Add proper test for symmetric matrices eigenValueDecomposition

Cosmetic change Double -> Float64

CHANGELOG.md

@@ -3,6 +3,7 @@
 ## Unreleased
 
 ### Added
+- Metropolis-Hastings sampler
 
 ### Changed
 
@@ -200,7 +201,7 @@
 - Full autodiff refactoring based on `Symbol`
 - `kmath-prob` renamed to `kmath-stat`
 - Grid generators moved to `kmath-for-real`
-- Use `Point<Double>` instead of specialized type in `kmath-for-real`
+- Use `Point<Float64>` 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`

benchmarks/build.gradle.kts

@@ -15,8 +15,6 @@ repositories {
     mavenCentral()
 }
 
-val multikVersion: String by rootProject.extra
-
 kotlin {
     jvm()
 
@@ -45,7 +43,7 @@ kotlin {
                 implementation(project(":kmath-for-real"))
                 implementation(project(":kmath-tensors"))
                 implementation(project(":kmath-multik"))
-                implementation("org.jetbrains.kotlinx:multik-default:$multikVersion")
+                implementation(libs.multik.default)
                 implementation(spclibs.kotlinx.benchmark.runtime)
             }
         }

benchmarks/src/jsMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt

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

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt

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

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/NDFieldBenchmark.kt

@@ -17,6 +17,7 @@ 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
@@ -37,28 +38,28 @@ internal class NDFieldBenchmark {
 
     @Benchmark
     fun specializedFieldAdd(blackhole: Blackhole) = with(specializedField) {
-        var res: StructureND<Double> = one(shape)
+        var res: StructureND<Float64> = one(shape)
         repeat(n) { res += 1.0 }
         blackhole.consume(res)
     }
 
     @Benchmark
     fun boxingFieldAdd(blackhole: Blackhole) = with(genericField) {
-        var res: StructureND<Double> = one(shape)
+        var res: StructureND<Float64> = one(shape)
         repeat(n) { res += 1.0 }
         blackhole.consume(res)
     }
 
     @Benchmark
     fun multikAdd(blackhole: Blackhole) = with(multikAlgebra) {
-        var res: StructureND<Double> = one(shape)
+        var res: StructureND<Float64> = one(shape)
         repeat(n) { res += 1.0 }
         blackhole.consume(res)
     }
 
     @Benchmark
     fun viktorAdd(blackhole: Blackhole) = with(viktorField) {
-        var res: StructureND<Double> = one(shape)
+        var res: StructureND<Float64> = one(shape)
         repeat(n) { res += 1.0 }
         blackhole.consume(res)
     }
@@ -87,7 +88,7 @@ internal class NDFieldBenchmark {
 
 //    @Benchmark
 //    fun nd4jAdd(blackhole: Blackhole) = with(nd4jField) {
-//        var res: StructureND<Double> = one(dim, dim)
+//        var res: StructureND<Float64> = one(dim, dim)
 //        repeat(n) { res += 1.0 }
 //        blackhole.consume(res)
 //    }

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorBenchmark.kt

@@ -15,6 +15,7 @@ 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)
@@ -23,7 +24,7 @@ internal class ViktorBenchmark {
     @Benchmark
     fun doubleFieldAddition(blackhole: Blackhole) {
         with(doubleField) {
-            var res: StructureND<Double> = one(shape)
+            var res: StructureND<Float64> = one(shape)
             repeat(n) { res += 1.0 }
             blackhole.consume(res)
         }

build.gradle.kts

@@ -3,7 +3,7 @@ import space.kscience.gradle.useSPCTeam
 
 plugins {
     id("space.kscience.gradle.project")
-    id("org.jetbrains.kotlinx.kover") version "0.7.6"
+    alias(spclibs.plugins.kotlinx.kover)
 }
 
 val attributesVersion by extra("0.2.0")
@@ -70,5 +70,3 @@ ksciencePublish {
 }
 
 apiValidation.nonPublicMarkers.add("space.kscience.kmath.UnstableKMathAPI")
-
-val multikVersion by extra("0.2.3")

docs/nd-structure.md

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

examples/build.gradle.kts

@@ -10,8 +10,6 @@ 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"))
@@ -33,7 +31,7 @@ dependencies {
     implementation(project(":kmath-jafama"))
     //multik
     implementation(project(":kmath-multik"))
-    implementation("org.jetbrains.kotlinx:multik-default:$multikVersion")
+    implementation(libs.multik.default)
 
     //datetime
     implementation("org.jetbrains.kotlinx:kotlinx-datetime:0.4.0")

examples/src/main/kotlin/space/kscience/kmath/functions/integrate.kt

@@ -14,11 +14,12 @@ 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<Double> = { x -> 3 * x.pow(2) + 2 * x + 1 }
+    val function: Function1D<Float64> = { 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)

examples/src/main/kotlin/space/kscience/kmath/functions/interpolate.kt

@@ -8,6 +8,7 @@ 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
@@ -23,7 +24,7 @@ fun main() {
         x to sin(x)
     }
 
-    val polynomial: PiecewisePolynomial<Double> = SplineInterpolator(Float64Field).interpolatePolynomials(data)
+    val polynomial: PiecewisePolynomial<Float64> = SplineInterpolator(Float64Field).interpolatePolynomials(data)
 
     val function = polynomial.asFunction(Float64Field, 0.0)
 

examples/src/main/kotlin/space/kscience/kmath/functions/interpolateSquare.kt

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

examples/src/main/kotlin/space/kscience/kmath/functions/matrixIntegration.kt

@@ -12,6 +12,7 @@ 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) {
@@ -22,7 +23,7 @@ fun main(): Unit = Double.algebra.withNdAlgebra(2, 2) {
     }
 
     //Define a function in a nd space
-    val function: (Double) -> StructureND<Double> = { x: Double -> 3 * x.pow(2) + 2 * diagonal(x) + 1 }
+    val function: (Double) -> StructureND<Float64> = { 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)

examples/src/main/kotlin/space/kscience/kmath/linear/eigenValueDecomposition.kt

@@ -0,0 +1,40 @@
+/*
+ * 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" )
+        }
+    }
+
+}

examples/src/main/kotlin/space/kscience/kmath/linear/gradient.kt

@@ -6,18 +6,19 @@
 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<Double>) -> Double = { x ->
+    val gaussian: (Point<Float64>) -> Double = { x ->
         require(x.size == x0.size)
         kotlin.math.exp(-((x - x0) / sigma).square().sum())
     }
 
-    fun ((Point<Double>) -> Double).grad(x: Point<Double>): Point<Double> {
+    fun ((Point<Float64>) -> Double).grad(x: Point<Float64>): Point<Float64> {
         require(x.size == x0.size)
         return Float64Buffer(x.size) { i ->
             val h = sigma[i] / 5

examples/src/main/kotlin/space/kscience/kmath/operations/mixedNDOperations.kt

@@ -11,6 +11,7 @@ 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
@@ -20,7 +21,7 @@ fun main() {
         if (i == j) 2.0 else 0.0
     }
 
-    val cmMatrix: Structure2D<Double> = CMLinearSpace.matrix(2, 2)(0.0, 1.0, 0.0, 3.0)
+    val cmMatrix: Structure2D<Float64> = CMLinearSpace.matrix(2, 2)(0.0, 1.0, 0.0, 3.0)
 
     val res: Float64BufferND = Float64Field.ndAlgebra {
         exp(viktorStructure) + 2.0 * cmMatrix

examples/src/main/kotlin/space/kscience/kmath/series/analyzeDif.kt

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

examples/src/main/kotlin/space/kscience/kmath/series/seriesBuilder.kt

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

examples/src/main/kotlin/space/kscience/kmath/stat/DistributionBenchmark.kt

@@ -37,7 +37,7 @@ private suspend fun runKMathChained(): Duration {
 }
 
 private fun runCMDirect(): Duration {
-    val rng = RandomSource.create(RandomSource.MT, 123L)
+    val rng = RandomSource.MT.create(123L)
 
     val sampler = CMGaussianSampler.of(
         BoxMullerNormalizedGaussianSampler.of(rng),

examples/src/main/kotlin/space/kscience/kmath/stat/DistributionDemo.kt

@@ -10,13 +10,14 @@ 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<Double>.mean(): Chain<Double> = combineWithState(AveragingChainState(), { it.copy() }) { chain ->
+private fun Chain<Float64>.mean(): Chain<Float64> = combineWithState(AveragingChainState(), { it.copy() }) { chain ->
     val next = chain.next()
     num++
     value += next

examples/src/main/kotlin/space/kscience/kmath/structures/ComplexND.kt

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

examples/src/main/kotlin/space/kscience/kmath/structures/NDField.kt

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

examples/src/main/kotlin/space/kscience/kmath/structures/StreamDoubleFieldND.kt

@@ -19,21 +19,21 @@ import java.util.stream.IntStream
  * execution.
  */
 class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64Field>,
-    NumbersAddOps<StructureND<Double>>,
-    ExtendedField<StructureND<Double>> {
+    NumbersAddOps<StructureND<Float64>>,
+    ExtendedField<StructureND<Float64>> {
 
     private val strides = ColumnStrides(shape)
     override val elementAlgebra: Float64Field get() = Float64Field
-    override val zero: BufferND<Double> by lazy { structureND(shape) { zero } }
-    override val one: BufferND<Double> by lazy { structureND(shape) { one } }
+    override val zero: BufferND<Float64> by lazy { structureND(shape) { zero } }
+    override val one: BufferND<Float64> by lazy { structureND(shape) { one } }
 
-    override fun number(value: Number): BufferND<Double> {
+    override fun number(value: Number): BufferND<Float64> {
         val d = value.toDouble() // minimize conversions
         return structureND(shape) { d }
     }
 
     @OptIn(PerformancePitfall::class)
-    private val StructureND<Double>.buffer: Float64Buffer
+    private val StructureND<Float64>.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<Double> {
+    override fun structureND(shape: ShapeND, initializer: Float64Field.(IntArray) -> Double): BufferND<Float64> {
         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<Double> {
+    ): MutableBufferND<Float64> {
         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<Double>.map(
+    override fun StructureND<Float64>.map(
         transform: Float64Field.(Double) -> Double,
-    ): BufferND<Double> {
+    ): BufferND<Float64> {
         val array = Arrays.stream(buffer.array).parallel().map { Float64Field.transform(it) }.toArray()
         return BufferND(strides, array.asBuffer())
     }
 
     @OptIn(PerformancePitfall::class)
-    override fun StructureND<Double>.mapIndexed(
+    override fun StructureND<Float64>.mapIndexed(
         transform: Float64Field.(index: IntArray, Double) -> Double,
-    ): BufferND<Double> {
+    ): BufferND<Float64> {
         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<Double>,
-        right: StructureND<Double>,
+        left: StructureND<Float64>,
+        right: StructureND<Float64>,
         transform: Float64Field.(Double, Double) -> Double,
-    ): BufferND<Double> {
+    ): BufferND<Float64> {
         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<Double>.unaryMinus(): StructureND<Double> = map { -it }
+    override fun StructureND<Float64>.unaryMinus(): StructureND<Float64> = map { -it }
 
-    override fun scale(a: StructureND<Double>, value: Double): StructureND<Double> = a.map { it * value }
+    override fun scale(a: StructureND<Float64>, value: Double): StructureND<Float64> = a.map { it * value }
 
-    override fun power(arg: StructureND<Double>, pow: Number): BufferND<Double> = arg.map { power(it, pow) }
+    override fun power(arg: StructureND<Float64>, pow: Number): BufferND<Float64> = arg.map { power(it, pow) }
 
-    override fun exp(arg: StructureND<Double>): BufferND<Double> = arg.map { exp(it) }
+    override fun exp(arg: StructureND<Float64>): BufferND<Float64> = arg.map { exp(it) }
 
