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166 changed files with 1204 additions and 1646 deletions

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@ -201,7 +201,7 @@
- Full autodiff refactoring based on `Symbol`
- `kmath-prob` renamed to `kmath-stat`
- Grid generators moved to `kmath-for-real`
- Use `Point<Float64>` instead of specialized type in `kmath-for-real`
- Use `Point<Double>` instead of specialized type in `kmath-for-real`
- Optimized dot product for buffer matrices moved to `kmath-for-real`
- EjmlMatrix context is an object
- Matrix LUP `inverse` renamed to `inverseWithLup`

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@ -1,119 +1,4 @@
# BenchmarksResult
## Report for benchmark configuration <code>main</code>
* Run on Java HotSpot(TM) 64-Bit Server VM (build 21.0.4+8-LTS-jvmci-23.1-b41) with Java process:
```
C:\Users\altavir\scoop\apps\graalvm-oracle-21jdk\current\bin\java.exe -XX:ThreadPriorityPolicy=1 -XX:+UnlockExperimentalVMOptions -XX:+EnableJVMCIProduct -XX:-UnlockExperimentalVMOptions -Dfile.encoding=UTF-8 -Duser.country=US -Duser.language=en -Duser.variant
```
* JMH 1.21 was used in `thrpt` mode with 5 warmup iterations by 10 s and 5 measurement iterations by 10 s.
### [ArrayBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ArrayBenchmark.kt)
| Benchmark | Score |
|:---------:|:-----:|
|`benchmarkArrayRead`|1.9E+07 &plusmn; 2.3E+05 ops/s|
|`benchmarkBufferRead`|1.4E+07 &plusmn; 8.7E+05 ops/s|
|`nativeBufferRead`|1.4E+07 &plusmn; 1.3E+06 ops/s|
### [BigIntBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BigIntBenchmark.kt)
| Benchmark | Score |
|:---------:|:-----:|
|`jvmAdd`|5.1E+07 &plusmn; 1.3E+06 ops/s|
|`jvmAddLarge`|5.1E+04 &plusmn; 8.2E+02 ops/s|
|`jvmMultiply`|8.5E+07 &plusmn; 9.7E+06 ops/s|
|`jvmMultiplyLarge`|2.5E+02 &plusmn; 15 ops/s|
|`jvmParsing10`|8.7E+06 &plusmn; 5.1E+05 ops/s|
|`jvmParsing16`|6.4E+06 &plusmn; 1.8E+05 ops/s|
|`jvmPower`|28 &plusmn; 0.79 ops/s|
|`jvmSmallAdd`|7.0E+07 &plusmn; 4.3E+06 ops/s|
|`kmAdd`|4.8E+07 &plusmn; 2.2E+06 ops/s|
|`kmAddLarge`|3.5E+04 &plusmn; 3.7E+03 ops/s|
|`kmMultiply`|6.7E+07 &plusmn; 1.5E+07 ops/s|
|`kmMultiplyLarge`|54 &plusmn; 4.2 ops/s|
|`kmParsing10`|4.5E+06 &plusmn; 8.3E+04 ops/s|
|`kmParsing16`|4.9E+06 &plusmn; 1.1E+05 ops/s|
|`kmPower`|10 &plusmn; 0.96 ops/s|
|`kmSmallAdd`|4.1E+07 &plusmn; 5.9E+05 ops/s|
### [BufferBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BufferBenchmark.kt)
| Benchmark | Score |
|:---------:|:-----:|
|`bufferViewReadWrite`|5.8E+06 &plusmn; 1.6E+05 ops/s|
|`bufferViewReadWriteSpecialized`|5.6E+06 &plusmn; 2.6E+05 ops/s|
|`complexBufferReadWrite`|6.6E+06 &plusmn; 2.7E+05 ops/s|
|`doubleArrayReadWrite`|7.5E+06 &plusmn; 1.0E+06 ops/s|
|`doubleBufferReadWrite`|8.0E+06 &plusmn; 6.7E+05 ops/s|
### [DotBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt)
| Benchmark | Score |
|:---------:|:-----:|
|`bufferedDot`|1.3 &plusmn; 0.020 ops/s|
|`cmDot`|0.47 &plusmn; 0.42 ops/s|
|`cmDotWithConversion`|0.76 &plusmn; 0.13 ops/s|
|`ejmlDot`|6.7 &plusmn; 0.091 ops/s|
|`ejmlDotWithConversion`|6.4 &plusmn; 0.82 ops/s|
|`multikDot`|40 &plusmn; 6.7 ops/s|
|`parallelDot`|12 &plusmn; 1.8 ops/s|
|`tensorDot`|1.2 &plusmn; 0.041 ops/s|
|`tfDot`|5.9 &plusmn; 0.49 ops/s|
### [ExpressionsInterpretersBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt)
| Benchmark | Score |
|:---------:|:-----:|
|`asmGenericExpression`|29 &plusmn; 1.2 ops/s|
|`asmPrimitiveExpression`|43 &plusmn; 1.3 ops/s|
|`asmPrimitiveExpressionArray`|71 &plusmn; 0.38 ops/s|
|`functionalExpression`|5.6 &plusmn; 0.11 ops/s|
|`justCalculate`|69 &plusmn; 9.0 ops/s|
|`mstExpression`|7.1 &plusmn; 0.020 ops/s|
|`rawExpression`|41 &plusmn; 1.5 ops/s|
### [IntegrationBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/IntegrationBenchmark.kt)
| Benchmark | Score |
|:---------:|:-----:|
|`complexIntegration`|3.6E+03 &plusmn; 1.9E+02 ops/s|
|`doubleIntegration`|3.7E+03 &plusmn; 12 ops/s|
### [JafamaBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/JafamaBenchmark.kt)
| Benchmark | Score |
|:---------:|:-----:|
|`core`|38 &plusmn; 0.64 ops/s|
|`jafama`|52 &plusmn; 0.36 ops/s|
|`strictJafama`|52 &plusmn; 4.0 ops/s|
### [MatrixInverseBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/MatrixInverseBenchmark.kt)
| Benchmark | Score |
|:---------:|:-----:|
|`cmLUPInversion`|2.2E+03 &plusmn; 76 ops/s|
|`ejmlInverse`|1.3E+03 &plusmn; 5.7 ops/s|
|`kmathLupInversion`|9.5E+02 &plusmn; 1.8E+02 ops/s|
|`kmathParallelLupInversion`|9.1E+02 &plusmn; 1.4E+02 ops/s|
### [NDFieldBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/NDFieldBenchmark.kt)
| Benchmark | Score |
|:---------:|:-----:|
|`boxingFieldAdd`|7.7 &plusmn; 0.79 ops/s|
|`multikAdd`|6.5 &plusmn; 0.33 ops/s|
|`multikInPlaceAdd`|64 &plusmn; 0.79 ops/s|
|`specializedFieldAdd`|8.0 &plusmn; 0.090 ops/s|
|`tensorAdd`|9.2 &plusmn; 0.053 ops/s|
|`tensorInPlaceAdd`|17 &plusmn; 10 ops/s|
|`viktorAdd`|7.6 &plusmn; 1.2 ops/s|
### [ViktorBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorBenchmark.kt)
| Benchmark | Score |
|:---------:|:-----:|
|`doubleFieldAddition`|7.7 &plusmn; 0.34 ops/s|
|`rawViktor`|5.9 &plusmn; 1.1 ops/s|
|`viktorFieldAddition`|7.3 &plusmn; 1.1 ops/s|
### [ViktorLogBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorLogBenchmark.kt)
| Benchmark | Score |
|:---------:|:-----:|
|`rawViktorLog`|1.4 &plusmn; 0.076 ops/s|
|`realFieldLog`|1.3 &plusmn; 0.069 ops/s|
|`viktorFieldLog`|1.3 &plusmn; 0.032 ops/s|
# Module benchmarks

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@ -1,14 +1,11 @@
import com.fasterxml.jackson.module.kotlin.jacksonObjectMapper
import com.fasterxml.jackson.module.kotlin.readValue
import kotlinx.benchmark.gradle.BenchmarksExtension
import java.time.LocalDateTime
import java.time.ZoneId
import java.util.*
@file:Suppress("UNUSED_VARIABLE")
import space.kscience.kmath.benchmarks.addBenchmarkProperties
plugins {
kotlin("multiplatform")
alias(spclibs.plugins.kotlin.plugin.allopen)
alias(spclibs.plugins.kotlinx.benchmark)
id("org.jetbrains.kotlinx.benchmark")
}
allOpen.annotation("org.openjdk.jmh.annotations.State")
@ -18,6 +15,8 @@ repositories {
mavenCentral()
}
val multikVersion: String by rootProject.extra
kotlin {
jvm()
@ -46,7 +45,7 @@ kotlin {
implementation(project(":kmath-for-real"))
implementation(project(":kmath-tensors"))
implementation(project(":kmath-multik"))
implementation(libs.multik.default)
implementation("org.jetbrains.kotlinx:multik-default:$multikVersion")
implementation(spclibs.kotlinx.benchmark.runtime)
}
}
@ -161,121 +160,8 @@ kotlin {
}
}
private data class JmhReport(
val jmhVersion: String,
val benchmark: String,
val mode: String,
val threads: Int,
val forks: Int,
val jvm: String,
val jvmArgs: List<String>,
val jdkVersion: String,
val vmName: String,
val vmVersion: String,
val warmupIterations: Int,
val warmupTime: String,
val warmupBatchSize: Int,
val measurementIterations: Int,
val measurementTime: String,
val measurementBatchSize: Int,
val params: Map<String, String> = emptyMap(),
val primaryMetric: PrimaryMetric,
val secondaryMetrics: Map<String, SecondaryMetric>,
) {
interface Metric {
val score: Double
val scoreError: Double
val scoreConfidence: List<Double>
val scorePercentiles: Map<Double, Double>
val scoreUnit: String
}
data class PrimaryMetric(
override val score: Double,
override val scoreError: Double,
override val scoreConfidence: List<Double>,
override val scorePercentiles: Map<Double, Double>,
override val scoreUnit: String,
val rawDataHistogram: List<List<List<List<Double>>>>? = null,
val rawData: List<List<Double>>? = null,
) : Metric
data class SecondaryMetric(
override val score: Double,
override val scoreError: Double,
override val scoreConfidence: List<Double>,
override val scorePercentiles: Map<Double, Double>,
override val scoreUnit: String,
val rawData: List<List<Double>>,
) : Metric
}
readme {
maturity = space.kscience.gradle.Maturity.EXPERIMENTAL
}
val jsonMapper = jacksonObjectMapper()
fun noun(number: Number, singular: String, plural: String) = if (number.toLong() == 1L) singular else plural
extensions.findByType(BenchmarksExtension::class.java)?.configurations?.forEach { cfg ->
val propertyName =
"benchmark${cfg.name.replaceFirstChar { if (it.isLowerCase()) it.titlecase(Locale.getDefault()) else it.toString() }}"
logger.info("Processing benchmark data from benchmark ${cfg.name} into readme property $propertyName")
val launches = layout.buildDirectory.dir("reports/benchmarks/${cfg.name}").get().asFile
if (!launches.exists()) return@forEach
property(propertyName) {
val resDirectory = launches.listFiles()?.maxByOrNull {
LocalDateTime.parse(it.name).atZone(ZoneId.systemDefault()).toInstant()
}
if (resDirectory == null || !(resDirectory.resolve("jvm.json")).exists()) {
"> **Can't find appropriate benchmark data. Try generating readme files after running benchmarks**."
} else {
val reports: List<JmhReport> =
jsonMapper.readValue<List<JmhReport>>(resDirectory.resolve("jvm.json"))
buildString {
appendLine("## Report for benchmark configuration <code>${cfg.name}</code>")
appendLine()
val first = reports.first()
appendLine("* Run on ${first.vmName} (build ${first.vmVersion}) with Java process:")
appendLine()
appendLine("```")
appendLine(
"${first.jvm} ${
first.jvmArgs.joinToString(" ")
}"
)
appendLine("```")
appendLine(
"* JMH ${first.jmhVersion} was used in `${first.mode}` mode with ${first.warmupIterations} warmup ${
noun(first.warmupIterations, "iteration", "iterations")
} by ${first.warmupTime} and ${first.measurementIterations} measurement ${
noun(first.measurementIterations, "iteration", "iterations")
} by ${first.measurementTime}."
)
reports.groupBy { it.benchmark.substringBeforeLast(".") }.forEach { (cl, compare) ->
appendLine("### [${cl.substringAfterLast(".")}](src/jvmMain/kotlin/${cl.replace(".","/")}.kt)")
appendLine()
appendLine("| Benchmark | Score |")
appendLine("|:---------:|:-----:|")
compare.forEach { report ->
val benchmarkName = report.benchmark.substringAfterLast(".")
val score = String.format("%.2G", report.primaryMetric.score)
val error = String.format("%.2G", report.primaryMetric.scoreError)
appendLine("|`$benchmarkName`|$score &plusmn; $error ${report.primaryMetric.scoreUnit}|")
}
}
}
}
}
}
}
addBenchmarkProperties()

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@ -1,5 +0,0 @@
# BenchmarksResult
${benchmarkMain}

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@ -14,7 +14,6 @@ import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.bindSymbol
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Float64
import kotlin.math.sin
import kotlin.random.Random
import space.kscience.kmath.estree.compileToExpression as estreeCompileToExpression
@ -68,7 +67,7 @@ class ExpressionsInterpretersBenchmark {
blackhole.consume(sum)
}
private fun invokeAndSum(expr: Expression<Float64>, blackhole: Blackhole) {
private fun invokeAndSum(expr: Expression<Double>, blackhole: Blackhole) {
val random = Random(0)
var sum = 0.0
val m = HashMap<Symbol, Double>()
@ -100,7 +99,7 @@ class ExpressionsInterpretersBenchmark {
private val wasm = node.wasmCompileToExpression(Float64Field)
private val estree = node.estreeCompileToExpression(Float64Field)
private val raw = Expression<Float64> { args ->
private val raw = Expression<Double> { args ->
val x = args.getValue(x)
x * 2.0 + 2.0 / x - 16.0 / sin(x)
}

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@ -15,7 +15,6 @@ import space.kscience.kmath.operations.Algebra
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.bindSymbol
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Float64
import kotlin.math.sin
import kotlin.random.Random
@ -84,7 +83,7 @@ internal class ExpressionsInterpretersBenchmark {
blackhole.consume(sum)
}
private fun invokeAndSum(expr: Expression<Float64>, blackhole: Blackhole) {
private fun invokeAndSum(expr: Expression<Double>, blackhole: Blackhole) {
val random = Random(0)
var sum = 0.0
val m = HashMap<Symbol, Double>()
@ -115,9 +114,9 @@ internal class ExpressionsInterpretersBenchmark {
private val asmPrimitive = node.compileToExpression(Float64Field)
private val xIdx = asmPrimitive.indexer.indexOf(x)
private val asmGeneric = node.compileToExpression(Float64Field as Algebra<Float64>)
private val asmGeneric = node.compileToExpression(Float64Field as Algebra<Double>)
private val raw = Expression<Float64> { args ->
private val raw = Expression<Double> { args ->
val x = args[x]!!
x * 2.0 + 2.0 / x - 16.0 / sin(x)
}

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@ -17,7 +17,6 @@ import space.kscience.kmath.UnsafeKMathAPI
import space.kscience.kmath.nd.*
import space.kscience.kmath.nd4j.nd4j
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.tensors.core.DoubleTensor
import space.kscience.kmath.tensors.core.one
import space.kscience.kmath.tensors.core.tensorAlgebra
@ -38,28 +37,28 @@ internal class NDFieldBenchmark {
@Benchmark
fun specializedFieldAdd(blackhole: Blackhole) = with(specializedField) {
var res: StructureND<Float64> = one(shape)
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
@Benchmark
fun boxingFieldAdd(blackhole: Blackhole) = with(genericField) {
var res: StructureND<Float64> = one(shape)
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
@Benchmark
fun multikAdd(blackhole: Blackhole) = with(multikAlgebra) {
var res: StructureND<Float64> = one(shape)
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
@Benchmark
fun viktorAdd(blackhole: Blackhole) = with(viktorField) {
var res: StructureND<Float64> = one(shape)
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
@ -88,7 +87,7 @@ internal class NDFieldBenchmark {
// @Benchmark
// fun nd4jAdd(blackhole: Blackhole) = with(nd4jField) {
// var res: StructureND<Float64> = one(dim, dim)
// var res: StructureND<Double> = one(dim, dim)
// repeat(n) { res += 1.0 }
// blackhole.consume(res)
// }

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@ -15,7 +15,6 @@ import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.nd.one
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.viktor.ViktorFieldND
@State(Scope.Benchmark)
@ -24,7 +23,7 @@ internal class ViktorBenchmark {
@Benchmark
fun doubleFieldAddition(blackhole: Blackhole) {
with(doubleField) {
var res: StructureND<Float64> = one(shape)
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}

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@ -2,8 +2,8 @@ import space.kscience.gradle.useApache2Licence
import space.kscience.gradle.useSPCTeam
plugins {
alias(spclibs.plugins.kscience.project)
alias(spclibs.plugins.kotlinx.kover)
id("space.kscience.gradle.project")
id("org.jetbrains.kotlinx.kover") version "0.7.6"
}
val attributesVersion by extra("0.2.0")
@ -70,3 +70,5 @@ ksciencePublish {
}
apiValidation.nonPublicMarkers.add("space.kscience.kmath.UnstableKMathAPI")
val multikVersion by extra("0.2.3")

33
buildSrc/build.gradle.kts Normal file
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@ -0,0 +1,33 @@
plugins {
kotlin("jvm") version "1.9.23"
`kotlin-dsl`
`version-catalog`
}
repositories {
mavenLocal()
maven("https://repo.kotlin.link")
mavenCentral()
gradlePluginPortal()
}
val toolsVersion = spclibs.versions.tools.get()
val kotlinVersion = spclibs.versions.kotlin.asProvider().get()
val benchmarksVersion = spclibs.versions.kotlinx.benchmark.get()
dependencies {
api("space.kscience:gradle-tools:$toolsVersion")
//plugins form benchmarks
api("org.jetbrains.kotlinx:kotlinx-benchmark-plugin:$benchmarksVersion")
//api("org.jetbrains.kotlin:kotlin-allopen:$kotlinVersion")
//to be used inside build-script only
//implementation(spclibs.kotlinx.serialization.json)
implementation("com.fasterxml.jackson.module:jackson-module-kotlin:2.14.+")
}
kotlin {
jvmToolchain(11)
compilerOptions {
optIn.add("kotlin.OptIn")
}
}

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@ -0,0 +1,38 @@
/*
* 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.
*/
enableFeaturePreview("TYPESAFE_PROJECT_ACCESSORS")
plugins {
id("org.gradle.toolchains.foojay-resolver-convention") version "0.8.0"
}
dependencyResolutionManagement {
val projectProperties = java.util.Properties()
file("../gradle.properties").inputStream().use {
projectProperties.load(it)
}
projectProperties.forEach { key, value ->
extra.set(key.toString(), value)
}
val toolsVersion: String = projectProperties["toolsVersion"].toString()
@Suppress("UnstableApiUsage")
repositories {
mavenLocal()
maven("https://repo.kotlin.link")
mavenCentral()
gradlePluginPortal()
}
versionCatalogs {
create("spclibs") {
from("space.kscience:version-catalog:$toolsVersion")
}
}
}

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@ -0,0 +1,55 @@
/*
* Copyright 2018-2024 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.benchmarks
data class JmhReport(
val jmhVersion: String,
val benchmark: String,
val mode: String,
val threads: Int,
val forks: Int,
val jvm: String,
val jvmArgs: List<String>,
val jdkVersion: String,
val vmName: String,
val vmVersion: String,
val warmupIterations: Int,
val warmupTime: String,
val warmupBatchSize: Int,
val measurementIterations: Int,
val measurementTime: String,
val measurementBatchSize: Int,
val params: Map<String, String> = emptyMap(),
val primaryMetric: PrimaryMetric,
val secondaryMetrics: Map<String, SecondaryMetric>,
) {
interface Metric {
val score: Double
val scoreError: Double
val scoreConfidence: List<Double>
val scorePercentiles: Map<Double, Double>
val scoreUnit: String
}
data class PrimaryMetric(
override val score: Double,
override val scoreError: Double,
override val scoreConfidence: List<Double>,
override val scorePercentiles: Map<Double, Double>,
override val scoreUnit: String,
val rawDataHistogram: List<List<List<List<Double>>>>? = null,
val rawData: List<List<Double>>? = null,
) : Metric
data class SecondaryMetric(
override val score: Double,
override val scoreError: Double,
override val scoreConfidence: List<Double>,
override val scorePercentiles: Map<Double, Double>,
override val scoreUnit: String,
val rawData: List<List<Double>>,
) : Metric
}

