.github/workflows/build.yml

@@ -13,25 +13,13 @@ jobs:
     runs-on: ${{matrix.os}}
     timeout-minutes: 40
     steps:
-      - name: Checkout the repo
+      - uses: actions/checkout@v3.0.0
-        uses: actions/checkout@v2
+      - uses: actions/setup-java@v3.0.0
-      - name: Set up JDK 11
-        uses: DeLaGuardo/setup-graalvm@4.0
         with:
-          graalvm: 21.2.0
+          java-version: 11
-          java: java11
+          distribution: liberica
-          arch: amd64
-      - name: Cache gradle
-        uses: actions/cache@v2
-        with:
-          path: |
-            ~/.gradle/caches
-            ~/.gradle/wrapper
-          key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
-          restore-keys: |
-            ${{ runner.os }}-gradle-
       - name: Cache konan
-        uses: actions/cache@v2
+        uses: actions/cache@v3.0.1
         with:
           path: ~/.konan
           key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
@@ -39,5 +27,6 @@ jobs:
             ${{ runner.os }}-gradle-
       - name: Gradle Wrapper Validation
         uses: gradle/wrapper-validation-action@v1.0.4
-      - name: Build
+      - uses: gradle/gradle-build-action@v2.1.5
-        run: ./gradlew build --build-cache --no-daemon --stacktrace
+        with:
+          arguments: build

.github/workflows/pages.yml

@@ -1,28 +1,31 @@
 name: Dokka publication
 
 on:
-  push:
+  workflow_dispatch:
-    branches: [ master ]
+  release:
+    types: [ created ]
 
 jobs:
   build:
     runs-on: ubuntu-20.04
     timeout-minutes: 40
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3.0.0
-      - uses: DeLaGuardo/setup-graalvm@4.0
+      - uses: actions/setup-java@v3.0.0
         with:
-          graalvm: 21.2.0
+          java-version: 11
-          java: java11
+          distribution: liberica
-          arch: amd64
+      - name: Cache konan
-      - uses: actions/cache@v2
+        uses: actions/cache@v3.0.1
         with:
-          path: ~/.gradle/caches
+          path: ~/.konan
           key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
           restore-keys: |
             ${{ runner.os }}-gradle-
-      - run: ./gradlew dokkaHtmlMultiModule --build-cache --no-daemon --no-parallel --stacktrace
+      - uses: gradle/gradle-build-action@v2.1.5
-      - uses: JamesIves/github-pages-deploy-action@4.1.0
+        with:
+          arguments: dokkaHtmlMultiModule --no-parallel
+      - uses: JamesIves/github-pages-deploy-action@v4.3.0
         with:
           branch: gh-pages
           folder: build/dokka/htmlMultiModule

.github/workflows/publish.yml

@@ -14,42 +14,38 @@ jobs:
         os: [ macOS-latest, windows-latest ]
     runs-on: ${{matrix.os}}
     steps:
-      - name: Checkout the repo
+      - uses: actions/checkout@v3.0.0
-        uses: actions/checkout@v2
+      - uses: actions/setup-java@v3.0.0
-      - name: Set up JDK 11
-        uses: DeLaGuardo/setup-graalvm@4.0
         with:
-          graalvm: 21.2.0
+          java-version: 11
-          java: java11
+          distribution: liberica
-          arch: amd64
-      - name: Cache gradle
-        uses: actions/cache@v2
-        with:
-          path: |
-            ~/.gradle/caches
-            ~/.gradle/wrapper
-          key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
-          restore-keys: |
-            ${{ runner.os }}-gradle-
       - name: Cache konan
-        uses: actions/cache@v2
+        uses: actions/cache@v3.0.1
         with:
           path: ~/.konan
           key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
           restore-keys: |
             ${{ runner.os }}-gradle-
-      - name: Gradle Wrapper Validation
+      - uses: gradle/wrapper-validation-action@v1.0.4
-        uses: gradle/wrapper-validation-action@v1.0.4
       - name: Publish Windows Artifacts
         if: matrix.os == 'windows-latest'
-        shell: cmd
+        uses: gradle/gradle-build-action@v2.1.5
-        run: >
+        with:
-          ./gradlew release --no-daemon --build-cache -Ppublishing.enabled=true
+          arguments: |
-          -Ppublishing.space.user=${{ secrets.SPACE_APP_ID }}
+            releaseAll
-          -Ppublishing.space.token=${{ secrets.SPACE_APP_SECRET }}
+            -Ppublishing.enabled=true
+            -Ppublishing.sonatype=false
+            -Ppublishing.space.user=${{ secrets.SPACE_APP_ID }}
+            -Ppublishing.space.token=${{ secrets.SPACE_APP_SECRET }}
       - name: Publish Mac Artifacts
         if: matrix.os == 'macOS-latest'
-        run: >
+        uses: gradle/gradle-build-action@v2.1.5
-          ./gradlew release --no-daemon --build-cache -Ppublishing.enabled=true -Ppublishing.platform=macosX64
+        with:
-          -Ppublishing.space.user=${{ secrets.SPACE_APP_ID }}
+          arguments: |
-          -Ppublishing.space.token=${{ secrets.SPACE_APP_SECRET }}
+            releaseMacosX64
+            releaseIosArm64
+            releaseIosX64
+            -Ppublishing.enabled=true
+            -Ppublishing.sonatype=false            
+            -Ppublishing.space.user=${{ secrets.SPACE_APP_ID }}
+            -Ppublishing.space.token=${{ secrets.SPACE_APP_SECRET }}

.gitignore

@@ -19,3 +19,4 @@ out/
 
 !/.idea/copyright/
 !/.idea/scopes/
+/kotlin-js-store/yarn.lock

CHANGELOG.md

@@ -2,6 +2,19 @@
 
 ## [Unreleased]
 ### Added
+
+### Changed
+
+### Deprecated
+
+### Removed
+
+### Fixed
+
+### Security
+
+## [0.3.0]
+### Added
 - `ScaleOperations` interface
 - `Field` extends `ScaleOperations`
 - Basic integration API
@@ -19,6 +32,12 @@
 - Complex power
 - Separate methods for UInt, Int and Number powers. NaN safety.
 - Tensorflow prototype
+- `ValueAndErrorField`
+- MST compilation to WASM: #286
+- Jafama integration: #176
+- `contentEquals` with tolerance: #364
+- Compilation to TeX for MST: #254
+
 
 ### Changed
 - Exponential operations merged with hyperbolic functions
@@ -48,10 +67,15 @@
 - Operations -> Ops
 - Default Buffer and ND algebras are now Ops and lack neutral elements (0, 1) as well as algebra-level shapes.
 - Tensor algebra takes read-only structures as input and inherits AlgebraND
+- `UnivariateDistribution` renamed to `Distribution1D`
+- Rework of histograms.
+- `UnivariateFunction` -> `Function1D`, `MultivariateFunction` -> `FunctionND`
+
 
 ### Deprecated
 - Specialized `DoubleBufferAlgebra`
 
+
 ### Removed
 - Nearest in Domain. To be implemented in geometry package.
 - Number multiplication and division in main Algebra chain
@@ -62,10 +86,12 @@
 - Second generic from DifferentiableExpression
 - Algebra elements are completely removed. Use algebra contexts instead.
 
+
 ### Fixed
 - Ring inherits RingOperations, not GroupOperations
 - Univariate histogram filling
 
+
 ### Security
 
 ## [0.2.0]
@@ -88,6 +114,7 @@
 - New `MatrixFeature` interfaces for matrix decompositions
 - Basic Quaternion vector support in `kmath-complex`.
 
+
 ### Changed
 - Package changed from `scientifik` to `space.kscience`
 - Gradle version: 6.6 -> 6.8.2
@@ -112,7 +139,6 @@
 - `symbol` method in `Algebra` renamed to `bindSymbol` to avoid ambiguity
 - Add `out` projection to `Buffer` generic
 
-### Deprecated
 
 ### Removed
 - `kmath-koma` module because it doesn't support Kotlin 1.4.
@@ -122,13 +148,11 @@
 - `Real` class
 - StructureND identity and equals
 
+
 ### Fixed
 - `symbol` method in `MstExtendedField` (https://github.com/mipt-npm/kmath/pull/140)
 
-### Security
-
 ## [0.1.4]
-
 ### Added
 - Functional Expressions API
 - Mathematical Syntax Tree, its interpreter and API
@@ -146,6 +170,7 @@
 - Full hyperbolic functions support and default implementations within `ExtendedField`
 - Norm support for `Complex`
 
+
 ### Changed
 - `readAsMemory` now has `throws IOException` in JVM signature.
 - Several functions taking functional types were made `inline`.
@@ -157,9 +182,10 @@
 - Gradle version: 6.3 -> 6.6
 - Moved probability distributions to commons-rng and to `kmath-prob`
 
+
 ### Fixed
 - Missing copy method in Memory implementation on JS (https://github.com/mipt-npm/kmath/pull/106)
 - D3.dim value in `kmath-dimensions`
 - Multiplication in integer rings in `kmath-core` (https://github.com/mipt-npm/kmath/pull/101)
 - Commons RNG compatibility (https://github.com/mipt-npm/kmath/issues/93)
-- Multiplication of BigInt by scalar
+- Multiplication of BigInt by scalar

README.md

@@ -52,21 +52,18 @@ module definitions below. The module stability could have the following levels:
 
 ## Modules
 
-<hr/>
 
-* ### [benchmarks](benchmarks)
+### [benchmarks](benchmarks)
 > 
 >
 > **Maturity**: EXPERIMENTAL
-<hr/>
 
-* ### [examples](examples)
+### [examples](examples)
 > 
 >
 > **Maturity**: EXPERIMENTAL
-<hr/>
 
-* ### [kmath-ast](kmath-ast)
+### [kmath-ast](kmath-ast)
 > 
 >
 > **Maturity**: EXPERIMENTAL
@@ -77,15 +74,13 @@ module definitions below. The module stability could have the following levels:
 > - [mst-js-codegen](kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/estree.kt) : Dynamic MST to JS compiler
 > - [rendering](kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt) : Extendable MST rendering
 
-<hr/>
 
-* ### [kmath-commons](kmath-commons)
+### [kmath-commons](kmath-commons)
 > 
 >
 > **Maturity**: EXPERIMENTAL
-<hr/>
 
-* ### [kmath-complex](kmath-complex)
+### [kmath-complex](kmath-complex)
 > Complex numbers and quaternions.
 >
 > **Maturity**: PROTOTYPE
@@ -94,9 +89,8 @@ module definitions below. The module stability could have the following levels:
 > - [complex](kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Complex.kt) : Complex Numbers
 > - [quaternion](kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Quaternion.kt) : Quaternions
 
-<hr/>
 
-* ### [kmath-core](kmath-core)
+### [kmath-core](kmath-core)
 > Core classes, algebra definitions, basic linear algebra
 >
 > **Maturity**: DEVELOPMENT
@@ -112,21 +106,18 @@ performance calculations to code generation.
 > - [domains](kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains) : Domains
 > - [autodiff](kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt) : Automatic differentiation
 
-<hr/>
 
-* ### [kmath-coroutines](kmath-coroutines)
+### [kmath-coroutines](kmath-coroutines)
 > 
 >
 > **Maturity**: EXPERIMENTAL
-<hr/>
 
-* ### [kmath-dimensions](kmath-dimensions)
+### [kmath-dimensions](kmath-dimensions)
 > 
 >
 > **Maturity**: PROTOTYPE
-<hr/>
 
-* ### [kmath-ejml](kmath-ejml)
+### [kmath-ejml](kmath-ejml)
 > 
 >
 > **Maturity**: PROTOTYPE
@@ -136,9 +127,8 @@ performance calculations to code generation.
 > - [ejml-matrix](kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlMatrix.kt) : Matrix implementation.
 > - [ejml-linear-space](kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt) : LinearSpace implementations.
 
-<hr/>
 
-* ### [kmath-for-real](kmath-for-real)
+### [kmath-for-real](kmath-for-real)
 > Extension module that should be used to achieve numpy-like behavior.
 All operations are specialized to work with `Double` numbers without declaring algebraic contexts.
 One can still use generic algebras though.
@@ -150,9 +140,8 @@ One can still use generic algebras though.
 > - [DoubleMatrix](kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/DoubleMatrix.kt) : Numpy-like operations for 2d real structures
 > - [grids](kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/structures/grids.kt) : Uniform grid generators
 
-<hr/>
 
-* ### [kmath-functions](kmath-functions)
+### [kmath-functions](kmath-functions)
 > 
 >
 > **Maturity**: EXPERIMENTAL
@@ -164,21 +153,18 @@ One can still use generic algebras though.
 > - [spline interpolation](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/SplineInterpolator.kt) : Cubic spline XY interpolator.
 > - [integration](kmath-functions/#) : Univariate and multivariate quadratures
 
-<hr/>
 
-* ### [kmath-geometry](kmath-geometry)
+### [kmath-geometry](kmath-geometry)
 > 
 >
 > **Maturity**: PROTOTYPE
-<hr/>
 
-* ### [kmath-histograms](kmath-histograms)
+### [kmath-histograms](kmath-histograms)
 > 
 >
 > **Maturity**: PROTOTYPE
-<hr/>
 
-* ### [kmath-jafama](kmath-jafama)
+### [kmath-jafama](kmath-jafama)
 > 
 >
 > **Maturity**: PROTOTYPE
@@ -186,15 +172,13 @@ One can still use generic algebras though.
 > **Features:**
 > - [jafama-double](kmath-jafama/src/main/kotlin/space/kscience/kmath/jafama/) : Double ExtendedField implementations based on Jafama
 
-<hr/>
 
-* ### [kmath-jupyter](kmath-jupyter)
+### [kmath-jupyter](kmath-jupyter)
 > 
 >
 > **Maturity**: PROTOTYPE
-<hr/>
 
-* ### [kmath-kotlingrad](kmath-kotlingrad)
+### [kmath-kotlingrad](kmath-kotlingrad)
 > 
 >
 > **Maturity**: EXPERIMENTAL
@@ -203,21 +187,18 @@ One can still use generic algebras though.
 > - [differentiable-mst-expression](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/KotlingradExpression.kt) : MST based DifferentiableExpression.
 > - [scalars-adapters](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/scalarsAdapters.kt) : Conversions between Kotlin∇'s SFun and MST
 
-<hr/>
 
-* ### [kmath-memory](kmath-memory)
+### [kmath-memory](kmath-memory)
 > An API and basic implementation for arranging objects in a continuous memory block.
 >
 > **Maturity**: DEVELOPMENT
-<hr/>
 
-* ### [kmath-multik](kmath-multik)
+### [kmath-multik](kmath-multik)
 > 
 >
 > **Maturity**: PROTOTYPE
-<hr/>
 
-* ### [kmath-nd4j](kmath-nd4j)
+### [kmath-nd4j](kmath-nd4j)
 > 
 >
 > **Maturity**: EXPERIMENTAL
@@ -227,33 +208,28 @@ One can still use generic algebras though.
 > - [nd4jarrayrings](kmath-nd4j/#) : Rings over Nd4jArrayStructure of Int and Long
 > - [nd4jarrayfields](kmath-nd4j/#) : Fields over Nd4jArrayStructure of Float and Double
 
-<hr/>
 
-* ### [kmath-optimization](kmath-optimization)
+### [kmath-optimization](kmath-optimization)
 > 
 >
 > **Maturity**: EXPERIMENTAL
-<hr/>
 
-* ### [kmath-stat](kmath-stat)
+### [kmath-stat](kmath-stat)
 > 
 >
 > **Maturity**: EXPERIMENTAL
-<hr/>
 
-* ### [kmath-symja](kmath-symja)
+### [kmath-symja](kmath-symja)
 > 
 >
 > **Maturity**: PROTOTYPE
-<hr/>
 
-* ### [kmath-tensorflow](kmath-tensorflow)
+### [kmath-tensorflow](kmath-tensorflow)
 > 
 >
 > **Maturity**: PROTOTYPE
-<hr/>
 
-* ### [kmath-tensors](kmath-tensors)
+### [kmath-tensors](kmath-tensors)
 > 
 >
 > **Maturity**: PROTOTYPE
@@ -263,13 +239,11 @@ One can still use generic algebras though.
 > - [tensor algebra with broadcasting](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt) : Basic linear algebra operations implemented with broadcasting.
 > - [linear algebra operations](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt) : Advanced linear algebra operations like LU decomposition, SVD, etc.
 