-    override fun ln(arg: StructureND<Double>): BufferND<Double> = arg.map { ln(it) }
+    override fun ln(arg: StructureND<Float64>): BufferND<Float64> = arg.map { ln(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 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 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) }
+    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) }
 }
 
 fun Float64Field.ndStreaming(vararg shape: Int): StreamDoubleFieldND = StreamDoubleFieldND(ShapeND(shape))

examples/src/main/kotlin/space/kscience/kmath/structures/mutableNd.kt

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

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StreamingLm/streamLm.kt

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

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/functionsToOptimize.kt

@@ -9,6 +9,7 @@ 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
@@ -19,24 +20,24 @@ 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<Double>,
+    var lm_matx_y_dat: MutableStructure2D<Float64>,
     var example_number: Int,
-    var p_init: MutableStructure2D<Double>,
-    var t: MutableStructure2D<Double>,
-    var y_dat: MutableStructure2D<Double>,
+    var p_init: MutableStructure2D<Float64>,
+    var t: MutableStructure2D<Float64>,
+    var y_dat: MutableStructure2D<Float64>,
     var weight: Double,
-    var dp: MutableStructure2D<Double>,
-    var p_min: MutableStructure2D<Double>,
-    var p_max: MutableStructure2D<Double>,
-    var consts: MutableStructure2D<Double>,
+    var dp: MutableStructure2D<Float64>,
+    var p_min: MutableStructure2D<Float64>,
+    var p_max: MutableStructure2D<Float64>,
+    var consts: MutableStructure2D<Float64>,
     var opts: DoubleArray,
 )
 
 fun funcEasyForLm(
-    t: MutableStructure2D<Double>,
-    p: MutableStructure2D<Double>,
+    t: MutableStructure2D<Float64>,
+    p: MutableStructure2D<Float64>,
     exampleNumber: Int,
-): MutableStructure2D<Double> {
+): MutableStructure2D<Float64> {
     val m = t.shape.component1()
     var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
 
@@ -59,10 +60,10 @@ fun funcEasyForLm(
 }
 
 fun funcMiddleForLm(
-    t: MutableStructure2D<Double>,
-    p: MutableStructure2D<Double>,
+    t: MutableStructure2D<Float64>,
+    p: MutableStructure2D<Float64>,
     exampleNumber: Int,
-): MutableStructure2D<Double> {
+): MutableStructure2D<Float64> {
     val m = t.shape.component1()
     var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
 
@@ -79,10 +80,10 @@ fun funcMiddleForLm(
 }
 
 fun funcDifficultForLm(
-    t: MutableStructure2D<Double>,
-    p: MutableStructure2D<Double>,
+    t: MutableStructure2D<Float64>,
+    p: MutableStructure2D<Float64>,
     exampleNumber: Int,
-): MutableStructure2D<Double> {
+): MutableStructure2D<Float64> {
     val m = t.shape.component1()
     var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
 

examples/src/main/kotlin/space/kscience/kmath/tensors/multik.kt

@@ -10,12 +10,13 @@ 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<Double>().wrap()
+    val a = Multik.ndarray(intArrayOf(1, 2, 3)).asType<Float64>().wrap()
     val b = Multik.ndarray(doubleArrayOf(1.0, 2.0, 3.0)).wrap()
     one(a.shape) - a + b * 3.0
 }

gradle/libs.versions.toml

@@ -0,0 +1,12 @@
+[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" }

kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestFolding.kt

@@ -9,6 +9,7 @@ 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
@@ -41,7 +42,7 @@ internal class TestFolding {
     @Test
     fun foldSymbol() = assertEquals(
         Float64Field.pi,
-        ("pi".parseMath().evaluateConstants(Float64Field) as? TypedMst.Constant<Double> ?: fail()).value,
+        ("pi".parseMath().evaluateConstants(Float64Field) as? TypedMst.Constant<Float64> ?: fail()).value,
     )
 
     @Test

kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/utils.kt

@@ -10,12 +10,13 @@ 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<Double>
+    fun MST.compileToExpression(algebra: Float64Field): Expression<Float64>
     fun MST.compile(algebra: Float64Field, arguments: Map<Symbol, Double>): Double
 
     fun MST.compile(algebra: Float64Field, vararg arguments: Pair<Symbol, Double>): Double =

kmath-ast/src/jsMain/kotlin/space/kscience/kmath/wasm/internal/WasmBuilder.kt

@@ -11,6 +11,7 @@ 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
 
@@ -85,13 +86,13 @@ internal sealed class WasmBuilder<T : Number, out E : Expression<T>>(
 }
 
 @UnstableKMathAPI
-internal class DoubleWasmBuilder(target: TypedMst<Double>) :
+internal class DoubleWasmBuilder(target: TypedMst<Float64>) :
     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<Double>()
+            override fun invoke(arguments: DoubleArray) = spreader(executable, arguments).unsafeCast<Float64>()
         }
     }
 
@@ -99,7 +100,7 @@ internal class DoubleWasmBuilder(target: TypedMst<Double>) :
 
     override fun visitNumber(number: Number) = ctx.f64.const(number.toDouble())
 
-    override fun visitUnary(node: TypedMst.Unary<Double>): ExpressionRef = when (node.operation) {
+    override fun visitUnary(node: TypedMst.Unary<Float64>): 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))
@@ -120,7 +121,7 @@ internal class DoubleWasmBuilder(target: TypedMst<Double>) :
         else -> super.visitUnary(node)
     }
 
-    override fun visitBinary(mst: TypedMst.Binary<Double>): ExpressionRef = when (mst.operation) {
+    override fun visitBinary(mst: TypedMst.Binary<Float64>): 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))

kmath-ast/src/jsMain/kotlin/space/kscience/kmath/wasm/wasm.kt

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

kmath-ast/src/jsTest/kotlin/space/kscience/kmath/ast/utils.kt

@@ -13,6 +13,7 @@ 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
@@ -24,7 +25,7 @@ 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<Double> = wasmCompileToExpression(algebra)
+    override fun MST.compileToExpression(algebra: Float64Field): Expression<Float64> = wasmCompileToExpression(algebra)
 
     override fun MST.compile(algebra: Float64Field, arguments: Map<Symbol, Double>): Double =
         wasmCompile(algebra, arguments)
@@ -33,7 +34,7 @@ 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<Double> = estreeCompileToExpression(algebra)
+    override fun MST.compileToExpression(algebra: Float64Field): Expression<Float64> = estreeCompileToExpression(algebra)
 
     override fun MST.compile(algebra: Float64Field, arguments: Map<Symbol, Double>): Double =
         estreeCompile(algebra, arguments)

kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/PrimitiveAsmBuilder.kt

@@ -15,6 +15,7 @@ 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
@@ -381,7 +382,7 @@ internal sealed class PrimitiveAsmBuilder<T : Number, out E : Expression<T>>(
 }
 
 @UnstableKMathAPI
-internal class DoubleAsmBuilder(target: TypedMst<Double>) : PrimitiveAsmBuilder<Double, DoubleExpression>(
+internal class DoubleAsmBuilder(target: TypedMst<Float64>) : PrimitiveAsmBuilder<Double, DoubleExpression>(
     Float64Field,
     java.lang.Double::class.java,
     java.lang.Double.TYPE,
@@ -410,7 +411,7 @@ internal class DoubleAsmBuilder(target: TypedMst<Double>) : PrimitiveAsmBuilder<
         false,
     )
 
-    override fun visitUnary(node: TypedMst.Unary<Double>) {
+    override fun visitUnary(node: TypedMst.Unary<Float64>) {
         super.visitUnary(node)
 
         when (node.operation) {
@@ -435,7 +436,7 @@ internal class DoubleAsmBuilder(target: TypedMst<Double>) : PrimitiveAsmBuilder<
         }
     }
 
-    override fun visitBinary(node: TypedMst.Binary<Double>) {
+    override fun visitBinary(node: TypedMst.Binary<Float64>) {
         super.visitBinary(node)
 
         when (node.operation) {

kmath-ast/src/jvmTest/kotlin/space/kscience/kmath/ast/utils.kt

@@ -12,6 +12,7 @@ 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
 
@@ -22,18 +23,18 @@ 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<Double> =
-        asmCompileToExpression(algebra as Algebra<Double>)
+    override fun MST.compileToExpression(algebra: Float64Field): Expression<Float64> =
+        asmCompileToExpression(algebra as Algebra<Float64>)
 
     override fun MST.compile(algebra: Float64Field, arguments: Map<Symbol, Double>): Double =
-        asmCompile(algebra as Algebra<Double>, arguments)
+        asmCompile(algebra as Algebra<Float64>, 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<Double> = asmCompileToExpression(algebra)
+    override fun MST.compileToExpression(algebra: Float64Field): Expression<Float64> = asmCompileToExpression(algebra)
 
     override fun MST.compile(algebra: Float64Field, arguments: Map<Symbol, Double>): Double =
         asmCompile(algebra, arguments)

kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/expressions/CmDsExpression.kt

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

kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/integration/CMGaussRuleIntegrator.kt

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

kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/integration/CMIntegrator.kt

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

kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/linear/CMMatrix.kt

@@ -12,6 +12,7 @@ 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
@@ -22,7 +23,7 @@ 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<Double> {
+public class CMMatrix(public val origin: RealMatrix) : Matrix<Float64> {
 
     override val rowNum: Int get() = origin.rowDimension
     override val colNum: Int get() = origin.columnDimension
@@ -31,12 +32,12 @@ public class CMMatrix(public val origin: RealMatrix) : Matrix<Double> {
 }
 
 @JvmInline
-public value class CMVector(public val origin: RealVector) : Point<Double> {
+public value class CMVector(public val origin: RealVector) : Point<Float64> {
     override val size: Int get() = origin.dimension
 
     override operator fun get(index: Int): Double = origin.getEntry(index)
 
-    override operator fun iterator(): Iterator<Double> = origin.toArray().iterator()
+    override operator fun iterator(): Iterator<Float64> = origin.toArray().iterator()
 
     override fun toString(): String = Buffer.toString(this)
 }
@@ -46,7 +47,7 @@ public fun RealVector.toPoint(): CMVector = CMVector(this)
 public object CMLinearSpace : LinearSpace<Double, Float64Field> {
     override val elementAlgebra: Float64Field get() = Float64Field
 
-    override val type: SafeType<Double> get() = DoubleField.type
+    override val type: SafeType<Float64> get() = DoubleField.type
 
     override fun buildMatrix(
         rows: Int,
@@ -58,7 +59,7 @@ public object CMLinearSpace : LinearSpace<Double, Float64Field> {
     }
 
     @OptIn(UnstableKMathAPI::class)
-    public fun Matrix<Double>.toCM(): CMMatrix = when (val matrix = origin) {
+    public fun Matrix<Float64>.toCM(): CMMatrix = when (val matrix = origin) {
         is CMMatrix -> matrix
         else -> {
             //TODO add feature analysis
@@ -67,7 +68,7 @@ public object CMLinearSpace : LinearSpace<Double, Float64Field> {
         }
     }
 
-    public fun Point<Double>.toCM(): CMVector = if (this is CMVector) this else {
+    public fun Point<Float64>.toCM(): CMVector = if (this is CMVector) this else {
         val array = DoubleArray(size) { this[it] }
         ArrayRealVector(array).wrap()
     }
@@ -75,40 +76,40 @@ 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<Double> =
+    override fun buildVector(size: Int, initializer: Float64Field.(Int) -> Double): Point<Float64> =
         ArrayRealVector(DoubleArray(size) { Float64Field.initializer(it) }).wrap()
 
-    override fun Matrix<Double>.plus(other: Matrix<Double>): CMMatrix =
+    override fun Matrix<Float64>.plus(other: Matrix<Float64>): CMMatrix =
         toCM().origin.add(other.toCM().origin).wrap()
 
-    override fun Point<Double>.plus(other: Point<Double>): CMVector =
+    override fun Point<Float64>.plus(other: Point<Float64>): CMVector =
         toCM().origin.add(other.toCM().origin).wrap()
 
-    override fun Point<Double>.minus(other: Point<Double>): CMVector =
+    override fun Point<Float64>.minus(other: Point<Float64>): CMVector =
         toCM().origin.subtract(other.toCM().origin).wrap()
 
-    override fun Matrix<Double>.dot(other: Matrix<Double>): CMMatrix =
+    override fun Matrix<Float64>.dot(other: Matrix<Float64>): CMMatrix =
         toCM().origin.multiply(other.toCM().origin).wrap()
 
-    override fun Matrix<Double>.dot(vector: Point<Double>): CMVector =
+    override fun Matrix<Float64>.dot(vector: Point<Float64>): CMVector =
         toCM().origin.preMultiply(vector.toCM().origin).wrap()
 
-    override operator fun Matrix<Double>.minus(other: Matrix<Double>): CMMatrix =
+    override operator fun Matrix<Float64>.minus(other: Matrix<Float64>): CMMatrix =
         toCM().origin.subtract(other.toCM().origin).wrap()
 