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@ -0,0 +1,110 @@
/*
* Copyright 2018-2024 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.benchmarks
import com.fasterxml.jackson.module.kotlin.jacksonObjectMapper
import com.fasterxml.jackson.module.kotlin.readValue
import kotlinx.benchmark.gradle.BenchmarksExtension
import org.gradle.api.Project
import space.kscience.gradle.KScienceReadmeExtension
import java.time.LocalDateTime
import java.time.ZoneId
import java.time.format.DateTimeFormatter
import java.time.format.DateTimeFormatterBuilder
import java.time.format.SignStyle
import java.time.temporal.ChronoField.*
import java.util.*
private val ISO_DATE_TIME: DateTimeFormatter = DateTimeFormatterBuilder().run {
parseCaseInsensitive()
appendValue(YEAR, 4, 10, SignStyle.EXCEEDS_PAD)
appendLiteral('-')
appendValue(MONTH_OF_YEAR, 2)
appendLiteral('-')
appendValue(DAY_OF_MONTH, 2)
appendLiteral('T')
appendValue(HOUR_OF_DAY, 2)
appendLiteral('.')
appendValue(MINUTE_OF_HOUR, 2)
optionalStart()
appendLiteral('.')
appendValue(SECOND_OF_MINUTE, 2)
optionalStart()
appendFraction(NANO_OF_SECOND, 0, 9, true)
optionalStart()
appendOffsetId()
optionalStart()
appendLiteral('[')
parseCaseSensitive()
appendZoneRegionId()
appendLiteral(']')
toFormatter()
}
private fun noun(number: Number, singular: String, plural: String) = if (number.toLong() == 1L) singular else plural
private val jsonMapper = jacksonObjectMapper()
fun Project.addBenchmarkProperties() {
val benchmarksProject = this
rootProject.subprojects.forEach { p ->
p.extensions.findByType(KScienceReadmeExtension::class.java)?.run {
benchmarksProject.extensions.findByType(BenchmarksExtension::class.java)?.configurations?.forEach { cfg ->
property("benchmark${cfg.name.replaceFirstChar { if (it.isLowerCase()) it.titlecase(Locale.getDefault()) else it.toString() }}") {
val launches = benchmarksProject.layout.buildDirectory.dir("reports/benchmarks/${cfg.name}").get()
val resDirectory = launches.files().maxByOrNull {
LocalDateTime.parse(it.name, ISO_DATE_TIME).atZone(ZoneId.systemDefault()).toInstant()
}
if (resDirectory == null || !(resDirectory.resolve("jvm.json")).exists()) {
"> **Can't find appropriate benchmark data. Try generating readme files after running benchmarks**."
} else {
val reports: List<JmhReport> =
jsonMapper.readValue<List<JmhReport>>(resDirectory.resolve("jvm.json"))
buildString {
appendLine("<details>")
appendLine("<summary>")
appendLine("Report for benchmark configuration <code>${cfg.name}</code>")
appendLine("</summary>")
appendLine()
val first = reports.first()
appendLine("* Run on ${first.vmName} (build ${first.vmVersion}) with Java process:")
appendLine()
appendLine("```")
appendLine(
"${first.jvm} ${
first.jvmArgs.joinToString(" ")
}"
)
appendLine("```")
appendLine(
"* JMH ${first.jmhVersion} was used in `${first.mode}` mode with ${first.warmupIterations} warmup ${
noun(first.warmupIterations, "iteration", "iterations")
} by ${first.warmupTime} and ${first.measurementIterations} measurement ${
noun(first.measurementIterations, "iteration", "iterations")
} by ${first.measurementTime}."
)
appendLine()
appendLine("| Benchmark | Score |")
appendLine("|:---------:|:-----:|")
reports.forEach { report ->
appendLine("|`${report.benchmark}`|${report.primaryMetric.score} &plusmn; ${report.primaryMetric.scoreError} ${report.primaryMetric.scoreUnit}|")
}
appendLine("</details>")
}
}
}
}
}
}
}

View File

@ -31,7 +31,7 @@ The code to run this looks like:
```kotlin
specializedField.run {
var res: NDBuffer<Float64> = one
var res: NDBuffer<Double> = one
repeat(n) {
res += 1.0
}
@ -103,7 +103,7 @@ The boxing field produced by
```kotlin
genericField.run {
var res: NDBuffer<Float64> = one
var res: NDBuffer<Double> = one
repeat(n) {
res += 1.0
}

View File

@ -10,6 +10,8 @@ repositories {
maven("https://maven.pkg.jetbrains.space/kotlin/p/kotlin/kotlin-js-wrappers")
}
val multikVersion: String by rootProject.extra
dependencies {
implementation(project(":kmath-ast"))
implementation(project(":kmath-kotlingrad"))
@ -31,7 +33,7 @@ dependencies {
implementation(project(":kmath-jafama"))
//multik
implementation(project(":kmath-multik"))
implementation(libs.multik.default)
implementation("org.jetbrains.kotlinx:multik-default:$multikVersion")
//datetime
implementation("org.jetbrains.kotlinx:kotlinx-datetime:0.4.0")

View File

@ -14,12 +14,11 @@ import space.kscience.kmath.integration.gaussIntegrator
import space.kscience.kmath.integration.integrate
import space.kscience.kmath.integration.value
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.structures.Float64
import kotlin.math.pow
fun main() {
//Define a function
val function: Function1D<Float64> = { x -> 3 * x.pow(2) + 2 * x + 1 }
val function: Function1D<Double> = { x -> 3 * x.pow(2) + 2 * x + 1 }
//get the result of the integration
val result = Float64Field.gaussIntegrator.integrate(0.0..10.0, function = function)

View File

@ -8,7 +8,6 @@ package space.kscience.kmath.functions
import space.kscience.kmath.interpolation.SplineInterpolator
import space.kscience.kmath.interpolation.interpolatePolynomials
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.structures.Float64
import space.kscience.plotly.Plotly
import space.kscience.plotly.UnstablePlotlyAPI
import space.kscience.plotly.makeFile
@ -24,7 +23,7 @@ fun main() {
x to sin(x)
}
val polynomial: PiecewisePolynomial<Float64> = SplineInterpolator(Float64Field).interpolatePolynomials(data)
val polynomial: PiecewisePolynomial<Double> = SplineInterpolator(Float64Field).interpolatePolynomials(data)
val function = polynomial.asFunction(Float64Field, 0.0)

View File

@ -10,7 +10,6 @@ import space.kscience.kmath.interpolation.splineInterpolator
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.real.map
import space.kscience.kmath.real.step
import space.kscience.kmath.structures.Float64
import space.kscience.plotly.Plotly
import space.kscience.plotly.UnstablePlotlyAPI
import space.kscience.plotly.makeFile
@ -19,7 +18,7 @@ import space.kscience.plotly.scatter
@OptIn(UnstablePlotlyAPI::class)
fun main() {
val function: Function1D<Float64> = { x ->
val function: Function1D<Double> = { x ->
if (x in 30.0..50.0) {
1.0
} else {
@ -29,7 +28,7 @@ fun main() {
val xs = 0.0..100.0 step 0.5
val ys = xs.map(function)
val polynomial: PiecewisePolynomial<Float64> = Float64Field.splineInterpolator.interpolatePolynomials(xs, ys)
val polynomial: PiecewisePolynomial<Double> = Float64Field.splineInterpolator.interpolatePolynomials(xs, ys)
val polyFunction = polynomial.asFunction(Float64Field, 0.0)

View File

@ -12,7 +12,6 @@ import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.structureND
import space.kscience.kmath.nd.withNdAlgebra
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.structures.Float64
import kotlin.math.pow
fun main(): Unit = Double.algebra.withNdAlgebra(2, 2) {
@ -23,7 +22,7 @@ fun main(): Unit = Double.algebra.withNdAlgebra(2, 2) {
}
//Define a function in a nd space
val function: (Double) -> StructureND<Float64> = { x: Double -> 3 * x.pow(2) + 2 * diagonal(x) + 1 }
val function: (Double) -> StructureND<Double> = { x: Double -> 3 * x.pow(2) + 2 * diagonal(x) + 1 }
//get the result of the integration
val result = gaussIntegrator.integrate(0.0..10.0, function = function)

View File

@ -1,40 +0,0 @@
/*
* Copyright 2018-2024 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.linear
import space.kscience.kmath.commons.linear.CMLinearSpace
import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.structures.Float64
import kotlin.random.Random
fun main() {
val dim = 46
val random = Random(123)
val u = Float64.algebra.linearSpace.buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
listOf(CMLinearSpace, EjmlLinearSpaceDDRM).forEach { algebra ->
with(algebra) {
//create a simmetric matrix
val matrix = buildMatrix(dim, dim) { row, col ->
if (row >= col) u[row, col] else u[col, row]
}
val eigen = matrix.getOrComputeAttribute(EIG) ?: error("Failed to compute eigenvalue decomposition")
check(
StructureND.contentEquals(
matrix,
eigen.v dot eigen.d dot eigen.v.transposed(),
1e-4
)
) { "$algebra decomposition failed" }
println("$algebra eigenvalue decomposition complete and checked" )
}
}
}

View File

@ -6,19 +6,18 @@
package space.kscience.kmath.linear
import space.kscience.kmath.real.*
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
fun main() {
val x0 = DoubleVector(0.0, 0.0, 0.0)
val sigma = DoubleVector(1.0, 1.0, 1.0)
val gaussian: (Point<Float64>) -> Double = { x ->
val gaussian: (Point<Double>) -> Double = { x ->
require(x.size == x0.size)
kotlin.math.exp(-((x - x0) / sigma).square().sum())
}
fun ((Point<Float64>) -> Double).grad(x: Point<Float64>): Point<Float64> {
fun ((Point<Double>) -> Double).grad(x: Point<Double>): Point<Double> {
require(x.size == x0.size)
return Float64Buffer(x.size) { i ->
val h = sigma[i] / 5

View File

@ -11,7 +11,6 @@ import space.kscience.kmath.nd.Float64BufferND
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.nd.mutableStructureND
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.viktor.viktorAlgebra
import kotlin.collections.component1
import kotlin.collections.component2
@ -21,7 +20,7 @@ fun main() {
if (i == j) 2.0 else 0.0
}
val cmMatrix: Structure2D<Float64> = CMLinearSpace.matrix(2, 2)(0.0, 1.0, 0.0, 3.0)
val cmMatrix: Structure2D<Double> = CMLinearSpace.matrix(2, 2)(0.0, 1.0, 0.0, 3.0)
val res: Float64BufferND = Float64Field.ndAlgebra {
exp(viktorStructure) + 2.0 * cmMatrix

View File

@ -14,7 +14,6 @@ import space.kscience.kmath.operations.toList
import space.kscience.kmath.stat.KMComparisonResult
import space.kscience.kmath.stat.ksComparisonStatistic
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.slice
import space.kscience.plotly.*
import kotlin.math.PI
@ -25,7 +24,7 @@ fun Double.Companion.seriesAlgebra() = Double.algebra.bufferAlgebra.seriesAlgebr
fun main() = with(Double.seriesAlgebra()) {
fun Plot.plotSeries(name: String, buffer: Buffer<Float64>) {
fun Plot.plotSeries(name: String, buffer: Buffer<Double>) {
scatter {
this.name = name
x.numbers = buffer.labels
@ -38,10 +37,10 @@ fun main() = with(Double.seriesAlgebra()) {
val s2 = s1.slice(20..50).moveTo(40)
val s3: Buffer<Float64> = s1.zip(s2) { l, r -> l + r } //s1 + s2
val s3: Buffer<Double> = s1.zip(s2) { l, r -> l + r } //s1 + s2
val s4 = s3.map { ln(it) }
val kmTest: KMComparisonResult<Float64> = ksComparisonStatistic(s1, s2)
val kmTest: KMComparisonResult<Double> = ksComparisonStatistic(s1, s2)
Plotly.page {
h1 { +"This is my plot" }

View File

@ -6,7 +6,10 @@
package space.kscience.kmath.series
import space.kscience.kmath.structures.*
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64Buffer
import space.kscience.kmath.structures.asBuffer
import space.kscience.kmath.structures.toDoubleArray
import space.kscience.plotly.*
import space.kscience.plotly.models.Scatter
import space.kscience.plotly.models.ScatterMode
@ -19,7 +22,7 @@ fun main(): Unit = with(Double.seriesAlgebra()) {
val arrayOfRandoms = DoubleArray(20) { random.nextDouble() }
val series1: Float64Buffer = arrayOfRandoms.asBuffer()
val series2: Series<Float64> = series1.moveBy(3)
val series2: Series<Double> = series1.moveBy(3)
val res = series2 - series1
@ -27,7 +30,7 @@ fun main(): Unit = with(Double.seriesAlgebra()) {
println(res)
fun Plot.series(name: String, buffer: Buffer<Float64>, block: Scatter.() -> Unit = {}) {
fun Plot.series(name: String, buffer: Buffer<Double>, block: Scatter.() -> Unit = {}) {
scatter {
this.name = name
x.numbers = buffer.offsetIndices

View File

@ -36,28 +36,8 @@ private suspend fun runKMathChained(): Duration {
return Duration.between(startTime, Instant.now())
}
private fun runKMathBlocking(): Duration {
val generator = RandomGenerator.fromSource(RandomSource.MT, 123L)
val normal = GaussianSampler(7.0, 2.0)
val chain = normal.sample(generator)
val startTime = Instant.now()
var sum = 0.0
repeat(10000001) { counter ->
sum += chain.nextBlocking()
if (counter % 100000 == 0) {
val duration = Duration.between(startTime, Instant.now())
val meanValue = sum / counter
println("Chain sampler completed $counter elements in $duration: $meanValue")
}
}
return Duration.between(startTime, Instant.now())
}
private fun runCMDirect(): Duration {
val rng = RandomSource.MT.create(123L)
val rng = RandomSource.create(RandomSource.MT, 123L)
val sampler = CMGaussianSampler.of(
BoxMullerNormalizedGaussianSampler.of(rng),
@ -87,8 +67,6 @@ private fun runCMDirect(): Duration {
fun main(): Unit = runBlocking(Dispatchers.Default) {
val directJob = async { runCMDirect() }
val chainJob = async { runKMathChained() }
val blockingJob = async { runKMathBlocking() }
println("KMath Chained: ${chainJob.await()}")
println("KMath Blocking: ${blockingJob.await()}")
println("Apache Direct: ${directJob.await()}")
}

View File

@ -10,14 +10,13 @@ import space.kscience.kmath.chains.Chain
import space.kscience.kmath.chains.combineWithState
import space.kscience.kmath.distributions.NormalDistribution
import space.kscience.kmath.random.RandomGenerator
import space.kscience.kmath.structures.Float64
private data class AveragingChainState(var num: Int = 0, var value: Double = 0.0)
/**
* Averaging.
*/
private fun Chain<Float64>.mean(): Chain<Float64> = combineWithState(AveragingChainState(), { it.copy() }) { chain ->
private fun Chain<Double>.mean(): Chain<Double> = combineWithState(AveragingChainState(), { it.copy() }) { chain ->
val next = chain.next()
num++
value += next

View File

@ -26,7 +26,7 @@ fun main() {
val realTime = measureTimeMillis {
realField {
var res: StructureND<Float64> = one
var res: StructureND<Double> = one
repeat(n) {
res += 1.0
}

View File

@ -45,35 +45,35 @@ fun main() {
measureAndPrint("Boxing addition") {
genericField {
var res: StructureND<Float64> = one(shape)
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Specialized addition") {
doubleField {
var res: StructureND<Float64> = one(shape)
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Nd4j specialized addition") {
nd4jField {
var res: StructureND<Float64> = one(shape)
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Viktor addition") {
viktorField {
var res: StructureND<Float64> = one
var res: StructureND<Double> = one
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Parallel stream addition") {
parallelField {
var res: StructureND<Float64> = one
var res: StructureND<Double> = one
repeat(n) { res += 1.0 }
}
}

View File

@ -19,21 +19,21 @@ import java.util.stream.IntStream
* execution.
*/
class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64Field>,
NumbersAddOps<StructureND<Float64>>,
ExtendedField<StructureND<Float64>> {
NumbersAddOps<StructureND<Double>>,
ExtendedField<StructureND<Double>> {
private val strides = ColumnStrides(shape)
override val elementAlgebra: Float64Field get() = Float64Field
override val zero: BufferND<Float64> by lazy { structureND(shape) { zero } }
override val one: BufferND<Float64> by lazy { structureND(shape) { one } }
override val zero: BufferND<Double> by lazy { structureND(shape) { zero } }
override val one: BufferND<Double> by lazy { structureND(shape) { one } }
override fun number(value: Number): BufferND<Float64> {
override fun number(value: Number): BufferND<Double> {
val d = value.toDouble() // minimize conversions
return structureND(shape) { d }
}
@OptIn(PerformancePitfall::class)
private val StructureND<Float64>.buffer: Float64Buffer
private val StructureND<Double>.buffer: Float64Buffer
get() = when {
shape != this@StreamDoubleFieldND.shape -> throw ShapeMismatchException(
this@StreamDoubleFieldND.shape,
@ -44,7 +44,7 @@ class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64
else -> Float64Buffer(strides.linearSize) { offset -> get(strides.index(offset)) }
}
override fun structureND(shape: ShapeND, initializer: Float64Field.(IntArray) -> Double): BufferND<Float64> {
override fun structureND(shape: ShapeND, initializer: Float64Field.(IntArray) -> Double): BufferND<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
val index = strides.index(offset)
Float64Field.initializer(index)
@ -56,7 +56,7 @@ class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64
override fun mutableStructureND(
shape: ShapeND,
initializer: DoubleField.(IntArray) -> Double,
): MutableBufferND<Float64> {
): MutableBufferND<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
val index = strides.index(offset)
DoubleField.initializer(index)
@ -66,17 +66,17 @@ class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64
}
@OptIn(PerformancePitfall::class)
override fun StructureND<Float64>.map(
override fun StructureND<Double>.map(
transform: Float64Field.(Double) -> Double,
): BufferND<Float64> {
): BufferND<Double> {
val array = Arrays.stream(buffer.array).parallel().map { Float64Field.transform(it) }.toArray()
return BufferND(strides, array.asBuffer())
}
@OptIn(PerformancePitfall::class)
override fun StructureND<Float64>.mapIndexed(
override fun StructureND<Double>.mapIndexed(
transform: Float64Field.(index: IntArray, Double) -> Double,
): BufferND<Float64> {
): BufferND<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
Float64Field.transform(
strides.index(offset),
@ -89,39 +89,39 @@ class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64
@OptIn(PerformancePitfall::class)
override fun zip(
left: StructureND<Float64>,
right: StructureND<Float64>,
left: StructureND<Double>,
right: StructureND<Double>,
transform: Float64Field.(Double, Double) -> Double,
): BufferND<Float64> {
): BufferND<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
Float64Field.transform(left.buffer.array[offset], right.buffer.array[offset])
}.toArray()
return BufferND(strides, array.asBuffer())
}
override fun StructureND<Float64>.unaryMinus(): StructureND<Float64> = map { -it }
override fun StructureND<Double>.unaryMinus(): StructureND<Double> = map { -it }
override fun scale(a: StructureND<Float64>, value: Double): StructureND<Float64> = a.map { it * value }
override fun scale(a: StructureND<Double>, value: Double): StructureND<Double> = a.map { it * value }
override fun power(arg: StructureND<Float64>, pow: Number): BufferND<Float64> = arg.map { power(it, pow) }
override fun power(arg: StructureND<Double>, pow: Number): BufferND<Double> = arg.map { power(it, pow) }
override fun exp(arg: StructureND<Float64>): BufferND<Float64> = arg.map { exp(it) }
override fun exp(arg: StructureND<Double>): BufferND<Double> = arg.map { exp(it) }
override fun ln(arg: StructureND<Float64>): BufferND<Float64> = arg.map { ln(it) }
override fun ln(arg: StructureND<Double>): BufferND<Double> = arg.map { ln(it) }
override fun sin(arg: StructureND<Float64>): BufferND<Float64> = arg.map { sin(it) }
override fun cos(arg: StructureND<Float64>): BufferND<Float64> = arg.map { cos(it) }
override fun tan(arg: StructureND<Float64>): BufferND<Float64> = arg.map { tan(it) }
override fun asin(arg: StructureND<Float64>): BufferND<Float64> = arg.map { asin(it) }
override fun acos(arg: StructureND<Float64>): BufferND<Float64> = arg.map { acos(it) }
override fun atan(arg: StructureND<Float64>): BufferND<Float64> = arg.map { atan(it) }
override fun sin(arg: StructureND<Double>): BufferND<Double> = arg.map { sin(it) }
override fun cos(arg: StructureND<Double>): BufferND<Double> = arg.map { cos(it) }
override fun tan(arg: StructureND<Double>): BufferND<Double> = arg.map { tan(it) }
override fun asin(arg: StructureND<Double>): BufferND<Double> = arg.map { asin(it) }
override fun acos(arg: StructureND<Double>): BufferND<Double> = arg.map { acos(it) }
override fun atan(arg: StructureND<Double>): BufferND<Double> = arg.map { atan(it) }
override fun sinh(arg: StructureND<Float64>): BufferND<Float64> = arg.map { sinh(it) }
override fun cosh(arg: StructureND<Float64>): BufferND<Float64> = arg.map { cosh(it) }
override fun tanh(arg: StructureND<Float64>): BufferND<Float64> = arg.map { tanh(it) }
override fun asinh(arg: StructureND<Float64>): BufferND<Float64> = arg.map { asinh(it) }
override fun acosh(arg: StructureND<Float64>): BufferND<Float64> = arg.map { acosh(it) }
override fun atanh(arg: StructureND<Float64>): BufferND<Float64> = arg.map { atanh(it) }
override fun sinh(arg: StructureND<Double>): BufferND<Double> = arg.map { sinh(it) }
override fun cosh(arg: StructureND<Double>): BufferND<Double> = arg.map { cosh(it) }
override fun tanh(arg: StructureND<Double>): BufferND<Double> = arg.map { tanh(it) }
override fun asinh(arg: StructureND<Double>): BufferND<Double> = arg.map { asinh(it) }
override fun acosh(arg: StructureND<Double>): BufferND<Double> = arg.map { acosh(it) }
override fun atanh(arg: StructureND<Double>): BufferND<Double> = arg.map { atanh(it) }
}
fun Float64Field.ndStreaming(vararg shape: Int): StreamDoubleFieldND = StreamDoubleFieldND(ShapeND(shape))

View File

@ -11,7 +11,7 @@ import space.kscience.kmath.operations.algebra
@OptIn(PerformancePitfall::class)
fun main(): Unit = with(Double.algebra.ndAlgebra) {
val structure: MutableStructure2D<Float64> = mutableStructureND(ShapeND(2, 2)) { (i, j) ->
val structure: MutableStructure2D<Double> = mutableStructureND(ShapeND(2, 2)) { (i, j) ->
i.toDouble() + j.toDouble()
}.as2D()