-<hr/>
 
-* ### [kmath-viktor](kmath-viktor)
+### [kmath-viktor](kmath-viktor)
 > 
 >
 > **Maturity**: DEVELOPMENT
-<hr/>
 
 
 ## Multi-platform support
@@ -308,8 +282,8 @@ repositories {
 }
 
 dependencies {
-    api("space.kscience:kmath-core:0.3.0-dev-17")
+    api("space.kscience:kmath-core:$version")
-    // api("space.kscience:kmath-core-jvm:0.3.0-dev-17") for jvm-specific version
+    // api("space.kscience:kmath-core-jvm:$version") for jvm-specific version
 }
 ```
 

benchmarks/README.md

@@ -0,0 +1,4 @@
+# Module benchmarks
+
+
+

benchmarks/build.gradle.kts

@@ -84,6 +84,11 @@ benchmark {
         iterationTimeUnit = "ms"
     }
 
+    configurations.register("svd") {
+        commonConfiguration()
+        include("SVDBenchmark")
+    }
+
     configurations.register("buffer") {
         commonConfiguration()
         include("BufferBenchmark")
@@ -155,7 +160,7 @@ kotlin.sourceSets.all {
     }
 }
 
-tasks.withType<org.jetbrains.kotlin.gradle.tasks.KotlinCompile> {
+tasks.withType<org.jetbrains.kotlin.gradle.dsl.KotlinJvmCompile> {
     kotlinOptions {
         jvmTarget = "11"
         freeCompilerArgs = freeCompilerArgs + "-Xjvm-default=all" + "-Xlambdas=indy"

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

@@ -15,8 +15,10 @@ import space.kscience.kmath.linear.invoke
 import space.kscience.kmath.linear.linearSpace
 import space.kscience.kmath.multik.multikAlgebra
 import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.structures.Buffer
 import space.kscience.kmath.tensorflow.produceWithTF
+import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.tensorAlgebra
 import kotlin.random.Random
 
@@ -90,4 +92,9 @@ internal class DotBenchmark {
     fun doubleDot(blackhole: Blackhole) = with(DoubleField.linearSpace) {
         blackhole.consume(matrix1 dot matrix2)
     }
+
+    @Benchmark
+    fun doubleTensorDot(blackhole: Blackhole) = DoubleTensorAlgebra.invoke {
+        blackhole.consume(matrix1 dot matrix2)
+    }
 }

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

@@ -0,0 +1,141 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.benchmarks
+import kotlinx.benchmark.Benchmark
+import kotlinx.benchmark.Blackhole
+import kotlinx.benchmark.Scope
+import kotlinx.benchmark.State
+import org.ejml.UtilEjml.assertTrue
+import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.diagonalEmbedding
+import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.dot
+import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.eq
+import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.svdGolubKahan
+import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.transpose
+import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.svdPowerMethod
+
+@State(Scope.Benchmark)
+class SVDBenchmark {
+    companion object {
+        val tensorSmall = DoubleTensorAlgebra.randomNormal(intArrayOf(5, 5), 0)
+        val tensorMedium = DoubleTensorAlgebra.randomNormal(intArrayOf(10, 10), 0)
+        val tensorLarge = DoubleTensorAlgebra.randomNormal(intArrayOf(50, 50), 0)
+        val tensorVeryLarge = DoubleTensorAlgebra.randomNormal(intArrayOf(100, 100), 0)
+        val epsilon = 1e-9
+    }
+
+    @Benchmark
+    fun svdPowerMethodSmall(blackhole: Blackhole) {
+        val svd = tensorSmall.svdPowerMethod()
+        val tensorSVD = svd.first
+            .dot(
+                diagonalEmbedding(svd.second)
+                    .dot(svd.third.transpose())
+            )
+        assertTrue(tensorSVD.eq(tensorSmall, epsilon))
+        blackhole.consume(
+            tensorSmall.svdPowerMethod()
+        )
+    }
+
+    @Benchmark
+    fun svdPowerMethodMedium(blackhole: Blackhole) {
+        val svd = tensorMedium.svdPowerMethod()
+        val tensorSVD = svd.first
+            .dot(
+                diagonalEmbedding(svd.second)
+                    .dot(svd.third.transpose())
+            )
+        assertTrue(tensorSVD.eq(tensorMedium, epsilon))
+        blackhole.consume(
+            tensorMedium.svdPowerMethod()
+        )
+    }
+
+    @Benchmark
+    fun svdPowerMethodLarge(blackhole: Blackhole) {
+        val svd = tensorLarge.svdPowerMethod()
+        val tensorSVD = svd.first
+            .dot(
+                diagonalEmbedding(svd.second)
+                    .dot(svd.third.transpose())
+            )
+        assertTrue(tensorSVD.eq(tensorLarge, epsilon))
+        blackhole.consume(
+            tensorLarge.svdPowerMethod()
+        )
+    }
+
+    @Benchmark
+    fun svdPowerMethodVeryLarge(blackhole: Blackhole) {
+        val svd = tensorVeryLarge.svdPowerMethod()
+        val tensorSVD = svd.first
+            .dot(
+                diagonalEmbedding(svd.second)
+                    .dot(svd.third.transpose())
+            )
+        assertTrue(tensorSVD.eq(tensorVeryLarge, epsilon))
+        blackhole.consume(
+            tensorVeryLarge.svdPowerMethod()
+        )
+    }
+
+    @Benchmark
+    fun svdGolubKahanSmall(blackhole: Blackhole) {
+        val svd = tensorSmall.svdGolubKahan()
+        val tensorSVD = svd.first
+            .dot(
+                diagonalEmbedding(svd.second)
+                    .dot(svd.third.transpose())
+            )
+        assertTrue(tensorSVD.eq(tensorSmall, epsilon))
+        blackhole.consume(
+            tensorSmall.svdGolubKahan()
+        )
+    }
+
+    @Benchmark
+    fun svdGolubKahanMedium(blackhole: Blackhole) {
+        val svd = tensorMedium.svdGolubKahan()
+        val tensorSVD = svd.first
+            .dot(
+                diagonalEmbedding(svd.second)
+                    .dot(svd.third.transpose())
+            )
+        assertTrue(tensorSVD.eq(tensorMedium, epsilon))
+        blackhole.consume(
+            tensorMedium.svdGolubKahan()
+        )
+    }
+
+    @Benchmark
+    fun svdGolubKahanLarge(blackhole: Blackhole) {
+        val svd = tensorLarge.svdGolubKahan()
+        val tensorSVD = svd.first
+            .dot(
+                diagonalEmbedding(svd.second)
+                    .dot(svd.third.transpose())
+            )
+        assertTrue(tensorSVD.eq(tensorLarge, epsilon))
+        blackhole.consume(
+            tensorLarge.svdGolubKahan()
+        )
+    }
+
+    @Benchmark
+    fun svdGolubKahanVeryLarge(blackhole: Blackhole) {
+        val svd = tensorVeryLarge.svdGolubKahan()
+        val tensorSVD = svd.first
+            .dot(
+                diagonalEmbedding(svd.second)
+                    .dot(svd.third.transpose())
+            )
+        assertTrue(tensorSVD.eq(tensorVeryLarge, epsilon))
+        blackhole.consume(
+            tensorVeryLarge.svdGolubKahan()
+        )
+    }
+}

build.gradle.kts

@@ -1,16 +1,17 @@
 plugins {
     id("ru.mipt.npm.gradle.project")
-    id("org.jetbrains.kotlinx.kover") version "0.5.0-RC"
+    id("org.jetbrains.kotlinx.kover") version "0.5.0"
 }
 
 allprojects {
     repositories {
+        maven("https://repo.kotlin.link")
         maven("https://oss.sonatype.org/content/repositories/snapshots")
         mavenCentral()
     }
 
     group = "space.kscience"
-    version = "0.3.0-dev-19"
+    version = "0.3.0"
 }
 
 subprojects {
@@ -55,7 +56,7 @@ subprojects {
 readme.readmeTemplate = file("docs/templates/README-TEMPLATE.md")
 
 ksciencePublish {
-    github("kmath")
+    github("kmath", addToRelease = false)
     space()
     sonatype()
 }

buildSrc/build.gradle.kts

@@ -1,20 +1,21 @@
 plugins {
     `kotlin-dsl`
     `version-catalog`
-    alias(npmlibs.plugins.kotlin.plugin.serialization)
+    alias(miptNpmLibs.plugins.kotlin.plugin.serialization)
 }
 
 java.targetCompatibility = JavaVersion.VERSION_11
 
 repositories {
+    mavenLocal()
     maven("https://repo.kotlin.link")
     mavenCentral()
     gradlePluginPortal()
 }
 
 val toolsVersion: String by extra
-val kotlinVersion = npmlibs.versions.kotlin.asProvider().get()
+val kotlinVersion = miptNpmLibs.versions.kotlin.asProvider().get()
-val benchmarksVersion = "0.4.2"
+val benchmarksVersion = miptNpmLibs.versions.kotlinx.benchmark.get()
 
 dependencies {
     api("ru.mipt.npm:gradle-tools:$toolsVersion")
@@ -22,7 +23,7 @@ dependencies {
     api("org.jetbrains.kotlinx:kotlinx-benchmark-plugin:$benchmarksVersion")
     api("org.jetbrains.kotlin:kotlin-allopen:$kotlinVersion")
     //to be used inside build-script only
-    implementation(npmlibs.kotlinx.serialization.json)
+    implementation(miptNpmLibs.kotlinx.serialization.json)
 }
 
 kotlin.sourceSets.all {

buildSrc/gradle.properties

@@ -4,11 +4,4 @@
 #
 
 kotlin.code.style=official
-kotlin.mpp.stability.nowarn=true
+toolsVersion=0.11.2-kotlin-1.6.10
-
-kotlin.jupyter.add.scanner=false
-
-org.gradle.configureondemand=true
-org.gradle.parallel=true
-
-toolsVersion=0.10.9-kotlin-1.6.10

buildSrc/settings.gradle.kts

@@ -3,21 +3,20 @@
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
-
 enableFeaturePreview("TYPESAFE_PROJECT_ACCESSORS")
-enableFeaturePreview("VERSION_CATALOGS")
 
 dependencyResolutionManagement {
-
     val toolsVersion: String by extra
 
     repositories {
+        mavenLocal()
         maven("https://repo.kotlin.link")
         mavenCentral()
+        gradlePluginPortal()
     }
 
     versionCatalogs {
-        create("npmlibs") {
+        create("miptNpmLibs") {
             from("ru.mipt.npm:version-catalog:$toolsVersion")
         }
     }

docs/templates/README-TEMPLATE.md

@@ -52,7 +52,7 @@ module definitions below. The module stability could have the following levels:
 
 ## Modules
 
-$modules
+${modules}
 
 ## Multi-platform support
 

examples/README.md

@@ -0,0 +1,4 @@
+# Module examples
+
+
+

examples/build.gradle.kts

@@ -58,7 +58,7 @@ kotlin.sourceSets.all {
     }
 }
 
-tasks.withType<org.jetbrains.kotlin.gradle.tasks.KotlinCompile> {
+tasks.withType<org.jetbrains.kotlin.gradle.dsl.KotlinJvmCompile> {
     kotlinOptions {
         jvmTarget = "11"
         freeCompilerArgs = freeCompilerArgs + "-Xjvm-default=all" + "-Xopt-in=kotlin.RequiresOptIn" + "-Xlambdas=indy"

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

@@ -13,7 +13,7 @@ import kotlin.math.pow
 
 fun main() {
     //Define a function
-    val function: UnivariateFunction<Double> = { x -> 3 * x.pow(2) + 2 * x + 1 }
+    val function: Function1D<Double> = { x -> 3 * x.pow(2) + 2 * x + 1 }
 
     //get the result of the integration
     val result = DoubleField.gaussIntegrator.integrate(0.0..10.0, function = function)

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

@@ -5,8 +5,8 @@
 
 package space.kscience.kmath.functions
 
-import space.kscience.kmath.interpolation.SplineInterpolator
 import space.kscience.kmath.interpolation.interpolatePolynomials
+import space.kscience.kmath.interpolation.splineInterpolator
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.real.map
 import space.kscience.kmath.real.step
@@ -18,7 +18,7 @@ import space.kscience.plotly.scatter
 
 @OptIn(UnstablePlotlyAPI::class)
 fun main() {
-    val function: UnivariateFunction<Double> = { x ->
+    val function: Function1D<Double> = { x ->
         if (x in 30.0..50.0) {
             1.0
         } else {
@@ -28,7 +28,7 @@ fun main() {
     val xs = 0.0..100.0 step 0.5
     val ys = xs.map(function)
 
-    val polynomial: PiecewisePolynomial<Double> = SplineInterpolator.double.interpolatePolynomials(xs, ys)
+    val polynomial: PiecewisePolynomial<Double> = DoubleField.splineInterpolator.interpolatePolynomials(xs, ys)
 
     val polyFunction = polynomial.asFunction(DoubleField, 0.0)
 

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

@@ -1,65 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.tensors
-
-import space.kscience.kmath.linear.transpose
-import space.kscience.kmath.misc.PerformancePitfall
-import space.kscience.kmath.nd.MutableStructure2D
-import space.kscience.kmath.nd.Structure2D
-import space.kscience.kmath.nd.as2D
-import space.kscience.kmath.tensors.core.*
-import space.kscience.kmath.tensors.core.tensorAlgebra
-import kotlin.math.*
-
-fun MutableStructure2D<Double>.print() {
-    val n = this.shape.component1()
-    val m = this.shape.component2()
-    for (i in 0 until n) {
-        for (j in 0 until m) {
-            val x = (this[i, j] * 100).roundToInt() / 100.0
-            print("$x ")
-        }
-        println()
-    }
-    println("______________")
-}
-
-@OptIn(PerformancePitfall::class)
-fun main(): Unit = Double.tensorAlgebra.withBroadcast {
-    val shape = intArrayOf(5, 3)
-    val buffer = doubleArrayOf(
-        1.000000, 2.000000, 3.000000,
-        2.000000, 3.000000, 4.000000,
-        3.000000, 4.000000, 5.000000,
-        4.000000, 5.000000, 6.000000,
-        5.000000, 6.000000, 7.000000
-    )
-    val buffer2 = doubleArrayOf(
-        0.000000, 0.000000, 0.000000,
-        0.000000, 0.000000, 0.000000,
-        0.000000, 0.000000, 0.000000
-    )
-    val tensor = fromArray(shape, buffer).as2D()
-    val v = fromArray(intArrayOf(3, 3), buffer2).as2D()
-    val w_shape = intArrayOf(3, 1)
-    var w_buffer = doubleArrayOf(0.000000)
-    for (i in 0 until 3 - 1) {
-        w_buffer += doubleArrayOf(0.000000)
-    }
-    val w = BroadcastDoubleTensorAlgebra.fromArray(w_shape, w_buffer).as2D()
-    tensor.print()
-    var ans = Pair(w, v)
-    tensor.svdGolabKahan(v, w)
-
-    println("u")
-    tensor.print()
-    println("w")
-    w.print()
-    println("v")
-    v.print()
-
-
-}

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

@@ -1,325 +0,0 @@
-package space.kscience.kmath.tensors
-
-import space.kscience.kmath.nd.*
-import kotlin.math.abs
-import kotlin.math.max
-import kotlin.math.min
-import kotlin.math.sqrt
-
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-fun pythag(a: Double, b: Double): Double {
-    val at: Double = abs(a)
-    val bt: Double = abs(b)
-    val ct: Double
-    val result: Double
-    if (at > bt) {
-        ct = bt / at
-        result = at * sqrt(1.0 + ct * ct)
-    } else if (bt > 0.0) {
-        ct = at / bt
-        result = bt * sqrt(1.0 + ct * ct)
-    } else result = 0.0
-    return result
-}
-
-fun SIGN(a: Double, b: Double): Double {
-    if (b >= 0.0)
-        return abs(a)
-    else
-        return -abs(a)
-}
-
-// matrix v is not transposed at the output
-
-internal fun MutableStructure2D<Double>.svdGolabKahan(v: MutableStructure2D<Double>, w: MutableStructure2D<Double>) {
-    val shape = this.shape
-    val m = shape.component1()
-    val n = shape.component2()
-    var f = 0.0
-    val rv1 = DoubleArray(n)
-    var s = 0.0
-    var scale = 0.0
-    var anorm = 0.0
-    var g = 0.0
-    var l = 0
-    for (i in 0 until n) {
-        /* left-hand reduction */
-        l = i + 1
-        rv1[i] = scale * g
-        g = 0.0
-        s = 0.0
-        scale = 0.0
-        if (i < m) {
-            for (k in i until m) {
-                scale += abs(this[k, i]);
-            }
-            if (scale != 0.0) {
-                for (k in i until m) {
-                    this[k, i] = (this[k, i] / scale)
-                    s += this[k, i] * this[k, i]
-                }
-                f = this[i, i]
-                if (f >= 0) {
-                    g = (-1) * abs(sqrt(s))
-                } else {
-                    g = abs(sqrt(s))
-                }
-                val h = f * g - s
-                this[i, i] = f - g
-                if (i != n - 1) {
-                    for (j in l until n) {
-                        s = 0.0
-                        for (k in i until m) {
-                            s += this[k, i] * this[k, j]
-                        }
-                        f = s / h
-                        for (k in i until m) {
-                            this[k, j] += f * this[k, i]
-                        }
-                    }
-                }
-                for (k in i until m) {
-                    this[k, i] = this[k, i] * scale
-                }
-            }
-        }
-
-        w[i, 0] = scale * g
-        /* right-hand reduction */
-        g = 0.0
-        s = 0.0
-        scale = 0.0
-        if (i < m && i != n - 1) {
-            for (k in l until n) {
-                scale += abs(this[i, k])
-            }
-            if (scale != 0.0) {
-                for (k in l until n) {
-                    this[i, k] = this[i, k] / scale
-                    s += this[i, k] * this[i, k]
-                }
-                f = this[i, l]
-                if (f >= 0) {
-                    g = (-1) * abs(sqrt(s))
-                } else {
-                    g = abs(sqrt(s))
-                }
-                val h = f * g - s
-                this[i, l] = f - g
-                for (k in l until n) {
-                    rv1[k] = this[i, k] / h
-                }
-                if (i != m - 1) {
-                    for (j in l until m) {
-                        s = 0.0
-                        for (k in l until n) {
-                            s += this[j, k] * this[i, k]
-                        }
-                        for (k in l until n) {
-                            this[j, k] += s * rv1[k]
-                        }
-                    }
-                }
-                for (k in l until n) {
-                    this[i, k] = this[i, k] * scale
-                }
-            }
-        }
-        anorm = max(anorm, (abs(w[i, 0]) + abs(rv1[i])));
-    }
-
-    for (i in n - 1 downTo 0) {
-        if (i < n - 1) {
-            if (g != 0.0) {
-                for (j in l until n) {
-                    v[j, i] = (this[i, j] / this[i, l]) / g
-                }
-                for (j in l until n) {
-                    s = 0.0
-                    for (k in l until n)
-                        s += this[i, k] * v[k, j]
-                    for (k in l until n)
-                        v[k, j] += s * v[k, i]
-                }
-            }
-            for (j in l until n) {
-                v[i, j] = 0.0
-                v[j, i] = 0.0
-            }
-        }
-        v[i, i] = 1.0
-        g = rv1[i]
-        l = i
-    }
-
-    // до этого момента все правильно считается
-    // дальше - нет
-
-    for (i in min(n, m) - 1 downTo 0) {
-        l = i + 1
-        g = w[i, 0]
-        for (j in l until n) {
-            this[i, j] = 0.0
-        }
-        if (g != 0.0) {
-            // !!!!! вот тут деление на почти ноль
-            g = 1.0 / g
-            for (j in l until n) {
-                s = 0.0
-                for (k in l until m) {
-                    s += this[k, i] * this[k, j]
-                }
-                f = (s / this[i, i]) * g
-                for (k in i until m) {
-                    this[k, j] += f * this[k, i]
-                }
-            }
-            for (j in i until m) {
-                this[j, i] *= g
-            }
-        } else {
-            for (j in i until m) {
-                this[j, i] = 0.0
-            }
-        }
-        this[i, i] += 1.0
-    }
-
-//    println("matrix")
-//    this.print()
-//    тут матрица должна выглядеть так:
-//    0.134840   -0.762770    0.522117
-//    -0.269680   -0.476731   -0.245388
-//    -0.404520   -0.190693   -0.527383
-//    -0.539360    0.095346   -0.297540
-//    -0.674200    0.381385    0.548193
-
-    this[0, 2] = 0.522117
-    this[1, 2] = -0.245388
-    this[2, 2] = -0.527383
-    this[3, 2] = -0.297540
-    this[4, 2] = 0.548193
-
-    // задала правильные значения, чтобы проверить правильность кода дальше
-    // дальше - все корректно
-
-    var flag = 0
-    var nm = 0
-    var c = 0.0
-    var h = 0.0
-    var y = 0.0
-    var z = 0.0
-    var x = 0.0
-    for (k in n - 1 downTo 0) {
-        for (its in 1 until 30) {
-            flag = 1
-            for (newl in k downTo 0) {
-                nm = newl - 1
-                if (abs(rv1[newl]) + anorm == anorm) {
-                    flag = 0
-                    l = newl
-                    break
-                }
-                if (abs(w[nm, 0]) + anorm == anorm) {
-                    l = newl
-                    break
-                }
-            }
-
-            if (flag != 0) {
-                c = 0.0
-                s = 1.0
-                for (i in l until k) {
-                    f = s * rv1[i]
-                    rv1[i] = c * rv1[i]
-                    if (abs(f) + anorm == anorm) {
-                        break
-                    }
-                    h = pythag(f, g)
-                    w[i, 0] = h
-                    h = 1.0 / h
-                    c = g * h
-                    s = (-f) * h
-                    for (j in 0 until m) {
-                        y = this[j, nm]
-                        z = this[j, i]
-                        this[j, nm] = y * c + z * s
-                        this[j, i] = z * c - y * s
-                    }
-                }
-            }
-
-            z = w[k, 0]
-            if (l == k) {
-                if (z < 0.0) {
-                    w[k, 0] = -z
-                    for (j in 0 until n)
-                        v[j, k] = -v[j, k]
-                }
-                break
-            }
-
-//            надо придумать, что сделать - выкинуть ошибку?
-//            if (its == 30) {
-//                return
-//            }
-
-            x = w[l, 0]
-            nm = k - 1
-            y = w[nm, 0]
-            g = rv1[nm]
-            h = rv1[k]
-            f = ((y - z) * (y + z) + (g - h) * (g + h)) / (2.0 * h * y)
-            g = pythag(f, 1.0)
-            f = ((x - z) * (x + z) + h * ((y / (f + SIGN(g, f))) - h)) / x
-            c = 1.0
-            s = 1.0
-
-            var i = 0
-            for (j in l until nm + 1) {
-                i = j + 1
-                g = rv1[i]
-                y = w[i, 0]
-                h = s * g
-                g = c * g
-                z = pythag(f, h)
-                rv1[j] = z
-                c = f / z
-                s = h / z
-                f = x * c + g * s
-                g = g * c - x * s
-                h = y * s
-                y *= c
-
-                for (jj in 0 until n) {
-                    x = v[jj, j];
-                    z = v[jj, i];
-                    v[jj, j] = x * c + z * s;
-                    v[jj, i] = z * c - x * s;
-                }
-                z = pythag(f, h)
-                w[j, 0] = z
-                if (z != 0.0) {
-                    z = 1.0 / z
-                    c = f * z
-                    s = h * z
-                }
-                f = c * g + s * y
-                x = c * y - s * g
-                for (jj in 0 until m) {
-                    y = this[jj, j]
-                    z = this[jj, i]
-                    this[jj, j] = y * c + z * s
-                    this[jj, i] = z * c - y * s
-                }
-            }
-            rv1[l] = 0.0
-            rv1[k] = f
-            w[k, 0] = x
-        }
-    }
-}

gradle.properties

@@ -2,14 +2,12 @@
 # Copyright 2018-2021 KMath contributors.
 # Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
 #
-
 kotlin.code.style=official
-kotlin.mpp.stability.nowarn=true
-
 kotlin.jupyter.add.scanner=false
+kotlin.mpp.stability.nowarn=true
+kotlin.native.ignoreDisabledTargets=true
+#kotlin.incremental.js.ir=true
 
 org.gradle.configureondemand=true
-org.gradle.parallel=true
 org.gradle.jvmargs=-XX:MaxMetaspaceSize=1G
-
+org.gradle.parallel=true
-toolsVersion=0.11.1-kotlin-1.6.10

gradle/wrapper/gradle-wrapper.properties

@@ -1,5 +1,5 @@
 distributionBase=GRADLE_USER_HOME
 distributionPath=wrapper/dists
-distributionUrl=https\://services.gradle.org/distributions/gradle-7.3.3-bin.zip
+distributionUrl=https\://services.gradle.org/distributions/gradle-7.4.2-bin.zip
 zipStoreBase=GRADLE_USER_HOME
 zipStorePath=wrapper/dists

kmath-ast/README.md

@@ -10,17 +10,17 @@ Extensions to MST API: transformations, dynamic compilation and visualization.
 
 ## Artifact:
 
-The Maven coordinates of this project are `space.kscience:kmath-ast:0.3.0-dev-17`.
+The Maven coordinates of this project are `space.kscience:kmath-ast:0.3.0`.
 
-**Gradle:**
+**Gradle Groovy:**
-```gradle
+```groovy
 repositories {
     maven { url 'https://repo.kotlin.link' }
     mavenCentral()
 }
 
 dependencies {
-    implementation 'space.kscience:kmath-ast:0.3.0-dev-17'
+    implementation 'space.kscience:kmath-ast:0.3.0'
 }
 ```
 **Gradle Kotlin DSL:**
@@ -31,7 +31,7 @@ repositories {
 }
 