-    override operator fun Matrix<Double>.times(value: Double): CMMatrix =
+    override operator fun Matrix<Float64>.times(value: Double): CMMatrix =
         toCM().origin.scalarMultiply(value).wrap()
 
-    override fun Double.times(m: Matrix<Double>): CMMatrix =
+    override fun Double.times(m: Matrix<Float64>): CMMatrix =
         m * this
 
-    override fun Point<Double>.times(value: Double): CMVector =
+    override fun Point<Float64>.times(value: Double): CMVector =
         toCM().origin.mapMultiply(value).wrap()
 
-    override fun Double.times(v: Point<Double>): CMVector =
+    override fun Double.times(v: Point<Float64>): CMVector =
         v * this
 
-    override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Double>, attribute: A): V? {
+    override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Float64>, attribute: A): V? {
 
         val origin = structure.toCM().origin
 
@@ -125,7 +126,7 @@ 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<Double> get() = cmCholesky.l.wrap()
+                override val l: Matrix<Float64> get() = cmCholesky.l.wrap()
             }
 
             QR -> object : QRDecomposition<Float64> {
@@ -144,6 +145,13 @@ 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")

kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/linear/CMSolver.kt

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

kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/optimization/CMOptimizer.kt

@@ -22,6 +22,7 @@ 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
 
@@ -33,7 +34,7 @@ public object CMOptimizerEngine : OptimizationAttribute<() -> MultivariateOptimi
 /**
  * Specify a Commons-maths optimization engine
  */
-public fun AttributesBuilder<FunctionOptimization<Double>>.cmEngine(optimizerBuilder: () -> MultivariateOptimizer) {
+public fun AttributesBuilder<FunctionOptimization<Float64>>.cmEngine(optimizerBuilder: () -> MultivariateOptimizer) {
     set(CMOptimizerEngine, optimizerBuilder)
 }
 
@@ -42,18 +43,18 @@ public object CMOptimizerData : SetAttribute<SymbolIndexer.() -> OptimizationDat
 /**
  * Specify Commons-maths optimization data.
  */
-public fun AttributesBuilder<FunctionOptimization<Double>>.cmOptimizationData(data: SymbolIndexer.() -> OptimizationData) {
+public fun AttributesBuilder<FunctionOptimization<Float64>>.cmOptimizationData(data: SymbolIndexer.() -> OptimizationData) {
     CMOptimizerData add data
 }
 
-public fun AttributesBuilder<FunctionOptimization<Double>>.simplexSteps(vararg steps: Pair<Symbol, Double>) {
+public fun AttributesBuilder<FunctionOptimization<Float64>>.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<Double>> {
+public object CMOptimizer : Optimizer<Double, FunctionOptimization<Float64>> {
 
     public const val DEFAULT_RELATIVE_TOLERANCE: Double = 1e-4
     public const val DEFAULT_ABSOLUTE_TOLERANCE: Double = 1e-4
@@ -67,12 +68,12 @@ public object CMOptimizer : Optimizer<Double, FunctionOptimization<Double>> {
 
 
     override suspend fun optimize(
-        problem: FunctionOptimization<Double>,
-    ): FunctionOptimization<Double> {
+        problem: FunctionOptimization<Float64>,
+    ): FunctionOptimization<Float64> {
         val startPoint = problem.startPoint
 
         val parameters = problem.attributes[OptimizationParameters]
-            ?: problem.attributes[OptimizationStartPoint<Double>()]?.keys
+            ?: problem.attributes[OptimizationStartPoint<Float64>()]?.keys
             ?: startPoint.keys
 
 

kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/transform/Transformations.kt

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

kmath-commons/src/jvmTest/kotlin/space/kscience/kmath/commons/optimization/OptimizeTest.kt

@@ -18,6 +18,7 @@ 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
@@ -70,7 +71,7 @@ internal class OptimizeTest {
             bindSymbol(a) * arg.pow(2) + bindSymbol(b) * arg + cWithDefault
         }
 
-        val result: FunctionOptimization<Double> = chi2.optimizeWith(
+        val result: FunctionOptimization<Float64> = chi2.optimizeWith(
             CMOptimizer,
             mapOf(a to 1.5, b to 0.9, c to 1.0),
         ) {

kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Quaternion.kt

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

kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/Domain1D.kt

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

kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/Float64Domain.kt

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

kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/HyperSquareDomain.kt

@@ -7,6 +7,7 @@ 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
 
@@ -15,7 +16,7 @@ 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<Double>, public val upper: Buffer<Double>) : Float64Domain {
+public class HyperSquareDomain(public val lower: Buffer<Float64>, public val upper: Buffer<Float64>) : Float64Domain {
     init {
         require(lower.size == upper.size) {
             "Domain borders size mismatch. Lower borders size is ${lower.size}, but upper borders size is ${upper.size}."
@@ -29,7 +30,7 @@ public class HyperSquareDomain(public val lower: Buffer<Double>, public val uppe
 
     public val center: Float64Buffer get() = Float64Buffer(dimension) { (lower[it] + upper[it]) / 2.0 }
 
-    override operator fun contains(point: Point<Double>): Boolean = point.indices.all { i ->
+    override operator fun contains(point: Point<Float64>): Boolean = point.indices.all { i ->
         point[i] in lower[i]..upper[i]
     }
 

kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/UnconstrainedDomain.kt

@@ -6,10 +6,11 @@ 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<Double>): Boolean = true
+    override operator fun contains(point: Point<Float64>): Boolean = true
 
     override fun getLowerBound(num: Int): Double = Double.NEGATIVE_INFINITY
 

kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/Expression.kt

@@ -13,6 +13,7 @@ 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
 
@@ -47,9 +48,9 @@ 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<Double> {
+public interface DoubleExpression : Expression<Float64> {
 
-    override val type: SafeType<Double> get() = DoubleField.type
+    override val type: SafeType<Float64> get() = DoubleField.type
 
     /**
      * The indexer of this expression's arguments that should be used to build array for [invoke].

kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/FunctionalExpressionAlgebra.kt

@@ -6,6 +6,7 @@
 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
@@ -197,5 +198,5 @@ public inline fun <T, A : ExtendedField<T>> A.expressionInExtendedField(
 ): Expression<T> = FunctionalExpressionExtendedField(this).block()
 
 public inline fun Float64Field.expression(
-    block: FunctionalExpressionExtendedField<Double, Float64Field>.() -> Expression<Double>,
-): Expression<Double> = FunctionalExpressionExtendedField(this).block()
+    block: FunctionalExpressionExtendedField<Double, Float64Field>.() -> Expression<Float64>,
+): Expression<Float64> = FunctionalExpressionExtendedField(this).block()

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/EigenDecomposition.kt

@@ -0,0 +1,30 @@
+/*
+ * 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
+
+public interface EigenDecomposition<T> {
+    /**
+     * Eigenvector matrix.
+     */
+    public val v: Matrix<T>
+
+    /**
+     * A diagonal matrix of eigenvalues. Must have [IsDiagonal]
+     */
+    public val d: Matrix<T>
+}
+
+public class EigenDecompositionAttribute<T> :
+    PolymorphicAttribute<EigenDecomposition<T>>(safeTypeOf()),
+    MatrixAttribute<EigenDecomposition<T>>
+
+@UnstableKMathAPI
+public val <T> MatrixScope<T>.EIG: EigenDecompositionAttribute<T>
+    get() = EigenDecompositionAttribute()

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/Float64LinearSpace.kt

@@ -6,12 +6,19 @@
 package space.kscience.kmath.linear
 
 import space.kscience.kmath.PerformancePitfall
-import space.kscience.kmath.nd.*
+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.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> {
 
@@ -21,36 +28,36 @@ public object Float64LinearSpace : LinearSpace<Double, Float64Field> {
         rows: Int,
         columns: Int,
         initializer: Float64Field.(i: Int, j: Int) -> Double,
-    ): Matrix<Double> = Floa64FieldOpsND.structureND(ShapeND(rows, columns)) { (i, j) ->
+    ): Matrix<Float64> = 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<Double>.unaryMinus(): Matrix<Double> = Floa64FieldOpsND {
+    override fun Matrix<Float64>.unaryMinus(): Matrix<Float64> = Floa64FieldOpsND {
         asND().map { -it }.as2D()
     }
 
-    override fun Matrix<Double>.plus(other: Matrix<Double>): Matrix<Double> = Floa64FieldOpsND {
+    override fun Matrix<Float64>.plus(other: Matrix<Float64>): Matrix<Float64> = Floa64FieldOpsND {
         require(shape == other.shape) { "Shape mismatch on Matrix::plus. Expected $shape but found ${other.shape}" }
         asND().plus(other.asND()).as2D()
     }
 
-    override fun Matrix<Double>.minus(other: Matrix<Double>): Matrix<Double> = Floa64FieldOpsND {
+    override fun Matrix<Float64>.minus(other: Matrix<Float64>): Matrix<Float64> = 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<Double>.linearize() = if (this is Float64Buffer) {
+    private fun Buffer<Float64>.linearize() = if (this is Float64Buffer) {
         this.array
     } else {
         DoubleArray(size) { get(it) }
     }
 
     @OptIn(PerformancePitfall::class)
-    override fun Matrix<Double>.dot(other: Matrix<Double>): Matrix<Double> {
+    override fun Matrix<Float64>.dot(other: Matrix<Float64>): Matrix<Float64> {
         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() }
@@ -67,7 +74,7 @@ public object Float64LinearSpace : LinearSpace<Double, Float64Field> {
     }
 
     @OptIn(PerformancePitfall::class)
-    override fun Matrix<Double>.dot(vector: Point<Double>): Float64Buffer {
+    override fun Matrix<Float64>.dot(vector: Point<Float64>): 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
@@ -82,27 +89,27 @@ public object Float64LinearSpace : LinearSpace<Double, Float64Field> {
 
     }
 
-    override fun Matrix<Double>.times(value: Double): Matrix<Double> = Floa64FieldOpsND {
+    override fun Matrix<Float64>.times(value: Double): Matrix<Float64> = Floa64FieldOpsND {
         asND().map { it * value }.as2D()
     }
 
-    public override fun Point<Double>.plus(other: Point<Double>): Float64Buffer = Float64BufferOps.run {
+    public override fun Point<Float64>.plus(other: Point<Float64>): Float64Buffer = Float64BufferOps.run {
         this@plus + other
     }
 
-    public override fun Point<Double>.minus(other: Point<Double>): Float64Buffer = Float64BufferOps.run {
+    public override fun Point<Float64>.minus(other: Point<Float64>): Float64Buffer = Float64BufferOps.run {
         this@minus - other
     }
 
-    public override fun Point<Double>.times(value: Double): Float64Buffer = Float64BufferOps.run {
+    public override fun Point<Float64>.times(value: Double): Float64Buffer = Float64BufferOps.run {
         scale(this@times, value)
     }
 
-    public operator fun Point<Double>.div(value: Double): Float64Buffer = Float64BufferOps.run {
+    public operator fun Point<Float64>.div(value: Double): Float64Buffer = Float64BufferOps.run {
         scale(this@div, 1.0 / value)
     }
 
-    public override fun Double.times(v: Point<Double>): Float64Buffer = v * this
+    public override fun Double.times(v: Point<Float64>): Float64Buffer = v * this
 
 
 }

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LupDecomposition.kt

@@ -163,9 +163,9 @@ public fun <T : Comparable<T>> Field<T>.lup(
 
 
 public fun Field<Float64>.lup(
-    matrix: Matrix<Double>,
+    matrix: Matrix<Float64>,
     singularityThreshold: Double = 1e-11,
-): GenericLupDecomposition<Double> = lup(matrix) { it < singularityThreshold }
+): GenericLupDecomposition<Float64> = 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<Double> =
+public fun LinearSpace<Double, Float64Field>.lupSolver(singularityThreshold: Double = 1e-11): LinearSolver<Float64> =
     lupSolver { it < singularityThreshold }

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/matrixAttributes.kt

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

kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/cumulative.kt

@@ -8,6 +8,7 @@ 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
 
 /**
@@ -47,7 +48,7 @@ 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<Double>.cumulativeSum(): Iterable<Double> = cumulative(0.0) { element, sum -> sum + element }
+public fun Iterable<Float64>.cumulativeSum(): Iterable<Float64> = cumulative(0.0) { element, sum -> sum + element }
 