View File

@ -12,7 +12,6 @@ import space.kscience.kmath.nd.MutableStructure2D
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.component1
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.tensors.LevenbergMarquardt.StartDataLm
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.zeros
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
@ -21,9 +20,9 @@ import space.kscience.kmath.tensors.core.levenbergMarquardt
import kotlin.random.Random
fun streamLm(
lm_func: (MutableStructure2D<Float64>, MutableStructure2D<Float64>, Int) -> (MutableStructure2D<Float64>),
lm_func: (MutableStructure2D<Double>, MutableStructure2D<Double>, Int) -> (MutableStructure2D<Double>),
startData: StartDataLm, launchFrequencyInMs: Long, numberOfLaunches: Int,
): Flow<MutableStructure2D<Float64>> = flow {
): Flow<MutableStructure2D<Double>> = flow {
var example_number = startData.example_number
var p_init = startData.p_init
@ -65,7 +64,7 @@ fun streamLm(
}
}
fun generateNewYDat(y_dat: MutableStructure2D<Float64>, delta: Double): MutableStructure2D<Float64> {
fun generateNewYDat(y_dat: MutableStructure2D<Double>, delta: Double): MutableStructure2D<Double> {
val n = y_dat.shape.component1()
val y_dat_new = zeros(ShapeND(intArrayOf(n, 1))).as2D()
for (i in 0 until n) {

View File

@ -9,7 +9,6 @@ import space.kscience.kmath.nd.MutableStructure2D
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.component1
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.div
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
@ -20,24 +19,24 @@ import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.times
import space.kscience.kmath.tensors.core.asDoubleTensor
public data class StartDataLm(
var lm_matx_y_dat: MutableStructure2D<Float64>,
var lm_matx_y_dat: MutableStructure2D<Double>,
var example_number: Int,
var p_init: MutableStructure2D<Float64>,
var t: MutableStructure2D<Float64>,
var y_dat: MutableStructure2D<Float64>,
var p_init: MutableStructure2D<Double>,
var t: MutableStructure2D<Double>,
var y_dat: MutableStructure2D<Double>,
var weight: Double,
var dp: MutableStructure2D<Float64>,
var p_min: MutableStructure2D<Float64>,
var p_max: MutableStructure2D<Float64>,
var consts: MutableStructure2D<Float64>,
var dp: MutableStructure2D<Double>,
var p_min: MutableStructure2D<Double>,
var p_max: MutableStructure2D<Double>,
var consts: MutableStructure2D<Double>,
var opts: DoubleArray,
)
fun funcEasyForLm(
t: MutableStructure2D<Float64>,
p: MutableStructure2D<Float64>,
t: MutableStructure2D<Double>,
p: MutableStructure2D<Double>,
exampleNumber: Int,
): MutableStructure2D<Float64> {
): MutableStructure2D<Double> {
val m = t.shape.component1()
var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
@ -60,10 +59,10 @@ fun funcEasyForLm(
}
fun funcMiddleForLm(
t: MutableStructure2D<Float64>,
p: MutableStructure2D<Float64>,
t: MutableStructure2D<Double>,
p: MutableStructure2D<Double>,
exampleNumber: Int,
): MutableStructure2D<Float64> {
): MutableStructure2D<Double> {
val m = t.shape.component1()
var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
@ -80,10 +79,10 @@ fun funcMiddleForLm(
}
fun funcDifficultForLm(
t: MutableStructure2D<Float64>,
p: MutableStructure2D<Float64>,
t: MutableStructure2D<Double>,
p: MutableStructure2D<Double>,
exampleNumber: Int,
): MutableStructure2D<Float64> {
): MutableStructure2D<Double> {
val m = t.shape.component1()
var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))

View File

@ -10,13 +10,12 @@ import org.jetbrains.kotlinx.multik.api.ndarray
import org.jetbrains.kotlinx.multik.default.DefaultEngine
import space.kscience.kmath.multik.MultikDoubleAlgebra
import space.kscience.kmath.nd.one
import space.kscience.kmath.structures.Float64
val multikAlgebra = MultikDoubleAlgebra(DefaultEngine())
fun main(): Unit = with(multikAlgebra) {
val a = Multik.ndarray(intArrayOf(1, 2, 3)).asType<Float64>().wrap()
val a = Multik.ndarray(intArrayOf(1, 2, 3)).asType<Double>().wrap()
val b = Multik.ndarray(doubleArrayOf(1.0, 2.0, 3.0)).wrap()
one(a.shape) - a + b * 3.0
}

View File

@ -10,4 +10,6 @@ org.gradle.jvmargs=-Xmx4096m
org.gradle.parallel=true
org.gradle.workers.max=4
toolsVersion=0.15.4-kotlin-2.0.0
toolsVersion=0.15.4-kotlin-2.0.0
#kotlin.experimental.tryK2=true
#kscience.wasm.disabled=true

View File

@ -1,14 +1,9 @@
[versions]
commons-rng = "1.6"
multik = "0.2.3"
[libraries]
commons-rng-simple = { module = "org.apache.commons:commons-rng-simple", version.ref = "commons-rng" }
commons-rng-sampling = { module = "org.apache.commons:commons-rng-sampling", version.ref = "commons-rng" }
multik-core = { module = "org.jetbrains.kotlinx:multik-core", version.ref = "multik" }
multik-default = { module = "org.jetbrains.kotlinx:multik-default", version.ref = "multik" }
[plugins]
commons-rng-simple = {module ="org.apache.commons:commons-rng-simple", version.ref = "commons-rng"}
commons-rng-sampling = {module ="org.apache.commons:commons-rng-sampling", version.ref = "commons-rng"}

View File

@ -9,7 +9,6 @@ import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.Int32Ring
import space.kscience.kmath.operations.Int8Ring
import space.kscience.kmath.operations.pi
import space.kscience.kmath.structures.Float64
import kotlin.test.Test
import kotlin.test.assertEquals
import kotlin.test.fail
@ -42,7 +41,7 @@ internal class TestFolding {
@Test
fun foldSymbol() = assertEquals(
Float64Field.pi,
("pi".parseMath().evaluateConstants(Float64Field) as? TypedMst.Constant<Float64> ?: fail()).value,
("pi".parseMath().evaluateConstants(Float64Field) as? TypedMst.Constant<Double> ?: fail()).value,
)
@Test

View File

@ -10,13 +10,12 @@ import space.kscience.kmath.expressions.MST
import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.Int32Ring
import space.kscience.kmath.structures.Float64
internal interface CompilerTestContext {
fun MST.compileToExpression(algebra: Int32Ring): Expression<Int>
fun MST.compile(algebra: Int32Ring, arguments: Map<Symbol, Int>): Int
fun MST.compile(algebra: Int32Ring, vararg arguments: Pair<Symbol, Int>): Int = compile(algebra, mapOf(*arguments))
fun MST.compileToExpression(algebra: Float64Field): Expression<Float64>
fun MST.compileToExpression(algebra: Float64Field): Expression<Double>
fun MST.compile(algebra: Float64Field, arguments: Map<Symbol, Double>): Double
fun MST.compile(algebra: Float64Field, vararg arguments: Pair<Symbol, Double>): Double =

View File

@ -11,7 +11,6 @@ import space.kscience.kmath.expressions.*
import space.kscience.kmath.internal.binaryen.*
import space.kscience.kmath.internal.webassembly.Instance
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.internal.binaryen.Module as BinaryenModule
import space.kscience.kmath.internal.webassembly.Module as WasmModule
@ -86,13 +85,13 @@ internal sealed class WasmBuilder<T : Number, out E : Expression<T>>(
}
@UnstableKMathAPI
internal class DoubleWasmBuilder(target: TypedMst<Float64>) :
internal class DoubleWasmBuilder(target: TypedMst<Double>) :
WasmBuilder<Double, DoubleExpression>(f64, Float64Field, target) {
override val instance by lazy {
object : DoubleExpression {
override val indexer = SimpleSymbolIndexer(keys)
override fun invoke(arguments: DoubleArray) = spreader(executable, arguments).unsafeCast<Float64>()
override fun invoke(arguments: DoubleArray) = spreader(executable, arguments).unsafeCast<Double>()
}
}
@ -100,7 +99,7 @@ internal class DoubleWasmBuilder(target: TypedMst<Float64>) :
override fun visitNumber(number: Number) = ctx.f64.const(number.toDouble())
override fun visitUnary(node: TypedMst.Unary<Float64>): ExpressionRef = when (node.operation) {
override fun visitUnary(node: TypedMst.Unary<Double>): ExpressionRef = when (node.operation) {
GroupOps.MINUS_OPERATION -> ctx.f64.neg(visit(node.value))
GroupOps.PLUS_OPERATION -> visit(node.value)
PowerOperations.SQRT_OPERATION -> ctx.f64.sqrt(visit(node.value))
@ -121,7 +120,7 @@ internal class DoubleWasmBuilder(target: TypedMst<Float64>) :
else -> super.visitUnary(node)
}
override fun visitBinary(mst: TypedMst.Binary<Float64>): ExpressionRef = when (mst.operation) {
override fun visitBinary(mst: TypedMst.Binary<Double>): ExpressionRef = when (mst.operation) {
GroupOps.PLUS_OPERATION -> ctx.f64.add(visit(mst.left), visit(mst.right))
GroupOps.MINUS_OPERATION -> ctx.f64.sub(visit(mst.left), visit(mst.right))
RingOps.TIMES_OPERATION -> ctx.f64.mul(visit(mst.left), visit(mst.right))

View File

@ -13,7 +13,6 @@ import space.kscience.kmath.ast.evaluateConstants
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.Int32Ring
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.wasm.internal.DoubleWasmBuilder
import space.kscience.kmath.wasm.internal.IntWasmBuilder
@ -59,7 +58,7 @@ public fun MST.compile(algebra: Int32Ring, vararg arguments: Pair<Symbol, Int>):
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public fun MST.compileToExpression(algebra: Float64Field): Expression<Float64> {
public fun MST.compileToExpression(algebra: Float64Field): Expression<Double> {
val typed = evaluateConstants(algebra)
return if (typed is TypedMst.Constant) object : DoubleExpression {

View File

@ -13,7 +13,6 @@ import space.kscience.kmath.expressions.MST
import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.Int32Ring
import space.kscience.kmath.structures.Float64
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
import space.kscience.kmath.estree.compile as estreeCompile
@ -25,7 +24,7 @@ import space.kscience.kmath.wasm.compileToExpression as wasmCompileToExpression
private object WasmCompilerTestContext : CompilerTestContext {
override fun MST.compileToExpression(algebra: Int32Ring): Expression<Int> = wasmCompileToExpression(algebra)
override fun MST.compile(algebra: Int32Ring, arguments: Map<Symbol, Int>): Int = wasmCompile(algebra, arguments)
override fun MST.compileToExpression(algebra: Float64Field): Expression<Float64> = wasmCompileToExpression(algebra)
override fun MST.compileToExpression(algebra: Float64Field): Expression<Double> = wasmCompileToExpression(algebra)
override fun MST.compile(algebra: Float64Field, arguments: Map<Symbol, Double>): Double =
wasmCompile(algebra, arguments)
@ -34,7 +33,7 @@ private object WasmCompilerTestContext : CompilerTestContext {
private object ESTreeCompilerTestContext : CompilerTestContext {
override fun MST.compileToExpression(algebra: Int32Ring): Expression<Int> = estreeCompileToExpression(algebra)
override fun MST.compile(algebra: Int32Ring, arguments: Map<Symbol, Int>): Int = estreeCompile(algebra, arguments)
override fun MST.compileToExpression(algebra: Float64Field): Expression<Float64> = estreeCompileToExpression(algebra)
override fun MST.compileToExpression(algebra: Float64Field): Expression<Double> = estreeCompileToExpression(algebra)
override fun MST.compile(algebra: Float64Field, arguments: Map<Symbol, Double>): Double =
estreeCompile(algebra, arguments)

View File

@ -15,7 +15,6 @@ import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.ast.TypedMst
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Float64
import java.lang.invoke.MethodHandles
import java.lang.invoke.MethodType
import java.nio.file.Paths
@ -382,7 +381,7 @@ internal sealed class PrimitiveAsmBuilder<T : Number, out E : Expression<T>>(
}
@UnstableKMathAPI
internal class DoubleAsmBuilder(target: TypedMst<Float64>) : PrimitiveAsmBuilder<Double, DoubleExpression>(
internal class DoubleAsmBuilder(target: TypedMst<Double>) : PrimitiveAsmBuilder<Double, DoubleExpression>(
Float64Field,
java.lang.Double::class.java,
java.lang.Double.TYPE,
@ -411,7 +410,7 @@ internal class DoubleAsmBuilder(target: TypedMst<Float64>) : PrimitiveAsmBuilder
false,
)
override fun visitUnary(node: TypedMst.Unary<Float64>) {
override fun visitUnary(node: TypedMst.Unary<Double>) {
super.visitUnary(node)
when (node.operation) {
@ -436,7 +435,7 @@ internal class DoubleAsmBuilder(target: TypedMst<Float64>) : PrimitiveAsmBuilder
}
}
override fun visitBinary(node: TypedMst.Binary<Float64>) {
override fun visitBinary(node: TypedMst.Binary<Double>) {
super.visitBinary(node)
when (node.operation) {

View File

@ -12,7 +12,6 @@ import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.operations.Algebra
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.Int32Ring
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.asm.compile as asmCompile
import space.kscience.kmath.asm.compileToExpression as asmCompileToExpression
@ -23,18 +22,18 @@ private object GenericAsmCompilerTestContext : CompilerTestContext {
override fun MST.compile(algebra: Int32Ring, arguments: Map<Symbol, Int>): Int =
asmCompile(algebra as Algebra<Int>, arguments)
override fun MST.compileToExpression(algebra: Float64Field): Expression<Float64> =
asmCompileToExpression(algebra as Algebra<Float64>)
override fun MST.compileToExpression(algebra: Float64Field): Expression<Double> =
asmCompileToExpression(algebra as Algebra<Double>)
override fun MST.compile(algebra: Float64Field, arguments: Map<Symbol, Double>): Double =
asmCompile(algebra as Algebra<Float64>, arguments)
asmCompile(algebra as Algebra<Double>, arguments)
}
@OptIn(UnstableKMathAPI::class)
private object PrimitiveAsmCompilerTestContext : CompilerTestContext {
override fun MST.compileToExpression(algebra: Int32Ring): Expression<Int> = asmCompileToExpression(algebra)
override fun MST.compile(algebra: Int32Ring, arguments: Map<Symbol, Int>): Int = asmCompile(algebra, arguments)
override fun MST.compileToExpression(algebra: Float64Field): Expression<Float64> = asmCompileToExpression(algebra)
override fun MST.compileToExpression(algebra: Float64Field): Expression<Double> = asmCompileToExpression(algebra)
override fun MST.compile(algebra: Float64Field, arguments: Map<Symbol, Double>): Double =
asmCompile(algebra, arguments)

View File

@ -14,7 +14,6 @@ import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.ExtendedField
import space.kscience.kmath.operations.NumbersAddOps
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.MutableBufferFactory
/**
@ -133,9 +132,9 @@ public object CmDsProcessor : AutoDiffProcessor<Double, DerivativeStructure, CmD
@Deprecated("Use generic DSAlgebra from the core")
public class CmDsExpression(
public val function: CmDsField.() -> DerivativeStructure,
) : DifferentiableExpression<Float64> {
) : DifferentiableExpression<Double> {
override val type: SafeType<Float64> get() = DoubleField.type
override val type: SafeType<Double> get() = DoubleField.type
override operator fun invoke(arguments: Map<Symbol, Double>): Double =
CmDsField(0, arguments).function().value
@ -143,7 +142,7 @@ public class CmDsExpression(
/**
* Get the derivative expression with given orders
*/
override fun derivativeOrNull(symbols: List<Symbol>): Expression<Float64> = Expression(type) { arguments ->
override fun derivativeOrNull(symbols: List<Symbol>): Expression<Double> = Expression(type) { arguments ->
with(CmDsField(symbols.size, arguments)) { function().derivative(symbols) }
}
}

View File

@ -7,7 +7,6 @@ package space.kscience.kmath.commons.integration
import org.apache.commons.math3.analysis.integration.gauss.GaussIntegrator
import org.apache.commons.math3.analysis.integration.gauss.GaussIntegratorFactory
import space.kscience.kmath.integration.*
import space.kscience.kmath.structures.Float64
/**
* A simple one-pass integrator based on Gauss rule
@ -15,9 +14,9 @@ import space.kscience.kmath.structures.Float64
public class CMGaussRuleIntegrator(
private val numpoints: Int,
private var type: GaussRule = GaussRule.LEGENDRE,
) : UnivariateIntegrator<Float64> {
) : UnivariateIntegrator<Double> {
override fun integrate(integrand: UnivariateIntegrand<Float64>): UnivariateIntegrand<Float64> {
override fun integrate(integrand: UnivariateIntegrand<Double>): UnivariateIntegrand<Double> {
val range = integrand[IntegrationRange]
?: error("Integration range is not provided")
val integrator: GaussIntegrator = getIntegrator(range)
@ -29,7 +28,7 @@ public class CMGaussRuleIntegrator(
}
}
private fun getIntegrator(range: ClosedRange<Float64>): GaussIntegrator {
private fun getIntegrator(range: ClosedRange<Double>): GaussIntegrator {
return when (type) {
GaussRule.LEGENDRE -> factory.legendre(
numpoints,
@ -78,7 +77,7 @@ public class CMGaussRuleIntegrator(
private val factory: GaussIntegratorFactory = GaussIntegratorFactory()
public fun integrate(
range: ClosedRange<Float64>,
range: ClosedRange<Double>,
numPoints: Int = 100,
type: GaussRule = GaussRule.LEGENDRE,
function: (Double) -> Double,

View File

@ -10,7 +10,6 @@ import org.apache.commons.math3.analysis.integration.SimpsonIntegrator
import space.kscience.attributes.Attributes
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.integration.*
import space.kscience.kmath.structures.Float64
import org.apache.commons.math3.analysis.integration.UnivariateIntegrator as CMUnivariateIntegrator
/**
@ -18,10 +17,10 @@ import org.apache.commons.math3.analysis.integration.UnivariateIntegrator as CMU
*/
public class CMIntegrator(
private val defaultMaxCalls: Int = 200,
public val integratorBuilder: (Integrand<Float64>) -> CMUnivariateIntegrator,
) : UnivariateIntegrator<Float64> {
public val integratorBuilder: (Integrand<Double>) -> CMUnivariateIntegrator,
) : UnivariateIntegrator<Double> {
override fun integrate(integrand: UnivariateIntegrand<Float64>): UnivariateIntegrand<Float64> {
override fun integrate(integrand: UnivariateIntegrand<Double>): UnivariateIntegrand<Double> {
val integrator = integratorBuilder(integrand)
val maxCalls = integrand[IntegrandMaxCalls] ?: defaultMaxCalls
val remainingCalls = maxCalls - integrand.calls

View File

@ -12,7 +12,6 @@ import space.kscience.attributes.SafeType
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.linear.*
import space.kscience.kmath.linear.CholeskyDecomposition
import space.kscience.kmath.linear.EigenDecomposition
import space.kscience.kmath.linear.QRDecomposition
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.nd.StructureAttribute
@ -23,7 +22,7 @@ import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.IntBuffer
import space.kscience.kmath.structures.asBuffer
public class CMMatrix(public val origin: RealMatrix) : Matrix<Float64> {
public class CMMatrix(public val origin: RealMatrix) : Matrix<Double> {
override val rowNum: Int get() = origin.rowDimension
override val colNum: Int get() = origin.columnDimension
@ -32,12 +31,12 @@ public class CMMatrix(public val origin: RealMatrix) : Matrix<Float64> {
}
@JvmInline
public value class CMVector(public val origin: RealVector) : Point<Float64> {
public value class CMVector(public val origin: RealVector) : Point<Double> {
override val size: Int get() = origin.dimension
override operator fun get(index: Int): Double = origin.getEntry(index)
override operator fun iterator(): Iterator<Float64> = origin.toArray().iterator()
override operator fun iterator(): Iterator<Double> = origin.toArray().iterator()
override fun toString(): String = Buffer.toString(this)
}
@ -47,7 +46,7 @@ public fun RealVector.toPoint(): CMVector = CMVector(this)
public object CMLinearSpace : LinearSpace<Double, Float64Field> {
override val elementAlgebra: Float64Field get() = Float64Field
override val type: SafeType<Float64> get() = DoubleField.type
override val type: SafeType<Double> get() = DoubleField.type
override fun buildMatrix(
rows: Int,
@ -59,7 +58,7 @@ public object CMLinearSpace : LinearSpace<Double, Float64Field> {
}
@OptIn(UnstableKMathAPI::class)
public fun Matrix<Float64>.toCM(): CMMatrix = when (val matrix = origin) {
public fun Matrix<Double>.toCM(): CMMatrix = when (val matrix = origin) {
is CMMatrix -> matrix
else -> {
//TODO add feature analysis
@ -68,7 +67,7 @@ public object CMLinearSpace : LinearSpace<Double, Float64Field> {
}
}
public fun Point<Float64>.toCM(): CMVector = if (this is CMVector) this else {
public fun Point<Double>.toCM(): CMVector = if (this is CMVector) this else {
val array = DoubleArray(size) { this[it] }
ArrayRealVector(array).wrap()
}
@ -76,41 +75,40 @@ public object CMLinearSpace : LinearSpace<Double, Float64Field> {
internal fun RealMatrix.wrap(): CMMatrix = CMMatrix(this)
internal fun RealVector.wrap(): CMVector = CMVector(this)
override fun buildVector(size: Int, initializer: Float64Field.(Int) -> Double): Point<Float64> =
override fun buildVector(size: Int, initializer: Float64Field.(Int) -> Double): Point<Double> =
ArrayRealVector(DoubleArray(size) { Float64Field.initializer(it) }).wrap()
override fun Matrix<Float64>.plus(other: Matrix<Float64>): CMMatrix =
override fun Matrix<Double>.plus(other: Matrix<Double>): CMMatrix =
toCM().origin.add(other.toCM().origin).wrap()
override fun Point<Float64>.plus(other: Point<Float64>): CMVector =
override fun Point<Double>.plus(other: Point<Double>): CMVector =
toCM().origin.add(other.toCM().origin).wrap()
override fun Point<Float64>.minus(other: Point<Float64>): CMVector =
override fun Point<Double>.minus(other: Point<Double>): CMVector =
toCM().origin.subtract(other.toCM().origin).wrap()
override fun Matrix<Float64>.dot(other: Matrix<Float64>): CMMatrix =
override fun Matrix<Double>.dot(other: Matrix<Double>): CMMatrix =
toCM().origin.multiply(other.toCM().origin).wrap()
override fun Matrix<Float64>.dot(vector: Point<Float64>): CMVector =
override fun Matrix<Double>.dot(vector: Point<Double>): CMVector =
toCM().origin.preMultiply(vector.toCM().origin).wrap()
override operator fun Matrix<Float64>.minus(other: Matrix<Float64>): CMMatrix =
override operator fun Matrix<Double>.minus(other: Matrix<Double>): CMMatrix =
toCM().origin.subtract(other.toCM().origin).wrap()
override operator fun Matrix<Float64>.times(value: Double): CMMatrix =
override operator fun Matrix<Double>.times(value: Double): CMMatrix =
toCM().origin.scalarMultiply(value).wrap()
override fun Double.times(m: Matrix<Float64>): CMMatrix =
override fun Double.times(m: Matrix<Double>): CMMatrix =
m * this
override fun Point<Float64>.times(value: Double): CMVector =
override fun Point<Double>.times(value: Double): CMVector =
toCM().origin.mapMultiply(value).wrap()
override fun Double.times(v: Point<Float64>): CMVector =
override fun Double.times(v: Point<Double>): CMVector =
v * this
@OptIn(UnstableKMathAPI::class)
override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Float64>, attribute: A): V? {
override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Double>, attribute: A): V? {
val origin = structure.toCM().origin
@ -127,7 +125,7 @@ public object CMLinearSpace : LinearSpace<Double, Float64Field> {
Cholesky -> object : CholeskyDecomposition<Float64> {
val cmCholesky by lazy { org.apache.commons.math3.linear.CholeskyDecomposition(origin) }
override val l: Matrix<Float64> get() = cmCholesky.l.wrap()
override val l: Matrix<Double> get() = cmCholesky.l.wrap()
}
QR -> object : QRDecomposition<Float64> {
@ -146,13 +144,6 @@ public object CMLinearSpace : LinearSpace<Double, Float64Field> {
}
EIG -> object : EigenDecomposition<Float64> {
val cmEigen by lazy { org.apache.commons.math3.linear.EigenDecomposition(origin) }
override val v: Matrix<Float64> get() = cmEigen.v.wrap()
override val d: Matrix<Float64> get() = cmEigen.d.wrap()
}
else -> null
}
@Suppress("UNCHECKED_CAST")