 dependencies {
-    implementation("space.kscience:kmath-ast:0.3.0-dev-17")
+    implementation("space.kscience:kmath-ast:0.3.0")
 }
 ```
 
@@ -66,20 +66,19 @@ For example, the following code:
 
 ```kotlin
 import space.kscience.kmath.asm.compileToExpression
-import space.kscience.kmath.complex.ComplexField
+import space.kscience.kmath.operations.DoubleField
 
-"x+2".parseMath().compileToExpression(ComplexField)
+"x^3-x+3".parseMath().compileToExpression(DoubleField)
 ```
 
 … leads to generation of bytecode, which can be decompiled to the following Java class:
 
 ```java
-import java.util.Map;
+import java.util.*;
-import kotlin.jvm.functions.Function2;
+import kotlin.jvm.functions.*;
-import space.kscience.kmath.asm.internal.MapIntrinsics;
+import space.kscience.kmath.asm.internal.*;
-import space.kscience.kmath.complex.Complex;
+import space.kscience.kmath.complex.*;
-import space.kscience.kmath.expressions.Expression;
+import space.kscience.kmath.expressions.*;
-import space.kscience.kmath.expressions.Symbol;
 
 public final class CompiledExpression_45045_0 implements Expression<Complex> {
     private final Object[] constants;
@@ -91,6 +90,32 @@ public final class CompiledExpression_45045_0 implements Expression<Complex> {
 }
 ```
 
+For `LongRing`, `IntRing`, and `DoubleField` specialization is supported for better performance:
+
+```java
+import java.util.*;
+import space.kscience.kmath.asm.internal.*;
+import space.kscience.kmath.expressions.*;
+
+public final class CompiledExpression_-386104628_0 implements DoubleExpression {
+    private final SymbolIndexer indexer;
+
+    public SymbolIndexer getIndexer() {
+        return this.indexer;
+    }
+
+    public double invoke(double[] arguments) {
+        double var2 = arguments[0];
+        return Math.pow(var2, 3.0D) - var2 + 3.0D;
+    }
+
+    public final Double invoke(Map<Symbol, ? extends Double> arguments) {
+        double var2 = ((Double)MapIntrinsics.getOrFail(arguments, "x")).doubleValue();
+        return Math.pow(var2, 3.0D) - var2 + 3.0D;
+    }
+}
+```
+
 Setting JVM system property `space.kscience.kmath.ast.dump.generated.classes` to `1` makes the translator dump class files to program's working directory, so they can be reviewed manually.
 
 #### Limitations
@@ -134,9 +159,9 @@ MstField { x + 2 }.compileToExpression(DoubleField)
 An example of emitted Wasm IR in the form of WAT:
 
 ```lisp
-(func $executable (param $0 f64) (result f64)
+(func \$executable (param \$0 f64) (result f64)
   (f64.add
-    (local.get $0)
+    (local.get \$0)
     (f64.const 2)
   )
 )

kmath-commons/README.md

@@ -0,0 +1,32 @@
+# Module kmath-commons
+
+Commons math binding for kmath
+
+## Usage
+
+## Artifact:
+
+The Maven coordinates of this project are `space.kscience:kmath-commons:0.3.0`.
+
+**Gradle Groovy:**
+```groovy
+repositories {
+    maven { url 'https://repo.kotlin.link' }
+    mavenCentral()
+}
+
+dependencies {
+    implementation 'space.kscience:kmath-commons:0.3.0'
+}
+```
+**Gradle Kotlin DSL:**
+```kotlin
+repositories {
+    maven("https://repo.kotlin.link")
+    mavenCentral()
+}
+
+dependencies {
+    implementation("space.kscience:kmath-commons:0.3.0")
+}
+```

kmath-complex/README.md

@@ -8,17 +8,17 @@ Complex and hypercomplex number systems in KMath.
 
 ## Artifact:
 
-The Maven coordinates of this project are `space.kscience:kmath-complex:0.3.0-dev-17`.
+The Maven coordinates of this project are `space.kscience:kmath-complex:0.3.0`.
 
-**Gradle:**
+**Gradle Groovy:**
-```gradle
+```groovy
 repositories {
     maven { url 'https://repo.kotlin.link' }
     mavenCentral()
 }
 
 dependencies {
-    implementation 'space.kscience:kmath-complex:0.3.0-dev-17'
+    implementation 'space.kscience:kmath-complex:0.3.0'
 }
 ```
 **Gradle Kotlin DSL:**
@@ -29,6 +29,6 @@ repositories {
 }
 
 dependencies {
-    implementation("space.kscience:kmath-complex:0.3.0-dev-17")
+    implementation("space.kscience:kmath-complex:0.3.0")
 }
 ```

kmath-core/README.md

@@ -15,17 +15,17 @@ performance calculations to code generation.
 
 ## Artifact:
 
-The Maven coordinates of this project are `space.kscience:kmath-core:0.3.0-dev-17`.
+The Maven coordinates of this project are `space.kscience:kmath-core:0.3.0`.
 
-**Gradle:**
+**Gradle Groovy:**
-```gradle
+```groovy
 repositories {
     maven { url 'https://repo.kotlin.link' }
     mavenCentral()
 }
 
 dependencies {
-    implementation 'space.kscience:kmath-core:0.3.0-dev-17'
+    implementation 'space.kscience:kmath-core:0.3.0'
 }
 ```
 **Gradle Kotlin DSL:**
@@ -36,6 +36,6 @@ repositories {
 }
 
 dependencies {
-    implementation("space.kscience:kmath-core:0.3.0-dev-17")
+    implementation("space.kscience:kmath-core:0.3.0")
 }
 ```

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

@@ -0,0 +1,59 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.domains
+
+import space.kscience.kmath.linear.Point
+import space.kscience.kmath.misc.UnstableKMathAPI
+
+@UnstableKMathAPI
+public abstract class Domain1D<T : Comparable<T>>(public val range: ClosedRange<T>) : Domain<T> {
+    override val dimension: Int get() = 1
+
+    public operator fun contains(value: T): Boolean = range.contains(value)
+
+    override operator fun contains(point: Point<T>): Boolean {
+        require(point.size == 0)
+        return contains(point[0])
+    }
+}
+
+@UnstableKMathAPI
+public class DoubleDomain1D(
+    @Suppress("CanBeParameter") public val doubleRange: ClosedFloatingPointRange<Double>,
+) : Domain1D<Double>(doubleRange), DoubleDomain {
+    override fun getLowerBound(num: Int): Double {
+        require(num == 0)
+        return range.start
+    }
+
+    override fun getUpperBound(num: Int): Double {
+        require(num == 0)
+        return range.endInclusive
+    }
+
+    override fun volume(): Double = range.endInclusive - range.start
+
+    override fun equals(other: Any?): Boolean {
+        if (this === other) return true
+        if (other == null || this::class != other::class) return false
+
+        other as DoubleDomain1D
+
+        if (doubleRange != other.doubleRange) return false
+
+        return true
+    }
+
+    override fun hashCode(): Int = doubleRange.hashCode()
+
+    override fun toString(): String = doubleRange.toString()
+
+
+}
+
+@UnstableKMathAPI
+public val Domain1D<Double>.center: Double
+    get() = (range.endInclusive + range.start) / 2

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

@@ -7,18 +7,28 @@ package space.kscience.kmath.domains
 import space.kscience.kmath.linear.Point
 import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.DoubleBuffer
 import space.kscience.kmath.structures.indices
 
 /**
- *
+ * A hyper-square (or hyper-cube) real-space domain. It is formed by a [Buffer] of [lower] boundaries
- * HyperSquareDomain class.
+ * and a [Buffer] of upper boundaries. Upper should be greater or equals than lower.
- *
- * @author Alexander Nozik
  */
 @UnstableKMathAPI
-public class HyperSquareDomain(private val lower: Buffer<Double>, private val upper: Buffer<Double>) : DoubleDomain {
+public class HyperSquareDomain(public val lower: Buffer<Double>, public val upper: Buffer<Double>) : DoubleDomain {
+    init {
+        require(lower.size == upper.size) {
+            "Domain borders size mismatch. Lower borders size is ${lower.size}, but upper borders size is ${upper.size}."
+        }
+        require(lower.indices.all { lower[it] <= upper[it] }) {
+            "Domain borders order mismatch. Lower borders must be less or equals than upper borders."
+        }
+    }
+
     override val dimension: Int get() = lower.size
 
+    public val center: DoubleBuffer get() = DoubleBuffer(dimension) { (lower[it] + upper[it]) / 2.0 }
+
     override operator fun contains(point: Point<Double>): Boolean = point.indices.all { i ->
         point[i] in lower[i]..upper[i]
     }

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

@@ -1,33 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.domains
-
-import space.kscience.kmath.linear.Point
-import space.kscience.kmath.misc.UnstableKMathAPI
-
-@UnstableKMathAPI
-public class UnivariateDomain(public val range: ClosedFloatingPointRange<Double>) : DoubleDomain {
-    override val dimension: Int get() = 1
-
-    public operator fun contains(d: Double): Boolean = range.contains(d)
-
-    override operator fun contains(point: Point<Double>): Boolean {
-        require(point.size == 0)
-        return contains(point[0])
-    }
-
-    override fun getLowerBound(num: Int): Double {
-        require(num == 0)
-        return range.start
-    }
-
-    override fun getUpperBound(num: Int): Double {
-        require(num == 0)
-        return range.endInclusive
-    }
-
-    override fun volume(): Double = range.endInclusive - range.start
-}

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

@@ -3,43 +3,71 @@
  * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
+
 package space.kscience.kmath.misc
 
-import kotlin.comparisons.*
 import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.VirtualBuffer
 
 /**
- * Return a new list filled with buffer indices. Indice order is defined by sorting associated buffer value.
+ * Return a new array filled with buffer indices. Indices order is defined by sorting associated buffer value.
- * This feature allows to sort buffer values without reordering its content.
+ * This feature allows sorting buffer values without reordering its content.
  *
- * @return List of buffer indices, sorted by associated value.
+ * @return Buffer indices, sorted by associated value.
  */
-@PerformancePitfall
 @UnstableKMathAPI
-public fun <V: Comparable<V>> Buffer<V>.permSort() : IntArray = _permSortWith(compareBy<Int> { get(it) })
+public fun <V : Comparable<V>> Buffer<V>.indicesSorted(): IntArray = permSortIndicesWith(compareBy { get(it) })
+
+/**
+ * Create a zero-copy virtual buffer that contains the same elements but in ascending order
+ */
+@OptIn(UnstableKMathAPI::class)
+public fun <V : Comparable<V>> Buffer<V>.sorted(): Buffer<V> {
+    val permutations = indicesSorted()
+    return VirtualBuffer(size) { this[permutations[it]] }
+}
 
-@PerformancePitfall
 @UnstableKMathAPI
-public fun <V: Comparable<V>> Buffer<V>.permSortDescending() : IntArray = _permSortWith(compareByDescending<Int> { get(it) })
+public fun <V : Comparable<V>> Buffer<V>.indicesSortedDescending(): IntArray =
+    permSortIndicesWith(compareByDescending { get(it) })
+
+/**
+ * Create a zero-copy virtual buffer that contains the same elements but in descending order
+ */
+@OptIn(UnstableKMathAPI::class)
+public fun <V : Comparable<V>> Buffer<V>.sortedDescending(): Buffer<V> {
+    val permutations = indicesSortedDescending()
+    return VirtualBuffer(size) { this[permutations[it]] }
+}
 
-@PerformancePitfall
 @UnstableKMathAPI
-public fun <V, C: Comparable<C>> Buffer<V>.permSortBy(selector: (V) -> C) : IntArray = _permSortWith(compareBy<Int> { selector(get(it)) })
+public fun <V, C : Comparable<C>> Buffer<V>.indicesSortedBy(selector: (V) -> C): IntArray =
+    permSortIndicesWith(compareBy { selector(get(it)) })
+
+@OptIn(UnstableKMathAPI::class)
+public fun <V, C : Comparable<C>> Buffer<V>.sortedBy(selector: (V) -> C): Buffer<V> {
+    val permutations = indicesSortedBy(selector)
+    return VirtualBuffer(size) { this[permutations[it]] }
+}
 
-@PerformancePitfall
 @UnstableKMathAPI
-public fun <V, C: Comparable<C>> Buffer<V>.permSortByDescending(selector: (V) -> C) : IntArray = _permSortWith(compareByDescending<Int> { selector(get(it)) })
+public fun <V, C : Comparable<C>> Buffer<V>.indicesSortedByDescending(selector: (V) -> C): IntArray =
+    permSortIndicesWith(compareByDescending { selector(get(it)) })
+
+@OptIn(UnstableKMathAPI::class)
+public fun <V, C : Comparable<C>> Buffer<V>.sortedByDescending(selector: (V) -> C): Buffer<V> {
+    val permutations = indicesSortedByDescending(selector)
+    return VirtualBuffer(size) { this[permutations[it]] }
+}
 
-@PerformancePitfall
 @UnstableKMathAPI
-public fun <V> Buffer<V>.permSortWith(comparator : Comparator<V>) : IntArray = _permSortWith { i1, i2 -> comparator.compare(get(i1), get(i2)) }
+public fun <V> Buffer<V>.indicesSortedWith(comparator: Comparator<V>): IntArray =
+    permSortIndicesWith { i1, i2 -> comparator.compare(get(i1), get(i2)) }
 
-@PerformancePitfall
+private fun <V> Buffer<V>.permSortIndicesWith(comparator: Comparator<Int>): IntArray {
-@UnstableKMathAPI
+    if (size < 2) return IntArray(size) { 0 }
-private fun <V> Buffer<V>._permSortWith(comparator : Comparator<Int>) : IntArray {
-    if (size < 2) return IntArray(size)
 
-    /* TODO: optimisation : keep a constant big array of indices (Ex: from 0 to 4096), then create indice
+    /* TODO: optimisation : keep a constant big array of indices (Ex: from 0 to 4096), then create indices
      * arrays more efficiently by copying subpart of cached one. For bigger needs, we could copy entire
      * cached array, then fill remaining indices manually. Not done for now, because:
      *  1. doing it right would require some statistics about common used buffer sizes.
@@ -53,3 +81,12 @@ private fun <V> Buffer<V>._permSortWith(comparator : Comparator<Int>) : IntArray
      */
     return packedIndices.sortedWith(comparator).toIntArray()
 }
+
+/**
+ * Checks that the [Buffer] is sorted (ascending) and throws [IllegalArgumentException] if it is not.
+ */
+public fun <T : Comparable<T>> Buffer<T>.requireSorted() {
+    for (i in 0..(size - 2)) {
+        require(get(i + 1) >= get(i)) { "The buffer is not sorted at index $i" }
+    }
+}

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

@@ -194,7 +194,7 @@ public interface RingOpsND<T, out A : RingOps<T>> : RingOps<StructureND<T>>, Gro
     override fun multiply(left: StructureND<T>, right: StructureND<T>): StructureND<T> =
         zip(left, right) { aValue, bValue -> multiply(aValue, bValue) }
 
-    //TODO move to extensions after KEEP-176
+    //TODO move to extensions with context receivers
 
     /**
      * Multiplies an ND structure by an element of it.