 @JvmName("cumulativeSumOfInt")
 public fun Iterable<Int>.cumulativeSum(): Iterable<Int> = cumulative(0) { element, sum -> sum + element }
@@ -59,7 +60,7 @@ 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<Double>.cumulativeSum(): Sequence<Double> = cumulative(0.0) { element, sum -> sum + element }
+public fun Sequence<Float64>.cumulativeSum(): Sequence<Float64> = cumulative(0.0) { element, sum -> sum + element }
 
 @JvmName("cumulativeSumOfInt")
 public fun Sequence<Int>.cumulativeSum(): Sequence<Int> = cumulative(0) { element, sum -> sum + element }
@@ -71,7 +72,7 @@ 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<Double>.cumulativeSum(): List<Double> = cumulative(0.0) { element, sum -> sum + element }
+public fun List<Float64>.cumulativeSum(): List<Float64> = cumulative(0.0) { element, sum -> sum + element }
 
 @JvmName("cumulativeSumOfInt")
 public fun List<Int>.cumulativeSum(): List<Int> = cumulative(0) { element, sum -> sum + element }

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Float64FieldND.kt

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

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt

@@ -14,6 +14,7 @@ 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>
@@ -76,8 +77,8 @@ public interface StructureND<out T> : AttributeContainer, WithShape {
 
         @PerformancePitfall
         public fun contentEquals(
-            st1: StructureND<Double>,
-            st2: StructureND<Double>,
+            st1: StructureND<Float64>,
+            st2: StructureND<Float64>,
             tolerance: Double = 1e-11,
         ): Boolean {
             if (st1 === st2) return true
@@ -210,7 +211,7 @@ 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<Double>.get(vararg index: Int): Double = getDouble(index)
+public operator fun StructureND<Float64>.get(vararg index: Int): Double = getDouble(index)
 
 public operator fun StructureND<Int>.get(vararg index: Int): Int = getInt(index)
 

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/VirtualStructureND.kt

@@ -7,6 +7,7 @@ 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,
@@ -24,7 +25,7 @@ public open class VirtualStructureND<T>(
 public class VirtualDoubleStructureND(
     shape: ShapeND,
     producer: (IntArray) -> Double,
-) : VirtualStructureND<Double>(shape, producer)
+) : VirtualStructureND<Float64>(shape, producer)
 
 @UnstableKMathAPI
 public class VirtualIntStructureND(

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/primitiveStructureND.kt

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

kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/BufferAlgebra.kt

@@ -6,6 +6,7 @@
 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
 
@@ -182,7 +183,7 @@ 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<Double> {
+public fun BufferField<Double, *>.buffer(vararg elements: Number): Buffer<Float64> {
     require(elements.size == size) { "Expected $size elements but found ${elements.size}" }
     return elementBufferFactory(size) { elements[it].toDouble() }
 }

kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Float64BufferField.kt

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

kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Float64BufferOps.kt

@@ -14,43 +14,43 @@ import kotlin.math.sqrt
 /**
  * [ExtendedFieldOps] over [Float64Buffer].
  */
-public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, ExtendedFieldOps<Buffer<Double>>,
-    Norm<Buffer<Double>, Double> {
+public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, ExtendedFieldOps<Buffer<Float64>>,
+    Norm<Buffer<Float64>, Double> {
 
     override val elementAlgebra: Float64Field get() = Float64Field
 
-    override val elementBufferFactory: MutableBufferFactory<Double> get() = elementAlgebra.bufferFactory
+    override val elementBufferFactory: MutableBufferFactory<Float64> get() = elementAlgebra.bufferFactory
 
     @Suppress("OVERRIDE_BY_INLINE")
     @OptIn(UnstableKMathAPI::class)
-    final override inline fun Buffer<Double>.map(block: Float64Field.(Double) -> Double): Float64Buffer =
+    final override inline fun Buffer<Float64>.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<Double>.mapIndexed(block: Float64Field.(index: Int, arg: Double) -> Double): Float64Buffer =
+    final override inline fun Buffer<Float64>.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<Double>.zip(
-        other: Buffer<Double>,
+    final override inline fun Buffer<Float64>.zip(
+        other: Buffer<Float64>,
         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<Double>) -> Buffer<Double> =
+    override fun unaryOperationFunction(operation: String): (arg: Buffer<Float64>) -> Buffer<Float64> =
         super<ExtendedFieldOps>.unaryOperationFunction(operation)
 
-    override fun binaryOperationFunction(operation: String): (left: Buffer<Double>, right: Buffer<Double>) -> Buffer<Double> =
+    override fun binaryOperationFunction(operation: String): (left: Buffer<Float64>, right: Buffer<Float64>) -> Buffer<Float64> =
         super<ExtendedFieldOps>.binaryOperationFunction(operation)
 
-    override fun Buffer<Double>.unaryMinus(): Float64Buffer = map { -it }
+    override fun Buffer<Float64>.unaryMinus(): Float64Buffer = map { -it }
 
-    override fun add(left: Buffer<Double>, right: Buffer<Double>): Float64Buffer {
+    override fun add(left: Buffer<Float64>, right: Buffer<Float64>): 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<Double>.plus(arg: Buffer<Double>): Float64Buffer = add(this, arg)
+    override fun Buffer<Float64>.plus(arg: Buffer<Float64>): Float64Buffer = add(this, arg)
 
-    override fun Buffer<Double>.minus(arg: Buffer<Double>): Float64Buffer {
+    override fun Buffer<Float64>.minus(arg: Buffer<Float64>): 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<Double>, k: Number): RealBuffer {
+//    override fun multiply(a: Buffer<Float64>, 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<Double>, k: Number): RealBuffer {
+//    override fun divide(a: Buffer<Float64>, 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<Double>, right: Buffer<Double>): Float64Buffer {
+    override fun multiply(left: Buffer<Float64>, right: Buffer<Float64>): 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<Double>, right: Buffer<Double>): Float64Buffer {
+    override fun divide(left: Buffer<Float64>, right: Buffer<Float64>): 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<Double>): Float64Buffer = arg.map { sin(it) }
+    override fun sin(arg: Buffer<Float64>): Float64Buffer = arg.map { sin(it) }
 
-    override fun cos(arg: Buffer<Double>): Float64Buffer = arg.map { cos(it) }
+    override fun cos(arg: Buffer<Float64>): Float64Buffer = arg.map { cos(it) }
 
-    override fun tan(arg: Buffer<Double>): Float64Buffer = arg.map { tan(it) }
+    override fun tan(arg: Buffer<Float64>): Float64Buffer = arg.map { tan(it) }
 
-    override fun asin(arg: Buffer<Double>): Float64Buffer = arg.map { asin(it) }
+    override fun asin(arg: Buffer<Float64>): Float64Buffer = arg.map { asin(it) }
 
-    override fun acos(arg: Buffer<Double>): Float64Buffer = arg.map { acos(it) }
+    override fun acos(arg: Buffer<Float64>): Float64Buffer = arg.map { acos(it) }
 
-    override fun atan(arg: Buffer<Double>): Float64Buffer = arg.map { atan(it) }
+    override fun atan(arg: Buffer<Float64>): Float64Buffer = arg.map { atan(it) }
 
-    override fun sinh(arg: Buffer<Double>): Float64Buffer = arg.map { sinh(it) }
+    override fun sinh(arg: Buffer<Float64>): Float64Buffer = arg.map { sinh(it) }
 
-    override fun cosh(arg: Buffer<Double>): Float64Buffer = arg.map { cosh(it) }
+    override fun cosh(arg: Buffer<Float64>): Float64Buffer = arg.map { cosh(it) }
 
-    override fun tanh(arg: Buffer<Double>): Float64Buffer = arg.map { tanh(it) }
+    override fun tanh(arg: Buffer<Float64>): Float64Buffer = arg.map { tanh(it) }
 
-    override fun asinh(arg: Buffer<Double>): Float64Buffer = arg.map { asinh(it) }
+    override fun asinh(arg: Buffer<Float64>): Float64Buffer = arg.map { asinh(it) }
 
-    override fun acosh(arg: Buffer<Double>): Float64Buffer = arg.map { acosh(it) }
+    override fun acosh(arg: Buffer<Float64>): Float64Buffer = arg.map { acosh(it) }
 
-    override fun atanh(arg: Buffer<Double>): Float64Buffer = arg.map { atanh(it) }
+    override fun atanh(arg: Buffer<Float64>): Float64Buffer = arg.map { atanh(it) }
 
-    override fun exp(arg: Buffer<Double>): Float64Buffer = arg.map { exp(it) }
+    override fun exp(arg: Buffer<Float64>): Float64Buffer = arg.map { exp(it) }
 
-    override fun ln(arg: Buffer<Double>): Float64Buffer = arg.map { ln(it) }
+    override fun ln(arg: Buffer<Float64>): Float64Buffer = arg.map { ln(it) }
 
-    override fun norm(arg: Buffer<Double>): Double = Float64L2Norm.norm(arg)
+    override fun norm(arg: Buffer<Float64>): Double = Float64L2Norm.norm(arg)
 
-    override fun scale(a: Buffer<Double>, value: Double): Float64Buffer = a.map { it * value }
+    override fun scale(a: Buffer<Float64>, value: Double): Float64Buffer = a.map { it * value }
 
-    override fun power(arg: Buffer<Double>, pow: Number): Buffer<Double> = if (pow is Int) {
+    override fun power(arg: Buffer<Float64>, pow: Number): Buffer<Float64> = 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<Double>, Double> {
-    override fun norm(arg: Point<Double>): Double = sqrt(arg.fold(0.0) { acc: Double, d: Double -> acc + d.pow(2) })
+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 fun Float64BufferOps.sum(buffer: Buffer<Double>): Double = buffer.reduce(Double::plus)
+public fun Float64BufferOps.sum(buffer: Buffer<Float64>): Double = buffer.reduce(Double::plus)
 
 /**
  * Sum of elements using given [conversion]
@@ -173,7 +173,7 @@ public fun Float64BufferOps.sum(buffer: Buffer<Double>): 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<Double>): Double = sum(buffer) / buffer.size
+public fun Float64BufferOps.average(buffer: Buffer<Float64>): Double = sum(buffer) / buffer.size
 
 /**
  * Average of elements using given [conversion]
@@ -181,14 +181,14 @@ public fun Float64BufferOps.average(buffer: Buffer<Double>): Double = sum(buffer
 public inline fun <T> Float64BufferOps.averageOf(buffer: Buffer<T>, conversion: (T) -> Double): Double =
     sumOf(buffer, conversion) / buffer.size
 
-public fun Float64BufferOps.dispersion(buffer: Buffer<Double>): Double {
+public fun Float64BufferOps.dispersion(buffer: Buffer<Float64>): 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<Double>): Double = sqrt(dispersion(buffer))
+public fun Float64BufferOps.std(buffer: Buffer<Float64>): Double = sqrt(dispersion(buffer))
 
-public fun Float64BufferOps.covariance(x: Buffer<Double>, y: Buffer<Double>): Double {
+public fun Float64BufferOps.covariance(x: Buffer<Float64>, y: Buffer<Float64>): 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)

kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/numbers.kt

@@ -4,6 +4,7 @@
  */
 package space.kscience.kmath.operations
 
+import space.kscience.kmath.structures.Float64
 import space.kscience.kmath.structures.MutableBufferFactory
 import kotlin.math.pow as kpow
 
@@ -66,8 +67,8 @@ 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<Double>, Norm<Double, Double>, ScaleOperations<Double> {
-    override val bufferFactory: MutableBufferFactory<Double> = MutableBufferFactory()
+public object Float64Field : ExtendedField<Float64>, Norm<Double, Double>, ScaleOperations<Float64> {
+    override val bufferFactory: MutableBufferFactory<Float64> = MutableBufferFactory()
 
     override val zero: Double get() = 0.0
     override val one: Double get() = 1.0

kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt

@@ -115,7 +115,7 @@ public fun <T> Buffer(
     size: Int,
     initializer: (Int) -> T,
 ): Buffer<T> = when (type.kType) {
-    typeOf<Double>() -> MutableBuffer.double(size) { initializer(it) as Double } as Buffer<T>
+    typeOf<Float64>() -> 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<Double>() -> MutableBuffer.double(size) { initializer(it) as Double } as Buffer<T>
+        typeOf<Float64>() -> 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>

kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Float64Buffer.kt

@@ -14,7 +14,7 @@ import kotlin.jvm.JvmInline
  * @property array the underlying array.
  */
 @JvmInline
-public value class Float64Buffer(public val array: DoubleArray) : PrimitiveBuffer<Double> {
+public value class Float64Buffer(public val array: DoubleArray) : PrimitiveBuffer<Float64> {
 