View File

@ -9,7 +9,6 @@ import org.apache.commons.math3.linear.*
import space.kscience.kmath.linear.LinearSolver
import space.kscience.kmath.linear.Matrix
import space.kscience.kmath.linear.Point
import space.kscience.kmath.structures.Float64
public enum class CMDecomposition {
LUP,
@ -20,7 +19,7 @@ public enum class CMDecomposition {
}
private fun CMLinearSpace.solver(
a: Matrix<Float64>,
a: Matrix<Double>,
decomposition: CMDecomposition = CMDecomposition.LUP,
): DecompositionSolver = when (decomposition) {
CMDecomposition.LUP -> LUDecomposition(a.toCM().origin).solver
@ -31,31 +30,31 @@ private fun CMLinearSpace.solver(
}
public fun CMLinearSpace.solve(
a: Matrix<Float64>,
b: Matrix<Float64>,
a: Matrix<Double>,
b: Matrix<Double>,
decomposition: CMDecomposition = CMDecomposition.LUP,
): CMMatrix = solver(a, decomposition).solve(b.toCM().origin).wrap()
public fun CMLinearSpace.solve(
a: Matrix<Float64>,
b: Point<Float64>,
a: Matrix<Double>,
b: Point<Double>,
decomposition: CMDecomposition = CMDecomposition.LUP,
): CMVector = solver(a, decomposition).solve(b.toCM().origin).toPoint()
public fun CMLinearSpace.inverse(
a: Matrix<Float64>,
a: Matrix<Double>,
decomposition: CMDecomposition = CMDecomposition.LUP,
): CMMatrix = solver(a, decomposition).inverse.wrap()
public fun CMLinearSpace.solver(decomposition: CMDecomposition): LinearSolver<Float64> = object : LinearSolver<Float64> {
override fun solve(a: Matrix<Float64>, b: Matrix<Float64>): Matrix<Float64> =
public fun CMLinearSpace.solver(decomposition: CMDecomposition): LinearSolver<Double> = object : LinearSolver<Double> {
override fun solve(a: Matrix<Double>, b: Matrix<Double>): Matrix<Double> =
solver(a, decomposition).solve(b.toCM().origin).wrap()
override fun solve(a: Matrix<Float64>, b: Point<Float64>): Point<Float64> =
override fun solve(a: Matrix<Double>, b: Point<Double>): Point<Double> =
solver(a, decomposition).solve(b.toCM().origin).toPoint()
override fun inverse(matrix: Matrix<Float64>): Matrix<Float64> = solver(matrix, decomposition).inverse.wrap()
override fun inverse(matrix: Matrix<Double>): Matrix<Double> = solver(matrix, decomposition).inverse.wrap()
}
public fun CMLinearSpace.lupSolver(): LinearSolver<Float64> = solver((CMDecomposition.LUP))
public fun CMLinearSpace.lupSolver(): LinearSolver<Double> = solver((CMDecomposition.LUP))

View File

@ -22,7 +22,6 @@ import space.kscience.kmath.expressions.SymbolIndexer
import space.kscience.kmath.expressions.derivative
import space.kscience.kmath.expressions.withSymbols
import space.kscience.kmath.optimization.*
import space.kscience.kmath.structures.Float64
import kotlin.collections.set
import kotlin.reflect.KClass
@ -34,7 +33,7 @@ public object CMOptimizerEngine : OptimizationAttribute<() -> MultivariateOptimi
/**
* Specify a Commons-maths optimization engine
*/
public fun AttributesBuilder<FunctionOptimization<Float64>>.cmEngine(optimizerBuilder: () -> MultivariateOptimizer) {
public fun AttributesBuilder<FunctionOptimization<Double>>.cmEngine(optimizerBuilder: () -> MultivariateOptimizer) {
set(CMOptimizerEngine, optimizerBuilder)
}
@ -43,18 +42,18 @@ public object CMOptimizerData : SetAttribute<SymbolIndexer.() -> OptimizationDat
/**
* Specify Commons-maths optimization data.
*/
public fun AttributesBuilder<FunctionOptimization<Float64>>.cmOptimizationData(data: SymbolIndexer.() -> OptimizationData) {
public fun AttributesBuilder<FunctionOptimization<Double>>.cmOptimizationData(data: SymbolIndexer.() -> OptimizationData) {
CMOptimizerData add data
}
public fun AttributesBuilder<FunctionOptimization<Float64>>.simplexSteps(vararg steps: Pair<Symbol, Double>) {
public fun AttributesBuilder<FunctionOptimization<Double>>.simplexSteps(vararg steps: Pair<Symbol, Double>) {
//TODO use convergence checker from features
cmEngine { SimplexOptimizer(CMOptimizer.defaultConvergenceChecker) }
cmOptimizationData { NelderMeadSimplex(mapOf(*steps).toDoubleArray()) }
}
@OptIn(UnstableKMathAPI::class)
public object CMOptimizer : Optimizer<Double, FunctionOptimization<Float64>> {
public object CMOptimizer : Optimizer<Double, FunctionOptimization<Double>> {
public const val DEFAULT_RELATIVE_TOLERANCE: Double = 1e-4
public const val DEFAULT_ABSOLUTE_TOLERANCE: Double = 1e-4
@ -68,12 +67,12 @@ public object CMOptimizer : Optimizer<Double, FunctionOptimization<Float64>> {
override suspend fun optimize(
problem: FunctionOptimization<Float64>,
): FunctionOptimization<Float64> {
problem: FunctionOptimization<Double>,
): FunctionOptimization<Double> {
val startPoint = problem.startPoint
val parameters = problem.attributes[OptimizationParameters]
?: problem.attributes[OptimizationStartPoint<Float64>()]?.keys
?: problem.attributes[OptimizationStartPoint<Double>()]?.keys
?: startPoint.keys

View File

@ -77,7 +77,7 @@ public fun Flow<Buffer<Complex>>.fft(
}
@JvmName("realFFT")
public fun Flow<Buffer<Float64>>.fft(
public fun Flow<Buffer<Double>>.fft(
normalization: DftNormalization = DftNormalization.STANDARD,
direction: TransformType = TransformType.FORWARD,
): Flow<Buffer<Complex>> {
@ -89,7 +89,7 @@ public fun Flow<Buffer<Float64>>.fft(
* Process a continuous flow of real numbers in FFT splitting it in chunks of [bufferSize].
*/
@JvmName("realFFT")
public fun Flow<Float64>.fft(
public fun Flow<Double>.fft(
bufferSize: Int = Int.MAX_VALUE,
normalization: DftNormalization = DftNormalization.STANDARD,
direction: TransformType = TransformType.FORWARD,
@ -99,4 +99,4 @@ public fun Flow<Float64>.fft(
* Map a complex flow into real flow by taking real part of each number
*/
@FlowPreview
public fun Flow<Complex>.real(): Flow<Float64> = map { it.re }
public fun Flow<Complex>.real(): Flow<Double> = map { it.re }

View File

@ -18,7 +18,6 @@ import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.optimization.*
import space.kscience.kmath.random.RandomGenerator
import space.kscience.kmath.stat.chiSquaredExpression
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
import space.kscience.kmath.structures.asBuffer
import kotlin.test.Test
@ -71,7 +70,7 @@ internal class OptimizeTest {
bindSymbol(a) * arg.pow(2) + bindSymbol(b) * arg + cWithDefault
}
val result: FunctionOptimization<Float64> = chi2.optimizeWith(
val result: FunctionOptimization<Double> = chi2.optimizeWith(
CMOptimizer,
mapOf(a to 1.5, b to 0.9, c to 1.0),
) {

View File

@ -11,7 +11,6 @@ import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.memory.*
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.MutableBuffer
import space.kscience.kmath.structures.MutableBufferFactory
import kotlin.math.*
@ -29,7 +28,7 @@ public class Quaternion(
public val x: Double,
public val y: Double,
public val z: Double,
) : Buffer<Float64> {
) : Buffer<Double> {
init {
require(!w.isNaN()) { "w-component of quaternion is not-a-number" }
require(!x.isNaN()) { "x-component of quaternion is not-a-number" }
@ -52,7 +51,7 @@ public class Quaternion(
else -> error("Index $index out of bounds [0,3]")
}
override fun iterator(): Iterator<Float64> = listOf(w, x, y, z).iterator()
override fun iterator(): Iterator<Double> = listOf(w, x, y, z).iterator()
override fun equals(other: Any?): Boolean {
if (this === other) return true

View File

@ -7,7 +7,6 @@ package space.kscience.kmath.domains
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.linear.Point
import space.kscience.kmath.structures.Float64
@UnstableKMathAPI
public abstract class Domain1D<T : Comparable<T>>(public val range: ClosedRange<T>) : Domain<T> {
@ -23,8 +22,8 @@ public abstract class Domain1D<T : Comparable<T>>(public val range: ClosedRange<
@UnstableKMathAPI
public class DoubleDomain1D(
@Suppress("CanBeParameter") public val doubleRange: ClosedFloatingPointRange<Float64>,
) : Domain1D<Float64>(doubleRange), Float64Domain {
@Suppress("CanBeParameter") public val doubleRange: ClosedFloatingPointRange<Double>,
) : Domain1D<Double>(doubleRange), Float64Domain {
override fun getLowerBound(num: Int): Double {
require(num == 0)
return range.start
@ -56,5 +55,5 @@ public class DoubleDomain1D(
}
@UnstableKMathAPI
public val Domain1D<Float64>.center: Double
public val Domain1D<Double>.center: Double
get() = (range.endInclusive + range.start) / 2

View File

@ -5,7 +5,6 @@
package space.kscience.kmath.domains
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.structures.Float64
/**
* n-dimensional volume
@ -13,7 +12,7 @@ import space.kscience.kmath.structures.Float64
* @author Alexander Nozik
*/
@UnstableKMathAPI
public interface Float64Domain : Domain<Float64> {
public interface Float64Domain : Domain<Double> {
/**
* Global lower edge
* @param num axis number

View File

@ -7,7 +7,6 @@ package space.kscience.kmath.domains
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.linear.Point
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
import space.kscience.kmath.structures.indices
@ -16,7 +15,7 @@ import space.kscience.kmath.structures.indices
* and a [Buffer] of upper boundaries. Upper should be greater or equals than lower.
*/
@UnstableKMathAPI
public class HyperSquareDomain(public val lower: Buffer<Float64>, public val upper: Buffer<Float64>) : Float64Domain {
public class HyperSquareDomain(public val lower: Buffer<Double>, public val upper: Buffer<Double>) : Float64Domain {
init {
require(lower.size == upper.size) {
"Domain borders size mismatch. Lower borders size is ${lower.size}, but upper borders size is ${upper.size}."
@ -30,7 +29,7 @@ public class HyperSquareDomain(public val lower: Buffer<Float64>, public val upp
public val center: Float64Buffer get() = Float64Buffer(dimension) { (lower[it] + upper[it]) / 2.0 }
override operator fun contains(point: Point<Float64>): Boolean = point.indices.all { i ->
override operator fun contains(point: Point<Double>): Boolean = point.indices.all { i ->
point[i] in lower[i]..upper[i]
}

View File

@ -6,11 +6,10 @@ package space.kscience.kmath.domains
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.linear.Point
import space.kscience.kmath.structures.Float64
@UnstableKMathAPI
public class UnconstrainedDomain(override val dimension: Int) : Float64Domain {
override operator fun contains(point: Point<Float64>): Boolean = true
override operator fun contains(point: Point<Double>): Boolean = true
override fun getLowerBound(num: Int): Double = Double.NEGATIVE_INFINITY

View File

@ -13,7 +13,6 @@ import space.kscience.kmath.operations.Algebra
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.IntRing
import space.kscience.kmath.operations.LongRing
import space.kscience.kmath.structures.Float64
import kotlin.jvm.JvmName
import kotlin.properties.ReadOnlyProperty
@ -48,9 +47,9 @@ public inline fun <reified T> Expression(noinline block: (Map<Symbol, T>) -> T):
* Specialization of [Expression] for [Double] allowing better performance because of using array.
*/
@UnstableKMathAPI
public interface DoubleExpression : Expression<Float64> {
public interface DoubleExpression : Expression<Double> {
override val type: SafeType<Float64> get() = DoubleField.type
override val type: SafeType<Double> get() = DoubleField.type
/**
* The indexer of this expression's arguments that should be used to build array for [invoke].

View File

@ -6,7 +6,6 @@
package space.kscience.kmath.expressions
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.MutableBufferFactory
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
@ -198,5 +197,5 @@ public inline fun <T, A : ExtendedField<T>> A.expressionInExtendedField(
): Expression<T> = FunctionalExpressionExtendedField(this).block()
public inline fun Float64Field.expression(
block: FunctionalExpressionExtendedField<Double, Float64Field>.() -> Expression<Float64>,
): Expression<Float64> = FunctionalExpressionExtendedField(this).block()
block: FunctionalExpressionExtendedField<Double, Float64Field>.() -> Expression<Double>,
): Expression<Double> = FunctionalExpressionExtendedField(this).block()

View File

@ -1,32 +0,0 @@
/*
* Copyright 2018-2024 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.linear
import space.kscience.attributes.PolymorphicAttribute
import space.kscience.attributes.safeTypeOf
import space.kscience.kmath.UnstableKMathAPI
@UnstableKMathAPI
public interface EigenDecomposition<T> {
/**
* Eigenvector matrix.
*/
public val v: Matrix<T>
/**
* A diagonal matrix of eigenvalues. Must have [IsDiagonal]
*/
public val d: Matrix<T>
}
@UnstableKMathAPI
public class EigenDecompositionAttribute<T> :
PolymorphicAttribute<EigenDecomposition<T>>(safeTypeOf()),
MatrixAttribute<EigenDecomposition<T>>
@UnstableKMathAPI
public val <T> MatrixScope<T>.EIG: EigenDecompositionAttribute<T>
get() = EigenDecompositionAttribute()

View File

@ -6,19 +6,12 @@
package space.kscience.kmath.linear
import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.nd.Floa64FieldOpsND
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.asND
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.Float64BufferOps
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
import kotlin.collections.component1
import kotlin.collections.component2
import kotlin.collections.map
public object Float64LinearSpace : LinearSpace<Double, Float64Field> {
@ -28,36 +21,36 @@ public object Float64LinearSpace : LinearSpace<Double, Float64Field> {
rows: Int,
columns: Int,
initializer: Float64Field.(i: Int, j: Int) -> Double,
): Matrix<Float64> = Floa64FieldOpsND.structureND(ShapeND(rows, columns)) { (i, j) ->
): Matrix<Double> = Floa64FieldOpsND.structureND(ShapeND(rows, columns)) { (i, j) ->
Float64Field.initializer(i, j)
}.as2D()
override fun buildVector(size: Int, initializer: Float64Field.(Int) -> Double): Float64Buffer =
Float64Buffer(size) { Float64Field.initializer(it) }
override fun Matrix<Float64>.unaryMinus(): Matrix<Float64> = Floa64FieldOpsND {
override fun Matrix<Double>.unaryMinus(): Matrix<Double> = Floa64FieldOpsND {
asND().map { -it }.as2D()
}
override fun Matrix<Float64>.plus(other: Matrix<Float64>): Matrix<Float64> = Floa64FieldOpsND {
override fun Matrix<Double>.plus(other: Matrix<Double>): Matrix<Double> = Floa64FieldOpsND {
require(shape == other.shape) { "Shape mismatch on Matrix::plus. Expected $shape but found ${other.shape}" }
asND().plus(other.asND()).as2D()
}
override fun Matrix<Float64>.minus(other: Matrix<Float64>): Matrix<Float64> = Floa64FieldOpsND {
override fun Matrix<Double>.minus(other: Matrix<Double>): Matrix<Double> = Floa64FieldOpsND {
require(shape == other.shape) { "Shape mismatch on Matrix::minus. Expected $shape but found ${other.shape}" }
asND().minus(other.asND()).as2D()
}
// Create a continuous in-memory representation of this vector for better memory layout handling
private fun Buffer<Float64>.linearize() = if (this is Float64Buffer) {
private fun Buffer<Double>.linearize() = if (this is Float64Buffer) {
this.array
} else {
DoubleArray(size) { get(it) }
}
@OptIn(PerformancePitfall::class)
override fun Matrix<Float64>.dot(other: Matrix<Float64>): Matrix<Float64> {
override fun Matrix<Double>.dot(other: Matrix<Double>): Matrix<Double> {
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
val rows = this@dot.rows.map { it.linearize() }
val columns = other.columns.map { it.linearize() }
@ -74,7 +67,7 @@ public object Float64LinearSpace : LinearSpace<Double, Float64Field> {
}
@OptIn(PerformancePitfall::class)
override fun Matrix<Float64>.dot(vector: Point<Float64>): Float64Buffer {
override fun Matrix<Double>.dot(vector: Point<Double>): Float64Buffer {
require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
val rows = this@dot.rows.map { it.linearize() }
val indices = 0 until this.colNum
@ -89,27 +82,27 @@ public object Float64LinearSpace : LinearSpace<Double, Float64Field> {
}
override fun Matrix<Float64>.times(value: Double): Matrix<Float64> = Floa64FieldOpsND {
override fun Matrix<Double>.times(value: Double): Matrix<Double> = Floa64FieldOpsND {
asND().map { it * value }.as2D()
}
public override fun Point<Float64>.plus(other: Point<Float64>): Float64Buffer = Float64BufferOps.run {
public override fun Point<Double>.plus(other: Point<Double>): Float64Buffer = Float64BufferOps.run {
this@plus + other
}
public override fun Point<Float64>.minus(other: Point<Float64>): Float64Buffer = Float64BufferOps.run {
public override fun Point<Double>.minus(other: Point<Double>): Float64Buffer = Float64BufferOps.run {
this@minus - other
}
public override fun Point<Float64>.times(value: Double): Float64Buffer = Float64BufferOps.run {
public override fun Point<Double>.times(value: Double): Float64Buffer = Float64BufferOps.run {
scale(this@times, value)
}
public operator fun Point<Float64>.div(value: Double): Float64Buffer = Float64BufferOps.run {
public operator fun Point<Double>.div(value: Double): Float64Buffer = Float64BufferOps.run {
scale(this@div, 1.0 / value)
}
public override fun Double.times(v: Point<Float64>): Float64Buffer = v * this
public override fun Double.times(v: Point<Double>): Float64Buffer = v * this
}

View File

@ -163,9 +163,9 @@ public fun <T : Comparable<T>> Field<T>.lup(
public fun Field<Float64>.lup(
matrix: Matrix<Float64>,
matrix: Matrix<Double>,
singularityThreshold: Double = 1e-11,
): GenericLupDecomposition<Float64> = lup(matrix) { it < singularityThreshold }
): GenericLupDecomposition<Double> = lup(matrix) { it < singularityThreshold }
private fun <T> Field<T>.solve(
lup: LupDecomposition<T>,
@ -235,5 +235,5 @@ public fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lupSolver(
override fun inverse(matrix: Matrix<T>): Matrix<T> = solve(matrix, one(matrix.rowNum, matrix.colNum))
}
public fun LinearSpace<Double, Float64Field>.lupSolver(singularityThreshold: Double = 1e-11): LinearSolver<Float64> =
public fun LinearSpace<Double, Float64Field>.lupSolver(singularityThreshold: Double = 1e-11): LinearSolver<Double> =
lupSolver { it < singularityThreshold }