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

@@ -32,18 +32,23 @@ public open class BufferND<out T>(
 /**
  * Transform structure to a new structure using provided [BufferFactory] and optimizing if argument is [BufferND]
  */
-public inline fun <T, reified R : Any> StructureND<T>.mapToBuffer(
+public inline fun <T, R : Any> StructureND<T>.mapToBuffer(
-    factory: BufferFactory<R> = Buffer.Companion::auto,
+    factory: BufferFactory<R>,
     crossinline transform: (T) -> R,
-): BufferND<R> {
+): BufferND<R> = if (this is BufferND<T>)
-    return if (this is BufferND<T>)
+    BufferND(this.indices, factory.invoke(indices.linearSize) { transform(buffer[it]) })
-        BufferND(this.indices, factory.invoke(indices.linearSize) { transform(buffer[it]) })
+else {
-    else {
+    val strides = DefaultStrides(shape)
-        val strides = DefaultStrides(shape)
+    BufferND(strides, factory.invoke(strides.linearSize) { transform(get(strides.index(it))) })
-        BufferND(strides, factory.invoke(strides.linearSize) { transform(get(strides.index(it))) })
-    }
 }
 
+/**
+ * Transform structure to a new structure using inferred [BufferFactory]
+ */
+public inline fun <T, reified R : Any> StructureND<T>.mapToBuffer(
+    crossinline transform: (T) -> R,
+): BufferND<R> = mapToBuffer(Buffer.Companion::auto, transform)
+
 /**
  * Represents [MutableStructureND] over [MutableBuffer].
  *

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

@@ -5,7 +5,6 @@
 
 package space.kscience.kmath.operations
 
-import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.structures.Buffer
 import space.kscience.kmath.structures.BufferFactory
 import space.kscience.kmath.structures.DoubleBuffer
@@ -53,7 +52,7 @@ public interface BufferAlgebra<T, out A : Algebra<T>> : Algebra<Buffer<T>> {
  */
 private inline fun <T, A : Algebra<T>> BufferAlgebra<T, A>.mapInline(
     buffer: Buffer<T>,
-    crossinline block: A.(T) -> T
+    crossinline block: A.(T) -> T,
 ): Buffer<T> = bufferFactory(buffer.size) { elementAlgebra.block(buffer[it]) }
 
 /**
@@ -61,7 +60,7 @@ private inline fun <T, A : Algebra<T>> BufferAlgebra<T, A>.mapInline(
  */
 private inline fun <T, A : Algebra<T>> BufferAlgebra<T, A>.mapIndexedInline(
     buffer: Buffer<T>,
-    crossinline block: A.(index: Int, arg: T) -> T
+    crossinline block: A.(index: Int, arg: T) -> T,
 ): Buffer<T> = bufferFactory(buffer.size) { elementAlgebra.block(it, buffer[it]) }
 
 /**
@@ -70,7 +69,7 @@ private inline fun <T, A : Algebra<T>> BufferAlgebra<T, A>.mapIndexedInline(
 private inline fun <T, A : Algebra<T>> BufferAlgebra<T, A>.zipInline(
     l: Buffer<T>,
     r: Buffer<T>,
-    crossinline block: A.(l: T, r: T) -> T
+    crossinline block: A.(l: T, r: T) -> T,
 ): Buffer<T> {
     require(l.size == r.size) { "Incompatible buffer sizes. left: ${l.size}, right: ${r.size}" }
     return bufferFactory(l.size) { elementAlgebra.block(l[it], r[it]) }
@@ -127,13 +126,13 @@ public fun <T, A : ExponentialOperations<T>> BufferAlgebra<T, A>.atanh(arg: Buff
     mapInline(arg) { atanh(it) }
 
 public fun <T, A : PowerOperations<T>> BufferAlgebra<T, A>.pow(arg: Buffer<T>, pow: Number): Buffer<T> =
-    mapInline(arg) {it.pow(pow) }
+    mapInline(arg) { it.pow(pow) }
 
 
-public open class BufferRingOps<T, A: Ring<T>>(
+public open class BufferRingOps<T, A : Ring<T>>(
     override val elementAlgebra: A,
     override val bufferFactory: BufferFactory<T>,
-) : BufferAlgebra<T, A>, RingOps<Buffer<T>>{
+) : BufferAlgebra<T, A>, RingOps<Buffer<T>> {
 
     override fun add(left: Buffer<T>, right: Buffer<T>): Buffer<T> = zipInline(left, right) { l, r -> l + r }
     override fun multiply(left: Buffer<T>, right: Buffer<T>): Buffer<T> = zipInline(left, right) { l, r -> l * r }
@@ -152,10 +151,11 @@ public val ShortRing.bufferAlgebra: BufferRingOps<Short, ShortRing>
 public open class BufferFieldOps<T, A : Field<T>>(
     elementAlgebra: A,
     bufferFactory: BufferFactory<T>,
-) : BufferRingOps<T, A>(elementAlgebra, bufferFactory), BufferAlgebra<T, A>, FieldOps<Buffer<T>>, ScaleOperations<Buffer<T>> {
+) : BufferRingOps<T, A>(elementAlgebra, bufferFactory), BufferAlgebra<T, A>, FieldOps<Buffer<T>>,
+    ScaleOperations<Buffer<T>> {
 
-    override fun add(left: Buffer<T>, right: Buffer<T>): Buffer<T> = zipInline(left, right) { l, r -> l + r }
+//    override fun add(left: Buffer<T>, right: Buffer<T>): Buffer<T> = zipInline(left, right) { l, r -> l + r }
-    override fun multiply(left: Buffer<T>, right: Buffer<T>): Buffer<T> = zipInline(left, right) { l, r -> l * r }
+//    override fun multiply(left: Buffer<T>, right: Buffer<T>): Buffer<T> = zipInline(left, right) { l, r -> l * r }
     override fun divide(left: Buffer<T>, right: Buffer<T>): Buffer<T> = zipInline(left, right) { l, r -> l / r }
 
     override fun scale(a: Buffer<T>, value: Double): Buffer<T> = a.map { scale(it, value) }
@@ -168,7 +168,7 @@ public open class BufferFieldOps<T, A : Field<T>>(
 public class BufferField<T, A : Field<T>>(
     elementAlgebra: A,
     bufferFactory: BufferFactory<T>,
-    override val size: Int
+    override val size: Int,
 ) : BufferFieldOps<T, A>(elementAlgebra, bufferFactory), Field<Buffer<T>>, WithSize {
 
     override val zero: Buffer<T> = bufferFactory(size) { elementAlgebra.zero }

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

@@ -105,6 +105,16 @@ public interface Buffer<out T> {
  */
 public val Buffer<*>.indices: IntRange get() = 0 until size
 
+public fun <T> Buffer<T>.first(): T {
+    require(size > 0) { "Can't get the first element of empty buffer" }
+    return get(0)
+}
+
+public fun <T> Buffer<T>.last(): T {
+    require(size > 0) { "Can't get the last element of empty buffer" }
+    return get(size - 1)
+}
+
 /**
  * Immutable wrapper for [MutableBuffer].
  *

kmath-core/src/commonTest/kotlin/space/kscience/kmath/misc/PermSortTest.kt

@@ -6,14 +6,13 @@
 package space.kscience.kmath.misc
 
 import space.kscience.kmath.misc.PermSortTest.Platform.*
-import kotlin.random.Random
-import kotlin.test.Test
-import kotlin.test.assertEquals
-import kotlin.test.assertTrue
-
 import space.kscience.kmath.structures.IntBuffer
 import space.kscience.kmath.structures.asBuffer
+import kotlin.random.Random
+import kotlin.test.Test
 import kotlin.test.assertContentEquals
+import kotlin.test.assertEquals
+import kotlin.test.assertTrue
 
 class PermSortTest {
 
@@ -29,9 +28,9 @@ class PermSortTest {
     @Test
     fun testOnEmptyBuffer() {
         val emptyBuffer = IntBuffer(0) {it}
-        var permutations = emptyBuffer.permSort()
+        var permutations = emptyBuffer.indicesSorted()
         assertTrue(permutations.isEmpty(), "permutation on an empty buffer should return an empty result")
-        permutations = emptyBuffer.permSortDescending()
+        permutations = emptyBuffer.indicesSortedDescending()
         assertTrue(permutations.isEmpty(), "permutation on an empty buffer should return an empty result")
     }
 
@@ -47,25 +46,25 @@ class PermSortTest {
 
     @Test
     fun testPermSortBy() {
-        val permutations = platforms.permSortBy { it.name }
+        val permutations = platforms.indicesSortedBy { it.name }
         val expected = listOf(ANDROID, JS, JVM, NATIVE, WASM)
         assertContentEquals(expected, permutations.map { platforms[it] }, "Ascending PermSort by name")
     }
 
     @Test
     fun testPermSortByDescending() {
-        val permutations = platforms.permSortByDescending { it.name }
+        val permutations = platforms.indicesSortedByDescending { it.name }
         val expected = listOf(WASM, NATIVE, JVM, JS, ANDROID)
         assertContentEquals(expected, permutations.map { platforms[it] }, "Descending PermSort by name")
     }
 
     @Test
     fun testPermSortWith() {
-        var permutations = platforms.permSortWith { p1, p2 -> p1.name.length.compareTo(p2.name.length) }
+        var permutations = platforms.indicesSortedWith { p1, p2 -> p1.name.length.compareTo(p2.name.length) }
         val expected = listOf(JS, JVM, WASM, NATIVE, ANDROID)
         assertContentEquals(expected, permutations.map { platforms[it] }, "PermSort using custom ascending comparator")
 
-        permutations = platforms.permSortWith(compareByDescending { it.name.length })
+        permutations = platforms.indicesSortedWith(compareByDescending { it.name.length })
         assertContentEquals(expected.reversed(), permutations.map { platforms[it] }, "PermSort using custom descending comparator")
     }
 
@@ -75,7 +74,7 @@ class PermSortTest {
         println("Test randomization seed: $seed")
 
         val buffer = Random(seed).buffer(bufferSize)
-        val indices = buffer.permSort()
+        val indices = buffer.indicesSorted()
 
         assertEquals(bufferSize, indices.size)
         // Ensure no doublon is present in indices
@@ -87,7 +86,7 @@ class PermSortTest {
             assertTrue(current <= next, "Permutation indices not properly sorted")
         }
 
-        val descIndices = buffer.permSortDescending()
+        val descIndices = buffer.indicesSortedDescending()
         assertEquals(bufferSize, descIndices.size) 
         // Ensure no doublon is present in indices
         assertEquals(descIndices.toSet().size, descIndices.size)

kmath-coroutines/README.md

@@ -0,0 +1,32 @@
+# Module kmath-coroutines
+
+
+
+## Usage
+
+## Artifact:
+
+The Maven coordinates of this project are `space.kscience:kmath-coroutines:0.3.0`.
+
+**Gradle Groovy:**
+```groovy
+repositories {
+    maven { url 'https://repo.kotlin.link' }
+    mavenCentral()
+}
+
+dependencies {
+    implementation 'space.kscience:kmath-coroutines:0.3.0'
+}
+```
+**Gradle Kotlin DSL:**
+```kotlin
+repositories {
+    maven("https://repo.kotlin.link")
+    mavenCentral()
+}
+
+dependencies {
+    implementation("space.kscience:kmath-coroutines:0.3.0")
+}
+```

kmath-dimensions/README.md

@@ -0,0 +1,32 @@
+# Module kmath-dimensions
+
+A proof of concept module for adding type-safe dimensions to structures
+
+## Usage
+
+## Artifact:
+
+The Maven coordinates of this project are `space.kscience:kmath-dimensions:0.3.0`.
+
+**Gradle Groovy:**
+```groovy
+repositories {
+    maven { url 'https://repo.kotlin.link' }
+    mavenCentral()
+}
+
+dependencies {
+    implementation 'space.kscience:kmath-dimensions:0.3.0'
+}
+```
+**Gradle Kotlin DSL:**
+```kotlin
+repositories {
+    maven("https://repo.kotlin.link")
+    mavenCentral()
+}
+
+dependencies {
+    implementation("space.kscience:kmath-dimensions:0.3.0")
+}
+```

kmath-ejml/README.md

@@ -9,17 +9,17 @@ EJML based linear algebra implementation.
 
 ## Artifact:
 
-The Maven coordinates of this project are `space.kscience:kmath-ejml:0.3.0-dev-17`.
+The Maven coordinates of this project are `space.kscience:kmath-ejml:0.3.0`.
 
-**Gradle:**
+**Gradle Groovy:**
-```gradle
+```groovy
 repositories {
     maven { url 'https://repo.kotlin.link' }
     mavenCentral()
 }
 
 dependencies {
-    implementation 'space.kscience:kmath-ejml:0.3.0-dev-17'
+    implementation 'space.kscience:kmath-ejml:0.3.0'
 }
 ```
 **Gradle Kotlin DSL:**
@@ -30,6 +30,6 @@ repositories {
 }
 
 dependencies {
-    implementation("space.kscience:kmath-ejml:0.3.0-dev-17")
+    implementation("space.kscience:kmath-ejml:0.3.0")
 }
 ```

kmath-for-real/README.md

@@ -9,17 +9,17 @@ Specialization of KMath APIs for Double numbers.
 
 ## Artifact:
 
-The Maven coordinates of this project are `space.kscience:kmath-for-real:0.3.0-dev-17`.
+The Maven coordinates of this project are `space.kscience:kmath-for-real:0.3.0`.
 
-**Gradle:**
+**Gradle Groovy:**
-```gradle
+```groovy
 repositories {
     maven { url 'https://repo.kotlin.link' }
     mavenCentral()
 }
 
 dependencies {
-    implementation 'space.kscience:kmath-for-real:0.3.0-dev-17'
+    implementation 'space.kscience:kmath-for-real:0.3.0'
 }
 ```
 **Gradle Kotlin DSL:**
@@ -30,6 +30,6 @@ repositories {
 }
 
 dependencies {
-    implementation("space.kscience:kmath-for-real:0.3.0-dev-17")
+    implementation("space.kscience:kmath-for-real:0.3.0")
 }
 ```

kmath-for-real/build.gradle.kts

@@ -21,19 +21,22 @@ readme {
 
     feature(
         id = "DoubleVector",
-        description = "Numpy-like operations for Buffers/Points",
         ref = "src/commonMain/kotlin/space/kscience/kmath/real/DoubleVector.kt"
-    )
+    ){
+        "Numpy-like operations for Buffers/Points"
+    }
 
     feature(
         id = "DoubleMatrix",
-        description = "Numpy-like operations for 2d real structures",
         ref = "src/commonMain/kotlin/space/kscience/kmath/real/DoubleMatrix.kt"
-    )
+    ){
+        "Numpy-like operations for 2d real structures"
+    }
 
     feature(
         id = "grids",
-        description = "Uniform grid generators",
         ref = "src/commonMain/kotlin/space/kscience/kmath/structures/grids.kt"
-    )
+    ){
+        "Uniform grid generators"
+    }
 }

kmath-functions/README.md

@@ -11,17 +11,17 @@ Functions and interpolations.
 
 ## Artifact:
 
-The Maven coordinates of this project are `space.kscience:kmath-functions:0.3.0-dev-17`.
+The Maven coordinates of this project are `space.kscience:kmath-functions:0.3.0`.
 
-**Gradle:**
+**Gradle Groovy:**
-```gradle
+```groovy
 repositories {
     maven { url 'https://repo.kotlin.link' }
     mavenCentral()
 }
 
 dependencies {
-    implementation 'space.kscience:kmath-functions:0.3.0-dev-17'
+    implementation 'space.kscience:kmath-functions:0.3.0'
 }
 ```
 **Gradle Kotlin DSL:**
@@ -32,6 +32,6 @@ repositories {
 }
 
 dependencies {
-    implementation("space.kscience:kmath-functions:0.3.0-dev-17")
+    implementation("space.kscience:kmath-functions:0.3.0")
 }
 ```

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

@@ -7,6 +7,6 @@ package space.kscience.kmath.functions
 
 import space.kscience.kmath.structures.Buffer
 
-public typealias UnivariateFunction<T> = (T) -> T
+public typealias Function1D<T> = (T) -> T
 
-public typealias MultivariateFunction<T> = (Buffer<T>) -> T
+public typealias FunctionND<T> = (Buffer<T>) -> T

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

@@ -28,6 +28,8 @@ public fun <T : Comparable<T>> PiecewisePolynomial<T>.integrate(algebra: Field<T
 /**
  * Compute definite integral of given [PiecewisePolynomial] piece by piece in a given [range]
  * Requires [UnivariateIntegrationNodes] or [IntegrationRange] and [IntegrandMaxCalls]
+ *
+ * TODO use context receiver for algebra
  */
 @UnstableKMathAPI
 public fun <T : Comparable<T>> PiecewisePolynomial<T>.integrate(
@@ -98,6 +100,7 @@ public object DoubleSplineIntegrator : UnivariateIntegrator<Double> {
     }
 }
 
+@Suppress("unused")
 @UnstableKMathAPI
 public inline val DoubleField.splineIntegrator: UnivariateIntegrator<Double>
     get() = DoubleSplineIntegrator

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

@@ -9,6 +9,7 @@ package space.kscience.kmath.interpolation
 
 import space.kscience.kmath.data.XYColumnarData
 import space.kscience.kmath.functions.PiecewisePolynomial
+import space.kscience.kmath.functions.asFunction
 import space.kscience.kmath.functions.value
 import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.operations.Ring
@@ -59,3 +60,33 @@ public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolatePolynomials(
     val pointSet = XYColumnarData.of(data.map { it.first }.asBuffer(), data.map { it.second }.asBuffer())
     return interpolatePolynomials(pointSet)
 }
+
+public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(
+    x: Buffer<T>,
+    y: Buffer<T>,
+): (T) -> T? = interpolatePolynomials(x, y).asFunction(algebra)
+
+public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(
+    data: Map<T, T>,
+): (T) -> T? = interpolatePolynomials(data).asFunction(algebra)
+
+public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(
+    data: List<Pair<T, T>>,
+): (T) -> T? = interpolatePolynomials(data).asFunction(algebra)
+
+
+public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(
+    x: Buffer<T>,
+    y: Buffer<T>,
+    defaultValue: T,
+): (T) -> T = interpolatePolynomials(x, y).asFunction(algebra, defaultValue)
+
+public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(
+    data: Map<T, T>,
+    defaultValue: T,
+): (T) -> T = interpolatePolynomials(data).asFunction(algebra, defaultValue)
+
+public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(
+    data: List<Pair<T, T>>,
+    defaultValue: T,
+): (T) -> T = interpolatePolynomials(data).asFunction(algebra, defaultValue)