     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<Double>.toDoubleArray(): DoubleArray = when (this) {
+public fun Buffer<Float64>.toDoubleArray(): DoubleArray = when (this) {
     is Float64Buffer -> array
     else -> DoubleArray(size, ::get)
 }
@@ -62,7 +62,7 @@ public fun Buffer<Double>.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<Double>.toFloat64Buffer(): Float64Buffer = when (this) {
+public fun Buffer<Float64>.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<Double>): Float64Buffer = arg.toFloat64Buffer()
+    override fun transform(arg: Buffer<Float64>): Float64Buffer = arg.toFloat64Buffer()
 }

kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/MutableBuffer.kt

@@ -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<Double>() -> MutableBuffer.double(size) { initializer(it) as Double } as MutableBuffer<T>
+    typeOf<Float64>() -> MutableBuffer.double(size) { initializer(it) as Double } as MutableBuffer<T>
     //TODO add unsigned types
     else -> MutableListBuffer(MutableList(size, initializer))
 }

kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/bufferPrimitiveAccess.kt

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

kmath-core/src/commonTest/kotlin/space/kscience/kmath/expressions/ExpressionFieldTest.kt

@@ -6,6 +6,7 @@
 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
@@ -37,7 +38,7 @@ class ExpressionFieldTest {
 
     @Test
     fun valueExpression() {
-        val expressionBuilder: FunctionalExpressionField<Double, *>.() -> Expression<Double> = {
+        val expressionBuilder: FunctionalExpressionField<Double, *>.() -> Expression<Float64> = {
             val x by binding
             x * x + 2 * x + one
         }

kmath-core/src/commonTest/kotlin/space/kscience/kmath/expressions/SimpleAutoDiffTest.kt

@@ -8,6 +8,7 @@ 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
@@ -21,18 +22,18 @@ internal class SimpleAutoDiffTest {
 
     fun dx(
         xBinding: Pair<Symbol, Double>,
-        body: SimpleAutoDiffField<Double, Float64Field>.(x: AutoDiffValue<Double>) -> AutoDiffValue<Double>,
-    ): DerivationResult<Double> = Float64Field.simpleAutoDiff(xBinding) { body(bindSymbol(xBinding.first)) }
+        body: SimpleAutoDiffField<Double, Float64Field>.(x: AutoDiffValue<Float64>) -> AutoDiffValue<Float64>,
+    ): DerivationResult<Float64> = Float64Field.simpleAutoDiff(xBinding) { body(bindSymbol(xBinding.first)) }
 
     fun dxy(
         xBinding: Pair<Symbol, Double>,
         yBinding: Pair<Symbol, Double>,
-        body: SimpleAutoDiffField<Double, Float64Field>.(x: AutoDiffValue<Double>, y: AutoDiffValue<Double>) -> AutoDiffValue<Double>,
-    ): DerivationResult<Double> = Float64Field.simpleAutoDiff(xBinding, yBinding) {
+        body: SimpleAutoDiffField<Double, Float64Field>.(x: AutoDiffValue<Float64>, y: AutoDiffValue<Float64>) -> AutoDiffValue<Float64>,
+    ): DerivationResult<Float64> = Float64Field.simpleAutoDiff(xBinding, yBinding) {
         body(bindSymbol(xBinding.first), bindSymbol(yBinding.first))
     }
 
-    fun diff(block: SimpleAutoDiffField<Double, Float64Field>.() -> AutoDiffValue<Double>): SimpleAutoDiffExpression<Double, Float64Field> {
+    fun diff(block: SimpleAutoDiffField<Double, Float64Field>.() -> AutoDiffValue<Float64>): SimpleAutoDiffExpression<Double, Float64Field> {
         return SimpleAutoDiffExpression(Float64Field, block)
     }
 

kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/MatrixTest.kt

@@ -10,6 +10,7 @@ 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
@@ -38,7 +39,7 @@ class MatrixTest {
 
     @Test
     fun testMatrixExtension() = Double.algebra.linearSpace.run {
-        val transitionMatrix: Matrix<Double> = VirtualMatrix(6, 6) { row, col ->
+        val transitionMatrix: Matrix<Float64> = VirtualMatrix(6, 6) { row, col ->
             when {
                 col == 0 -> .50
                 row + 1 == col -> .50
@@ -47,7 +48,7 @@ class MatrixTest {
             }
         }
 
-        infix fun Matrix<Double>.pow(power: Int): Matrix<Double> {
+        infix fun Matrix<Float64>.pow(power: Int): Matrix<Float64> {
             var res = this
             repeat(power - 1) {
                 res = res dot this@pow

kmath-core/src/jvmMain/kotlin/space/kscience/kmath/linear/Float64ParallelLinearSpace.kt

@@ -11,6 +11,7 @@ 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
@@ -25,7 +26,7 @@ public object Float64ParallelLinearSpace : LinearSpace<Double, Float64Field> {
         rows: Int,
         columns: Int,
         initializer: Float64Field.(i: Int, j: Int) -> Double,
-    ): Matrix<Double> {
+    ): Matrix<Float64> {
         val shape = ShapeND(rows, columns)
         val indexer = BufferAlgebraND.defaultIndexerBuilder(shape)
 
@@ -43,29 +44,29 @@ 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<Double>.unaryMinus(): Matrix<Double> = Floa64FieldOpsND {
+    override fun Matrix<Float64>.unaryMinus(): Matrix<Float64> = Floa64FieldOpsND {
         asND().map { -it }.as2D()
     }
 
-    override fun Matrix<Double>.plus(other: Matrix<Double>): Matrix<Double> = Floa64FieldOpsND {
+    override fun Matrix<Float64>.plus(other: Matrix<Float64>): Matrix<Float64> = Floa64FieldOpsND {
         require(shape == other.shape) { "Shape mismatch on Matrix::plus. Expected $shape but found ${other.shape}" }
         asND().plus(other.asND()).as2D()
     }
 
-    override fun Matrix<Double>.minus(other: Matrix<Double>): Matrix<Double> = Floa64FieldOpsND {
+    override fun Matrix<Float64>.minus(other: Matrix<Float64>): Matrix<Float64> = 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<Double>.linearize() = if (this is Float64Buffer) {
+    private fun Buffer<Float64>.linearize() = if (this is Float64Buffer) {
         this.array
     } else {
         DoubleArray(size) { get(it) }
     }
 
     @OptIn(PerformancePitfall::class)
-    override fun Matrix<Double>.dot(other: Matrix<Double>): Matrix<Double> {
+    override fun Matrix<Float64>.dot(other: Matrix<Float64>): Matrix<Float64> {
         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() }
@@ -82,7 +83,7 @@ public object Float64ParallelLinearSpace : LinearSpace<Double, Float64Field> {
     }
 
     @OptIn(PerformancePitfall::class)
-    override fun Matrix<Double>.dot(vector: Point<Double>): Float64Buffer {
+    override fun Matrix<Float64>.dot(vector: Point<Float64>): 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
@@ -97,27 +98,27 @@ public object Float64ParallelLinearSpace : LinearSpace<Double, Float64Field> {
 
     }
 
-    override fun Matrix<Double>.times(value: Double): Matrix<Double> = Floa64FieldOpsND {
+    override fun Matrix<Float64>.times(value: Double): Matrix<Float64> = Floa64FieldOpsND {
         asND().map { it * value }.as2D()
     }
 
-    public override fun Point<Double>.plus(other: Point<Double>): Float64Buffer = Float64BufferOps.run {
+    public override fun Point<Float64>.plus(other: Point<Float64>): Float64Buffer = Float64BufferOps.run {
         this@plus + other
     }
 
-    public override fun Point<Double>.minus(other: Point<Double>): Float64Buffer = Float64BufferOps.run {
+    public override fun Point<Float64>.minus(other: Point<Float64>): Float64Buffer = Float64BufferOps.run {
         this@minus - other
     }
 
-    public override fun Point<Double>.times(value: Double): Float64Buffer = Float64BufferOps.run {
+    public override fun Point<Float64>.times(value: Double): Float64Buffer = Float64BufferOps.run {
         scale(this@times, value)
     }
 
-    public operator fun Point<Double>.div(value: Double): Float64Buffer = Float64BufferOps.run {
+    public operator fun Point<Float64>.div(value: Double): Float64Buffer = Float64BufferOps.run {
         scale(this@div, 1.0 / value)
     }
 
-    public override fun Double.times(v: Point<Double>): Float64Buffer = v * this
+    public override fun Double.times(v: Point<Float64>): Float64Buffer = v * this
 
 
 }

kmath-core/src/jvmMain/kotlin/space/kscience/kmath/structures/parallelMutableBuffer.kt

@@ -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<Double>() -> IntStream.range(0, size).parallel().mapToDouble { initializer(it) as Float64 }.toArray()
+    typeOf<Float64>() -> 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>())

kmath-core/src/jvmTest/kotlin/space/kscience/kmath/linear/ParallelMatrixTest.kt

@@ -9,6 +9,7 @@ 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
@@ -37,7 +38,7 @@ class ParallelMatrixTest {
 
     @Test
     fun testMatrixExtension() = Float64Field.linearSpace.parallel {
-        val transitionMatrix: Matrix<Double> = VirtualMatrix(6, 6) { row, col ->
+        val transitionMatrix: Matrix<Float64> = VirtualMatrix(6, 6) { row, col ->
             when {
                 col == 0 -> .50
                 row + 1 == col -> .50
@@ -46,7 +47,7 @@ class ParallelMatrixTest {
             }
         }
 
-        infix fun Matrix<Double>.pow(power: Int): Matrix<Double> {
+        infix fun Matrix<Float64>.pow(power: Int): Matrix<Float64> {
             var res = this
             repeat(power - 1) {
                 res = res dot this@pow

kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/BlockingChain.kt

@@ -40,13 +40,13 @@ public interface BlockingBufferChain<out T> : BlockingChain<T>, BufferChain<T> {
 }
 
 
-public suspend inline fun <reified T : Any> Chain<T>.nextBuffer(size: Int): Buffer<T> = if (this is BufferChain) {
+public suspend inline fun <reified T> Chain<T>.nextBuffer(size: Int): Buffer<T> = if (this is BufferChain) {
     nextBuffer(size)
 } else {
     Buffer(size) { next() }
 }
 
-public inline fun <reified T : Any> BlockingChain<T>.nextBufferBlocking(
+public inline fun <reified T> BlockingChain<T>.nextBufferBlocking(
     size: Int,
 ): Buffer<T> = if (this is BlockingBufferChain) {
     nextBufferBlocking(size)

kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/BlockingDoubleChain.kt

@@ -5,12 +5,13 @@
 
 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<Double> {
+public interface BlockingDoubleChain : BlockingBufferChain<Float64> {
 
     /**
      * Returns an [DoubleArray] chunk of [size] values of [next].

kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/Chain.kt

@@ -23,7 +23,7 @@ public interface Chain<out T> : Flow<T> {
     public suspend fun next(): T
 
     /**
-     * Create a copy of current chain state. Consuming resulting chain does not affect initial chain.
+     * Create a copy of the current chain state. Consuming the resulting chain does not affect the initial chain.
      */
     public suspend fun fork(): Chain<T>
 
@@ -47,7 +47,7 @@ public class SimpleChain<out R>(private val gen: suspend () -> R) : Chain<R> {
 /**
  * A stateless Markov chain
  */
-public class MarkovChain<out R : Any>(private val seed: suspend () -> R, private val gen: suspend (R) -> R) : Chain<R> {
+public class MarkovChain<out R>(private val seed: suspend () -> R, private val gen: suspend (R) -> R) : Chain<R> {
     private val mutex: Mutex = Mutex()
     private var value: R? = null
 
@@ -71,8 +71,8 @@ public class MarkovChain<out R : Any>(private val seed: suspend () -> R, private
  */
 public class StatefulChain<S, out R>(
     private val state: S,
-    private val seed: S.() -> R,
-    private val forkState: ((S) -> S),
+    private val seed: suspend S.() -> R,
+    private val forkState: suspend ((S) -> S),
     private val gen: suspend S.(R) -> R,
 ) : Chain<R> {
     private val mutex: Mutex = Mutex()
@@ -97,6 +97,11 @@ public class ConstantChain<out T>(public val value: T) : Chain<T> {
     override suspend fun fork(): Chain<T> = this
 }
 