View File

@ -67,14 +67,13 @@ public fun <T : Any, A : Ring<T>> MatrixBuilder<T, A>.symmetric(
): Matrix<T> {
require(columns == rows) { "In order to build symmetric matrix, number of rows $rows should be equal to number of columns $columns" }
return with(BufferAccessor2D<T?>(rows, rows, MutableBufferFactory(type))) {
val cache = HashMap<IntArray, T>()
val cache = factory(rows * rows) { null }
linearSpace.buildMatrix(rows, rows) { i, j ->
val index = intArrayOf(i, j)
val cached = cache[index]
val cached = cache[i, j]
if (cached == null) {
val value = if (i <= j) builder(i, j) else builder(j, i)
cache[index] = value
cache[index] = value
cache[i, j] = value
cache[j, i] = value
value
} else {
cached

View File

@ -124,7 +124,7 @@ public val <T> MatrixScope<T>.QR: QRDecompositionAttribute<T>
public interface CholeskyDecomposition<T> {
/**
* The lower triangular matrix in this decomposition. It should have [LowerTriangular].
* The triangular matrix in this decomposition. It may have either [UpperTriangular] or [LowerTriangular].
*/
public val l: Matrix<T>
}

View File

@ -8,7 +8,6 @@ package space.kscience.kmath.misc
import space.kscience.kmath.operations.Ring
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import kotlin.jvm.JvmName
/**
@ -48,7 +47,7 @@ public fun <T> Iterable<T>.cumulativeSum(ring: Ring<T>): Iterable<T> =
ring { cumulative(zero) { element: T, sum: T -> sum + element } }
@JvmName("cumulativeSumOfDouble")
public fun Iterable<Float64>.cumulativeSum(): Iterable<Float64> = cumulative(0.0) { element, sum -> sum + element }
public fun Iterable<Double>.cumulativeSum(): Iterable<Double> = cumulative(0.0) { element, sum -> sum + element }
@JvmName("cumulativeSumOfInt")
public fun Iterable<Int>.cumulativeSum(): Iterable<Int> = cumulative(0) { element, sum -> sum + element }
@ -60,7 +59,7 @@ public fun <T> Sequence<T>.cumulativeSum(ring: Ring<T>): Sequence<T> =
ring { cumulative(zero) { element: T, sum: T -> sum + element } }
@JvmName("cumulativeSumOfDouble")
public fun Sequence<Float64>.cumulativeSum(): Sequence<Float64> = cumulative(0.0) { element, sum -> sum + element }
public fun Sequence<Double>.cumulativeSum(): Sequence<Double> = cumulative(0.0) { element, sum -> sum + element }
@JvmName("cumulativeSumOfInt")
public fun Sequence<Int>.cumulativeSum(): Sequence<Int> = cumulative(0) { element, sum -> sum + element }
@ -72,7 +71,7 @@ public fun <T> List<T>.cumulativeSum(group: Ring<T>): List<T> =
group { cumulative(zero) { element: T, sum: T -> sum + element } }
@JvmName("cumulativeSumOfDouble")
public fun List<Float64>.cumulativeSum(): List<Float64> = cumulative(0.0) { element, sum -> sum + element }
public fun List<Double>.cumulativeSum(): List<Double> = cumulative(0.0) { element, sum -> sum + element }
@JvmName("cumulativeSumOfInt")
public fun List<Int>.cumulativeSum(): List<Int> = cumulative(0) { element, sum -> sum + element }

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@ -8,7 +8,6 @@ package space.kscience.kmath.nd
import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
@ -21,7 +20,7 @@ import kotlin.math.pow as kpow
public class Float64BufferND(
indexes: ShapeIndexer,
override val buffer: Float64Buffer,
) : MutableBufferND<Float64>(indexes, buffer), MutableStructureNDOfDouble {
) : MutableBufferND<Double>(indexes, buffer), MutableStructureNDOfDouble {
override fun getDouble(index: IntArray): Double = buffer[indices.offset(index)]
@ -31,11 +30,11 @@ public class Float64BufferND(
}
public sealed class Floa64FieldOpsND : BufferedFieldOpsND<Float64, Float64Field>(Float64Field.bufferAlgebra),
ScaleOperations<StructureND<Float64>>, ExtendedFieldOps<StructureND<Float64>> {
public sealed class Floa64FieldOpsND : BufferedFieldOpsND<Double, Float64Field>(Float64Field.bufferAlgebra),
ScaleOperations<StructureND<Double>>, ExtendedFieldOps<StructureND<Double>> {
@OptIn(PerformancePitfall::class)
override fun StructureND<Float64>.toBufferND(): Float64BufferND = when (this) {
override fun StructureND<Double>.toBufferND(): Float64BufferND = when (this) {
is Float64BufferND -> this
else -> {
val indexer = indexerBuilder(shape)
@ -65,16 +64,16 @@ public sealed class Floa64FieldOpsND : BufferedFieldOpsND<Float64, Float64Field>
}
@OptIn(PerformancePitfall::class)
override fun StructureND<Float64>.map(transform: Float64Field.(Double) -> Double): BufferND<Float64> =
override fun StructureND<Double>.map(transform: Float64Field.(Double) -> Double): BufferND<Double> =
mapInline(toBufferND()) { Float64Field.transform(it) }
@OptIn(PerformancePitfall::class)
override fun zip(
left: StructureND<Float64>,
right: StructureND<Float64>,
left: StructureND<Double>,
right: StructureND<Double>,
transform: Float64Field.(Double, Double) -> Double,
): BufferND<Float64> = zipInline(left.toBufferND(), right.toBufferND()) { l, r -> Float64Field.transform(l, r) }
): BufferND<Double> = zipInline(left.toBufferND(), right.toBufferND()) { l, r -> Float64Field.transform(l, r) }
override fun mutableStructureND(shape: ShapeND, initializer: Float64Field.(IntArray) -> Double): Float64BufferND {
val indexer = indexerBuilder(shape)
@ -86,102 +85,102 @@ public sealed class Floa64FieldOpsND : BufferedFieldOpsND<Float64, Float64Field>
)
}
override fun add(left: StructureND<Float64>, right: StructureND<Float64>): Float64BufferND =
override fun add(left: StructureND<Double>, right: StructureND<Double>): Float64BufferND =
zipInline(left.toBufferND(), right.toBufferND()) { l, r -> l + r }
override fun multiply(left: StructureND<Float64>, right: StructureND<Float64>): Float64BufferND =
override fun multiply(left: StructureND<Double>, right: StructureND<Double>): Float64BufferND =
zipInline(left.toBufferND(), right.toBufferND()) { l, r -> l * r }
override fun StructureND<Float64>.unaryMinus(): Float64BufferND = mapInline(toBufferND()) { -it }
override fun StructureND<Double>.unaryMinus(): Float64BufferND = mapInline(toBufferND()) { -it }
override fun StructureND<Float64>.div(arg: StructureND<Float64>): Float64BufferND =
override fun StructureND<Double>.div(arg: StructureND<Double>): Float64BufferND =
zipInline(toBufferND(), arg.toBufferND()) { l, r -> l / r }
override fun divide(left: StructureND<Float64>, right: StructureND<Float64>): Float64BufferND =
override fun divide(left: StructureND<Double>, right: StructureND<Double>): Float64BufferND =
zipInline(left.toBufferND(), right.toBufferND()) { l: Double, r: Double -> l / r }
override fun StructureND<Float64>.div(arg: Double): Float64BufferND =
override fun StructureND<Double>.div(arg: Double): Float64BufferND =
mapInline(toBufferND()) { it / arg }
override fun Double.div(arg: StructureND<Float64>): Float64BufferND =
override fun Double.div(arg: StructureND<Double>): Float64BufferND =
mapInline(arg.toBufferND()) { this / it }
override fun StructureND<Float64>.unaryPlus(): Float64BufferND = toBufferND()
override fun StructureND<Double>.unaryPlus(): Float64BufferND = toBufferND()
override fun StructureND<Float64>.plus(arg: StructureND<Float64>): Float64BufferND =
override fun StructureND<Double>.plus(arg: StructureND<Double>): Float64BufferND =
zipInline(toBufferND(), arg.toBufferND()) { l: Double, r: Double -> l + r }
override fun StructureND<Float64>.minus(arg: StructureND<Float64>): Float64BufferND =
override fun StructureND<Double>.minus(arg: StructureND<Double>): Float64BufferND =
zipInline(toBufferND(), arg.toBufferND()) { l: Double, r: Double -> l - r }
override fun StructureND<Float64>.times(arg: StructureND<Float64>): Float64BufferND =
override fun StructureND<Double>.times(arg: StructureND<Double>): Float64BufferND =
zipInline(toBufferND(), arg.toBufferND()) { l: Double, r: Double -> l * r }
override fun StructureND<Float64>.times(k: Number): Float64BufferND =
override fun StructureND<Double>.times(k: Number): Float64BufferND =
mapInline(toBufferND()) { it * k.toDouble() }
override fun StructureND<Float64>.div(k: Number): Float64BufferND =
override fun StructureND<Double>.div(k: Number): Float64BufferND =
mapInline(toBufferND()) { it / k.toDouble() }
override fun Number.times(arg: StructureND<Float64>): Float64BufferND = arg * this
override fun Number.times(arg: StructureND<Double>): Float64BufferND = arg * this
override fun StructureND<Float64>.plus(arg: Double): Float64BufferND = mapInline(toBufferND()) { it + arg }
override fun StructureND<Double>.plus(arg: Double): Float64BufferND = mapInline(toBufferND()) { it + arg }
override fun StructureND<Float64>.minus(arg: Double): StructureND<Float64> = mapInline(toBufferND()) { it - arg }
override fun StructureND<Double>.minus(arg: Double): StructureND<Double> = mapInline(toBufferND()) { it - arg }
override fun Double.plus(arg: StructureND<Float64>): StructureND<Float64> = arg + this
override fun Double.plus(arg: StructureND<Double>): StructureND<Double> = arg + this
override fun Double.minus(arg: StructureND<Float64>): StructureND<Float64> = mapInline(arg.toBufferND()) { this - it }
override fun Double.minus(arg: StructureND<Double>): StructureND<Double> = mapInline(arg.toBufferND()) { this - it }
override fun scale(a: StructureND<Float64>, value: Double): Float64BufferND =
override fun scale(a: StructureND<Double>, value: Double): Float64BufferND =
mapInline(a.toBufferND()) { it * value }
override fun exp(arg: StructureND<Float64>): Float64BufferND =
override fun exp(arg: StructureND<Double>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.exp(it) }
override fun ln(arg: StructureND<Float64>): Float64BufferND =
override fun ln(arg: StructureND<Double>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.ln(it) }
override fun sin(arg: StructureND<Float64>): Float64BufferND =
override fun sin(arg: StructureND<Double>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.sin(it) }
override fun cos(arg: StructureND<Float64>): Float64BufferND =
override fun cos(arg: StructureND<Double>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.cos(it) }
override fun tan(arg: StructureND<Float64>): Float64BufferND =
override fun tan(arg: StructureND<Double>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.tan(it) }
override fun asin(arg: StructureND<Float64>): Float64BufferND =
override fun asin(arg: StructureND<Double>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.asin(it) }
override fun acos(arg: StructureND<Float64>): Float64BufferND =
override fun acos(arg: StructureND<Double>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.acos(it) }
override fun atan(arg: StructureND<Float64>): Float64BufferND =
override fun atan(arg: StructureND<Double>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.atan(it) }
override fun sinh(arg: StructureND<Float64>): Float64BufferND =
override fun sinh(arg: StructureND<Double>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.sinh(it) }
override fun cosh(arg: StructureND<Float64>): Float64BufferND =
override fun cosh(arg: StructureND<Double>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.cosh(it) }
override fun tanh(arg: StructureND<Float64>): Float64BufferND =
override fun tanh(arg: StructureND<Double>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.tanh(it) }
override fun asinh(arg: StructureND<Float64>): Float64BufferND =
override fun asinh(arg: StructureND<Double>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.asinh(it) }
override fun acosh(arg: StructureND<Float64>): Float64BufferND =
override fun acosh(arg: StructureND<Double>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.acosh(it) }
override fun atanh(arg: StructureND<Float64>): Float64BufferND =
override fun atanh(arg: StructureND<Double>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.atanh(it) }
override fun power(
arg: StructureND<Float64>,
arg: StructureND<Double>,
pow: Number,
): StructureND<Float64> = if (pow is Int) {
): StructureND<Double> = if (pow is Int) {
mapInline(arg.toBufferND()) { it.pow(pow) }
} else {
mapInline(arg.toBufferND()) { it.pow(pow.toDouble()) }
@ -192,18 +191,18 @@ public sealed class Floa64FieldOpsND : BufferedFieldOpsND<Float64, Float64Field>
@OptIn(UnstableKMathAPI::class)
public class Float64FieldND(override val shape: ShapeND) :
Floa64FieldOpsND(), FieldND<Double, Float64Field>, NumbersAddOps<StructureND<Float64>>,
ExtendedField<StructureND<Float64>> {
Floa64FieldOpsND(), FieldND<Double, Float64Field>, NumbersAddOps<StructureND<Double>>,
ExtendedField<StructureND<Double>> {
override fun power(arg: StructureND<Float64>, pow: UInt): Float64BufferND = mapInline(arg.toBufferND()) {
override fun power(arg: StructureND<Double>, pow: UInt): Float64BufferND = mapInline(arg.toBufferND()) {
it.kpow(pow.toInt())
}
override fun power(arg: StructureND<Float64>, pow: Int): Float64BufferND = mapInline(arg.toBufferND()) {
override fun power(arg: StructureND<Double>, pow: Int): Float64BufferND = mapInline(arg.toBufferND()) {
it.kpow(pow)
}
override fun power(arg: StructureND<Float64>, pow: Number): Float64BufferND = if (pow.isInteger()) {
override fun power(arg: StructureND<Double>, pow: Number): Float64BufferND = if (pow.isInteger()) {
power(arg, pow.toInt())
} else {
val dpow = pow.toDouble()
@ -213,17 +212,17 @@ public class Float64FieldND(override val shape: ShapeND) :
}
}
override fun sinh(arg: StructureND<Float64>): Float64BufferND = super<Floa64FieldOpsND>.sinh(arg)
override fun sinh(arg: StructureND<Double>): Float64BufferND = super<Floa64FieldOpsND>.sinh(arg)
override fun cosh(arg: StructureND<Float64>): Float64BufferND = super<Floa64FieldOpsND>.cosh(arg)
override fun cosh(arg: StructureND<Double>): Float64BufferND = super<Floa64FieldOpsND>.cosh(arg)
override fun tanh(arg: StructureND<Float64>): Float64BufferND = super<Floa64FieldOpsND>.tan(arg)
override fun tanh(arg: StructureND<Double>): Float64BufferND = super<Floa64FieldOpsND>.tan(arg)
override fun asinh(arg: StructureND<Float64>): Float64BufferND = super<Floa64FieldOpsND>.asinh(arg)
override fun asinh(arg: StructureND<Double>): Float64BufferND = super<Floa64FieldOpsND>.asinh(arg)
override fun acosh(arg: StructureND<Float64>): Float64BufferND = super<Floa64FieldOpsND>.acosh(arg)
override fun acosh(arg: StructureND<Double>): Float64BufferND = super<Floa64FieldOpsND>.acosh(arg)
override fun atanh(arg: StructureND<Float64>): Float64BufferND = super<Floa64FieldOpsND>.atanh(arg)
override fun atanh(arg: StructureND<Double>): Float64BufferND = super<Floa64FieldOpsND>.atanh(arg)
override fun number(value: Number): Float64BufferND {
val d = value.toDouble() // minimize conversions

View File

@ -14,7 +14,6 @@ import space.kscience.kmath.linear.LinearSpace
import space.kscience.kmath.operations.Ring
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import kotlin.math.abs
public interface StructureAttribute<T> : Attribute<T>
@ -77,8 +76,8 @@ public interface StructureND<out T> : AttributeContainer, WithShape {
@PerformancePitfall
public fun contentEquals(
st1: StructureND<Float64>,
st2: StructureND<Float64>,
st1: StructureND<Double>,
st2: StructureND<Double>,
tolerance: Double = 1e-11,
): Boolean {
if (st1 === st2) return true
@ -211,7 +210,7 @@ public fun <T : Comparable<T>> LinearSpace<T, Ring<T>>.contentEquals(
@PerformancePitfall
public operator fun <T> StructureND<T>.get(vararg index: Int): T = get(index)
public operator fun StructureND<Float64>.get(vararg index: Int): Double = getDouble(index)
public operator fun StructureND<Double>.get(vararg index: Int): Double = getDouble(index)
public operator fun StructureND<Int>.get(vararg index: Int): Int = getInt(index)

View File

@ -7,7 +7,6 @@ package space.kscience.kmath.nd
import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.structures.Float64
public open class VirtualStructureND<T>(
override val shape: ShapeND,
@ -25,7 +24,7 @@ public open class VirtualStructureND<T>(
public class VirtualDoubleStructureND(
shape: ShapeND,
producer: (IntArray) -> Double,
) : VirtualStructureND<Float64>(shape, producer)
) : VirtualStructureND<Double>(shape, producer)
@UnstableKMathAPI
public class VirtualIntStructureND(

View File

@ -6,9 +6,8 @@
package space.kscience.kmath.nd
import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.structures.Float64
public interface StructureNDOfDouble : StructureND<Float64> {
public interface StructureNDOfDouble : StructureND<Double> {
/**
* Guaranteed non-blocking access to content
@ -20,10 +19,10 @@ public interface StructureNDOfDouble : StructureND<Float64> {
* Optimized method to access primitive without boxing if possible
*/
@OptIn(PerformancePitfall::class)
public fun StructureND<Float64>.getDouble(index: IntArray): Double =
public fun StructureND<Double>.getDouble(index: IntArray): Double =
if (this is StructureNDOfDouble) getDouble(index) else get(index)
public interface MutableStructureNDOfDouble : StructureNDOfDouble, MutableStructureND<Float64> {
public interface MutableStructureNDOfDouble : StructureNDOfDouble, MutableStructureND<Double> {
/**
* Guaranteed non-blocking access to content
@ -32,7 +31,7 @@ public interface MutableStructureNDOfDouble : StructureNDOfDouble, MutableStruct
}
@OptIn(PerformancePitfall::class)
public fun MutableStructureND<Float64>.getDouble(index: IntArray): Double =
public fun MutableStructureND<Double>.getDouble(index: IntArray): Double =
if (this is StructureNDOfDouble) getDouble(index) else get(index)

View File

@ -6,7 +6,6 @@
package space.kscience.kmath.operations
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.MutableBuffer
import space.kscience.kmath.structures.MutableBufferFactory
@ -183,7 +182,7 @@ public fun <T, A : Field<T>> BufferFieldOps<T, A>.withSize(size: Int): BufferFie
//Double buffer specialization
public fun BufferField<Double, *>.buffer(vararg elements: Number): Buffer<Float64> {
public fun BufferField<Double, *>.buffer(vararg elements: Number): Buffer<Double> {
require(elements.size == size) { "Expected $size elements but found ${elements.size}" }
return elementBufferFactory(size) { elements[it].toDouble() }
}

View File

@ -6,7 +6,6 @@
package space.kscience.kmath.operations
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
/**
@ -14,23 +13,23 @@ import space.kscience.kmath.structures.Float64Buffer
*
* @property size the size of buffers to operate on.
*/
public class Float64BufferField(public val size: Int) : ExtendedField<Buffer<Float64>>, Float64BufferOps() {
override val zero: Buffer<Float64> by lazy { Float64Buffer(size) { 0.0 } }
override val one: Buffer<Float64> by lazy { Float64Buffer(size) { 1.0 } }
public class Float64BufferField(public val size: Int) : ExtendedField<Buffer<Double>>, Float64BufferOps() {
override val zero: Buffer<Double> by lazy { Float64Buffer(size) { 0.0 } }
override val one: Buffer<Double> by lazy { Float64Buffer(size) { 1.0 } }
override fun sinh(arg: Buffer<Float64>): Float64Buffer = super<Float64BufferOps>.sinh(arg)
override fun sinh(arg: Buffer<Double>): Float64Buffer = super<Float64BufferOps>.sinh(arg)
override fun cosh(arg: Buffer<Float64>): Float64Buffer = super<Float64BufferOps>.cosh(arg)
override fun cosh(arg: Buffer<Double>): Float64Buffer = super<Float64BufferOps>.cosh(arg)
override fun tanh(arg: Buffer<Float64>): Float64Buffer = super<Float64BufferOps>.tanh(arg)
override fun tanh(arg: Buffer<Double>): Float64Buffer = super<Float64BufferOps>.tanh(arg)
override fun asinh(arg: Buffer<Float64>): Float64Buffer = super<Float64BufferOps>.asinh(arg)
override fun asinh(arg: Buffer<Double>): Float64Buffer = super<Float64BufferOps>.asinh(arg)
override fun acosh(arg: Buffer<Float64>): Float64Buffer = super<Float64BufferOps>.acosh(arg)
override fun acosh(arg: Buffer<Double>): Float64Buffer = super<Float64BufferOps>.acosh(arg)
override fun atanh(arg: Buffer<Float64>): Float64Buffer = super<Float64BufferOps>.atanh(arg)
override fun atanh(arg: Buffer<Double>): Float64Buffer = super<Float64BufferOps>.atanh(arg)
override fun power(arg: Buffer<Float64>, pow: Number): Float64Buffer = if (pow.isInteger()) {
override fun power(arg: Buffer<Double>, pow: Number): Float64Buffer = if (pow.isInteger()) {
arg.map { it.pow(pow.toInt()) }
} else {
arg.map {
@ -39,6 +38,6 @@ public class Float64BufferField(public val size: Int) : ExtendedField<Buffer<Flo
}
}
override fun unaryOperationFunction(operation: String): (arg: Buffer<Float64>) -> Buffer<Float64> =
override fun unaryOperationFunction(operation: String): (arg: Buffer<Double>) -> Buffer<Double> =
super<ExtendedField>.unaryOperationFunction(operation)
}

