ZigguratNormalizedGaussianSampler,
|
||||
): NormalDistribution = NormalDistribution(GaussianSampler(mean, standardDeviation, normalized))
|
||||
|
@ -67,3 +67,12 @@ public fun Sampler<Double>.sampleBuffer(generator: RandomGenerator, size: Int):
|
||||
@JvmName("sampleIntBuffer")
|
||||
public fun Sampler<Int>.sampleBuffer(generator: RandomGenerator, size: Int): Chain<Buffer<Int>> =
|
||||
sampleBuffer(generator, size, ::IntBuffer)
|
||||
|
||||
|
||||
/**
|
||||
* Samples a [Buffer] of values from this [Sampler].
|
||||
*/
|
||||
public suspend fun Sampler<Double>.nextBuffer(generator: RandomGenerator, size: Int): Buffer<Double> =
|
||||
sampleBuffer(generator, size).first()
|
||||
|
||||
//TODO add `context(RandomGenerator) Sampler.nextBuffer
|
@ -8,9 +8,9 @@ package space.kscience.kmath.stat
|
||||
import space.kscience.kmath.chains.Chain
|
||||
import space.kscience.kmath.chains.SimpleChain
|
||||
import space.kscience.kmath.distributions.Distribution
|
||||
import space.kscience.kmath.distributions.UnivariateDistribution
|
||||
import space.kscience.kmath.distributions.Distribution1D
|
||||
|
||||
public class UniformDistribution(public val range: ClosedFloatingPointRange<Double>) : UnivariateDistribution<Double> {
|
||||
public class UniformDistribution(public val range: ClosedFloatingPointRange<Double>) : Distribution1D<Double> {
|
||||
private val length: Double = range.endInclusive - range.start
|
||||
|
||||
override fun probability(arg: Double): Double = if (arg in range) 1.0 / length else 0.0
|
||||
|
Loading…
Reference in New Issue
Block a user