+/**
+ * Discard a fixed number of samples
+ */
+public suspend inline fun <reified T> Chain<T>.discard(number: Int): Chain<T> = apply { nextBuffer<T>(number) }
+
 /**
  * Map the chain result using suspended transformation. Initial chain result can no longer be safely consumed
  * since mapped chain consumes tokens. Accepts regular transformation function.

kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/streaming/BufferFlow.kt

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

kmath-ejml/build.gradle.kts

@@ -1,15 +1,20 @@
 plugins {
-    id("space.kscience.gradle.jvm")
+    id("space.kscience.gradle.mpp")
 }
 
 val ejmlVerision = "0.43.1"
 
-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)
+kscience {
+    jvm()
+    jvmMain {
+        api(projects.kmathCore)
+        api(projects.kmathComplex)
+        api("org.ejml:ejml-all:$ejmlVerision")
+    }
+
+    jvmTest {
+        implementation(projects.testUtils)
+    }
 }
 
 readme {
@@ -30,12 +35,4 @@ 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))
-//}
+}

kmath-ejml/src/jvmMain/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt

@@ -6,9 +6,13 @@
 package space.kscience.kmath.ejml
 
 import space.kscience.kmath.UnstableKMathAPI
-import space.kscience.kmath.linear.*
-import space.kscience.kmath.operations.Float64Field
+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.operations.Ring
+import space.kscience.kmath.structures.Float64
 
 /**
  * [LinearSpace] implementation specialized for a certain EJML type.
@@ -38,6 +42,6 @@ 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 EjmlMatrix<T, *>.inverted(): Matrix<Double> =
-        computeAttribute(this, Float64Field.linearSpace.Inverted)!!
+    public fun Structure2D<T>.inverted(): Matrix<Float64> =
+        computeAttribute(this, Inverted()) ?: error("Can't invert matrix")
 }

kmath-ejml/src/jvmMain/kotlin/space/kscience/kmath/ejml/implementations.kt

@@ -12,6 +12,8 @@ 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
@@ -19,6 +21,7 @@ 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.complex.Complex
 import space.kscience.kmath.linear.*
 import space.kscience.kmath.linear.Matrix
 import space.kscience.kmath.nd.Structure2D
@@ -27,9 +30,15 @@ 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].
  */
@@ -79,16 +88,16 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
      */
     override val elementAlgebra: Float64Field get() = Float64Field
 
-    override val type: SafeType<Double> get() = safeTypeOf()
+    override val type: SafeType<Float64> get() = safeTypeOf()
 
     @Suppress("UNCHECKED_CAST")
-    override fun Matrix<Double>.toEjml(): EjmlDoubleMatrix<DMatrixRMaj> = when {
+    override fun Matrix<Float64>.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<Double>.toEjml(): EjmlDoubleVector<DMatrixRMaj> = when {
+    override fun Point<Float64>.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) }
@@ -115,21 +124,21 @@ 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<Double>.unaryMinus(): Matrix<Double> = this * elementAlgebra { -one }
+    override fun Matrix<Float64>.unaryMinus(): Matrix<Float64> = this * elementAlgebra { -one }
 
-    override fun Matrix<Double>.dot(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixRMaj> {
+    override fun Matrix<Float64>.dot(other: Matrix<Float64>): EjmlDoubleMatrix<DMatrixRMaj> {
         val out = DMatrixRMaj(1, 1)
         CommonOps_DDRM.mult(toEjml().origin, other.toEjml().origin, out)
         return out.wrapMatrix()
     }
 
-    override fun Matrix<Double>.dot(vector: Point<Double>): EjmlDoubleVector<DMatrixRMaj> {
+    override fun Matrix<Float64>.dot(vector: Point<Float64>): EjmlDoubleVector<DMatrixRMaj> {
         val out = DMatrixRMaj(1, 1)
         CommonOps_DDRM.mult(toEjml().origin, vector.toEjml().origin, out)
         return out.wrapVector()
     }
 
-    override operator fun Matrix<Double>.minus(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixRMaj> {
+    override operator fun Matrix<Float64>.minus(other: Matrix<Float64>): EjmlDoubleMatrix<DMatrixRMaj> {
         val out = DMatrixRMaj(1, 1)
 
         CommonOps_DDRM.add(
@@ -143,19 +152,19 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
         return out.wrapMatrix()
     }
 
-    override operator fun Matrix<Double>.times(value: Double): EjmlDoubleMatrix<DMatrixRMaj> {
+    override operator fun Matrix<Float64>.times(value: Double): EjmlDoubleMatrix<DMatrixRMaj> {
         val res = DMatrixRMaj(1, 1)
         CommonOps_DDRM.scale(value, toEjml().origin, res)
         return res.wrapMatrix()
     }
 
-    override fun Point<Double>.unaryMinus(): EjmlDoubleVector<DMatrixRMaj> {
+    override fun Point<Float64>.unaryMinus(): EjmlDoubleVector<DMatrixRMaj> {
         val res = DMatrixRMaj(1, 1)
         CommonOps_DDRM.changeSign(toEjml().origin, res)
         return res.wrapVector()
     }
 
-    override fun Matrix<Double>.plus(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixRMaj> {
+    override fun Matrix<Float64>.plus(other: Matrix<Float64>): EjmlDoubleMatrix<DMatrixRMaj> {
         val out = DMatrixRMaj(1, 1)
 
         CommonOps_DDRM.add(
@@ -169,7 +178,7 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
         return out.wrapMatrix()
     }
 
-    override fun Point<Double>.plus(other: Point<Double>): EjmlDoubleVector<DMatrixRMaj> {
+    override fun Point<Float64>.plus(other: Point<Float64>): EjmlDoubleVector<DMatrixRMaj> {
         val out = DMatrixRMaj(1, 1)
 
         CommonOps_DDRM.add(
@@ -183,7 +192,7 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
         return out.wrapVector()
     }
 
-    override fun Point<Double>.minus(other: Point<Double>): EjmlDoubleVector<DMatrixRMaj> {
+    override fun Point<Float64>.minus(other: Point<Float64>): EjmlDoubleVector<DMatrixRMaj> {
         val out = DMatrixRMaj(1, 1)
 
         CommonOps_DDRM.add(
@@ -197,18 +206,18 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
         return out.wrapVector()
     }
 
-    override fun Double.times(m: Matrix<Double>): EjmlDoubleMatrix<DMatrixRMaj> = m * this
+    override fun Double.times(m: Matrix<Float64>): EjmlDoubleMatrix<DMatrixRMaj> = m * this
 
-    override fun Point<Double>.times(value: Double): EjmlDoubleVector<DMatrixRMaj> {
+    override fun Point<Float64>.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<Double>): EjmlDoubleVector<DMatrixRMaj> = v * this
+    override fun Double.times(v: Point<Float64>): EjmlDoubleVector<DMatrixRMaj> = v * this
 
-    override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Double>, attribute: A): V? {
-        val origin = structure.toEjml().origin
+    override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Float64>, attribute: A): V? {
+        val origin: DMatrixRMaj = structure.toEjml().origin
 
         val raw: Any? = when (attribute) {
             Inverted -> {
@@ -218,28 +227,28 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
             }
 
             Determinant -> CommonOps_DDRM.det(origin)
-            SVD -> object : SingularValueDecomposition<Double> {
+            SVD -> object : SingularValueDecomposition<Float64> {
                 val ejmlSvd by lazy {
                     DecompositionFactory_DDRM
                         .svd(origin.numRows, origin.numCols, true, true, false)
                         .apply { decompose(origin.copy()) }
                 }
-                override val u: Matrix<Double> get() = ejmlSvd.getU(null, false).wrapMatrix()
+                override val u: Matrix<Float64> get() = ejmlSvd.getU(null, false).wrapMatrix()
 
-                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()
+                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()
 
             }
 
-            QR -> object : QRDecomposition<Double> {
+            QR -> object : QRDecomposition<Float64> {
                 val ejmlQr by lazy { DecompositionFactory_DDRM.qr().apply { decompose(origin.copy()) } }
-                override val q: Matrix<Double> get() = ejmlQr.getQ(null, false).wrapMatrix()
-                override val r: Matrix<Double> get() = ejmlQr.getR(null, false).wrapMatrix()
+                override val q: Matrix<Float64> get() = ejmlQr.getQ(null, false).wrapMatrix()
+                override val r: Matrix<Float64> get() = ejmlQr.getR(null, false).wrapMatrix()
             }
 
-            Cholesky -> object : CholeskyDecomposition<Double> {
-                override val l: Matrix<Double> by lazy {
+            Cholesky -> object : CholeskyDecomposition<Float64> {
+                override val l: Matrix<Float64> by lazy {
                     val cholesky =
                         DecompositionFactory_DDRM.chol(structure.rowNum, true).apply { decompose(origin.copy()) }
 
@@ -247,20 +256,49 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
                 }
             }
 
-            LUP -> object : LupDecomposition<Double> {
+            LUP -> object : LupDecomposition<Float64> {
                 private val lup by lazy {
                     DecompositionFactory_DDRM.lu(origin.numRows, origin.numCols).apply { decompose(origin.copy()) }
                 }
 
-                override val l: Matrix<Double>
+                override val l: Matrix<Float64>
                     get() = lup.getLower(null).wrapMatrix().withAttribute(LowerTriangular)
 
-
-                override val u: Matrix<Double>
+                override val u: Matrix<Float64>
                     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
         }
 
@@ -275,7 +313,7 @@ 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<Double>, b: Matrix<Double>): EjmlDoubleMatrix<DMatrixRMaj> {
+    public fun solve(a: Matrix<Float64>, b: Matrix<Float64>): EjmlDoubleMatrix<DMatrixRMaj> {
         val res = DMatrixRMaj(1, 1)
         CommonOps_DDRM.solve(DMatrixRMaj(a.toEjml().origin), DMatrixRMaj(b.toEjml().origin), res)
         return res.wrapMatrix()
@@ -288,7 +326,7 @@ 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<Double>, b: Point<Double>): EjmlDoubleVector<DMatrixRMaj> {
+    public fun solve(a: Matrix<Float64>, b: Point<Float64>): EjmlDoubleVector<DMatrixRMaj> {
         val res = DMatrixRMaj(1, 1)
         CommonOps_DDRM.solve(DMatrixRMaj(a.toEjml().origin), DMatrixRMaj(b.toEjml().origin), res)
         return EjmlDoubleVector(res)
@@ -488,6 +526,35 @@ 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
         }
 
@@ -533,16 +600,16 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
      */
     override val elementAlgebra: Float64Field get() = Float64Field
 
-    override val type: SafeType<Double> get() = safeTypeOf()
+    override val type: SafeType<Float64> get() = safeTypeOf()
 
     @Suppress("UNCHECKED_CAST")
-    override fun Matrix<Double>.toEjml(): EjmlDoubleMatrix<DMatrixSparseCSC> = when {
+    override fun Matrix<Float64>.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<Double>.toEjml(): EjmlDoubleVector<DMatrixSparseCSC> = when {
+    override fun Point<Float64>.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) }
@@ -569,21 +636,21 @@ 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<Double>.unaryMinus(): Matrix<Double> = this * elementAlgebra { -one }
+    override fun Matrix<Float64>.unaryMinus(): Matrix<Float64> = this * elementAlgebra { -one }
 
-    override fun Matrix<Double>.dot(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixSparseCSC> {
+    override fun Matrix<Float64>.dot(other: Matrix<Float64>): EjmlDoubleMatrix<DMatrixSparseCSC> {
         val out = DMatrixSparseCSC(1, 1)
         CommonOps_DSCC.mult(toEjml().origin, other.toEjml().origin, out)
         return out.wrapMatrix()
     }
 
-    override fun Matrix<Double>.dot(vector: Point<Double>): EjmlDoubleVector<DMatrixSparseCSC> {
+    override fun Matrix<Float64>.dot(vector: Point<Float64>): EjmlDoubleVector<DMatrixSparseCSC> {
         val out = DMatrixSparseCSC(1, 1)
         CommonOps_DSCC.mult(toEjml().origin, vector.toEjml().origin, out)
         return out.wrapVector()
     }
 
-    override operator fun Matrix<Double>.minus(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixSparseCSC> {
+    override operator fun Matrix<Float64>.minus(other: Matrix<Float64>): EjmlDoubleMatrix<DMatrixSparseCSC> {
         val out = DMatrixSparseCSC(1, 1)
 