View File

@ -14,43 +14,43 @@ import kotlin.math.sqrt
/**
* [ExtendedFieldOps] over [Float64Buffer].
*/
public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, ExtendedFieldOps<Buffer<Float64>>,
Norm<Buffer<Float64>, Double> {
public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, ExtendedFieldOps<Buffer<Double>>,
Norm<Buffer<Double>, Double> {
override val elementAlgebra: Float64Field get() = Float64Field
override val elementBufferFactory: MutableBufferFactory<Float64> get() = elementAlgebra.bufferFactory
override val elementBufferFactory: MutableBufferFactory<Double> get() = elementAlgebra.bufferFactory
@Suppress("OVERRIDE_BY_INLINE")
@OptIn(UnstableKMathAPI::class)
final override inline fun Buffer<Float64>.map(block: Float64Field.(Double) -> Double): Float64Buffer =
final override inline fun Buffer<Double>.map(block: Float64Field.(Double) -> Double): Float64Buffer =
DoubleArray(size) { Float64Field.block(getDouble(it)) }.asBuffer()
@OptIn(UnstableKMathAPI::class)
@Suppress("OVERRIDE_BY_INLINE")
final override inline fun Buffer<Float64>.mapIndexed(block: Float64Field.(index: Int, arg: Double) -> Double): Float64Buffer =
final override inline fun Buffer<Double>.mapIndexed(block: Float64Field.(index: Int, arg: Double) -> Double): Float64Buffer =
Float64Buffer(size) { Float64Field.block(it, getDouble(it)) }
@OptIn(UnstableKMathAPI::class)
@Suppress("OVERRIDE_BY_INLINE")
final override inline fun Buffer<Float64>.zip(
other: Buffer<Float64>,
final override inline fun Buffer<Double>.zip(
other: Buffer<Double>,
block: Float64Field.(left: Double, right: Double) -> Double,
): Float64Buffer {
require(size == other.size) { "Incompatible buffer sizes. left: ${size}, right: ${other.size}" }
return Float64Buffer(size) { Float64Field.block(getDouble(it), other.getDouble(it)) }
}
override fun unaryOperationFunction(operation: String): (arg: Buffer<Float64>) -> Buffer<Float64> =
override fun unaryOperationFunction(operation: String): (arg: Buffer<Double>) -> Buffer<Double> =
super<ExtendedFieldOps>.unaryOperationFunction(operation)
override fun binaryOperationFunction(operation: String): (left: Buffer<Float64>, right: Buffer<Float64>) -> Buffer<Float64> =
override fun binaryOperationFunction(operation: String): (left: Buffer<Double>, right: Buffer<Double>) -> Buffer<Double> =
super<ExtendedFieldOps>.binaryOperationFunction(operation)
override fun Buffer<Float64>.unaryMinus(): Float64Buffer = map { -it }
override fun Buffer<Double>.unaryMinus(): Float64Buffer = map { -it }
override fun add(left: Buffer<Float64>, right: Buffer<Float64>): Float64Buffer {
override fun add(left: Buffer<Double>, right: Buffer<Double>): Float64Buffer {
require(right.size == left.size) {
"The size of the first buffer ${left.size} should be the same as for second one: ${right.size} "
}
@ -62,9 +62,9 @@ public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, Ex
} else Float64Buffer(DoubleArray(left.size) { left[it] + right[it] })
}
override fun Buffer<Float64>.plus(arg: Buffer<Float64>): Float64Buffer = add(this, arg)
override fun Buffer<Double>.plus(arg: Buffer<Double>): Float64Buffer = add(this, arg)
override fun Buffer<Float64>.minus(arg: Buffer<Float64>): Float64Buffer {
override fun Buffer<Double>.minus(arg: Buffer<Double>): Float64Buffer {
require(arg.size == this.size) {
"The size of the first buffer ${this.size} should be the same as for second one: ${arg.size} "
}
@ -77,7 +77,7 @@ public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, Ex
}
//
// override fun multiply(a: Buffer<Float64>, k: Number): RealBuffer {
// override fun multiply(a: Buffer<Double>, k: Number): RealBuffer {
// val kValue = k.toDouble()
//
// return if (a is RealBuffer) {
@ -86,7 +86,7 @@ public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, Ex
// } else RealBuffer(DoubleArray(a.size) { a[it] * kValue })
// }
//
// override fun divide(a: Buffer<Float64>, k: Number): RealBuffer {
// override fun divide(a: Buffer<Double>, k: Number): RealBuffer {
// val kValue = k.toDouble()
//
// return if (a is RealBuffer) {
@ -96,7 +96,7 @@ public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, Ex
// }
@UnstableKMathAPI
override fun multiply(left: Buffer<Float64>, right: Buffer<Float64>): Float64Buffer {
override fun multiply(left: Buffer<Double>, right: Buffer<Double>): Float64Buffer {
require(right.size == left.size) {
"The size of the first buffer ${left.size} should be the same as for second one: ${right.size} "
}
@ -108,7 +108,7 @@ public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, Ex
} else Float64Buffer(DoubleArray(left.size) { left[it] * right[it] })
}
override fun divide(left: Buffer<Float64>, right: Buffer<Float64>): Float64Buffer {
override fun divide(left: Buffer<Double>, right: Buffer<Double>): Float64Buffer {
require(right.size == left.size) {
"The size of the first buffer ${left.size} should be the same as for second one: ${right.size} "
}
@ -120,39 +120,39 @@ public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, Ex
} else Float64Buffer(DoubleArray(left.size) { left[it] / right[it] })
}
override fun sin(arg: Buffer<Float64>): Float64Buffer = arg.map { sin(it) }
override fun sin(arg: Buffer<Double>): Float64Buffer = arg.map { sin(it) }
override fun cos(arg: Buffer<Float64>): Float64Buffer = arg.map { cos(it) }
override fun cos(arg: Buffer<Double>): Float64Buffer = arg.map { cos(it) }
override fun tan(arg: Buffer<Float64>): Float64Buffer = arg.map { tan(it) }
override fun tan(arg: Buffer<Double>): Float64Buffer = arg.map { tan(it) }
override fun asin(arg: Buffer<Float64>): Float64Buffer = arg.map { asin(it) }
override fun asin(arg: Buffer<Double>): Float64Buffer = arg.map { asin(it) }
override fun acos(arg: Buffer<Float64>): Float64Buffer = arg.map { acos(it) }
override fun acos(arg: Buffer<Double>): Float64Buffer = arg.map { acos(it) }
override fun atan(arg: Buffer<Float64>): Float64Buffer = arg.map { atan(it) }
override fun atan(arg: Buffer<Double>): Float64Buffer = arg.map { atan(it) }
override fun sinh(arg: Buffer<Float64>): Float64Buffer = arg.map { sinh(it) }
override fun sinh(arg: Buffer<Double>): Float64Buffer = arg.map { sinh(it) }
override fun cosh(arg: Buffer<Float64>): Float64Buffer = arg.map { cosh(it) }
override fun cosh(arg: Buffer<Double>): Float64Buffer = arg.map { cosh(it) }
override fun tanh(arg: Buffer<Float64>): Float64Buffer = arg.map { tanh(it) }
override fun tanh(arg: Buffer<Double>): Float64Buffer = arg.map { tanh(it) }
override fun asinh(arg: Buffer<Float64>): Float64Buffer = arg.map { asinh(it) }
override fun asinh(arg: Buffer<Double>): Float64Buffer = arg.map { asinh(it) }
override fun acosh(arg: Buffer<Float64>): Float64Buffer = arg.map { acosh(it) }
override fun acosh(arg: Buffer<Double>): Float64Buffer = arg.map { acosh(it) }
override fun atanh(arg: Buffer<Float64>): Float64Buffer = arg.map { atanh(it) }
override fun atanh(arg: Buffer<Double>): Float64Buffer = arg.map { atanh(it) }
override fun exp(arg: Buffer<Float64>): Float64Buffer = arg.map { exp(it) }
override fun exp(arg: Buffer<Double>): Float64Buffer = arg.map { exp(it) }
override fun ln(arg: Buffer<Float64>): Float64Buffer = arg.map { ln(it) }
override fun ln(arg: Buffer<Double>): Float64Buffer = arg.map { ln(it) }
override fun norm(arg: Buffer<Float64>): Double = Float64L2Norm.norm(arg)
override fun norm(arg: Buffer<Double>): Double = Float64L2Norm.norm(arg)
override fun scale(a: Buffer<Float64>, value: Double): Float64Buffer = a.map { it * value }
override fun scale(a: Buffer<Double>, value: Double): Float64Buffer = a.map { it * value }
override fun power(arg: Buffer<Float64>, pow: Number): Buffer<Float64> = if (pow is Int) {
override fun power(arg: Buffer<Double>, pow: Number): Buffer<Double> = if (pow is Int) {
arg.map { it.pow(pow) }
} else {
arg.map { it.pow(pow.toDouble()) }
@ -161,11 +161,11 @@ public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, Ex
public companion object : Float64BufferOps()
}
public object Float64L2Norm : Norm<Point<Float64>, Double> {
override fun norm(arg: Point<Float64>): Double = sqrt(arg.fold(0.0) { acc: Double, d: Double -> acc + d.pow(2) })
public object Float64L2Norm : Norm<Point<Double>, Double> {
override fun norm(arg: Point<Double>): Double = sqrt(arg.fold(0.0) { acc: Double, d: Double -> acc + d.pow(2) })
}
public fun Float64BufferOps.sum(buffer: Buffer<Float64>): Double = buffer.reduce(Double::plus)
public fun Float64BufferOps.sum(buffer: Buffer<Double>): Double = buffer.reduce(Double::plus)
/**
* Sum of elements using given [conversion]
@ -173,7 +173,7 @@ public fun Float64BufferOps.sum(buffer: Buffer<Float64>): Double = buffer.reduce
public inline fun <T> Float64BufferOps.sumOf(buffer: Buffer<T>, conversion: (T) -> Double): Double =
buffer.fold(0.0) { acc, value -> acc + conversion(value) }
public fun Float64BufferOps.average(buffer: Buffer<Float64>): Double = sum(buffer) / buffer.size
public fun Float64BufferOps.average(buffer: Buffer<Double>): Double = sum(buffer) / buffer.size
/**
* Average of elements using given [conversion]
@ -181,14 +181,14 @@ public fun Float64BufferOps.average(buffer: Buffer<Float64>): Double = sum(buffe
public inline fun <T> Float64BufferOps.averageOf(buffer: Buffer<T>, conversion: (T) -> Double): Double =
sumOf(buffer, conversion) / buffer.size
public fun Float64BufferOps.dispersion(buffer: Buffer<Float64>): Double {
public fun Float64BufferOps.dispersion(buffer: Buffer<Double>): Double {
val av = average(buffer)
return buffer.fold(0.0) { acc, value -> acc + (value - av).pow(2) } / buffer.size
}
public fun Float64BufferOps.std(buffer: Buffer<Float64>): Double = sqrt(dispersion(buffer))
public fun Float64BufferOps.std(buffer: Buffer<Double>): Double = sqrt(dispersion(buffer))
public fun Float64BufferOps.covariance(x: Buffer<Float64>, y: Buffer<Float64>): Double {
public fun Float64BufferOps.covariance(x: Buffer<Double>, y: Buffer<Double>): Double {
require(x.size == y.size) { "Expected buffers of the same size, but x.size == ${x.size} and y.size == ${y.size}" }
val xMean = average(x)
val yMean = average(y)

View File

@ -4,7 +4,6 @@
*/
package space.kscience.kmath.operations
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.MutableBufferFactory
import kotlin.math.pow as kpow
@ -67,8 +66,8 @@ public interface ExtendedField<T> : ExtendedFieldOps<T>, Field<T>, NumericAlgebr
* A field for [Double] without boxing. Does not produce an appropriate field element.
*/
@Suppress("EXTENSION_SHADOWED_BY_MEMBER")
public object Float64Field : ExtendedField<Float64>, Norm<Double, Double>, ScaleOperations<Float64> {
override val bufferFactory: MutableBufferFactory<Float64> = MutableBufferFactory()
public object Float64Field : ExtendedField<Double>, Norm<Double, Double>, ScaleOperations<Double> {
override val bufferFactory: MutableBufferFactory<Double> = MutableBufferFactory()
override val zero: Double get() = 0.0
override val one: Double get() = 1.0

View File

@ -115,7 +115,7 @@ public fun <T> Buffer(
size: Int,
initializer: (Int) -> T,
): Buffer<T> = when (type.kType) {
typeOf<Float64>() -> MutableBuffer.double(size) { initializer(it) as Double } as Buffer<T>
typeOf<Double>() -> MutableBuffer.double(size) { initializer(it) as Double } as Buffer<T>
typeOf<Short>() -> MutableBuffer.short(size) { initializer(it) as Short } as Buffer<T>
typeOf<Int>() -> MutableBuffer.int(size) { initializer(it) as Int } as Buffer<T>
typeOf<Long>() -> MutableBuffer.long(size) { initializer(it) as Long } as Buffer<T>
@ -134,7 +134,7 @@ public inline fun <reified T> Buffer(size: Int, initializer: (Int) -> T): Buffer
//code duplication here because we want to inline initializers
val type = safeTypeOf<T>()
return when (type.kType) {
typeOf<Float64>() -> MutableBuffer.double(size) { initializer(it) as Double } as Buffer<T>
typeOf<Double>() -> MutableBuffer.double(size) { initializer(it) as Double } as Buffer<T>
typeOf<Short>() -> MutableBuffer.short(size) { initializer(it) as Short } as Buffer<T>
typeOf<Int>() -> MutableBuffer.int(size) { initializer(it) as Int } as Buffer<T>
typeOf<Long>() -> MutableBuffer.long(size) { initializer(it) as Long } as Buffer<T>

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@ -14,7 +14,7 @@ import kotlin.jvm.JvmInline
* @property array the underlying array.
*/
@JvmInline
public value class Float64Buffer(public val array: DoubleArray) : PrimitiveBuffer<Float64> {
public value class Float64Buffer(public val array: DoubleArray) : PrimitiveBuffer<Double> {
override val size: Int get() = array.size
@ -54,7 +54,7 @@ public fun Float64Buffer(vararg doubles: Double): Float64Buffer = Float64Buffer(
/**
* Returns a new [DoubleArray] containing all the elements of this [Buffer].
*/
public fun Buffer<Float64>.toDoubleArray(): DoubleArray = when (this) {
public fun Buffer<Double>.toDoubleArray(): DoubleArray = when (this) {
is Float64Buffer -> array
else -> DoubleArray(size, ::get)
}
@ -62,7 +62,7 @@ public fun Buffer<Float64>.toDoubleArray(): DoubleArray = when (this) {
/**
* Represent this buffer as [Float64Buffer]. Does not guarantee that changes in the original buffer are reflected on this buffer.
*/
public fun Buffer<Float64>.toFloat64Buffer(): Float64Buffer = when (this) {
public fun Buffer<Double>.toFloat64Buffer(): Float64Buffer = when (this) {
is Float64Buffer -> this
else -> DoubleArray(size, ::get).asBuffer()
}
@ -79,5 +79,5 @@ public fun DoubleArray.asBuffer(): Float64Buffer = Float64Buffer(this)
public fun interface Float64BufferTransform : BufferTransform<Double, Double> {
public fun transform(arg: Float64Buffer): Float64Buffer
override fun transform(arg: Buffer<Float64>): Float64Buffer = arg.toFloat64Buffer()
override fun transform(arg: Buffer<Double>): Float64Buffer = arg.toFloat64Buffer()
}

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@ -99,7 +99,7 @@ public inline fun <T> MutableBuffer(
typeOf<Int32>() -> MutableBuffer.int(size) { initializer(it) as Int32 } as MutableBuffer<T>
typeOf<Int64>() -> MutableBuffer.long(size) { initializer(it) as Int64 } as MutableBuffer<T>
typeOf<Float>() -> MutableBuffer.float(size) { initializer(it) as Float } as MutableBuffer<T>
typeOf<Float64>() -> MutableBuffer.double(size) { initializer(it) as Double } as MutableBuffer<T>
typeOf<Double>() -> MutableBuffer.double(size) { initializer(it) as Double } as MutableBuffer<T>
//TODO add unsigned types
else -> MutableListBuffer(MutableList(size, initializer))
}

View File

@ -11,7 +11,7 @@ import space.kscience.kmath.UnstableKMathAPI
* Non-boxing access to primitive [Double]
*/
@UnstableKMathAPI
public fun Buffer<Float64>.getDouble(index: Int): Double = if (this is BufferView) {
public fun Buffer<Double>.getDouble(index: Int): Double = if (this is BufferView) {
val originIndex = originIndex(index)
if (originIndex >= 0) {
origin.getDouble(originIndex)

View File

@ -6,7 +6,6 @@
package space.kscience.kmath.expressions
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.structures.Float64
import kotlin.test.Test
import kotlin.test.assertEquals
import kotlin.test.assertFails
@ -38,7 +37,7 @@ class ExpressionFieldTest {
@Test
fun valueExpression() {
val expressionBuilder: FunctionalExpressionField<Double, *>.() -> Expression<Float64> = {
val expressionBuilder: FunctionalExpressionField<Double, *>.() -> Expression<Double> = {
val x by binding
x * x + 2 * x + one
}

View File

@ -8,7 +8,6 @@ package space.kscience.kmath.expressions
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.bindSymbol
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.asBuffer
import kotlin.math.E
import kotlin.math.PI
@ -22,18 +21,18 @@ internal class SimpleAutoDiffTest {
fun dx(
xBinding: Pair<Symbol, Double>,
body: SimpleAutoDiffField<Double, Float64Field>.(x: AutoDiffValue<Float64>) -> AutoDiffValue<Float64>,
): DerivationResult<Float64> = Float64Field.simpleAutoDiff(xBinding) { body(bindSymbol(xBinding.first)) }
body: SimpleAutoDiffField<Double, Float64Field>.(x: AutoDiffValue<Double>) -> AutoDiffValue<Double>,
): DerivationResult<Double> = Float64Field.simpleAutoDiff(xBinding) { body(bindSymbol(xBinding.first)) }
fun dxy(
xBinding: Pair<Symbol, Double>,
yBinding: Pair<Symbol, Double>,
body: SimpleAutoDiffField<Double, Float64Field>.(x: AutoDiffValue<Float64>, y: AutoDiffValue<Float64>) -> AutoDiffValue<Float64>,
): DerivationResult<Float64> = Float64Field.simpleAutoDiff(xBinding, yBinding) {
body: SimpleAutoDiffField<Double, Float64Field>.(x: AutoDiffValue<Double>, y: AutoDiffValue<Double>) -> AutoDiffValue<Double>,
): DerivationResult<Double> = Float64Field.simpleAutoDiff(xBinding, yBinding) {
body(bindSymbol(xBinding.first), bindSymbol(yBinding.first))
}
fun diff(block: SimpleAutoDiffField<Double, Float64Field>.() -> AutoDiffValue<Float64>): SimpleAutoDiffExpression<Double, Float64Field> {
fun diff(block: SimpleAutoDiffField<Double, Float64Field>.() -> AutoDiffValue<Double>): SimpleAutoDiffExpression<Double, Float64Field> {
return SimpleAutoDiffExpression(Float64Field, block)
}

View File

@ -10,7 +10,6 @@ import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.structures.Float64
import kotlin.test.Test
import kotlin.test.assertEquals
import kotlin.test.assertTrue
@ -39,7 +38,7 @@ class MatrixTest {
@Test
fun testMatrixExtension() = Double.algebra.linearSpace.run {
val transitionMatrix: Matrix<Float64> = VirtualMatrix(6, 6) { row, col ->
val transitionMatrix: Matrix<Double> = VirtualMatrix(6, 6) { row, col ->
when {
col == 0 -> .50
row + 1 == col -> .50
@ -48,7 +47,7 @@ class MatrixTest {
}
}
infix fun Matrix<Float64>.pow(power: Int): Matrix<Float64> {
infix fun Matrix<Double>.pow(power: Int): Matrix<Double> {
var res = this
repeat(power - 1) {
res = res dot this@pow