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

@@ -22,6 +22,7 @@ internal fun <T : Comparable<T>> insureSorted(points: XYColumnarData<*, T, *>) {
  * Reference JVM implementation: https://github.com/apache/commons-math/blob/master/src/main/java/org/apache/commons/math4/analysis/interpolation/LinearInterpolator.java
  */
 public class LinearInterpolator<T : Comparable<T>>(override val algebra: Field<T>) : PolynomialInterpolator<T> {
+
     @OptIn(UnstableKMathAPI::class)
     override fun interpolatePolynomials(points: XYColumnarData<T, T, T>): PiecewisePolynomial<T> = algebra {
         require(points.size > 0) { "Point array should not be empty" }
@@ -37,3 +38,6 @@ public class LinearInterpolator<T : Comparable<T>>(override val algebra: Field<T
         }
     }
 }
+
+public val <T : Comparable<T>> Field<T>.linearInterpolator: LinearInterpolator<T>
+    get() = LinearInterpolator(this)

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

@@ -63,8 +63,8 @@ public class SplineInterpolator<T : Comparable<T>>(
                 //Shift coefficients to represent absolute polynomial instead of one with an offset
                 val polynomial = Polynomial(
                     a - b * x0 + c * x02 - d * x03,
-                    b - 2*c*x0 + 3*d*x02,
+                    b - 2 * c * x0 + 3 * d * x02,
-                    c - 3*d*x0,
+                    c - 3 * d * x0,
                     d
                 )
                 cOld = c
@@ -72,8 +72,12 @@ public class SplineInterpolator<T : Comparable<T>>(
             }
         }
     }
-
-    public companion object {
-        public val double: SplineInterpolator<Double> = SplineInterpolator(DoubleField, ::DoubleBuffer)
-    }
 }
+
+
+public fun <T : Comparable<T>> Field<T>.splineInterpolator(
+    bufferFactory: MutableBufferFactory<T>,
+): SplineInterpolator<T> = SplineInterpolator(this, bufferFactory)
+
+public val DoubleField.splineInterpolator: SplineInterpolator<Double>
+    get() = SplineInterpolator(this, ::DoubleBuffer)

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

@@ -5,8 +5,6 @@
 
 package space.kscience.kmath.interpolation
 
-import space.kscience.kmath.functions.PiecewisePolynomial
-import space.kscience.kmath.functions.asFunction
 import space.kscience.kmath.operations.DoubleField
 import kotlin.test.Test
 import kotlin.test.assertEquals
@@ -21,8 +19,8 @@ internal class LinearInterpolatorTest {
             3.0 to 4.0
         )
 
-        val polynomial: PiecewisePolynomial<Double> = LinearInterpolator(DoubleField).interpolatePolynomials(data)
+        //val polynomial: PiecewisePolynomial<Double> = DoubleField.linearInterpolator.interpolatePolynomials(data)
-        val function = polynomial.asFunction(DoubleField)
+        val function = DoubleField.linearInterpolator.interpolate(data)
         assertEquals(null, function(-1.0))
         assertEquals(0.5, function(0.5))
         assertEquals(2.0, function(1.5))

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

@@ -5,8 +5,6 @@
 
 package space.kscience.kmath.interpolation
 
-import space.kscience.kmath.functions.PiecewisePolynomial
-import space.kscience.kmath.functions.asFunction
 import space.kscience.kmath.operations.DoubleField
 import kotlin.math.PI
 import kotlin.math.sin
@@ -21,9 +19,10 @@ internal class SplineInterpolatorTest {
             x to sin(x)
         }
 
-        val polynomial: PiecewisePolynomial<Double> = SplineInterpolator.double.interpolatePolynomials(data)
+        //val polynomial: PiecewisePolynomial<Double> = DoubleField.splineInterpolator.interpolatePolynomials(data)
+
+        val function = DoubleField.splineInterpolator.interpolate(data, Double.NaN)
 
-        val function = polynomial.asFunction(DoubleField, Double.NaN)
         assertEquals(Double.NaN, function(-1.0))
         assertEquals(sin(0.5), function(0.5), 0.1)
         assertEquals(sin(1.5), function(1.5), 0.1)

kmath-geometry/README.md

@@ -0,0 +1,32 @@
+# Module kmath-geometry
+
+
+
+## Usage
+
+## Artifact:
+
+The Maven coordinates of this project are `space.kscience:kmath-geometry:0.3.0`.
+
+**Gradle Groovy:**
+```groovy
+repositories {
+    maven { url 'https://repo.kotlin.link' }
+    mavenCentral()
+}
+
+dependencies {
+    implementation 'space.kscience:kmath-geometry:0.3.0'
+}
+```
+**Gradle Kotlin DSL:**
+```kotlin
+repositories {
+    maven("https://repo.kotlin.link")
+    mavenCentral()
+}
+
+dependencies {
+    implementation("space.kscience:kmath-geometry:0.3.0")
+}
+```

kmath-histograms/README.md

@@ -0,0 +1,32 @@
+# Module kmath-histograms
+
+
+
+## Usage
+
+## Artifact:
+
+The Maven coordinates of this project are `space.kscience:kmath-histograms:0.3.0`.
+
+**Gradle Groovy:**
+```groovy
+repositories {
+    maven { url 'https://repo.kotlin.link' }
+    mavenCentral()
+}
+
+dependencies {
+    implementation 'space.kscience:kmath-histograms:0.3.0'
+}
+```
+**Gradle Kotlin DSL:**
+```kotlin
+repositories {
+    maven("https://repo.kotlin.link")
+    mavenCentral()
+}
+
+dependencies {
+    implementation("space.kscience:kmath-histograms:0.3.0")
+}
+```

kmath-histograms/build.gradle.kts

@@ -17,6 +17,8 @@ kotlin.sourceSets {
     commonTest {
         dependencies {
             implementation(project(":kmath-for-real"))
+            implementation(projects.kmath.kmathStat)
+            implementation("org.jetbrains.kotlinx:kotlinx-coroutines-test:1.6.0")
         }
     }
 }

kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/Counter.kt

@@ -18,7 +18,8 @@ public interface Counter<T : Any> {
     public val value: T
 
     public companion object {
-        public fun double(): ObjectCounter<Double> = ObjectCounter(DoubleField)
+        public fun ofDouble(): ObjectCounter<Double> = ObjectCounter(DoubleField)
+        public fun <T: Any> of(group: Group<T>): ObjectCounter<T> = ObjectCounter(group)
     }
 }
 

kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/DoubleHistogramSpace.kt

@@ -1,130 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.histogram
-
-import space.kscience.kmath.domains.Domain
-import space.kscience.kmath.domains.HyperSquareDomain
-import space.kscience.kmath.misc.UnstableKMathAPI
-import space.kscience.kmath.nd.*
-import space.kscience.kmath.operations.DoubleField
-import space.kscience.kmath.structures.*
-import kotlin.math.floor
-
-public class DoubleHistogramSpace(
-    private val lower: Buffer<Double>,
-    private val upper: Buffer<Double>,
-    private val binNums: IntArray = IntArray(lower.size) { 20 },
-) : IndexedHistogramSpace<Double, Double> {
-
-    init {
-        // argument checks
-        require(lower.size == upper.size) { "Dimension mismatch in histogram lower and upper limits." }
-        require(lower.size == binNums.size) { "Dimension mismatch in bin count." }
-        require(!lower.indices.any { upper[it] - lower[it] < 0 }) { "Range for one of axis is not strictly positive" }
-    }
-
-    public val dimension: Int get() = lower.size
-
-    override val shape: IntArray = IntArray(binNums.size) { binNums[it] + 2 }
-    override val histogramValueSpace: DoubleFieldND = DoubleField.ndAlgebra(*shape)
-
-    private val binSize = DoubleBuffer(dimension) { (upper[it] - lower[it]) / binNums[it] }
-
-    /**
-     * Get internal [StructureND] bin index for given axis
-     */
-    private fun getIndex(axis: Int, value: Double): Int = when {
-        value >= upper[axis] -> binNums[axis] + 1 // overflow
-        value < lower[axis] -> 0 // underflow
-        else -> floor((value - lower[axis]) / binSize[axis]).toInt()
-    }
-
-    override fun getIndex(point: Buffer<Double>): IntArray = IntArray(dimension) {
-        getIndex(it, point[it])
-    }
-
-    @OptIn(UnstableKMathAPI::class)
-    override fun getDomain(index: IntArray): Domain<Double> {
-        val lowerBoundary = index.mapIndexed { axis, i ->
-            when (i) {
-                0 -> Double.NEGATIVE_INFINITY
-                shape[axis] - 1 -> upper[axis]
-                else -> lower[axis] + (i.toDouble()) * binSize[axis]
-            }
-        }.asBuffer()
-
-        val upperBoundary = index.mapIndexed { axis, i ->
-            when (i) {
-                0 -> lower[axis]
-                shape[axis] - 1 -> Double.POSITIVE_INFINITY
-                else -> lower[axis] + (i.toDouble() + 1) * binSize[axis]
-            }
-        }.asBuffer()
-
-        return HyperSquareDomain(lowerBoundary, upperBoundary)
-    }
-
-
-    override fun produceBin(index: IntArray, value: Double): Bin<Double> {
-        val domain = getDomain(index)
-        return DomainBin(domain, value)
-    }
-
-    override fun produce(builder: HistogramBuilder<Double>.() -> Unit): IndexedHistogram<Double, Double> {
-        val ndCounter = StructureND.auto(shape) { Counter.double() }
-        val hBuilder = HistogramBuilder<Double> { point, value ->
-            val index = getIndex(point)
-            ndCounter[index].add(value.toDouble())
-        }
-        hBuilder.apply(builder)
-        val values: BufferND<Double> = ndCounter.mapToBuffer { it.value }
-        return IndexedHistogram(this, values)
-    }
-
-    override fun IndexedHistogram<Double, Double>.unaryMinus(): IndexedHistogram<Double, Double> = this * (-1)
-
-    public companion object {
-        /**
-         * Use it like
-         * ```
-         *FastHistogram.fromRanges(
-         *  (-1.0..1.0),
-         *  (-1.0..1.0)
-         *)
-         *```
-         */
-        public fun fromRanges(vararg ranges: ClosedFloatingPointRange<Double>): DoubleHistogramSpace = DoubleHistogramSpace(
-            ranges.map(ClosedFloatingPointRange<Double>::start).asBuffer(),
-            ranges.map(ClosedFloatingPointRange<Double>::endInclusive).asBuffer()
-        )
-
-        /**
-         * Use it like
-         * ```
-         *FastHistogram.fromRanges(
-         *  (-1.0..1.0) to 50,
-         *  (-1.0..1.0) to 32
-         *)
-         *```
-         */
-        public fun fromRanges(vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>): DoubleHistogramSpace =
-            DoubleHistogramSpace(
-                ListBuffer(
-                    ranges
-                        .map(Pair<ClosedFloatingPointRange<Double>, Int>::first)
-                        .map(ClosedFloatingPointRange<Double>::start)
-                ),
-
-                ListBuffer(
-                    ranges
-                        .map(Pair<ClosedFloatingPointRange<Double>, Int>::first)
-                        .map(ClosedFloatingPointRange<Double>::endInclusive)
-                ),
-
-                ranges.map(Pair<ClosedFloatingPointRange<Double>, Int>::second).toIntArray()
-            )
-    }
-}

kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/Histogram.kt

@@ -13,14 +13,23 @@ import space.kscience.kmath.structures.asBuffer
 /**
  * The binned data element. Could be a histogram bin with a number of counts or an artificial construct.
  */
-public interface Bin<in T : Any> : Domain<T> {
+public interface Bin<in T : Any, out V> : Domain<T> {
     /**
      * The value of this bin.
      */
-    public val value: Number
+    public val binValue: V
 }
 
-public interface Histogram<in T : Any, out B : Bin<T>> {
+/**
+ * A simple histogram bin based on domain
+ */
+public data class DomainBin<in T : Comparable<T>, D : Domain<T>, out V>(
+    public val domain: D,
+    override val binValue: V,
+) : Bin<T, V>, Domain<T> by domain
+
+
+public interface Histogram<in T : Any, out V, out B : Bin<T, V>> {
     /**
      * Find existing bin, corresponding to given coordinates
      */
@@ -32,29 +41,38 @@ public interface Histogram<in T : Any, out B : Bin<T>> {
     public val dimension: Int
 
     public val bins: Iterable<B>
+
+    public companion object {
+        //A discoverability root
+    }
 }
 
-public fun interface HistogramBuilder<in T : Any> {
+public interface HistogramBuilder<in T : Any, V : Any> {
 
     /**
-     * Increment appropriate bin
+     * The default value increment for a bin
      */
-    public fun putValue(point: Point<out T>, value: Number)
+    public val defaultValue: V
+
+    /**
+     * Increment appropriate bin with given value
+     */
+    public fun putValue(point: Point<out T>, value: V = defaultValue)
 
 }
 
-public fun <T : Any, B : Bin<T>> HistogramBuilder<T>.put(point: Point<out T>): Unit = putValue(point, 1.0)
+public fun <T : Any> HistogramBuilder<T, *>.put(point: Point<out T>): Unit = putValue(point)
 
-public fun <T : Any> HistogramBuilder<T>.put(vararg point: T): Unit = put(point.asBuffer())
+public fun <T : Any> HistogramBuilder<T, *>.put(vararg point: T): Unit = put(point.asBuffer())
 
-public fun HistogramBuilder<Double>.put(vararg point: Number): Unit =
+public fun HistogramBuilder<Double, *>.put(vararg point: Number): Unit =
     put(DoubleBuffer(point.map { it.toDouble() }.toDoubleArray()))
 
-public fun HistogramBuilder<Double>.put(vararg point: Double): Unit = put(DoubleBuffer(point))
+public fun HistogramBuilder<Double, *>.put(vararg point: Double): Unit = put(DoubleBuffer(point))
-public fun <T : Any> HistogramBuilder<T>.fill(sequence: Iterable<Point<T>>): Unit = sequence.forEach { put(it) }
+public fun <T : Any> HistogramBuilder<T, *>.fill(sequence: Iterable<Point<T>>): Unit = sequence.forEach { put(it) }
 
 /**
  * Pass a sequence builder into histogram
  */
-public fun <T : Any> HistogramBuilder<T>.fill(block: suspend SequenceScope<Point<T>>.() -> Unit): Unit =
+public fun <T : Any> HistogramBuilder<T, *>.fill(block: suspend SequenceScope<Point<T>>.() -> Unit): Unit =
     fill(sequence(block).asIterable())

kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/Histogram1D.kt

@@ -0,0 +1,64 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.histogram
+
+import space.kscience.kmath.domains.Domain1D
+import space.kscience.kmath.domains.center
+import space.kscience.kmath.linear.Point
+import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.operations.asSequence
+import space.kscience.kmath.structures.Buffer
+
+
+/**
+ * A univariate bin based on a range
+ *
+ * @property binValue The value of histogram including weighting
+ */
+@UnstableKMathAPI
+public data class Bin1D<T : Comparable<T>, out V>(
+    public val domain: Domain1D<T>,
+    override val binValue: V,
+) : Bin<T, V>, ClosedRange<T> by domain.range {
+
+    override val dimension: Int get() = 1
+
+    override fun contains(point: Buffer<T>): Boolean = point.size == 1 && contains(point[0])
+}
+
+@OptIn(UnstableKMathAPI::class)
+public interface Histogram1D<T : Comparable<T>, V> : Histogram<T, V, Bin1D<T, V>> {
+    override val dimension: Int get() = 1
+    public operator fun get(value: T): Bin1D<T, V>?
+    override operator fun get(point: Buffer<T>): Bin1D<T, V>? = get(point[0])
+}
+
+public interface Histogram1DBuilder<in T : Any, V : Any> : HistogramBuilder<T, V> {
+    /**
+     * Thread safe put operation
+     */
+    public fun putValue(at: T, value: V = defaultValue)
+
+    override fun putValue(point: Point<out T>, value: V) {
+        require(point.size == 1) { "Only points with single value could be used in Histogram1D" }
+        putValue(point[0], value)
+    }
+}
+
+@UnstableKMathAPI
+public fun Histogram1DBuilder<Double, *>.fill(items: Iterable<Double>): Unit =
+    items.forEach(this::putValue)
+
+@UnstableKMathAPI
+public fun Histogram1DBuilder<Double, *>.fill(array: DoubleArray): Unit =
+    array.forEach(this::putValue)
+
+@UnstableKMathAPI
+public fun <T : Any> Histogram1DBuilder<T, *>.fill(buffer: Buffer<T>): Unit =
+    buffer.asSequence().forEach(this::putValue)
+
+@OptIn(UnstableKMathAPI::class)
+public val Bin1D<Double, *>.center: Double get() = domain.center

kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/HistogramND.kt

@@ -0,0 +1,76 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.histogram
+
+import space.kscience.kmath.domains.Domain
+import space.kscience.kmath.linear.Point
+import space.kscience.kmath.nd.DefaultStrides
+import space.kscience.kmath.nd.FieldOpsND
+import space.kscience.kmath.nd.Shape
+import space.kscience.kmath.nd.StructureND
+import space.kscience.kmath.operations.Group
+import space.kscience.kmath.operations.ScaleOperations
+import space.kscience.kmath.operations.invoke
+
+/**
+ * @param T the type of the argument space
+ * @param V the type of bin value
+ */
+public class HistogramND<T : Comparable<T>, D : Domain<T>, V : Any>(
+    public val group: HistogramGroupND<T, D, V>,
+    internal val values: StructureND<V>,
+) : Histogram<T, V, DomainBin<T, D, V>> {
+
+    override fun get(point: Point<T>): DomainBin<T, D, V>? {
+        val index = group.getIndexOrNull(point) ?: return null
+        return group.produceBin(index, values[index])
+    }
+
+    override val dimension: Int get() = group.shape.size
+
+    override val bins: Iterable<DomainBin<T, D, V>>
+        get() = DefaultStrides(group.shape).asSequence().map {
+            group.produceBin(it, values[it])
+        }.asIterable()
+}
+
+/**
+ * A space for producing histograms with values in a NDStructure
+ */
+public interface HistogramGroupND<T : Comparable<T>, D : Domain<T>, V : Any> :
+    Group<HistogramND<T, D, V>>, ScaleOperations<HistogramND<T, D, V>> {
+    public val shape: Shape
+    public val valueAlgebraND: FieldOpsND<V, *> //= NDAlgebra.space(valueSpace, Buffer.Companion::boxing, *shape),
+
+    /**
+     * Resolve index of the bin including given [point]. Return null if point is outside histogram area
+     */
+    public fun getIndexOrNull(point: Point<T>): IntArray?
+
+    /**
+     * Get a bin domain represented by given index
+     */
+    public fun getDomain(index: IntArray): Domain<T>?
+
+    public fun produceBin(index: IntArray, value: V): DomainBin<T, D, V>
+
+    public fun produce(builder: HistogramBuilder<T, V>.() -> Unit): HistogramND<T, D, V>
+
+    override fun add(left: HistogramND<T, D, V>, right: HistogramND<T, D, V>): HistogramND<T, D, V> {
+        require(left.group == this && right.group == this) {
+            "A histogram belonging to a different group cannot be operated."
+        }
+        return HistogramND(this, valueAlgebraND { left.values + right.values })
+    }
+
+    override fun scale(a: HistogramND<T, D, V>, value: Double): HistogramND<T, D, V> {
+        require(a.group == this) { "A histogram belonging to a different group cannot be operated." }
+        return HistogramND(this, valueAlgebraND { a.values * value })
+    }
+
+    override val zero: HistogramND<T, D, V> get() = produce { }
+}
+

kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/IndexedHistogramSpace.kt

@@ -1,82 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.histogram
-
-import space.kscience.kmath.domains.Domain
-import space.kscience.kmath.linear.Point
-import space.kscience.kmath.misc.UnstableKMathAPI
-import space.kscience.kmath.nd.DefaultStrides
-import space.kscience.kmath.nd.FieldND
-import space.kscience.kmath.nd.Shape
-import space.kscience.kmath.nd.StructureND
-import space.kscience.kmath.operations.Group
-import space.kscience.kmath.operations.ScaleOperations
-import space.kscience.kmath.operations.invoke
-
-/**
- * A simple histogram bin based on domain
- */
-public data class DomainBin<in T : Comparable<T>>(
-    public val domain: Domain<T>,
-    override val value: Number,
-) : Bin<T>, Domain<T> by domain
-
-@OptIn(UnstableKMathAPI::class)
-public class IndexedHistogram<T : Comparable<T>, V : Any>(
-    public val context: IndexedHistogramSpace<T, V>,
-    public val values: StructureND<V>,
-) : Histogram<T, Bin<T>> {
-
-    override fun get(point: Point<T>): Bin<T>? {
-        val index = context.getIndex(point) ?: return null
-        return context.produceBin(index, values[index])
-    }
-
-    override val dimension: Int get() = context.shape.size
-
-    override val bins: Iterable<Bin<T>>
-        get() = DefaultStrides(context.shape).asSequence().map {
-            context.produceBin(it, values[it])
-        }.asIterable()
-}
-
-/**
- * A space for producing histograms with values in a NDStructure
- */
-public interface IndexedHistogramSpace<T : Comparable<T>, V : Any>
-    : Group<IndexedHistogram<T, V>>, ScaleOperations<IndexedHistogram<T, V>> {
-    //public val valueSpace: Space<V>
-    public val shape: Shape
-    public val histogramValueSpace: FieldND<V, *> //= NDAlgebra.space(valueSpace, Buffer.Companion::boxing, *shape),
-
-    /**
-     * Resolve index of the bin including given [point]
-     */
-    public fun getIndex(point: Point<T>): IntArray?
-
-    /**
-     * Get a bin domain represented by given index
-     */
-    public fun getDomain(index: IntArray): Domain<T>?
-
-    public fun produceBin(index: IntArray, value: V): Bin<T>
-
-    public fun produce(builder: HistogramBuilder<T>.() -> Unit): IndexedHistogram<T, V>
-
-    override fun add(left: IndexedHistogram<T, V>, right: IndexedHistogram<T, V>): IndexedHistogram<T, V> {
-        require(left.context == this) { "Can't operate on a histogram produced by external space" }
-        require(right.context == this) { "Can't operate on a histogram produced by external space" }
-        return IndexedHistogram(this, histogramValueSpace { left.values + right.values })
-    }
-
-    override fun scale(a: IndexedHistogram<T, V>, value: Double): IndexedHistogram<T, V> {
-        require(a.context == this) { "Can't operate on a histogram produced by external space" }
-        return IndexedHistogram(this, histogramValueSpace { a.values * value })
-    }
-
-    override val zero: IndexedHistogram<T, V> get() = produce { }
-}
-

kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/UniformHistogram1D.kt

@@ -0,0 +1,156 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.histogram
+
+import space.kscience.kmath.domains.DoubleDomain1D
+import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.operations.Group
+import space.kscience.kmath.operations.Ring
+import space.kscience.kmath.operations.ScaleOperations
+import space.kscience.kmath.operations.invoke
+import space.kscience.kmath.structures.Buffer
+import kotlin.math.floor
+
+@OptIn(UnstableKMathAPI::class)
+public class UniformHistogram1D<V : Any>(
+    public val group: UniformHistogram1DGroup<V, *>,
+    internal val values: Map<Int, V>,
+) : Histogram1D<Double, V> {
+
+    private val startPoint get() = group.startPoint
+    private val binSize get() = group.binSize
+
+    private fun produceBin(index: Int, value: V): Bin1D<Double, V> {
+        val domain = DoubleDomain1D((startPoint + index * binSize)..(startPoint + (index + 1) * binSize))
+        return Bin1D(domain, value)
+    }
+
+    override val bins: Iterable<Bin1D<Double, V>> get() = values.map { produceBin(it.key, it.value) }
+
+    override fun get(value: Double): Bin1D<Double, V>? {
+        val index: Int = group.getIndex(value)
+        val v = values[index]
+        return v?.let { produceBin(index, it) }
+    }
+}
+
+/**
+ * An algebra for uniform histograms in 1D real space
+ */
+public class UniformHistogram1DGroup<V : Any, A>(
+    public val valueAlgebra: A,
+    public val binSize: Double,
+    public val startPoint: Double = 0.0,
+) : Group<Histogram1D<Double, V>>, ScaleOperations<Histogram1D<Double, V>> where A : Ring<V>, A : ScaleOperations<V> {
+
+    override val zero: UniformHistogram1D<V> = UniformHistogram1D(this, emptyMap())
+
+    /**
+     * Get index of a bin
+     */
+    @PublishedApi
+    internal fun getIndex(at: Double): Int = floor((at - startPoint) / binSize).toInt()
+
+    override fun add(
+        left: Histogram1D<Double, V>,
+        right: Histogram1D<Double, V>,
+    ): UniformHistogram1D<V> = valueAlgebra {
+        val leftUniform = produceFrom(left)
+        val rightUniform = produceFrom(right)
+        val keys = leftUniform.values.keys + rightUniform.values.keys
+        UniformHistogram1D(
+            this@UniformHistogram1DGroup,
+            keys.associateWith {
+                (leftUniform.values[it] ?: valueAlgebra.zero) + (rightUniform.values[it] ?: valueAlgebra.zero)
+            }
+        )
+    }
+
+    override fun Histogram1D<Double, V>.unaryMinus(): UniformHistogram1D<V> = valueAlgebra {
+        UniformHistogram1D(this@UniformHistogram1DGroup, produceFrom(this@unaryMinus).values.mapValues { -it.value })
+    }
+
+    override fun scale(
+        a: Histogram1D<Double, V>,
+        value: Double,
+    ): UniformHistogram1D<V> = UniformHistogram1D(
+        this@UniformHistogram1DGroup,
+        produceFrom(a).values.mapValues { valueAlgebra.scale(it.value, value) }
+    )
+
+    /**
+     * Fill histogram.
+     */
+    public inline fun produce(block: Histogram1DBuilder<Double, V>.() -> Unit): UniformHistogram1D<V> {
+        val map = HashMap<Int, V>()
+        val builder = object : Histogram1DBuilder<Double, V> {
+            override val defaultValue: V get() = valueAlgebra.zero
+
+            override fun putValue(at: Double, value: V) {
+                val index = getIndex(at)
+                map[index] = with(valueAlgebra) { (map[index] ?: zero) + one }
+            }
+        }
+        builder.block()
+        return UniformHistogram1D(this, map)
+    }
+
+    /**
+     * Re-bin given histogram to be compatible if exiting bin fully falls inside existing bin, this bin value
+     * is increased by one. If not, all bins including values from this bin are increased by fraction
+     * (conserving the norming).
+     */
+    @OptIn(UnstableKMathAPI::class)
+    public fun produceFrom(
+        histogram: Histogram1D<Double, V>,
+    ): UniformHistogram1D<V> = if ((histogram as? UniformHistogram1D)?.group == this) {
+        histogram
+    } else {
+        val map = HashMap<Int, V>()
+        histogram.bins.forEach { bin ->
+            val range = bin.domain.range
+            val indexOfLeft = getIndex(range.start)
+            val indexOfRight = getIndex(range.endInclusive)
+            val numBins = indexOfRight - indexOfLeft + 1
+            for (i in indexOfLeft..indexOfRight) {
+                map[indexOfLeft] = with(valueAlgebra) {
+                    (map[indexOfLeft] ?: zero) + bin.binValue / numBins
+                }
+            }
+        }
+        UniformHistogram1D(this, map)
+    }
+}
+
+public fun <V : Any, A> Histogram.Companion.uniform1D(
+    valueAlgebra: A,
+    binSize: Double,
+    startPoint: Double = 0.0,
+): UniformHistogram1DGroup<V, A> where A : Ring<V>, A : ScaleOperations<V> =
+    UniformHistogram1DGroup(valueAlgebra, binSize, startPoint)
+
+@UnstableKMathAPI
+public fun <V : Any> UniformHistogram1DGroup<V, *>.produce(
+    buffer: Buffer<Double>,
+): UniformHistogram1D<V> = produce { fill(buffer) }
+
+/**
+ * Map of bin centers to bin values
+ */
+@OptIn(UnstableKMathAPI::class)
+public val <V : Any> UniformHistogram1D<V>.binValues: Map<Double, V>
+    get() = bins.associate { it.center to it.binValue }
+
+
+//TODO add normalized values inside Field-based histogram spaces with context receivers
+///**
+// * Map of bin centers to normalized bin values (bin size as normalization)
+// */
+//@OptIn(UnstableKMathAPI::class)
+//public val <V : Any> UniformHistogram1D<V>.binValuesNormalized: Map<Double, V>
+//    get() = group.valueAlgebra {
+//        bins.associate { it.center to it.binValue / group.binSize }
+//    }

kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/UniformHistogramGroupND.kt

@@ -0,0 +1,163 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+@file:OptIn(UnstableKMathAPI::class)
+
+package space.kscience.kmath.histogram
+
+import space.kscience.kmath.domains.HyperSquareDomain
+import space.kscience.kmath.linear.Point
+import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.nd.*
+import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.operations.Field
+import space.kscience.kmath.operations.invoke
+import space.kscience.kmath.structures.*
+import kotlin.math.floor
+
+public typealias HyperSquareBin<V> = DomainBin<Double, HyperSquareDomain, V>
+
+/**
+ * Multivariate histogram space for hyper-square real-field bins.
+ * @param bufferFactory is an optional parameter used to optimize buffer production.
+ */
+public class UniformHistogramGroupND<V : Any, A : Field<V>>(
+    override val valueAlgebraND: FieldOpsND<V, A>,
+    private val lower: Buffer<Double>,
+    private val upper: Buffer<Double>,
+    private val binNums: IntArray = IntArray(lower.size) { 20 },
+    private val bufferFactory: BufferFactory<V> = Buffer.Companion::boxing,
+) : HistogramGroupND<Double, HyperSquareDomain, V> {
+
+    init {
+        // argument checks
+        require(lower.size == upper.size) { "Dimension mismatch in histogram lower and upper limits." }
+        require(lower.size == binNums.size) { "Dimension mismatch in bin count." }
+        require(!lower.indices.any { upper[it] - lower[it] < 0 }) { "Range for one of axis is not strictly positive" }
+    }
+
+    public val dimension: Int get() = lower.size
+
+    override val shape: IntArray = IntArray(binNums.size) { binNums[it] + 2 }
+
+    private val binSize = DoubleBuffer(dimension) { (upper[it] - lower[it]) / binNums[it] }
+
+    /**
+     * Get internal [StructureND] bin index for given axis
+     */
+    private fun getIndex(axis: Int, value: Double): Int = when {
+        value >= upper[axis] -> binNums[axis] + 1 // overflow
+        value < lower[axis] -> 0 // underflow
+        else -> floor((value - lower[axis]) / binSize[axis]).toInt()
+    }
+
+    override fun getIndexOrNull(point: Buffer<Double>): IntArray = IntArray(dimension) {
+        getIndex(it, point[it])
+    }
+
+    override fun getDomain(index: IntArray): HyperSquareDomain {
+        val lowerBoundary = index.mapIndexed { axis, i ->
+            when (i) {
+                0 -> Double.NEGATIVE_INFINITY
+                shape[axis] - 1 -> upper[axis]
+                else -> lower[axis] + (i.toDouble()) * binSize[axis]
+            }
+        }.asBuffer()
+
+        val upperBoundary = index.mapIndexed { axis, i ->
+            when (i) {
+                0 -> lower[axis]
+                shape[axis] - 1 -> Double.POSITIVE_INFINITY
+                else -> lower[axis] + (i.toDouble() + 1) * binSize[axis]
+            }
+        }.asBuffer()
+
+        return HyperSquareDomain(lowerBoundary, upperBoundary)
+    }
+
+    override fun produceBin(index: IntArray, value: V): HyperSquareBin<V> {
+        val domain = getDomain(index)
+        return DomainBin(domain, value)
+    }
+
+
+    override fun produce(builder: HistogramBuilder<Double, V>.() -> Unit): HistogramND<Double, HyperSquareDomain, V> {
+        val ndCounter = StructureND.buffered(shape) { Counter.of(valueAlgebraND.elementAlgebra) }
+        val hBuilder = object : HistogramBuilder<Double, V> {
+            override val defaultValue: V get() = valueAlgebraND.elementAlgebra.one
+
+            override fun putValue(point: Point<out Double>, value: V) = with(valueAlgebraND.elementAlgebra) {
+                val index = getIndexOrNull(point)
+                ndCounter[index].add(value)
+            }
+        }
+        hBuilder.apply(builder)
+        val values: BufferND<V> = ndCounter.mapToBuffer(bufferFactory) { it.value }
+        return HistogramND(this, values)
+    }
+
+    override fun HistogramND<Double, HyperSquareDomain, V>.unaryMinus(): HistogramND<Double, HyperSquareDomain, V> =
+        this * (-1)
+}
+
+/**
+ * Use it like
+ * ```
+ *FastHistogram.fromRanges(
+ *  (-1.0..1.0),
+ *  (-1.0..1.0)
+ *)
+ *```
+ */
+public fun <V : Any, A : Field<V>> Histogram.Companion.uniformNDFromRanges(
+    valueAlgebraND: FieldOpsND<V, A>,
+    vararg ranges: ClosedFloatingPointRange<Double>,
+    bufferFactory: BufferFactory<V> = Buffer.Companion::boxing,
+): UniformHistogramGroupND<V, A> = UniformHistogramGroupND(
+    valueAlgebraND,
+    ranges.map(ClosedFloatingPointRange<Double>::start).asBuffer(),
+    ranges.map(ClosedFloatingPointRange<Double>::endInclusive).asBuffer(),
+    bufferFactory = bufferFactory
+)
+
+public fun Histogram.Companion.uniformDoubleNDFromRanges(
+    vararg ranges: ClosedFloatingPointRange<Double>,
+): UniformHistogramGroupND<Double, DoubleField> =
+    uniformNDFromRanges(DoubleFieldOpsND, *ranges, bufferFactory = ::DoubleBuffer)
+
+
+/**
+ * Use it like
+ * ```
+ *FastHistogram.fromRanges(
+ *  (-1.0..1.0) to 50,
+ *  (-1.0..1.0) to 32
+ *)
+ *```
+ */
+public fun <V : Any, A : Field<V>> Histogram.Companion.uniformNDFromRanges(
+    valueAlgebraND: FieldOpsND<V, A>,
+    vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>,
+    bufferFactory: BufferFactory<V> = Buffer.Companion::boxing,
+): UniformHistogramGroupND<V, A> = UniformHistogramGroupND(
+    valueAlgebraND,
+    ListBuffer(
+        ranges
+            .map(Pair<ClosedFloatingPointRange<Double>, Int>::first)
+            .map(ClosedFloatingPointRange<Double>::start)
+    ),
+    ListBuffer(
+        ranges
+            .map(Pair<ClosedFloatingPointRange<Double>, Int>::first)
+            .map(ClosedFloatingPointRange<Double>::endInclusive)
+    ),
+    ranges.map(Pair<ClosedFloatingPointRange<Double>, Int>::second).toIntArray(),
+    bufferFactory = bufferFactory
+)
+
+public fun Histogram.Companion.uniformDoubleNDFromRanges(
+    vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>,
+): UniformHistogramGroupND<Double, DoubleField> =
+    uniformNDFromRanges(DoubleFieldOpsND, *ranges, bufferFactory = ::DoubleBuffer)

kmath-histograms/src/commonTest/kotlin/space/kscience/kmath/histogram/MultivariateHistogramTest.kt

@@ -3,8 +3,11 @@
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
+@file:OptIn(UnstableKMathAPI::class)
+
 package space.kscience.kmath.histogram
 
+import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.nd.DefaultStrides
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.real.DoubleVector
@@ -14,14 +17,14 @@ import kotlin.test.*
 internal class MultivariateHistogramTest {
     @Test
     fun testSinglePutHistogram() {
-        val hSpace = DoubleHistogramSpace.fromRanges(
+        val hSpace = Histogram.uniformDoubleNDFromRanges(
             (-1.0..1.0),
             (-1.0..1.0)
         )
         val histogram = hSpace.produce {
             put(0.55, 0.55)
         }
-        val bin = histogram.bins.find { it.value.toInt() > 0 } ?: fail()
+        val bin = histogram.bins.find { it.binValue.toInt() > 0 } ?: fail()
         assertTrue { bin.contains(DoubleVector(0.55, 0.55)) }
         assertTrue { bin.contains(DoubleVector(0.6, 0.5)) }
         assertFalse { bin.contains(DoubleVector(-0.55, 0.55)) }
@@ -29,7 +32,7 @@ internal class MultivariateHistogramTest {
 
     @Test
     fun testSequentialPut() {
-        val hSpace = DoubleHistogramSpace.fromRanges(
+        val hSpace = Histogram.uniformDoubleNDFromRanges(
             (-1.0..1.0),
             (-1.0..1.0),
             (-1.0..1.0)
@@ -44,12 +47,12 @@ internal class MultivariateHistogramTest {
                 put(nextDouble(), nextDouble(), nextDouble())
             }
         }
-        assertEquals(n, histogram.bins.sumOf { it.value.toInt() })
+        assertEquals(n, histogram.bins.sumOf { it.binValue.toInt() })
     }
 
     @Test
     fun testHistogramAlgebra() {
-        DoubleHistogramSpace.fromRanges(
+        Histogram.uniformDoubleNDFromRanges(
             (-1.0..1.0),
             (-1.0..1.0),
             (-1.0..1.0)
@@ -77,7 +80,7 @@ internal class MultivariateHistogramTest {
             assertTrue {
                 res.bins.count() >= histogram1.bins.count()
             }
-            assertEquals(0.0, res.bins.sumOf { it.value.toDouble() })
+            assertEquals(0.0, res.bins.sumOf { it.binValue.toDouble() })
         }
     }
 }

kmath-histograms/src/commonTest/kotlin/space/kscience/kmath/histogram/UniformHistogram1DTest.kt