         CommonOps_DSCC.add(
@@ -599,19 +666,19 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
         return out.wrapMatrix()
     }
 
-    override operator fun Matrix<Double>.times(value: Double): EjmlDoubleMatrix<DMatrixSparseCSC> {
+    override operator fun Matrix<Float64>.times(value: Double): EjmlDoubleMatrix<DMatrixSparseCSC> {
         val res = DMatrixSparseCSC(1, 1)
         CommonOps_DSCC.scale(value, toEjml().origin, res)
         return res.wrapMatrix()
     }
 
-    override fun Point<Double>.unaryMinus(): EjmlDoubleVector<DMatrixSparseCSC> {
+    override fun Point<Float64>.unaryMinus(): EjmlDoubleVector<DMatrixSparseCSC> {
         val res = DMatrixSparseCSC(1, 1)
         CommonOps_DSCC.changeSign(toEjml().origin, res)
         return res.wrapVector()
     }
 
-    override fun Matrix<Double>.plus(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixSparseCSC> {
+    override fun Matrix<Float64>.plus(other: Matrix<Float64>): EjmlDoubleMatrix<DMatrixSparseCSC> {
         val out = DMatrixSparseCSC(1, 1)
 
         CommonOps_DSCC.add(
@@ -627,7 +694,7 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
         return out.wrapMatrix()
     }
 
-    override fun Point<Double>.plus(other: Point<Double>): EjmlDoubleVector<DMatrixSparseCSC> {
+    override fun Point<Float64>.plus(other: Point<Float64>): EjmlDoubleVector<DMatrixSparseCSC> {
         val out = DMatrixSparseCSC(1, 1)
 
         CommonOps_DSCC.add(
@@ -643,7 +710,7 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
         return out.wrapVector()
     }
 
-    override fun Point<Double>.minus(other: Point<Double>): EjmlDoubleVector<DMatrixSparseCSC> {
+    override fun Point<Float64>.minus(other: Point<Float64>): EjmlDoubleVector<DMatrixSparseCSC> {
         val out = DMatrixSparseCSC(1, 1)
 
         CommonOps_DSCC.add(
@@ -659,17 +726,17 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
         return out.wrapVector()
     }
 
-    override fun Double.times(m: Matrix<Double>): EjmlDoubleMatrix<DMatrixSparseCSC> = m * this
+    override fun Double.times(m: Matrix<Float64>): EjmlDoubleMatrix<DMatrixSparseCSC> = m * this
 
-    override fun Point<Double>.times(value: Double): EjmlDoubleVector<DMatrixSparseCSC> {
+    override fun Point<Float64>.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<Double>): EjmlDoubleVector<DMatrixSparseCSC> = v * this
+    override fun Double.times(v: Point<Float64>): EjmlDoubleVector<DMatrixSparseCSC> = v * this
 
-    override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Double>, attribute: A): V? {
+    override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Float64>, attribute: A): V? {
         val origin = structure.toEjml().origin
 
         val raw: Any? = when (attribute) {
@@ -681,16 +748,16 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
 
             Determinant -> CommonOps_DSCC.det(origin)
 
-            QR -> object : QRDecomposition<Double> {
+            QR -> object : QRDecomposition<Float64> {
                 val ejmlQr by lazy {
                     DecompositionFactory_DSCC.qr(FillReducing.NONE).apply { decompose(origin.copy()) }
                 }
-                override val q: Matrix<Double> get() = ejmlQr.getQ(null, false).wrapMatrix()
-                override val r: Matrix<Double> get() = ejmlQr.getR(null, false).wrapMatrix()
+                override val q: Matrix<Float64> get() = ejmlQr.getQ(null, false).wrapMatrix()
+                override val r: Matrix<Float64> get() = ejmlQr.getR(null, false).wrapMatrix()
             }
 
-            Cholesky -> object : CholeskyDecomposition<Double> {
-                override val l: Matrix<Double> by lazy {
+            Cholesky -> object : CholeskyDecomposition<Float64> {
+                override val l: Matrix<Float64> by lazy {
                     val cholesky =
                         DecompositionFactory_DSCC.cholesky().apply { decompose(origin.copy()) }
 
@@ -698,16 +765,16 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
                 }
             }
 
-            LUP -> object : LupDecomposition<Double> {
+            LUP -> object : LupDecomposition<Float64> {
                 private val lup by lazy {
                     DecompositionFactory_DSCC.lu(FillReducing.NONE).apply { decompose(origin.copy()) }
                 }
 
-                override val l: Matrix<Double>
+                override val l: Matrix<Float64>
                     get() = lup.getLower(null).wrapMatrix().withAttribute(LowerTriangular)
 
 
-                override val u: Matrix<Double>
+                override val u: Matrix<Float64>
                     get() = lup.getUpper(null).wrapMatrix().withAttribute(UpperTriangular)
                 override val pivot: IntBuffer get() = lup.getRowPivotV(null).asBuffer()
             }
@@ -726,7 +793,7 @@ 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<Double>, b: Matrix<Double>): EjmlDoubleMatrix<DMatrixSparseCSC> {
+    public fun solve(a: Matrix<Float64>, b: Matrix<Float64>): EjmlDoubleMatrix<DMatrixSparseCSC> {
         val res = DMatrixSparseCSC(1, 1)
         CommonOps_DSCC.solve(DMatrixSparseCSC(a.toEjml().origin), DMatrixSparseCSC(b.toEjml().origin), res)
         return res.wrapMatrix()
@@ -739,7 +806,7 @@ 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<Double>, b: Point<Double>): EjmlDoubleVector<DMatrixSparseCSC> {
+    public fun solve(a: Matrix<Float64>, b: Point<Float64>): EjmlDoubleVector<DMatrixSparseCSC> {
         val res = DMatrixSparseCSC(1, 1)
         CommonOps_DSCC.solve(DMatrixSparseCSC(a.toEjml().origin), DMatrixSparseCSC(b.toEjml().origin), res)
         return EjmlDoubleVector(res)

kmath-ejml/src/jvmTest/kotlin/space/kscience/kmath/ejml/EjmlMatrixTest.kt

@@ -17,12 +17,16 @@ 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>) {
-    assertTrue { StructureND.contentEquals(expected, actual) }
+    expected.elements().forEach { (index, value) ->
+        assertEquals(value, actual[index], "Structure element with index ${index.toList()} should be equal to $value but is ${actual[index]}")
+    }
 }
 
 @OptIn(UnstableKMathAPI::class)
@@ -63,7 +67,7 @@ internal class EjmlMatrixTest {
         val w = EjmlDoubleMatrix(m)
         val det: Double = w.getOrComputeAttribute(Determinant) ?: fail()
         assertEquals(CommonOps_DDRM.det(m), det)
-        val lup: LupDecomposition<Double> = w.getOrComputeAttribute(LUP) ?: fail()
+        val lup: LupDecomposition<Float64> = w.getOrComputeAttribute(LUP) ?: fail()
 
         val ludecompositionF64 = DecompositionFactory_DDRM.lu(m.numRows, m.numCols)
             .also { it.decompose(m.copy()) }
@@ -104,4 +108,15 @@ 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())
+    }
 }

kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/DoubleVector.kt

@@ -9,12 +9,13 @@ 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<Double>
+public typealias DoubleVector = Point<Float64>
 
 public fun DoubleVector(vararg doubles: Double): DoubleVector = doubles.asBuffer()
 
@@ -41,7 +42,7 @@ 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<Double> = map { -it }
+public operator fun DoubleVector.unaryMinus(): Buffer<Float64> = map { -it }
 
 public operator fun DoubleVector.minus(other: DoubleVector): DoubleVector {
     require(size == other.size) { "Vector size $size expected but ${other.size} found" }

kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/RealMatrix.kt

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

kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/dot.kt

@@ -8,11 +8,12 @@ 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<Double>.dot(other: Matrix<Double>): Matrix<Double> = Double.algebra.linearSpace.run {
+public infix fun Matrix<Float64>.dot(other: Matrix<Float64>): Matrix<Float64> = Double.algebra.linearSpace.run {
     this@dot dot other
 }

kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/grids.kt

@@ -7,16 +7,17 @@ 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<Double>.length: Double get() = endInclusive - start
+public val ClosedFloatingPointRange<Float64>.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<Double>, numberOfPoints: Int): Float64Buffer {
+public fun Buffer.Companion.fromRange(range: ClosedFloatingPointRange<Float64>, numberOfPoints: Int): Float64Buffer {
     require(numberOfPoints >= 2) { "Number of points in grid must be more than 1" }
     val normalizedRange = when {
         range.endInclusive > range.start -> range
@@ -31,7 +32,7 @@ public fun Buffer.Companion.fromRange(range: ClosedFloatingPointRange<Double>, n
  * 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<Double>, step: Double): Float64Buffer {
+public fun Buffer.Companion.withFixedStep(range: ClosedFloatingPointRange<Float64>, step: Double): Float64Buffer {
     require(step > 0) { "The grid step must be positive" }
     val normalizedRange = when {
         range.endInclusive > range.start -> range
@@ -51,4 +52,4 @@ public fun Buffer.Companion.withFixedStep(range: ClosedFloatingPointRange<Double
  * If step is negative, the same goes from upper boundary downwards
  */
 @UnstableKMathAPI
-public infix fun ClosedFloatingPointRange<Double>.step(step: Double): Float64Buffer = Buffer.withFixedStep(this, step)
+public infix fun ClosedFloatingPointRange<Float64>.step(step: Double): Float64Buffer = Buffer.withFixedStep(this, step)

kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/realND.kt

@@ -7,12 +7,13 @@ 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<Double>.mapInline(crossinline transform: Float64Field.(Double) -> Double): BufferND<Double> {
+public inline fun BufferND<Float64>.mapInline(crossinline transform: Float64Field.(Double) -> Double): BufferND<Float64> {
     val array = DoubleArray(indices.linearSize) { offset -> Float64Field.transform(buffer[offset]) }
     return BufferND(indices, Float64Buffer(array))
 }
@@ -20,7 +21,7 @@ public inline fun BufferND<Double>.mapInline(crossinline transform: Float64Field
 /**
  * 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<Double>): BufferND<Double> =
+public operator fun Function1<Double, Double>.invoke(elementND: BufferND<Float64>): BufferND<Float64> =
     elementND.mapInline { this@invoke(it) }
 
 /* plus and minus */
@@ -28,9 +29,9 @@ public operator fun Function1<Double, Double>.invoke(elementND: BufferND<Double>
 /**
  * Summation operation for [BufferND] and single element
  */
-public operator fun BufferND<Double>.plus(arg: Double): BufferND<Double> = mapInline { it + arg }
+public operator fun BufferND<Float64>.plus(arg: Double): BufferND<Float64> = mapInline { it + arg }
 
 /**
  * Subtraction operation between [BufferND] and single element
  */
-public operator fun BufferND<Double>.minus(arg: Double): BufferND<Double> = mapInline { it - arg }
+public operator fun BufferND<Float64>.minus(arg: Double): BufferND<Float64> = mapInline { it - arg }

kmath-for-real/src/commonTest/kotlin/space/kscience/kmath/real/DoubleMatrixTest.kt

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

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/polynomialUtil.kt

@@ -7,6 +7,7 @@ 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
@@ -31,7 +32,7 @@ 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<Double>.value(arg: Double): Double =
+public fun Polynomial<Float64>.value(arg: Double): Double =
     coefficients.reduceIndexedOrNull { index, acc, c ->
         acc + c * arg.pow(index)
     } ?: .0

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/GaussIntegrator.kt

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

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/GaussIntegratorRuleFactory.kt

@@ -8,16 +8,17 @@ 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<Double>, Buffer<Double>>
+    public fun build(numPoints: Int): Pair<Buffer<Float64>, Buffer<Float64>>
 
     public companion object : IntegrandAttribute<GaussIntegratorRuleFactory> {
-        public fun double(numPoints: Int, range: ClosedRange<Double>): Pair<Buffer<Double>, Buffer<Double>> =
+        public fun double(numPoints: Int, range: ClosedRange<Float64>): Pair<Buffer<Float64>, Buffer<Float64>> =
             GaussLegendreRuleFactory.build(numPoints, range)
     }
 }
@@ -28,9 +29,9 @@ public interface GaussIntegratorRuleFactory {
  */
 public fun GaussIntegratorRuleFactory.build(
     numPoints: Int,
-    range: ClosedRange<Double>,
-): Pair<Buffer<Double>, Buffer<Double>> {
-    val normalized: Pair<Buffer<Double>, Buffer<Double>> = build(numPoints)
+    range: ClosedRange<Float64>,
+): Pair<Buffer<Float64>, Buffer<Float64>> {
+    val normalized: Pair<Buffer<Float64>, Buffer<Float64>> = build(numPoints)
     val length = range.endInclusive - range.start
 
     val points = normalized.first.mapToBuffer(Float64Field.bufferFactory) {
@@ -55,13 +56,13 @@ public fun GaussIntegratorRuleFactory.build(
 @ThreadLocal
 public object GaussLegendreRuleFactory : GaussIntegratorRuleFactory {
 