View File

@ -11,7 +11,6 @@ import space.kscience.kmath.operations.Float64BufferOps
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
import space.kscience.kmath.structures.asBuffer
import java.util.stream.IntStream
@ -26,7 +25,7 @@ public object Float64ParallelLinearSpace : LinearSpace<Double, Float64Field> {
rows: Int,
columns: Int,
initializer: Float64Field.(i: Int, j: Int) -> Double,
): Matrix<Float64> {
): Matrix<Double> {
val shape = ShapeND(rows, columns)
val indexer = BufferAlgebraND.defaultIndexerBuilder(shape)
@ -44,29 +43,29 @@ public object Float64ParallelLinearSpace : LinearSpace<Double, Float64Field> {
override fun buildVector(size: Int, initializer: Float64Field.(Int) -> Double): Float64Buffer =
IntStream.range(0, size).parallel().mapToDouble { Float64Field.initializer(it) }.toArray().asBuffer()
override fun Matrix<Float64>.unaryMinus(): Matrix<Float64> = Floa64FieldOpsND {
override fun Matrix<Double>.unaryMinus(): Matrix<Double> = Floa64FieldOpsND {
asND().map { -it }.as2D()
}
override fun Matrix<Float64>.plus(other: Matrix<Float64>): Matrix<Float64> = Floa64FieldOpsND {
override fun Matrix<Double>.plus(other: Matrix<Double>): Matrix<Double> = Floa64FieldOpsND {
require(shape == other.shape) { "Shape mismatch on Matrix::plus. Expected $shape but found ${other.shape}" }
asND().plus(other.asND()).as2D()
}
override fun Matrix<Float64>.minus(other: Matrix<Float64>): Matrix<Float64> = Floa64FieldOpsND {
override fun Matrix<Double>.minus(other: Matrix<Double>): Matrix<Double> = Floa64FieldOpsND {
require(shape == other.shape) { "Shape mismatch on Matrix::minus. Expected $shape but found ${other.shape}" }
asND().minus(other.asND()).as2D()
}
// Create a continuous in-memory representation of this vector for better memory layout handling
private fun Buffer<Float64>.linearize() = if (this is Float64Buffer) {
private fun Buffer<Double>.linearize() = if (this is Float64Buffer) {
this.array
} else {
DoubleArray(size) { get(it) }
}
@OptIn(PerformancePitfall::class)
override fun Matrix<Float64>.dot(other: Matrix<Float64>): Matrix<Float64> {
override fun Matrix<Double>.dot(other: Matrix<Double>): Matrix<Double> {
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
val rows = this@dot.rows.map { it.linearize() }
val columns = other.columns.map { it.linearize() }
@ -83,7 +82,7 @@ public object Float64ParallelLinearSpace : LinearSpace<Double, Float64Field> {
}
@OptIn(PerformancePitfall::class)
override fun Matrix<Float64>.dot(vector: Point<Float64>): Float64Buffer {
override fun Matrix<Double>.dot(vector: Point<Double>): Float64Buffer {
require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
val rows = this@dot.rows.map { it.linearize() }
val indices = 0 until this.colNum
@ -98,27 +97,27 @@ public object Float64ParallelLinearSpace : LinearSpace<Double, Float64Field> {
}
override fun Matrix<Float64>.times(value: Double): Matrix<Float64> = Floa64FieldOpsND {
override fun Matrix<Double>.times(value: Double): Matrix<Double> = Floa64FieldOpsND {
asND().map { it * value }.as2D()
}
public override fun Point<Float64>.plus(other: Point<Float64>): Float64Buffer = Float64BufferOps.run {
public override fun Point<Double>.plus(other: Point<Double>): Float64Buffer = Float64BufferOps.run {
this@plus + other
}
public override fun Point<Float64>.minus(other: Point<Float64>): Float64Buffer = Float64BufferOps.run {
public override fun Point<Double>.minus(other: Point<Double>): Float64Buffer = Float64BufferOps.run {
this@minus - other
}
public override fun Point<Float64>.times(value: Double): Float64Buffer = Float64BufferOps.run {
public override fun Point<Double>.times(value: Double): Float64Buffer = Float64BufferOps.run {
scale(this@times, value)
}
public operator fun Point<Float64>.div(value: Double): Float64Buffer = Float64BufferOps.run {
public operator fun Point<Double>.div(value: Double): Float64Buffer = Float64BufferOps.run {
scale(this@div, 1.0 / value)
}
public override fun Double.times(v: Point<Float64>): Float64Buffer = v * this
public override fun Double.times(v: Point<Double>): Float64Buffer = v * this
}

View File

@ -30,7 +30,7 @@ public fun <T> MutableBuffer.Companion.parallel(
.asBuffer() as MutableBuffer<T>
typeOf<Float>() -> Float32Buffer(size) { initializer(it) as Float } as MutableBuffer<T>
typeOf<Float64>() -> IntStream.range(0, size).parallel().mapToDouble { initializer(it) as Float64 }.toArray()
typeOf<Double>() -> IntStream.range(0, size).parallel().mapToDouble { initializer(it) as Float64 }.toArray()
.asBuffer() as MutableBuffer<T>
//TODO add unsigned types
else -> IntStream.range(0, size).parallel().mapToObj { initializer(it) }.collect(Collectors.toList<T>())

View File

@ -9,7 +9,6 @@ import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.structures.Float64
import kotlin.test.Test
import kotlin.test.assertEquals
import kotlin.test.assertTrue
@ -38,7 +37,7 @@ class ParallelMatrixTest {
@Test
fun testMatrixExtension() = Float64Field.linearSpace.parallel {
val transitionMatrix: Matrix<Float64> = VirtualMatrix(6, 6) { row, col ->
val transitionMatrix: Matrix<Double> = VirtualMatrix(6, 6) { row, col ->
when {
col == 0 -> .50
row + 1 == col -> .50
@ -47,7 +46,7 @@ class ParallelMatrixTest {
}
}
infix fun Matrix<Float64>.pow(power: Int): Matrix<Float64> {
infix fun Matrix<Double>.pow(power: Int): Matrix<Double> {
var res = this
repeat(power - 1) {
res = res dot this@pow

View File

@ -5,13 +5,12 @@
package space.kscience.kmath.chains
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
/**
* Chunked, specialized chain for double values, which supports blocking [nextBlocking] operation
*/
public interface BlockingDoubleChain : BlockingBufferChain<Float64> {
public interface BlockingDoubleChain : BlockingBufferChain<Double> {
/**
* Returns an [DoubleArray] chunk of [size] values of [next].

View File

@ -17,7 +17,6 @@ import space.kscience.kmath.chains.BlockingDoubleChain
import space.kscience.kmath.operations.Group
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.BufferFactory
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
/**
@ -57,7 +56,7 @@ public fun <T> Flow<T>.chunked(bufferSize: Int, bufferFactory: BufferFactory<T>)
/**
* Specialized flow chunker for real buffer
*/
public fun Flow<Float64>.chunked(bufferSize: Int): Flow<Float64Buffer> = flow {
public fun Flow<Double>.chunked(bufferSize: Int): Flow<Float64Buffer> = flow {
require(bufferSize > 0) { "Resulting chunk size must be more than zero" }
if (this@chunked is BlockingDoubleChain) {

View File

@ -1,20 +1,15 @@
plugins {
id("space.kscience.gradle.mpp")
id("space.kscience.gradle.jvm")
}
val ejmlVerision = "0.43.1"
kscience {
jvm()
jvmMain {
api(projects.kmathCore)
api(projects.kmathComplex)
api("org.ejml:ejml-all:$ejmlVerision")
}
jvmTest {
implementation(projects.testUtils)
}
dependencies {
api("org.ejml:ejml-ddense:$ejmlVerision")
api("org.ejml:ejml-fdense:$ejmlVerision")
api("org.ejml:ejml-dsparse:$ejmlVerision")
api("org.ejml:ejml-fsparse:$ejmlVerision")
api(projects.kmathCore)
}
readme {
@ -35,4 +30,12 @@ readme {
id = "ejml-linear-space",
ref = "src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt"
) { "LinearSpace implementations." }
}
}
//kotlin.sourceSets.main {
// val codegen by tasks.creating {
// ejmlCodegen(kotlin.srcDirs.first().absolutePath + "/space/kscience/kmath/ejml/_generated.kt")
// }
//
// kotlin.srcDirs(files().builtBy(codegen))
//}

View File

@ -6,13 +6,9 @@
package space.kscience.kmath.ejml
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.linear.Inverted
import space.kscience.kmath.linear.LinearSpace
import space.kscience.kmath.linear.Matrix
import space.kscience.kmath.linear.Point
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.linear.*
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.Ring
import space.kscience.kmath.structures.Float64
/**
* [LinearSpace] implementation specialized for a certain EJML type.
@ -42,6 +38,6 @@ public abstract class EjmlLinearSpace<T : Any, out A : Ring<T>, out M : org.ejml
public abstract override fun buildVector(size: Int, initializer: A.(Int) -> T): EjmlVector<T, M>
@UnstableKMathAPI
public fun Structure2D<T>.inverted(): Matrix<Float64> =
computeAttribute(this, Inverted()) ?: error("Can't invert matrix")
public fun EjmlMatrix<T, *>.inverted(): Matrix<Double> =
computeAttribute(this, Float64Field.linearSpace.Inverted)!!
}

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@ -12,8 +12,6 @@ import org.ejml.dense.row.CommonOps_DDRM
import org.ejml.dense.row.CommonOps_FDRM
import org.ejml.dense.row.factory.DecompositionFactory_DDRM
import org.ejml.dense.row.factory.DecompositionFactory_FDRM
import org.ejml.interfaces.decomposition.EigenDecomposition_F32
import org.ejml.interfaces.decomposition.EigenDecomposition_F64
import org.ejml.sparse.FillReducing
import org.ejml.sparse.csc.CommonOps_DSCC
import org.ejml.sparse.csc.CommonOps_FSCC
@ -21,8 +19,6 @@ import org.ejml.sparse.csc.factory.DecompositionFactory_DSCC
import org.ejml.sparse.csc.factory.DecompositionFactory_FSCC
import space.kscience.attributes.SafeType
import space.kscience.attributes.safeTypeOf
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.complex.Complex
import space.kscience.kmath.linear.*
import space.kscience.kmath.linear.Matrix
import space.kscience.kmath.nd.Structure2D
@ -31,15 +27,9 @@ import space.kscience.kmath.operations.Float32Field
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Float32
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.IntBuffer
import space.kscience.kmath.structures.asBuffer
/**
* Copy EJML [Complex_F64] into KMath [Complex]
*/
public fun Complex_F64.toKMathComplex(): Complex = Complex(real, imaginary)
/**
* [EjmlVector] specialization for [Double].
*/
@ -89,16 +79,16 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
*/
override val elementAlgebra: Float64Field get() = Float64Field
override val type: SafeType<Float64> get() = safeTypeOf()
override val type: SafeType<Double> get() = safeTypeOf()
@Suppress("UNCHECKED_CAST")
override fun Matrix<Float64>.toEjml(): EjmlDoubleMatrix<DMatrixRMaj> = when {
override fun Matrix<Double>.toEjml(): EjmlDoubleMatrix<DMatrixRMaj> = when {
this is EjmlDoubleMatrix<*> && origin is DMatrixRMaj -> this as EjmlDoubleMatrix<DMatrixRMaj>
else -> buildMatrix(rowNum, colNum) { i, j -> get(i, j) }
}
@Suppress("UNCHECKED_CAST")
override fun Point<Float64>.toEjml(): EjmlDoubleVector<DMatrixRMaj> = when {
override fun Point<Double>.toEjml(): EjmlDoubleVector<DMatrixRMaj> = when {
this is EjmlDoubleVector<*> && origin is DMatrixRMaj -> this as EjmlDoubleVector<DMatrixRMaj>
else -> EjmlDoubleVector(DMatrixRMaj(size, 1).also {
(0 until it.numRows).forEach { row -> it[row, 0] = get(row) }
@ -125,21 +115,21 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
private fun <T : DMatrix> T.wrapMatrix() = EjmlDoubleMatrix(this)
private fun <T : DMatrix> T.wrapVector() = EjmlDoubleVector(this)
override fun Matrix<Float64>.unaryMinus(): Matrix<Float64> = this * elementAlgebra { -one }
override fun Matrix<Double>.unaryMinus(): Matrix<Double> = this * elementAlgebra { -one }
override fun Matrix<Float64>.dot(other: Matrix<Float64>): EjmlDoubleMatrix<DMatrixRMaj> {
override fun Matrix<Double>.dot(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixRMaj> {
val out = DMatrixRMaj(1, 1)
CommonOps_DDRM.mult(toEjml().origin, other.toEjml().origin, out)
return out.wrapMatrix()
}
override fun Matrix<Float64>.dot(vector: Point<Float64>): EjmlDoubleVector<DMatrixRMaj> {
override fun Matrix<Double>.dot(vector: Point<Double>): EjmlDoubleVector<DMatrixRMaj> {
val out = DMatrixRMaj(1, 1)
CommonOps_DDRM.mult(toEjml().origin, vector.toEjml().origin, out)
return out.wrapVector()
}
override operator fun Matrix<Float64>.minus(other: Matrix<Float64>): EjmlDoubleMatrix<DMatrixRMaj> {
override operator fun Matrix<Double>.minus(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixRMaj> {
val out = DMatrixRMaj(1, 1)
CommonOps_DDRM.add(
@ -153,19 +143,19 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
return out.wrapMatrix()
}
override operator fun Matrix<Float64>.times(value: Double): EjmlDoubleMatrix<DMatrixRMaj> {
override operator fun Matrix<Double>.times(value: Double): EjmlDoubleMatrix<DMatrixRMaj> {
val res = DMatrixRMaj(1, 1)
CommonOps_DDRM.scale(value, toEjml().origin, res)
return res.wrapMatrix()
}
override fun Point<Float64>.unaryMinus(): EjmlDoubleVector<DMatrixRMaj> {
override fun Point<Double>.unaryMinus(): EjmlDoubleVector<DMatrixRMaj> {
val res = DMatrixRMaj(1, 1)
CommonOps_DDRM.changeSign(toEjml().origin, res)
return res.wrapVector()
}
override fun Matrix<Float64>.plus(other: Matrix<Float64>): EjmlDoubleMatrix<DMatrixRMaj> {
override fun Matrix<Double>.plus(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixRMaj> {
val out = DMatrixRMaj(1, 1)
CommonOps_DDRM.add(
@ -179,7 +169,7 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
return out.wrapMatrix()
}
override fun Point<Float64>.plus(other: Point<Float64>): EjmlDoubleVector<DMatrixRMaj> {
override fun Point<Double>.plus(other: Point<Double>): EjmlDoubleVector<DMatrixRMaj> {
val out = DMatrixRMaj(1, 1)
CommonOps_DDRM.add(
@ -193,7 +183,7 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
return out.wrapVector()
}
override fun Point<Float64>.minus(other: Point<Float64>): EjmlDoubleVector<DMatrixRMaj> {
override fun Point<Double>.minus(other: Point<Double>): EjmlDoubleVector<DMatrixRMaj> {
val out = DMatrixRMaj(1, 1)
CommonOps_DDRM.add(
@ -207,19 +197,18 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
return out.wrapVector()
}
override fun Double.times(m: Matrix<Float64>): EjmlDoubleMatrix<DMatrixRMaj> = m * this
override fun Double.times(m: Matrix<Double>): EjmlDoubleMatrix<DMatrixRMaj> = m * this
override fun Point<Float64>.times(value: Double): EjmlDoubleVector<DMatrixRMaj> {
override fun Point<Double>.times(value: Double): EjmlDoubleVector<DMatrixRMaj> {
val res = DMatrixRMaj(1, 1)
CommonOps_DDRM.scale(value, toEjml().origin, res)
return res.wrapVector()
}
override fun Double.times(v: Point<Float64>): EjmlDoubleVector<DMatrixRMaj> = v * this
override fun Double.times(v: Point<Double>): EjmlDoubleVector<DMatrixRMaj> = v * this
@OptIn(UnstableKMathAPI::class)
override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Float64>, attribute: A): V? {
val origin: DMatrixRMaj = structure.toEjml().origin
override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Double>, attribute: A): V? {
val origin = structure.toEjml().origin
val raw: Any? = when (attribute) {
Inverted -> {
@ -229,28 +218,28 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
}
Determinant -> CommonOps_DDRM.det(origin)
SVD -> object : SingularValueDecomposition<Float64> {
SVD -> object : SingularValueDecomposition<Double> {
val ejmlSvd by lazy {
DecompositionFactory_DDRM
.svd(origin.numRows, origin.numCols, true, true, false)
.apply { decompose(origin.copy()) }
}
override val u: Matrix<Float64> get() = ejmlSvd.getU(null, false).wrapMatrix()
override val u: Matrix<Double> get() = ejmlSvd.getU(null, false).wrapMatrix()
override val s: Matrix<Float64> get() = ejmlSvd.getW(null).wrapMatrix()
override val v: Matrix<Float64> get() = ejmlSvd.getV(null, false).wrapMatrix()
override val singularValues: Point<Float64> get() = ejmlSvd.singularValues.asBuffer()
override val s: Matrix<Double> get() = ejmlSvd.getW(null).wrapMatrix()
override val v: Matrix<Double> get() = ejmlSvd.getV(null, false).wrapMatrix()
override val singularValues: Point<Double> get() = ejmlSvd.singularValues.asBuffer()
}
QR -> object : QRDecomposition<Float64> {
QR -> object : QRDecomposition<Double> {
val ejmlQr by lazy { DecompositionFactory_DDRM.qr().apply { decompose(origin.copy()) } }
override val q: Matrix<Float64> get() = ejmlQr.getQ(null, false).wrapMatrix()
override val r: Matrix<Float64> get() = ejmlQr.getR(null, false).wrapMatrix()
override val q: Matrix<Double> get() = ejmlQr.getQ(null, false).wrapMatrix()
override val r: Matrix<Double> get() = ejmlQr.getR(null, false).wrapMatrix()
}
Cholesky -> object : CholeskyDecomposition<Float64> {
override val l: Matrix<Float64> by lazy {
Cholesky -> object : CholeskyDecomposition<Double> {
override val l: Matrix<Double> by lazy {
val cholesky =
DecompositionFactory_DDRM.chol(structure.rowNum, true).apply { decompose(origin.copy()) }
@ -258,49 +247,20 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
}
}
LUP -> object : LupDecomposition<Float64> {
LUP -> object : LupDecomposition<Double> {
private val lup by lazy {
DecompositionFactory_DDRM.lu(origin.numRows, origin.numCols).apply { decompose(origin.copy()) }
}
override val l: Matrix<Float64>
override val l: Matrix<Double>
get() = lup.getLower(null).wrapMatrix().withAttribute(LowerTriangular)
override val u: Matrix<Float64>
override val u: Matrix<Double>
get() = lup.getUpper(null).wrapMatrix().withAttribute(UpperTriangular)
override val pivot: IntBuffer get() = lup.getRowPivotV(null).asBuffer()
}
EIG -> {
check(origin.numCols == origin.numRows) { "Eigenvalue decomposition requires symmetric matrix" }
object : EigenDecomposition<Float64> {
val cmEigen: EigenDecomposition_F64<DMatrixRMaj> by lazy {
DecompositionFactory_DDRM.eig(origin.numRows, true).apply { decompose(origin) }
}
override val v: Matrix<Float64> by lazy {
val eigenvectors = List(origin.numRows) { cmEigen.getEigenVector(it) }.filterNotNull()
buildMatrix(eigenvectors.size, origin.numCols) { row, column ->
eigenvectors[row][column]
}
}
override val d: Matrix<Float64> by lazy {
val eigenvalues = List(origin.numRows) { cmEigen.getEigenvalue(it) }
buildMatrix(origin.numRows, origin.numCols) { row, column ->
when (row) {
column -> eigenvalues[row].real
column - 1 -> eigenvalues[row].imaginary
column + 1 -> -eigenvalues[row].imaginary
else -> 0.0
}
}
}
}
}
else -> null
}
@ -315,7 +275,7 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
* @param b n by p matrix.
* @return the solution for *x* that is n by p.
*/
public fun solve(a: Matrix<Float64>, b: Matrix<Float64>): EjmlDoubleMatrix<DMatrixRMaj> {
public fun solve(a: Matrix<Double>, b: Matrix<Double>): EjmlDoubleMatrix<DMatrixRMaj> {
val res = DMatrixRMaj(1, 1)
CommonOps_DDRM.solve(DMatrixRMaj(a.toEjml().origin), DMatrixRMaj(b.toEjml().origin), res)
return res.wrapMatrix()
@ -328,7 +288,7 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
* @param b n by p vector.
* @return the solution for *x* that is n by p.
*/
public fun solve(a: Matrix<Float64>, b: Point<Float64>): EjmlDoubleVector<DMatrixRMaj> {
public fun solve(a: Matrix<Double>, b: Point<Double>): EjmlDoubleVector<DMatrixRMaj> {
val res = DMatrixRMaj(1, 1)
CommonOps_DDRM.solve(DMatrixRMaj(a.toEjml().origin), DMatrixRMaj(b.toEjml().origin), res)
return EjmlDoubleVector(res)
@ -474,7 +434,6 @@ public object EjmlLinearSpaceFDRM : EjmlLinearSpace<Float, Float32Field, FMatrix
override fun Float.times(v: Point<Float>): EjmlFloatVector<FMatrixRMaj> = v * this
@OptIn(UnstableKMathAPI::class)
override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Float32>, attribute: A): V? {
val origin = structure.toEjml().origin
@ -529,35 +488,6 @@ public object EjmlLinearSpaceFDRM : EjmlLinearSpace<Float, Float32Field, FMatrix
override val pivot: IntBuffer get() = lup.getRowPivotV(null).asBuffer()
}
EIG -> {
check(origin.numCols == origin.numRows) { "Eigenvalue decomposition requires symmetric matrix" }
object : EigenDecomposition<Float32> {
val cmEigen: EigenDecomposition_F32<FMatrixRMaj> by lazy {
DecompositionFactory_FDRM.eig(origin.numRows, true).apply { decompose(origin) }
}
override val v by lazy {
val eigenvectors: List<FMatrixRMaj> = List(origin.numRows) { cmEigen.getEigenVector(it) }
buildMatrix(origin.numRows, origin.numCols) { row, column ->
eigenvectors[row][column]
}
}
override val d: Matrix<Float32> by lazy {
val eigenvalues = List(origin.numRows) { cmEigen.getEigenvalue(it) }
buildMatrix(origin.numRows, origin.numCols) { row, column ->
when (row) {
column -> eigenvalues[row].real
column - 1 -> eigenvalues[row].imaginary
column + 1 -> -eigenvalues[row].imaginary
else -> 0f
}
}
}
}
}
else -> null
}
@ -603,16 +533,16 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
*/
override val elementAlgebra: Float64Field get() = Float64Field
override val type: SafeType<Float64> get() = safeTypeOf()
override val type: SafeType<Double> get() = safeTypeOf()
@Suppress("UNCHECKED_CAST")
override fun Matrix<Float64>.toEjml(): EjmlDoubleMatrix<DMatrixSparseCSC> = when {
override fun Matrix<Double>.toEjml(): EjmlDoubleMatrix<DMatrixSparseCSC> = when {
this is EjmlDoubleMatrix<*> && origin is DMatrixSparseCSC -> this as EjmlDoubleMatrix<DMatrixSparseCSC>
else -> buildMatrix(rowNum, colNum) { i, j -> get(i, j) }
}
@Suppress("UNCHECKED_CAST")
override fun Point<Float64>.toEjml(): EjmlDoubleVector<DMatrixSparseCSC> = when {
override fun Point<Double>.toEjml(): EjmlDoubleVector<DMatrixSparseCSC> = when {
this is EjmlDoubleVector<*> && origin is DMatrixSparseCSC -> this as EjmlDoubleVector<DMatrixSparseCSC>
else -> EjmlDoubleVector(DMatrixSparseCSC(size, 1).also {
(0 until it.numRows).forEach { row -> it[row, 0] = get(row) }
@ -639,21 +569,21 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
private fun <T : DMatrix> T.wrapMatrix() = EjmlDoubleMatrix(this)
private fun <T : DMatrix> T.wrapVector() = EjmlDoubleVector(this)
override fun Matrix<Float64>.unaryMinus(): Matrix<Float64> = this * elementAlgebra { -one }
override fun Matrix<Double>.unaryMinus(): Matrix<Double> = this * elementAlgebra { -one }
override fun Matrix<Float64>.dot(other: Matrix<Float64>): EjmlDoubleMatrix<DMatrixSparseCSC> {
override fun Matrix<Double>.dot(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixSparseCSC> {
val out = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.mult(toEjml().origin, other.toEjml().origin, out)
return out.wrapMatrix()
}
override fun Matrix<Float64>.dot(vector: Point<Float64>): EjmlDoubleVector<DMatrixSparseCSC> {
override fun Matrix<Double>.dot(vector: Point<Double>): EjmlDoubleVector<DMatrixSparseCSC> {
val out = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.mult(toEjml().origin, vector.toEjml().origin, out)
return out.wrapVector()
}
override operator fun Matrix<Float64>.minus(other: Matrix<Float64>): EjmlDoubleMatrix<DMatrixSparseCSC> {
override operator fun Matrix<Double>.minus(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixSparseCSC> {
val out = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.add(
@ -669,19 +599,19 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
return out.wrapMatrix()
}
override operator fun Matrix<Float64>.times(value: Double): EjmlDoubleMatrix<DMatrixSparseCSC> {
override operator fun Matrix<Double>.times(value: Double): EjmlDoubleMatrix<DMatrixSparseCSC> {
val res = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.scale(value, toEjml().origin, res)
return res.wrapMatrix()
}
override fun Point<Float64>.unaryMinus(): EjmlDoubleVector<DMatrixSparseCSC> {
override fun Point<Double>.unaryMinus(): EjmlDoubleVector<DMatrixSparseCSC> {
val res = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.changeSign(toEjml().origin, res)
return res.wrapVector()
}
override fun Matrix<Float64>.plus(other: Matrix<Float64>): EjmlDoubleMatrix<DMatrixSparseCSC> {
override fun Matrix<Double>.plus(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixSparseCSC> {
val out = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.add(
@ -697,7 +627,7 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
return out.wrapMatrix()
}
override fun Point<Float64>.plus(other: Point<Float64>): EjmlDoubleVector<DMatrixSparseCSC> {
override fun Point<Double>.plus(other: Point<Double>): EjmlDoubleVector<DMatrixSparseCSC> {
val out = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.add(
@ -713,7 +643,7 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
return out.wrapVector()
}
override fun Point<Float64>.minus(other: Point<Float64>): EjmlDoubleVector<DMatrixSparseCSC> {
override fun Point<Double>.minus(other: Point<Double>): EjmlDoubleVector<DMatrixSparseCSC> {
val out = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.add(
@ -729,17 +659,17 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
return out.wrapVector()
}
override fun Double.times(m: Matrix<Float64>): EjmlDoubleMatrix<DMatrixSparseCSC> = m * this
override fun Double.times(m: Matrix<Double>): EjmlDoubleMatrix<DMatrixSparseCSC> = m * this
override fun Point<Float64>.times(value: Double): EjmlDoubleVector<DMatrixSparseCSC> {
override fun Point<Double>.times(value: Double): EjmlDoubleVector<DMatrixSparseCSC> {
val res = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.scale(value, toEjml().origin, res)
return res.wrapVector()
}
override fun Double.times(v: Point<Float64>): EjmlDoubleVector<DMatrixSparseCSC> = v * this
override fun Double.times(v: Point<Double>): EjmlDoubleVector<DMatrixSparseCSC> = v * this
override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Float64>, attribute: A): V? {
override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Double>, attribute: A): V? {
val origin = structure.toEjml().origin
val raw: Any? = when (attribute) {
@ -751,16 +681,16 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
Determinant -> CommonOps_DSCC.det(origin)
QR -> object : QRDecomposition<Float64> {
QR -> object : QRDecomposition<Double> {
val ejmlQr by lazy {
DecompositionFactory_DSCC.qr(FillReducing.NONE).apply { decompose(origin.copy()) }
}
override val q: Matrix<Float64> get() = ejmlQr.getQ(null, false).wrapMatrix()
override val r: Matrix<Float64> get() = ejmlQr.getR(null, false).wrapMatrix()
override val q: Matrix<Double> get() = ejmlQr.getQ(null, false).wrapMatrix()
override val r: Matrix<Double> get() = ejmlQr.getR(null, false).wrapMatrix()
}
Cholesky -> object : CholeskyDecomposition<Float64> {
override val l: Matrix<Float64> by lazy {
Cholesky -> object : CholeskyDecomposition<Double> {
override val l: Matrix<Double> by lazy {
val cholesky =
DecompositionFactory_DSCC.cholesky().apply { decompose(origin.copy()) }
@ -768,16 +698,16 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
}
}
LUP -> object : LupDecomposition<Float64> {
LUP -> object : LupDecomposition<Double> {
private val lup by lazy {
DecompositionFactory_DSCC.lu(FillReducing.NONE).apply { decompose(origin.copy()) }
}
override val l: Matrix<Float64>
override val l: Matrix<Double>
get() = lup.getLower(null).wrapMatrix().withAttribute(LowerTriangular)
override val u: Matrix<Float64>
override val u: Matrix<Double>
get() = lup.getUpper(null).wrapMatrix().withAttribute(UpperTriangular)
override val pivot: IntBuffer get() = lup.getRowPivotV(null).asBuffer()
}
@ -796,7 +726,7 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
* @param b n by p matrix.
* @return the solution for *x* that is n by p.
*/
public fun solve(a: Matrix<Float64>, b: Matrix<Float64>): EjmlDoubleMatrix<DMatrixSparseCSC> {
public fun solve(a: Matrix<Double>, b: Matrix<Double>): EjmlDoubleMatrix<DMatrixSparseCSC> {
val res = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.solve(DMatrixSparseCSC(a.toEjml().origin), DMatrixSparseCSC(b.toEjml().origin), res)
return res.wrapMatrix()
@ -809,7 +739,7 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
* @param b n by p vector.
* @return the solution for *x* that is n by p.
*/
public fun solve(a: Matrix<Float64>, b: Point<Float64>): EjmlDoubleVector<DMatrixSparseCSC> {
public fun solve(a: Matrix<Double>, b: Point<Double>): EjmlDoubleVector<DMatrixSparseCSC> {
val res = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.solve(DMatrixSparseCSC(a.toEjml().origin), DMatrixSparseCSC(b.toEjml().origin), res)
return EjmlDoubleVector(res)