@@ -0,0 +1,56 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.histogram
+
+import kotlinx.coroutines.ExperimentalCoroutinesApi
+import kotlinx.coroutines.test.runTest
+import space.kscience.kmath.distributions.NormalDistribution
+import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.stat.RandomGenerator
+import space.kscience.kmath.stat.nextBuffer
+import kotlin.native.concurrent.ThreadLocal
+import kotlin.test.Test
+import kotlin.test.assertEquals
+
+@OptIn(ExperimentalCoroutinesApi::class, UnstableKMathAPI::class)
+internal class UniformHistogram1DTest {
+
+    @Test
+    fun normal() = runTest {
+        val distribution = NormalDistribution(0.0, 1.0)
+        with(Histogram.uniform1D(DoubleField, 0.1)) {
+            val h1 = produce(distribution.nextBuffer(generator, 10000))
+
+            val h2 = produce(distribution.nextBuffer(generator, 50000))
+
+            val h3 = h1 + h2
+
+            assertEquals(60000, h3.bins.sumOf { it.binValue }.toInt())
+        }
+    }
+
+    @Test
+    fun rebinDown() = runTest {
+        val h1 = Histogram.uniform1D(DoubleField, 0.01).produce(generator.nextDoubleBuffer(10000))
+        val h2 = Histogram.uniform1D(DoubleField,0.03).produceFrom(h1)
+
+        assertEquals(10000, h2.bins.sumOf { it.binValue }.toInt())
+    }
+
+    @Test
+    fun rebinUp() = runTest {
+        val h1 = Histogram.uniform1D(DoubleField, 0.03).produce(generator.nextDoubleBuffer(10000))
+        val h2 = Histogram.uniform1D(DoubleField,0.01).produceFrom(h1)
+
+        assertEquals(10000, h2.bins.sumOf { it.binValue }.toInt())
+    }
+
+    @ThreadLocal
+    companion object{
+        private val generator = RandomGenerator.default(123)
+    }
+}

kmath-histograms/src/jvmMain/kotlin/space/kscience/kmath/histogram/TreeHistogramGroup.kt

@@ -0,0 +1,179 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+@file:OptIn(UnstableKMathAPI::class)
+
+package space.kscience.kmath.histogram
+
+import space.kscience.kmath.domains.DoubleDomain1D
+import space.kscience.kmath.domains.center
+import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.misc.sorted
+import space.kscience.kmath.operations.Group
+import space.kscience.kmath.operations.Ring
+import space.kscience.kmath.operations.ScaleOperations
+import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.first
+import space.kscience.kmath.structures.indices
+import space.kscience.kmath.structures.last
+import java.util.*
+
+private fun <B : ClosedRange<Double>> TreeMap<Double, B>.getBin(value: Double): B? {
+    // check ceiling entry and return it if it is what needed
+    val ceil = ceilingEntry(value)?.value
+    if (ceil != null && value in ceil) return ceil
+    //check floor entry
+    val floor = floorEntry(value)?.value
+    if (floor != null && value in floor) return floor
+    //neither is valid, not found
+    return null
+}
+
+//public data class ValueAndError(val value: Double, val error: Double)
+//
+//public typealias WeightedBin1D = Bin1D<Double, ValueAndError>
+
+/**
+ * A histogram based on a tree map of values
+ */
+public class TreeHistogram<V : Any>(
+    private val binMap: TreeMap<Double, Bin1D<Double, V>>,
+) : Histogram1D<Double, V> {
+    override fun get(value: Double): Bin1D<Double, V>? = binMap.getBin(value)
+    override val bins: Collection<Bin1D<Double, V>> get() = binMap.values
+}
+
+/**
+ * A space for univariate histograms with variable bin borders based on a tree map
+ */
+public class TreeHistogramGroup<V : Any, A>(
+    public val valueAlgebra: A,
+    @PublishedApi internal val binFactory: (Double) -> DoubleDomain1D,
+) : Group<TreeHistogram<V>>, ScaleOperations<TreeHistogram<V>> where A : Ring<V>, A : ScaleOperations<V> {
+
+    internal inner class DomainCounter(val domain: DoubleDomain1D, val counter: Counter<V> = Counter.of(valueAlgebra)) :
+        ClosedRange<Double> by domain.range
+
+    @PublishedApi
+    internal inner class TreeHistogramBuilder : Histogram1DBuilder<Double, V> {
+
+        override val defaultValue: V get() = valueAlgebra.one
+
+        private val bins: TreeMap<Double, DomainCounter> = TreeMap()
+
+        private fun createBin(value: Double): DomainCounter {
+            val binDefinition: DoubleDomain1D = binFactory(value)
+            val newBin = DomainCounter(binDefinition)
+            synchronized(this) {
+                bins[binDefinition.center] = newBin
+            }
+            return newBin
+        }
+
+        /**
+         * Thread safe put operation
+         */
+        override fun putValue(at: Double, value: V) {
+            (bins.getBin(at) ?: createBin(at)).counter.add(value)
+        }
+
+        fun build(): TreeHistogram<V> {
+            val map = bins.mapValuesTo(TreeMap<Double, Bin1D<Double, V>>()) { (_, binCounter) ->
+                Bin1D(binCounter.domain, binCounter.counter.value)
+            }
+            return TreeHistogram(map)
+        }
+    }
+
+    public inline fun produce(block: Histogram1DBuilder<Double, V>.() -> Unit): TreeHistogram<V> =
+        TreeHistogramBuilder().apply(block).build()
+
+    override fun add(
+        left: TreeHistogram<V>,
+        right: TreeHistogram<V>,
+    ): TreeHistogram<V> {
+        val bins = TreeMap<Double, Bin1D<Double, V>>().apply {
+            (left.bins.map { it.domain } union right.bins.map { it.domain }).forEach { def ->
+                put(
+                    def.center,
+                    Bin1D(
+                        def,
+                        with(valueAlgebra) {
+                            (left[def.center]?.binValue ?: zero) + (right[def.center]?.binValue ?: zero)
+                        }
+                    )
+                )
+            }
+        }
+        return TreeHistogram(bins)
+    }
+
+    override fun scale(a: TreeHistogram<V>, value: Double): TreeHistogram<V> {
+        val bins = TreeMap<Double, Bin1D<Double, V>>().apply {
+            a.bins.forEach { bin ->
+                put(
+                    bin.domain.center,
+                    Bin1D(bin.domain, valueAlgebra.scale(bin.binValue, value))
+                )
+            }
+        }
+
+        return TreeHistogram(bins)
+    }
+
+    override fun TreeHistogram<V>.unaryMinus(): TreeHistogram<V> = this * (-1)
+
+    override val zero: TreeHistogram<V> = produce { }
+}
+
+
+///**
+// * Build and fill a histogram with custom borders. Returns a read-only histogram.
+// */
+//public inline fun Histogram.custom(
+//    borders: DoubleArray,
+//    builder: Histogram1DBuilder<Double, Double>.() -> Unit,
+//): TreeHistogram = custom(borders).fill(builder)
+//
+//
+///**
+// * Build and fill a [DoubleHistogram1D]. Returns a read-only histogram.
+// */
+//public fun uniform(
+//    binSize: Double,
+//    start: Double = 0.0,
+//): TreeHistogramSpace = TreeHistogramSpace { value ->
+//    val center = start + binSize * floor((value - start) / binSize + 0.5)
+//    DoubleDomain1D((center - binSize / 2)..(center + binSize / 2))
+//}
+
+/**
+ * Create a histogram group with custom cell borders
+ */
+public fun <V : Any, A> Histogram.Companion.custom1D(
+    valueAlgebra: A,
+    borders: Buffer<Double>,
+): TreeHistogramGroup<V, A> where A : Ring<V>, A : ScaleOperations<V> {
+    val sorted = borders.sorted()
+
+    return TreeHistogramGroup(valueAlgebra) { value ->
+        when {
+            value <= sorted.first() -> DoubleDomain1D(
+                Double.NEGATIVE_INFINITY..sorted.first()
+            )
+
+            value > sorted.last() -> DoubleDomain1D(
+                sorted.last()..Double.POSITIVE_INFINITY
+            )
+
+            else -> {
+                val index = sorted.indices.first { value <= sorted[it] }
+                val left = sorted[index - 1]
+                val right = sorted[index]
+                DoubleDomain1D(left..right)
+            }
+        }
+    }
+}

kmath-histograms/src/jvmMain/kotlin/space/kscience/kmath/histogram/TreeHistogramSpace.kt

@@ -1,171 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.histogram
-
-import space.kscience.kmath.domains.UnivariateDomain
-import space.kscience.kmath.misc.UnstableKMathAPI
-import space.kscience.kmath.operations.Group
-import space.kscience.kmath.operations.ScaleOperations
-import space.kscience.kmath.structures.Buffer
-import java.util.*
-import kotlin.math.abs
-import kotlin.math.floor
-import kotlin.math.sqrt
-
-private fun <B : ClosedFloatingPointRange<Double>> TreeMap<Double, B>.getBin(value: Double): B? {
-    // check ceiling entry and return it if it is what needed
-    val ceil = ceilingEntry(value)?.value
-    if (ceil != null && value in ceil) return ceil
-    //check floor entry
-    val floor = floorEntry(value)?.value
-    if (floor != null && value in floor) return floor
-    //neither is valid, not found
-    return null
-}
-
-@UnstableKMathAPI
-public class TreeHistogram(
-    private val binMap: TreeMap<Double, out UnivariateBin>,
-) : UnivariateHistogram {
-    override fun get(value: Double): UnivariateBin? = binMap.getBin(value)
-    override val dimension: Int get() = 1
-    override val bins: Collection<UnivariateBin> get() = binMap.values
-}
-
-@OptIn(UnstableKMathAPI::class)
-@PublishedApi
-internal class TreeHistogramBuilder(val binFactory: (Double) -> UnivariateDomain) : UnivariateHistogramBuilder {
-
-    internal class BinCounter(val domain: UnivariateDomain, val counter: Counter<Double> = Counter.double()) :
-        ClosedFloatingPointRange<Double> by domain.range
-
-    private val bins: TreeMap<Double, BinCounter> = TreeMap()
-
-    fun get(value: Double): BinCounter? = bins.getBin(value)
-
-    fun createBin(value: Double): BinCounter {
-        val binDefinition = binFactory(value)
-        val newBin = BinCounter(binDefinition)
-        synchronized(this) { bins[binDefinition.center] = newBin }
-        return newBin
-    }
-
-    /**
-     * Thread safe put operation
-     */
-    override fun putValue(at: Double, value: Double) {
-        (get(at) ?: createBin(at)).apply {
-            counter.add(value)
-        }
-    }
-
-    override fun putValue(point: Buffer<Double>, value: Number) {
-        require(point.size == 1) { "Only points with single value could be used in univariate histogram" }
-        putValue(point[0], value.toDouble())
-    }
-
-    fun build(): TreeHistogram {
-        val map = bins.mapValuesTo(TreeMap<Double, UnivariateBin>()) { (_, binCounter) ->
-            val count = binCounter.counter.value
-            UnivariateBin(binCounter.domain, count, sqrt(count))
-        }
-        return TreeHistogram(map)
-    }
-}
-
-/**
- * A space for univariate histograms with variable bin borders based on a tree map
- */
-@UnstableKMathAPI
-public class TreeHistogramSpace(
-    @PublishedApi internal val binFactory: (Double) -> UnivariateDomain,
-) : Group<UnivariateHistogram>, ScaleOperations<UnivariateHistogram> {
-
-    public inline fun fill(block: UnivariateHistogramBuilder.() -> Unit): UnivariateHistogram =
-        TreeHistogramBuilder(binFactory).apply(block).build()
-
-    override fun add(
-        left: UnivariateHistogram,
-        right: UnivariateHistogram,
-    ): UnivariateHistogram {
-//        require(a.context == this) { "Histogram $a does not belong to this context" }
-//        require(b.context == this) { "Histogram $b does not belong to this context" }
-        val bins = TreeMap<Double, UnivariateBin>().apply {
-            (left.bins.map { it.domain } union right.bins.map { it.domain }).forEach { def ->
-                put(
-                    def.center,
-                    UnivariateBin(
-                        def,
-                        value = (left[def.center]?.value ?: 0.0) + (right[def.center]?.value ?: 0.0),
-                        standardDeviation = (left[def.center]?.standardDeviation
-                            ?: 0.0) + (right[def.center]?.standardDeviation ?: 0.0)
-                    )
-                )
-            }
-        }
-        return TreeHistogram(bins)
-    }
-
-    override fun scale(a: UnivariateHistogram, value: Double): UnivariateHistogram {
-        val bins = TreeMap<Double, UnivariateBin>().apply {
-            a.bins.forEach { bin ->
-                put(
-                    bin.domain.center,
-                    UnivariateBin(
-                        bin.domain,
-                        value = bin.value * value,
-                        standardDeviation = abs(bin.standardDeviation * value)
-                    )
-                )
-            }
-        }
-
-        return TreeHistogram(bins)
-    }
-
-    override fun UnivariateHistogram.unaryMinus(): UnivariateHistogram = this * (-1)
-
-    override val zero: UnivariateHistogram by lazy { fill { } }
-
-    public companion object {
-        /**
-         * Build and fill a [UnivariateHistogram]. Returns a read-only histogram.
-         */
-        public fun uniform(
-            binSize: Double,
-            start: Double = 0.0,
-        ): TreeHistogramSpace = TreeHistogramSpace { value ->
-            val center = start + binSize * floor((value - start) / binSize + 0.5)
-            UnivariateDomain((center - binSize / 2)..(center + binSize / 2))
-        }
-
-        /**
-         * Create a histogram with custom cell borders
-         */
-        public fun custom(borders: DoubleArray): TreeHistogramSpace {
-            val sorted = borders.sortedArray()
-
-            return TreeHistogramSpace { value ->
-                when {
-                    value < sorted.first() -> UnivariateDomain(
-                        Double.NEGATIVE_INFINITY..sorted.first()
-                    )
-
-                    value > sorted.last() -> UnivariateDomain(
-                        sorted.last()..Double.POSITIVE_INFINITY
-                    )
-
-                    else -> {
-                        val index = sorted.indices.first { value > sorted[it] }
-                        val left = sorted[index]
-                        val right = sorted[index + 1]
-                        UnivariateDomain(left..right)
-                    }
-                }
-            }
-        }
-    }
-}

kmath-histograms/src/jvmMain/kotlin/space/kscience/kmath/histogram/UnivariateHistogram.kt

@@ -1,79 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.histogram
-
-import space.kscience.kmath.domains.UnivariateDomain
-import space.kscience.kmath.misc.UnstableKMathAPI
-import space.kscience.kmath.operations.asSequence
-import space.kscience.kmath.structures.Buffer
-
-
-@UnstableKMathAPI
-public val UnivariateDomain.center: Double
-    get() = (range.endInclusive + range.start) / 2
-
-/**
- * A univariate bin based on a range
- *
- * @property value The value of histogram including weighting
- * @property standardDeviation Standard deviation of the bin value. Zero or negative if not applicable
- */
-@UnstableKMathAPI
-public class UnivariateBin(
-    public val domain: UnivariateDomain,
-    override val value: Double,
-    public val standardDeviation: Double,
-) : Bin<Double>, ClosedFloatingPointRange<Double> by domain.range {
-
-    override val dimension: Int get() = 1
-
-    override fun contains(point: Buffer<Double>): Boolean = point.size == 1 && contains(point[0])
-}
-
-@OptIn(UnstableKMathAPI::class)
-public interface UnivariateHistogram : Histogram<Double, UnivariateBin> {
-    public operator fun get(value: Double): UnivariateBin?
-    override operator fun get(point: Buffer<Double>): UnivariateBin? = get(point[0])
-
-    public companion object {
-        /**
-         * Build and fill a [UnivariateHistogram]. Returns a read-only histogram.
-         */
-        public inline fun uniform(
-            binSize: Double,
-            start: Double = 0.0,
-            builder: UnivariateHistogramBuilder.() -> Unit,
-        ): UnivariateHistogram = TreeHistogramSpace.uniform(binSize, start).fill(builder)
-
-        /**
-         * Build and fill a histogram with custom borders. Returns a read-only histogram.
-         */
-        public inline fun custom(
-            borders: DoubleArray,
-            builder: UnivariateHistogramBuilder.() -> Unit,
-        ): UnivariateHistogram = TreeHistogramSpace.custom(borders).fill(builder)
-
-    }
-}
-
-@UnstableKMathAPI
-public interface UnivariateHistogramBuilder : HistogramBuilder<Double> {
-    /**
-     * Thread safe put operation
-     */
-    public fun putValue(at: Double, value: Double = 1.0)
-
-    override fun putValue(point: Buffer<Double>, value: Number)
-}
-
-@UnstableKMathAPI
-public fun UnivariateHistogramBuilder.fill(items: Iterable<Double>): Unit = items.forEach(this::putValue)
-
-@UnstableKMathAPI
-public fun UnivariateHistogramBuilder.fill(array: DoubleArray): Unit = array.forEach(this::putValue)
-
-@UnstableKMathAPI
-public fun UnivariateHistogramBuilder.fill(buffer: Buffer<Double>): Unit = buffer.asSequence().forEach(this::putValue)

kmath-histograms/src/jvmTest/kotlin/space/kscience/kmath/histogram/TreeHistogramTest.kt

@@ -6,19 +6,24 @@
 package space.kscience.kmath.histogram
 
 import org.junit.jupiter.api.Test
+import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.real.step
 import kotlin.random.Random
+import kotlin.test.assertEquals
 import kotlin.test.assertTrue
 
 class TreeHistogramTest {
 
     @Test
     fun normalFill() {
-        val histogram = UnivariateHistogram.uniform(0.1) {
+        val random  = Random(123)
+        val histogram = Histogram.custom1D(DoubleField, 0.0..1.0 step 0.1).produce {
             repeat(100_000) {
-                putValue(Random.nextDouble())
+                putValue(random.nextDouble())
             }
         }
 
-        assertTrue { histogram.bins.count() > 10 }
+        assertTrue { histogram.bins.count() > 8}
+        assertEquals(100_000, histogram.bins.sumOf { it.binValue }.toInt())
     }
 }

kmath-jafama/README.md

@@ -7,17 +7,17 @@ Integration with [Jafama](https://github.com/jeffhain/jafama).
 
 ## Artifact:
 
-The Maven coordinates of this project are `space.kscience:kmath-jafama:0.3.0-dev-17`.
+The Maven coordinates of this project are `space.kscience:kmath-jafama:0.3.0`.
 
-**Gradle:**
+**Gradle Groovy:**
-```gradle
+```groovy
 repositories {
     maven { url 'https://repo.kotlin.link' }
     mavenCentral()
 }
 
 dependencies {
-    implementation 'space.kscience:kmath-jafama:0.3.0-dev-17'
+    implementation 'space.kscience:kmath-jafama:0.3.0'
 }
 ```
 **Gradle Kotlin DSL:**
@@ -28,7 +28,7 @@ repositories {
 }
 
 dependencies {
-    implementation("space.kscience:kmath-jafama:0.3.0-dev-17")
+    implementation("space.kscience:kmath-jafama:0.3.0")
 }
 ```
 

kmath-jupyter/README.md

@@ -0,0 +1,32 @@
+# Module kmath-jupyter
+
+
+
+## Usage
+
+## Artifact:
+
+The Maven coordinates of this project are `space.kscience:kmath-jupyter:0.3.0`.
+
+**Gradle Groovy:**
+```groovy
+repositories {
+    maven { url 'https://repo.kotlin.link' }
+    mavenCentral()
+}
+
+dependencies {
+    implementation 'space.kscience:kmath-jupyter:0.3.0'
+}
+```
+**Gradle Kotlin DSL:**
+```kotlin
+repositories {
+    maven("https://repo.kotlin.link")
+    mavenCentral()
+}
+
+dependencies {
+    implementation("space.kscience:kmath-jupyter:0.3.0")
+}
+```

kmath-jupyter/api/kmath-jupyter.api

@@ -0,0 +1,4 @@
+public final class space/kscience/kmath/jupyter/KMathJupyterKt {
+	public static final fun toMst (Ljava/lang/Number;)Lspace/kscience/kmath/expressions/MST$Numeric;
+}
+

kmath-kotlingrad/README.md

@@ -8,17 +8,17 @@
 
 ## Artifact:
 
-The Maven coordinates of this project are `space.kscience:kmath-kotlingrad:0.3.0-dev-17`.
+The Maven coordinates of this project are `space.kscience:kmath-kotlingrad:0.3.0`.
 