-    private val cache = HashMap<Int, Pair<Buffer<Double>, Buffer<Double>>>()
+    private val cache = HashMap<Int, Pair<Buffer<Float64>, Buffer<Float64>>>()
 
-    private fun getOrBuildRule(numPoints: Int): Pair<Buffer<Double>, Buffer<Double>> =
+    private fun getOrBuildRule(numPoints: Int): Pair<Buffer<Float64>, Buffer<Float64>> =
         cache.getOrPut(numPoints) { buildRule(numPoints) }
 
 
-    private fun buildRule(numPoints: Int): Pair<Buffer<Double>, Buffer<Double>> {
+    private fun buildRule(numPoints: Int): Pair<Buffer<Float64>, Buffer<Float64>> {
         if (numPoints == 1) {
             // Break recursion.
             return Pair(
@@ -73,7 +74,7 @@ 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<Double> = getOrBuildRule(numPoints - 1).first
+        val previousPoints: Buffer<Float64> = getOrBuildRule(numPoints - 1).first
 
         // Compute next rule.
         val points = DoubleArray(numPoints)
@@ -162,7 +163,7 @@ public object GaussLegendreRuleFactory : GaussIntegratorRuleFactory {
         return Pair(points.asBuffer(), weights.asBuffer())
     }
 
-    override fun build(numPoints: Int): Pair<Buffer<Double>, Buffer<Double>> = getOrBuildRule(numPoints)
+    override fun build(numPoints: Int): Pair<Buffer<Float64>, Buffer<Float64>> = getOrBuildRule(numPoints)
 
     override fun toString(): String = "GaussLegendreRule"
 }

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/Integrand.kt

@@ -6,6 +6,7 @@
 package space.kscience.kmath.integration
 
 import space.kscience.attributes.*
+import space.kscience.kmath.structures.Float64
 
 public interface IntegrandAttribute<T> : Attribute<T>
 
@@ -44,9 +45,9 @@ 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<Double>
+public object IntegrandRelativeAccuracy : IntegrandAttribute<Float64>
 
-public object IntegrandAbsoluteAccuracy : IntegrandAttribute<Double>
+public object IntegrandAbsoluteAccuracy : IntegrandAttribute<Float64>
 
 public object IntegrandCallsPerformed : IntegrandAttribute<Int>
 

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/SimpsonIntegrator.kt

@@ -10,6 +10,7 @@ import space.kscience.kmath.operations.Field
 import space.kscience.kmath.operations.Float64Field
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.operations.sum
+import space.kscience.kmath.structures.Float64
 
 /**
  * Use double pass Simpson rule integration with a fixed number of points.
@@ -24,7 +25,7 @@ public class SimpsonIntegrator<T : Any>(
 ) : UnivariateIntegrator<T> {
 
     private fun integrateRange(
-        integrand: UnivariateIntegrand<T>, range: ClosedRange<Double>, numPoints: Int,
+        integrand: UnivariateIntegrand<T>, range: ClosedRange<Float64>, numPoints: Int,
     ): T = algebra {
         val h: Double = (range.endInclusive - range.start) / (numPoints - 1)
         val values: List<T> = List(numPoints) { i ->
@@ -75,9 +76,9 @@ public val <T : Any> Field<T>.simpsonIntegrator: SimpsonIntegrator<T> get() = Si
  * * [IntegrandMaxCalls]&mdash;the maximum number of function calls during integration. For non-iterative rules, always uses
  * the maximum number of points. By default, uses 10 points.
  */
-public object DoubleSimpsonIntegrator : UnivariateIntegrator<Double> {
+public object DoubleSimpsonIntegrator : UnivariateIntegrator<Float64> {
     private fun integrateRange(
-        integrand: UnivariateIntegrand<Double>, range: ClosedRange<Double>, numPoints: Int,
+        integrand: UnivariateIntegrand<Float64>, range: ClosedRange<Float64>, numPoints: Int,
     ): Double {
         val h: Double = (range.endInclusive - range.start) / (numPoints - 1)
         val values = DoubleArray(numPoints) { i ->
@@ -96,7 +97,7 @@ public object DoubleSimpsonIntegrator : UnivariateIntegrator<Double> {
         return res
     }
 
-    override fun integrate(integrand: UnivariateIntegrand<Double>): UnivariateIntegrand<Double> {
+    override fun integrate(integrand: UnivariateIntegrand<Float64>): UnivariateIntegrand<Float64> {
         val ranges = integrand[UnivariateIntegrandRanges]
         return if (ranges != null) {
             val res = ranges.ranges.sumOf { integrateRange(integrand, it.first, it.second) }

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/SplineIntegrator.kt

@@ -14,6 +14,7 @@ import space.kscience.kmath.interpolation.SplineInterpolator
 import space.kscience.kmath.interpolation.interpolatePolynomials
 import space.kscience.kmath.operations.*
 import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.Float64
 import space.kscience.kmath.structures.Float64Buffer
 import space.kscience.kmath.structures.MutableBufferFactory
 
@@ -59,7 +60,7 @@ public class SplineIntegrator<T : Comparable<T>>(
 
         val interpolator: PolynomialInterpolator<T> = SplineInterpolator(algebra, bufferFactory)
 
-        val nodes: Buffer<Double> = integrand[UnivariateIntegrationNodes] ?: run {
+        val nodes: Buffer<Float64> = integrand[UnivariateIntegrationNodes] ?: run {
             val numPoints = integrand[IntegrandMaxCalls] ?: 100
             val step = (range.endInclusive - range.start) / (numPoints - 1)
             Float64Buffer(numPoints) { i -> range.start + i * step }
@@ -85,12 +86,12 @@ public class SplineIntegrator<T : Comparable<T>>(
  * uses the maximum number of points. By default, uses 10 points.
  */
 @UnstableKMathAPI
-public object DoubleSplineIntegrator : UnivariateIntegrator<Double> {
-    override fun integrate(integrand: UnivariateIntegrand<Double>): UnivariateIntegrand<Double> {
+public object DoubleSplineIntegrator : UnivariateIntegrator<Float64> {
+    override fun integrate(integrand: UnivariateIntegrand<Float64>): UnivariateIntegrand<Float64> {
         val range = integrand[IntegrationRange] ?: 0.0..1.0
-        val interpolator: PolynomialInterpolator<Double> = SplineInterpolator(Float64Field, Float64Field.bufferFactory)
+        val interpolator: PolynomialInterpolator<Float64> = SplineInterpolator(Float64Field, Float64Field.bufferFactory)
 
-        val nodes: Buffer<Double> = integrand[UnivariateIntegrationNodes] ?: run {
+        val nodes: Buffer<Float64> = integrand[UnivariateIntegrationNodes] ?: run {
             val numPoints = integrand[IntegrandMaxCalls] ?: 100
             val step = (range.endInclusive - range.start) / (numPoints - 1)
             Float64Buffer(numPoints) { i -> range.start + i * step }
@@ -108,5 +109,5 @@ public object DoubleSplineIntegrator : UnivariateIntegrator<Double> {
 
 @Suppress("unused")
 @UnstableKMathAPI
-public inline val Float64Field.splineIntegrator: UnivariateIntegrator<Double>
+public inline val Float64Field.splineIntegrator: UnivariateIntegrator<Float64>
     get() = DoubleSplineIntegrator

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/UnivariateIntegrand.kt

@@ -8,6 +8,7 @@ package space.kscience.kmath.integration
 import space.kscience.attributes.*
 import space.kscience.kmath.UnstableKMathAPI
 import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.Float64
 import space.kscience.kmath.structures.Float64Buffer
 
 public class UnivariateIntegrand<T>(
@@ -36,15 +37,15 @@ public inline fun <reified T : Any> UnivariateIntegrand(
 
 public typealias UnivariateIntegrator<T> = Integrator<T, UnivariateIntegrand<T>>
 
-public object IntegrationRange : IntegrandAttribute<ClosedRange<Double>>
+public object IntegrationRange : IntegrandAttribute<ClosedRange<Float64>>
 
 
 /**
  * Set of univariate integration ranges. First components correspond to the ranges themselves, second components to
  * the number of integration nodes per range.
  */
-public class UnivariateIntegrandRanges(public val ranges: List<Pair<ClosedRange<Double>, Int>>) {
-    public constructor(vararg pairs: Pair<ClosedRange<Double>, Int>) : this(pairs.toList())
+public class UnivariateIntegrandRanges(public val ranges: List<Pair<ClosedRange<Float64>, Int>>) {
+    public constructor(vararg pairs: Pair<ClosedRange<Float64>, Int>) : this(pairs.toList())
 
     override fun toString(): String {
         val rangesString = ranges.joinToString(separator = ",") { (range, points) ->
@@ -56,7 +57,7 @@ public class UnivariateIntegrandRanges(public val ranges: List<Pair<ClosedRange<
     public companion object : IntegrandAttribute<UnivariateIntegrandRanges>
 }
 
-public object UnivariateIntegrationNodes : IntegrandAttribute<Buffer<Double>>
+public object UnivariateIntegrationNodes : IntegrandAttribute<Buffer<Float64>>
 
 public fun AttributesBuilder<UnivariateIntegrand<*>>.integrationNodes(vararg nodes: Double) {
     UnivariateIntegrationNodes(Float64Buffer(nodes))
@@ -78,7 +79,7 @@ public inline fun <reified T : Any> UnivariateIntegrator<T>.integrate(
  */
 @UnstableKMathAPI
 public inline fun <reified T : Any> UnivariateIntegrator<T>.integrate(
-    range: ClosedRange<Double>,
+    range: ClosedRange<Float64>,
     noinline attributeBuilder: AttributesBuilder<UnivariateIntegrand<T>>.() -> Unit = {},
     noinline function: (Double) -> T,
 ): UnivariateIntegrand<T> {

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/SplineInterpolator.kt

@@ -12,6 +12,7 @@ import space.kscience.kmath.functions.Polynomial
 import space.kscience.kmath.operations.Field
 import space.kscience.kmath.operations.Float64Field
 import space.kscience.kmath.operations.invoke
+import space.kscience.kmath.structures.Float64
 import space.kscience.kmath.structures.MutableBufferFactory
 
 /**
@@ -78,5 +79,5 @@ public fun <T : Comparable<T>> Field<T>.splineInterpolator(
     bufferFactory: MutableBufferFactory<T>,
 ): SplineInterpolator<T> = SplineInterpolator(this, bufferFactory)
 
-public val Float64Field.splineInterpolator: SplineInterpolator<Double>
+public val Float64Field.splineInterpolator: SplineInterpolator<Float64>
     get() = SplineInterpolator(this, bufferFactory)

kmath-functions/src/commonTest/kotlin/space/kscience/kmath/interpolation/LinearInterpolatorTest.kt

@@ -19,7 +19,7 @@ internal class LinearInterpolatorTest {
             3.0 to 4.0
         )
 
-        //val polynomial: PiecewisePolynomial<Double> = DoubleField.linearInterpolator.interpolatePolynomials(data)
+        //val polynomial: PiecewisePolynomial<Float64> = DoubleField.linearInterpolator.interpolatePolynomials(data)
         val function = Float64Field.linearInterpolator.interpolate(data)
         assertEquals(null, function(-1.0))
         assertEquals(0.5, function(0.5))

kmath-functions/src/commonTest/kotlin/space/kscience/kmath/interpolation/SplineInterpolatorTest.kt

@@ -19,7 +19,7 @@ internal class SplineInterpolatorTest {
             x to sin(x)
         }
 
-        //val polynomial: PiecewisePolynomial<Double> = DoubleField.splineInterpolator.interpolatePolynomials(data)
+        //val polynomial: PiecewisePolynomial<Float64> = DoubleField.splineInterpolator.interpolatePolynomials(data)
 
         val function = Float64Field.splineInterpolator.interpolate(data, Double.NaN)