View File

@ -17,16 +17,12 @@ import space.kscience.kmath.linear.*
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.toArray
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.testutils.assertStructureEquals
import kotlin.random.Random
import kotlin.random.asJavaRandom
import kotlin.test.*
internal fun <T : Any> assertMatrixEquals(expected: StructureND<T>, actual: StructureND<T>) {
expected.elements().forEach { (index, value) ->
assertEquals(value, actual[index], "Structure element with index ${index.toList()} should be equal to $value but is ${actual[index]}")
}
assertTrue { StructureND.contentEquals(expected, actual) }
}
@OptIn(UnstableKMathAPI::class)
@ -67,7 +63,7 @@ internal class EjmlMatrixTest {
val w = EjmlDoubleMatrix(m)
val det: Double = w.getOrComputeAttribute(Determinant) ?: fail()
assertEquals(CommonOps_DDRM.det(m), det)
val lup: LupDecomposition<Float64> = w.getOrComputeAttribute(LUP) ?: fail()
val lup: LupDecomposition<Double> = w.getOrComputeAttribute(LUP) ?: fail()
val ludecompositionF64 = DecompositionFactory_DDRM.lu(m.numRows, m.numCols)
.also { it.decompose(m.copy()) }
@ -108,15 +104,4 @@ internal class EjmlMatrixTest {
assertTrue { StructureND.contentEquals(one(dim, dim), res, 1e-3) }
}
@Test
fun eigenValueDecomposition() = EjmlLinearSpaceDDRM {
val dim = 46
val u = buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
val matrix = buildMatrix(dim, dim) { row, col ->
if (row >= col) u[row, col] else u[col, row]
}
val eigen = matrix.getOrComputeAttribute(EIG) ?: fail()
assertStructureEquals(matrix, eigen.v dot eigen.d dot eigen.v.transposed())
}
}

View File

@ -9,13 +9,12 @@ import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.linear.Point
import space.kscience.kmath.operations.Float64L2Norm
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.MutableBuffer.Companion.double
import space.kscience.kmath.structures.asBuffer
import space.kscience.kmath.structures.indices
import kotlin.math.pow
public typealias DoubleVector = Point<Float64>
public typealias DoubleVector = Point<Double>
public fun DoubleVector(vararg doubles: Double): DoubleVector = doubles.asBuffer()
@ -42,7 +41,7 @@ public operator fun DoubleVector.plus(number: Number): DoubleVector = map { it +
public operator fun Number.plus(vector: DoubleVector): DoubleVector = vector + this
public operator fun DoubleVector.unaryMinus(): Buffer<Float64> = map { -it }
public operator fun DoubleVector.unaryMinus(): Buffer<Double> = map { -it }
public operator fun DoubleVector.minus(other: DoubleVector): DoubleVector {
require(size == other.size) { "Vector size $size expected but ${other.size} found" }

View File

@ -15,7 +15,6 @@ import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.operations.asIterable
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
import kotlin.math.pow
@ -31,7 +30,7 @@ import kotlin.math.pow
* Functions that help create a real (Double) matrix
*/
public typealias RealMatrix = Matrix<Float64>
public typealias RealMatrix = Matrix<Double>
public fun realMatrix(rowNum: Int, colNum: Int, initializer: Float64Field.(i: Int, j: Int) -> Double): RealMatrix =
Double.algebra.linearSpace.buildMatrix(rowNum, colNum, initializer)
@ -115,7 +114,7 @@ public operator fun RealMatrix.minus(other: RealMatrix): RealMatrix =
* Operations on columns
*/
public inline fun RealMatrix.appendColumn(crossinline mapper: (Buffer<Float64>) -> Double): RealMatrix =
public inline fun RealMatrix.appendColumn(crossinline mapper: (Buffer<Double>) -> Double): RealMatrix =
Double.algebra.linearSpace.buildMatrix(rowNum, colNum + 1) { row, col ->
if (col < colNum)
get(row, col)

View File

@ -8,12 +8,11 @@ package space.kscience.kmath.real
import space.kscience.kmath.linear.Matrix
import space.kscience.kmath.linear.linearSpace
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.structures.Float64
/**
* Optimized dot product for real matrices
*/
public infix fun Matrix<Float64>.dot(other: Matrix<Float64>): Matrix<Float64> = Double.algebra.linearSpace.run {
public infix fun Matrix<Double>.dot(other: Matrix<Double>): Matrix<Double> = Double.algebra.linearSpace.run {
this@dot dot other
}

View File

@ -7,17 +7,16 @@ package space.kscience.kmath.real
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
import kotlin.math.floor
public val ClosedFloatingPointRange<Float64>.length: Double get() = endInclusive - start
public val ClosedFloatingPointRange<Double>.length: Double get() = endInclusive - start
/**
* Create a Buffer-based grid with equally distributed [numberOfPoints] points. The range could be increasing or decreasing.
* If range has a zero size, then the buffer consisting of [numberOfPoints] equal values is returned.
*/
public fun Buffer.Companion.fromRange(range: ClosedFloatingPointRange<Float64>, numberOfPoints: Int): Float64Buffer {
public fun Buffer.Companion.fromRange(range: ClosedFloatingPointRange<Double>, numberOfPoints: Int): Float64Buffer {
require(numberOfPoints >= 2) { "Number of points in grid must be more than 1" }
val normalizedRange = when {
range.endInclusive > range.start -> range
@ -32,7 +31,7 @@ public fun Buffer.Companion.fromRange(range: ClosedFloatingPointRange<Float64>,
* Create a Buffer-based grid with equally distributed points with a fixed [step]. The range could be increasing or decreasing.
* If the step is larger than the range size, single point is returned.
*/
public fun Buffer.Companion.withFixedStep(range: ClosedFloatingPointRange<Float64>, step: Double): Float64Buffer {
public fun Buffer.Companion.withFixedStep(range: ClosedFloatingPointRange<Double>, step: Double): Float64Buffer {
require(step > 0) { "The grid step must be positive" }
val normalizedRange = when {
range.endInclusive > range.start -> range
@ -52,4 +51,4 @@ public fun Buffer.Companion.withFixedStep(range: ClosedFloatingPointRange<Float6
* If step is negative, the same goes from upper boundary downwards
*/
@UnstableKMathAPI
public infix fun ClosedFloatingPointRange<Float64>.step(step: Double): Float64Buffer = Buffer.withFixedStep(this, step)
public infix fun ClosedFloatingPointRange<Double>.step(step: Double): Float64Buffer = Buffer.withFixedStep(this, step)

View File

@ -7,13 +7,12 @@ package space.kscience.kmath.real
import space.kscience.kmath.nd.BufferND
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
/**
* Map one [BufferND] using function without indices.
*/
public inline fun BufferND<Float64>.mapInline(crossinline transform: Float64Field.(Double) -> Double): BufferND<Float64> {
public inline fun BufferND<Double>.mapInline(crossinline transform: Float64Field.(Double) -> Double): BufferND<Double> {
val array = DoubleArray(indices.linearSize) { offset -> Float64Field.transform(buffer[offset]) }
return BufferND(indices, Float64Buffer(array))
}
@ -21,7 +20,7 @@ public inline fun BufferND<Float64>.mapInline(crossinline transform: Float64Fiel
/**
* Element by element application of any operation on elements to the whole array. Just like in numpy.
*/
public operator fun Function1<Double, Double>.invoke(elementND: BufferND<Float64>): BufferND<Float64> =
public operator fun Function1<Double, Double>.invoke(elementND: BufferND<Double>): BufferND<Double> =
elementND.mapInline { this@invoke(it) }
/* plus and minus */
@ -29,9 +28,9 @@ public operator fun Function1<Double, Double>.invoke(elementND: BufferND<Float64
/**
* Summation operation for [BufferND] and single element
*/
public operator fun BufferND<Float64>.plus(arg: Double): BufferND<Float64> = mapInline { it + arg }
public operator fun BufferND<Double>.plus(arg: Double): BufferND<Double> = mapInline { it + arg }
/**
* Subtraction operation between [BufferND] and single element
*/
public operator fun BufferND<Float64>.minus(arg: Double): BufferND<Float64> = mapInline { it - arg }
public operator fun BufferND<Double>.minus(arg: Double): BufferND<Double> = mapInline { it - arg }

View File

@ -170,7 +170,7 @@ internal class DoubleMatrixTest {
assertEquals(matrix1.average(), 1.375)
}
// fun printMatrix(m: Matrix<Float64>) {
// fun printMatrix(m: Matrix<Double>) {
// for (row in 0 until m.shape[0]) {
// for (col in 0 until m.shape[1]) {
// print(m[row, col])

View File

@ -7,7 +7,6 @@ package space.kscience.kmath.functions
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Float64
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
import kotlin.math.max
@ -32,7 +31,7 @@ public inline fun <C, A, R> A.polynomialSpace(block: PolynomialSpace<C, A>.() ->
/**
* Evaluates value of [this] Double polynomial on provided Double argument.
*/
public fun Polynomial<Float64>.value(arg: Double): Double =
public fun Polynomial<Double>.value(arg: Double): Double =
coefficients.reduceIndexedOrNull { index, acc, c ->
acc + c * arg.pow(index)
} ?: .0

View File

@ -8,7 +8,6 @@ import space.kscience.attributes.AttributesBuilder
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.operations.Field
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.asBuffer
import space.kscience.kmath.structures.indices
@ -28,7 +27,7 @@ public class GaussIntegrator<T : Any>(
public val algebra: Field<T>,
) : UnivariateIntegrator<T> {
private fun buildRule(integrand: UnivariateIntegrand<T>): Pair<Buffer<Float64>, Buffer<Float64>> {
private fun buildRule(integrand: UnivariateIntegrand<T>): Pair<Buffer<Double>, Buffer<Double>> {
val factory = integrand[GaussIntegratorRuleFactory] ?: GaussLegendreRuleFactory
val predefinedRanges = integrand[UnivariateIntegrandRanges]
if (predefinedRanges == null || predefinedRanges.ranges.isEmpty()) {
@ -90,7 +89,7 @@ public val <T : Any> Field<T>.gaussIntegrator: GaussIntegrator<T> get() = GaussI
*/
@UnstableKMathAPI
public inline fun <reified T : Any> GaussIntegrator<T>.integrate(
range: ClosedRange<Float64>,
range: ClosedRange<Double>,
order: Int = 10,
intervals: Int = 10,
noinline attributesBuilder: AttributesBuilder<UnivariateIntegrand<T>>.() -> Unit = {},

View File

@ -8,17 +8,16 @@ package space.kscience.kmath.integration
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.mapToBuffer
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
import space.kscience.kmath.structures.asBuffer
import kotlin.math.ulp
import kotlin.native.concurrent.ThreadLocal
public interface GaussIntegratorRuleFactory {
public fun build(numPoints: Int): Pair<Buffer<Float64>, Buffer<Float64>>
public fun build(numPoints: Int): Pair<Buffer<Double>, Buffer<Double>>
public companion object : IntegrandAttribute<GaussIntegratorRuleFactory> {
public fun double(numPoints: Int, range: ClosedRange<Float64>): Pair<Buffer<Float64>, Buffer<Float64>> =
public fun double(numPoints: Int, range: ClosedRange<Double>): Pair<Buffer<Double>, Buffer<Double>> =
GaussLegendreRuleFactory.build(numPoints, range)
}
}
@ -29,9 +28,9 @@ public interface GaussIntegratorRuleFactory {
*/
public fun GaussIntegratorRuleFactory.build(
numPoints: Int,
range: ClosedRange<Float64>,
): Pair<Buffer<Float64>, Buffer<Float64>> {
val normalized: Pair<Buffer<Float64>, Buffer<Float64>> = build(numPoints)
range: ClosedRange<Double>,
): Pair<Buffer<Double>, Buffer<Double>> {
val normalized: Pair<Buffer<Double>, Buffer<Double>> = build(numPoints)
val length = range.endInclusive - range.start
val points = normalized.first.mapToBuffer(Float64Field.bufferFactory) {
@ -56,13 +55,13 @@ public fun GaussIntegratorRuleFactory.build(
@ThreadLocal
public object GaussLegendreRuleFactory : GaussIntegratorRuleFactory {
private val cache = HashMap<Int, Pair<Buffer<Float64>, Buffer<Float64>>>()
private val cache = HashMap<Int, Pair<Buffer<Double>, Buffer<Double>>>()
private fun getOrBuildRule(numPoints: Int): Pair<Buffer<Float64>, Buffer<Float64>> =
private fun getOrBuildRule(numPoints: Int): Pair<Buffer<Double>, Buffer<Double>> =
cache.getOrPut(numPoints) { buildRule(numPoints) }
private fun buildRule(numPoints: Int): Pair<Buffer<Float64>, Buffer<Float64>> {
private fun buildRule(numPoints: Int): Pair<Buffer<Double>, Buffer<Double>> {
if (numPoints == 1) {
// Break recursion.
return Pair(
@ -74,7 +73,7 @@ public object GaussLegendreRuleFactory : GaussIntegratorRuleFactory {
// Get previous rule.
// If it has not been computed, yet it will trigger a recursive call
// to this method.
val previousPoints: Buffer<Float64> = getOrBuildRule(numPoints - 1).first
val previousPoints: Buffer<Double> = getOrBuildRule(numPoints - 1).first
// Compute next rule.
val points = DoubleArray(numPoints)
@ -163,7 +162,7 @@ public object GaussLegendreRuleFactory : GaussIntegratorRuleFactory {
return Pair(points.asBuffer(), weights.asBuffer())
}
override fun build(numPoints: Int): Pair<Buffer<Float64>, Buffer<Float64>> = getOrBuildRule(numPoints)
override fun build(numPoints: Int): Pair<Buffer<Double>, Buffer<Double>> = getOrBuildRule(numPoints)
override fun toString(): String = "GaussLegendreRule"
}

View File

@ -6,7 +6,6 @@
package space.kscience.kmath.integration
import space.kscience.attributes.*
import space.kscience.kmath.structures.Float64
public interface IntegrandAttribute<T> : Attribute<T>
@ -45,9 +44,9 @@ public inline val <reified T : Any> Integrand<T>.valueOrNull: T? get() = attribu
*/
public inline val <reified T : Any> Integrand<T>.value: T get() = valueOrNull ?: error("No value in the integrand")
public object IntegrandRelativeAccuracy : IntegrandAttribute<Float64>
public object IntegrandRelativeAccuracy : IntegrandAttribute<Double>
public object IntegrandAbsoluteAccuracy : IntegrandAttribute<Float64>
public object IntegrandAbsoluteAccuracy : IntegrandAttribute<Double>
public object IntegrandCallsPerformed : IntegrandAttribute<Int>

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