-**Gradle:**
+**Gradle Groovy:**
-```gradle
+```groovy
 repositories {
     maven { url 'https://repo.kotlin.link' }
     mavenCentral()
 }
 
 dependencies {
-    implementation 'space.kscience:kmath-kotlingrad:0.3.0-dev-17'
+    implementation 'space.kscience:kmath-kotlingrad:0.3.0'
 }
 ```
 **Gradle Kotlin DSL:**
@@ -29,6 +29,6 @@ repositories {
 }
 
 dependencies {
-    implementation("space.kscience:kmath-kotlingrad:0.3.0-dev-17")
+    implementation("space.kscience:kmath-kotlingrad:0.3.0")
 }
 ```

kmath-memory/README.md

@@ -0,0 +1,32 @@
+# Module kmath-memory
+
+
+
+## Usage
+
+## Artifact:
+
+The Maven coordinates of this project are `space.kscience:kmath-memory:0.3.0`.
+
+**Gradle Groovy:**
+```groovy
+repositories {
+    maven { url 'https://repo.kotlin.link' }
+    mavenCentral()
+}
+
+dependencies {
+    implementation 'space.kscience:kmath-memory:0.3.0'
+}
+```
+**Gradle Kotlin DSL:**
+```kotlin
+repositories {
+    maven("https://repo.kotlin.link")
+    mavenCentral()
+}
+
+dependencies {
+    implementation("space.kscience:kmath-memory:0.3.0")
+}
+```

kmath-multik/README.md

@@ -0,0 +1,32 @@
+# Module kmath-multik
+
+JetBrains Multik connector
+
+## Usage
+
+## Artifact:
+
+The Maven coordinates of this project are `space.kscience:kmath-multik:0.3.0`.
+
+**Gradle Groovy:**
+```groovy
+repositories {
+    maven { url 'https://repo.kotlin.link' }
+    mavenCentral()
+}
+
+dependencies {
+    implementation 'space.kscience:kmath-multik:0.3.0'
+}
+```
+**Gradle Kotlin DSL:**
+```kotlin
+repositories {
+    maven("https://repo.kotlin.link")
+    mavenCentral()
+}
+
+dependencies {
+    implementation("space.kscience:kmath-multik:0.3.0")
+}
+```

kmath-multik/src/test/kotlin/space/kscience/kmath/multik/MultikNDTest.kt

@@ -6,13 +6,38 @@
 package space.kscience.kmath.multik
 
 import org.junit.jupiter.api.Test
+import space.kscience.kmath.nd.StructureND
 import space.kscience.kmath.nd.one
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.operations.invoke
+import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.tensorAlgebra
+import kotlin.test.assertTrue
 
 internal class MultikNDTest {
     @Test
     fun basicAlgebra(): Unit = DoubleField.multikAlgebra{
         one(2,2) + 1.0
     }
+
+    @Test
+    fun dotResult(){
+        val dim = 100
+
+        val tensor1 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12224)
+        val tensor2 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12225)
+
+        val multikResult = with(DoubleField.multikAlgebra){
+            tensor1 dot tensor2
+        }
+
+        val defaultResult = with(DoubleField.tensorAlgebra){
+            tensor1 dot tensor2
+        }
+
+        assertTrue {
+            StructureND.contentEquals(multikResult, defaultResult)
+        }
+
+    }
 }

kmath-nd4j/README.md

@@ -9,17 +9,17 @@ ND4J based implementations of KMath abstractions.
 
 ## Artifact:
 
-The Maven coordinates of this project are `space.kscience:kmath-nd4j:0.3.0-dev-17`.
+The Maven coordinates of this project are `space.kscience:kmath-nd4j:0.3.0`.
 
-**Gradle:**
+**Gradle Groovy:**
-```gradle
+```groovy
 repositories {
     maven { url 'https://repo.kotlin.link' }
     mavenCentral()
 }
 
 dependencies {
-    implementation 'space.kscience:kmath-nd4j:0.3.0-dev-17'
+    implementation 'space.kscience:kmath-nd4j:0.3.0'
 }
 ```
 **Gradle Kotlin DSL:**
@@ -30,7 +30,7 @@ repositories {
 }
 
 dependencies {
-    implementation("space.kscience:kmath-nd4j:0.3.0-dev-17")
+    implementation("space.kscience:kmath-nd4j:0.3.0")
 }
 ```
 

kmath-optimization/README.md

@@ -0,0 +1,32 @@
+# Module kmath-optimization
+
+
+
+## Usage
+
+## Artifact:
+
+The Maven coordinates of this project are `space.kscience:kmath-optimization:0.3.0`.
+
+**Gradle Groovy:**
+```groovy
+repositories {
+    maven { url 'https://repo.kotlin.link' }
+    mavenCentral()
+}
+
+dependencies {
+    implementation 'space.kscience:kmath-optimization:0.3.0'
+}
+```
+**Gradle Kotlin DSL:**
+```kotlin
+repositories {
+    maven("https://repo.kotlin.link")
+    mavenCentral()
+}
+
+dependencies {
+    implementation("space.kscience:kmath-optimization:0.3.0")
+}
+```

kmath-stat/README.md

@@ -0,0 +1,32 @@
+# Module kmath-stat
+
+
+
+## Usage
+
+## Artifact:
+
+The Maven coordinates of this project are `space.kscience:kmath-stat:0.3.0`.
+
+**Gradle Groovy:**
+```groovy
+repositories {
+    maven { url 'https://repo.kotlin.link' }
+    mavenCentral()
+}
+
+dependencies {
+    implementation 'space.kscience:kmath-stat:0.3.0'
+}
+```
+**Gradle Kotlin DSL:**
+```kotlin
+repositories {
+    maven("https://repo.kotlin.link")
+    mavenCentral()
+}
+
+dependencies {
+    implementation("space.kscience:kmath-stat:0.3.0")
+}
+```

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/distributions/Distribution.kt

@@ -27,7 +27,7 @@ public interface Distribution<T : Any> : Sampler<T> {
     public companion object
 }
 
-public interface UnivariateDistribution<T : Comparable<T>> : Distribution<T> {
+public interface Distribution1D<T : Comparable<T>> : Distribution<T> {
     /**
      * Cumulative distribution for ordered parameter (CDF)
      */
@@ -37,7 +37,7 @@ public interface UnivariateDistribution<T : Comparable<T>> : Distribution<T> {
 /**
  * Compute probability integral in an interval
  */
-public fun <T : Comparable<T>> UnivariateDistribution<T>.integral(from: T, to: T): Double {
+public fun <T : Comparable<T>> Distribution1D<T>.integral(from: T, to: T): Double {
     require(to > from)
     return cumulative(to) - cumulative(from)
 }

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/distributions/NormalDistribution.kt

@@ -14,14 +14,9 @@ import space.kscience.kmath.stat.RandomGenerator
 import kotlin.math.*
 
 /**
- * Implements [UnivariateDistribution] for the normal (gaussian) distribution.
+ * Implements [Distribution1D] for the normal (gaussian) distribution.
  */
-public class NormalDistribution(public val sampler: GaussianSampler) : UnivariateDistribution<Double> {
+public class NormalDistribution(public val sampler: GaussianSampler) : Distribution1D<Double> {
-    public constructor(
-        mean: Double,
-        standardDeviation: Double,
-        normalized: NormalizedGaussianSampler = ZigguratNormalizedGaussianSampler,
-    ) : this(GaussianSampler(mean, standardDeviation, normalized))
 
     override fun probability(arg: Double): Double {
         val x1 = (arg - sampler.mean) / sampler.standardDeviation
@@ -43,3 +38,9 @@ public class NormalDistribution(public val sampler: GaussianSampler) : Univariat
         private val SQRT2 = sqrt(2.0)
     }
 }
+
+public fun NormalDistribution(
+    mean: Double,
+    standardDeviation: Double,
+    normalized: NormalizedGaussianSampler = ZigguratNormalizedGaussianSampler,
+): NormalDistribution = NormalDistribution(GaussianSampler(mean, standardDeviation, normalized))

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Sampler.kt

@@ -67,3 +67,12 @@ public fun Sampler<Double>.sampleBuffer(generator: RandomGenerator, size: Int):
 @JvmName("sampleIntBuffer")
 public fun Sampler<Int>.sampleBuffer(generator: RandomGenerator, size: Int): Chain<Buffer<Int>> =
     sampleBuffer(generator, size, ::IntBuffer)
+
+
+/**
+ * Samples a [Buffer] of values from this [Sampler].
+ */
+public suspend fun Sampler<Double>.nextBuffer(generator: RandomGenerator, size: Int): Buffer<Double> =
+    sampleBuffer(generator, size).first()
+
+//TODO add `context(RandomGenerator) Sampler.nextBuffer

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/UniformDistribution.kt

@@ -8,9 +8,9 @@ package space.kscience.kmath.stat
 import space.kscience.kmath.chains.Chain
 import space.kscience.kmath.chains.SimpleChain
 import space.kscience.kmath.distributions.Distribution
-import space.kscience.kmath.distributions.UnivariateDistribution
+import space.kscience.kmath.distributions.Distribution1D
 
-public class UniformDistribution(public val range: ClosedFloatingPointRange<Double>) : UnivariateDistribution<Double> {
+public class UniformDistribution(public val range: ClosedFloatingPointRange<Double>) : Distribution1D<Double> {
     private val length: Double = range.endInclusive - range.start
 
     override fun probability(arg: Double): Double = if (arg in range) 1.0 / length else 0.0

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/ValueAndErrorField.kt

@@ -0,0 +1,56 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.stat
+
+import space.kscience.kmath.operations.Field
+import kotlin.math.pow
+import kotlin.math.sqrt
+
+/**
+ * A combination of a random [value] and its [dispersion].
+ *
+ * [dispersion] must be positive.
+ */
+public data class ValueAndError(val value: Double, val dispersion: Double) {
+    init {
+        require(dispersion >= 0) { "Dispersion must be non-negative" }
+    }
+
+    val error: Double get() = sqrt(dispersion)
+}
+
+/**
+ * An algebra for double value + its error combination. The multiplication assumes linear error propagation
+ */
+public object ValueAndErrorField : Field<ValueAndError> {
+
+    override val zero: ValueAndError = ValueAndError(0.0, 0.0)
+
+    override val one: ValueAndError = ValueAndError(1.0, 0.0)
+
+    override fun add(left: ValueAndError, right: ValueAndError): ValueAndError =
+        ValueAndError(left.value + right.value, left.dispersion + right.dispersion)
+
+    override fun ValueAndError.unaryMinus(): ValueAndError =
+        ValueAndError(-value, dispersion)
+
+    //TODO study performance impact of pow(2). On JVM it does not exist: https://stackoverflow.com/questions/29144275/xx-vs-math-powx-2-java-performance
+
+    override fun multiply(left: ValueAndError, right: ValueAndError): ValueAndError {
+        val value = left.value * right.value
+        val dispersion = (left.dispersion / left.value.pow(2) + right.dispersion / right.value.pow(2)) * value.pow(2)
+        return ValueAndError(value, dispersion)
+    }
+
+    override fun divide(left: ValueAndError, right: ValueAndError): ValueAndError {
+        val value = left.value / right.value
+        val dispersion = (left.dispersion / left.value.pow(2) + right.dispersion / right.value.pow(2)) * value.pow(2)
+        return ValueAndError(value, dispersion)
+    }
+
+    override fun scale(a: ValueAndError, value: Double): ValueAndError =
+        ValueAndError(a.value * value, a.dispersion * value.pow(2))
+}

kmath-symja/README.md

@@ -0,0 +1,32 @@
+# Module kmath-symja
+
+Symja integration module
+
+## Usage
+
+## Artifact:
+
+The Maven coordinates of this project are `space.kscience:kmath-symja:0.3.0`.
+
+**Gradle Groovy:**
+```groovy
+repositories {
+    maven { url 'https://repo.kotlin.link' }
+    mavenCentral()
+}
+
+dependencies {
+    implementation 'space.kscience:kmath-symja:0.3.0'
+}
+```
+**Gradle Kotlin DSL:**
+```kotlin
+repositories {
+    maven("https://repo.kotlin.link")
+    mavenCentral()
+}
+
+dependencies {
+    implementation("space.kscience:kmath-symja:0.3.0")
+}
+```

kmath-tensorflow/README.md

@@ -0,0 +1,32 @@
+# Module kmath-tensorflow
+
+Google tensorflow connector
+
+## Usage
+
+## Artifact:
+
+The Maven coordinates of this project are `space.kscience:kmath-tensorflow:0.3.0`.
+
+**Gradle Groovy:**
+```groovy
+repositories {
+    maven { url 'https://repo.kotlin.link' }
+    mavenCentral()
+}
+
+dependencies {
+    implementation 'space.kscience:kmath-tensorflow:0.3.0'
+}
+```
+**Gradle Kotlin DSL:**
+```kotlin
+repositories {
+    maven("https://repo.kotlin.link")
+    mavenCentral()
+}
+
+dependencies {
+    implementation("space.kscience:kmath-tensorflow:0.3.0")
+}
+```

kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/TensorFlowAlgebra.kt

@@ -6,11 +6,9 @@ import org.tensorflow.Operand
 import org.tensorflow.Output
 import org.tensorflow.Session
 import org.tensorflow.ndarray.NdArray
+import org.tensorflow.ndarray.index.Indices
 import org.tensorflow.op.Ops
-import org.tensorflow.op.core.Constant
+import org.tensorflow.op.core.*
-import org.tensorflow.op.core.Max
-import org.tensorflow.op.core.Min
-import org.tensorflow.op.core.Sum
 import org.tensorflow.types.TInt32
 import org.tensorflow.types.family.TNumber
 import org.tensorflow.types.family.TType
@@ -182,7 +180,7 @@ public abstract class TensorFlowAlgebra<T, TT : TNumber, A : Ring<T>> internal c
     override fun StructureND<T>.unaryMinus(): TensorFlowOutput<T, TT> = operate(ops.math::neg)
 
     override fun Tensor<T>.get(i: Int): Tensor<T> = operate {
-        TODO("Not yet implemented")
+        StridedSliceHelper.stridedSlice(ops.scope(), it, Indices.at(i.toLong()))
     }
 
     override fun Tensor<T>.transpose(i: Int, j: Int): Tensor<T> = operate {
@@ -210,7 +208,13 @@ public abstract class TensorFlowAlgebra<T, TT : TNumber, A : Ring<T>> internal c
         dim1: Int,
         dim2: Int,
     ): TensorFlowOutput<T, TT> = diagonalEntries.operate {
-        TODO("Not yet implemented")
+        ops.linalg.matrixDiagV3(
+            /* diagonal = */ it,
+            /* k = */ ops.constant(offset),
+            /* numRows = */ ops.constant(dim1),
+            /* numCols = */ ops.constant(dim2),
+            /* paddingValue = */ const(elementAlgebra.zero)
+        )
     }
 
     override fun StructureND<T>.sum(): T = operate {

kmath-tensorflow/src/test/kotlin/space/kscience/kmath/tensorflow/DoubleTensorFlowOps.kt

@@ -6,7 +6,6 @@ import space.kscience.kmath.nd.structureND
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.sum
-import kotlin.random.Random
 import kotlin.test.assertEquals
 
 class DoubleTensorFlowOps {
@@ -23,7 +22,6 @@ class DoubleTensorFlowOps {
 
     @Test
     fun dot(){
-        val random = Random(12224)
         val dim = 1000
 
         val tensor1 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12224)

kmath-tensors/README.md

@@ -9,17 +9,17 @@ Common linear algebra operations on tensors.
 
 ## Artifact:
 
-The Maven coordinates of this project are `space.kscience:kmath-tensors:0.3.0-dev-17`.
+The Maven coordinates of this project are `space.kscience:kmath-tensors:0.3.0`.
 
-**Gradle:**
+**Gradle Groovy:**
-```gradle
+```groovy
 repositories {
     maven { url 'https://repo.kotlin.link' }
     mavenCentral()
 }
 
 dependencies {
-    implementation 'space.kscience:kmath-tensors:0.3.0-dev-17'
+    implementation 'space.kscience:kmath-tensors:0.3.0'
 }
 ```
 **Gradle Kotlin DSL:**
@@ -30,6 +30,6 @@ repositories {
 }
 
 dependencies {
-    implementation("space.kscience:kmath-tensors:0.3.0-dev-17")
+    implementation("space.kscience:kmath-tensors:0.3.0")
 }
 ```

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt

@@ -27,7 +27,7 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.indices.linearSize) { i ->
-            newThis.mutableBuffer.array()[i] + newOther.mutableBuffer.array()[i]
+            newThis.mutableBuffer[i] + newOther.mutableBuffer[i]
         }
         return DoubleTensor(newThis.shape, resBuffer)
     }
@@ -35,8 +35,8 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
     override fun Tensor<Double>.plusAssign(arg: StructureND<Double>) {
         val newOther = broadcastTo(arg.tensor, tensor.shape)
         for (i in 0 until tensor.indices.linearSize) {
-            tensor.mutableBuffer.array()[tensor.bufferStart + i] +=
+            tensor.mutableBuffer[tensor.bufferStart + i] +=
-                newOther.mutableBuffer.array()[tensor.bufferStart + i]
+                newOther.mutableBuffer[tensor.bufferStart + i]
         }
     }
 
@@ -45,7 +45,7 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.indices.linearSize) { i ->
-            newThis.mutableBuffer.array()[i] - newOther.mutableBuffer.array()[i]
+            newThis.mutableBuffer[i] - newOther.mutableBuffer[i]
         }
         return DoubleTensor(newThis.shape, resBuffer)
     }
@@ -53,8 +53,8 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
     override fun Tensor<Double>.minusAssign(arg: StructureND<Double>) {
         val newOther = broadcastTo(arg.tensor, tensor.shape)
         for (i in 0 until tensor.indices.linearSize) {
-            tensor.mutableBuffer.array()[tensor.bufferStart + i] -=
+            tensor.mutableBuffer[tensor.bufferStart + i] -=
-                newOther.mutableBuffer.array()[tensor.bufferStart + i]
+                newOther.mutableBuffer[tensor.bufferStart + i]
         }
     }
 
@@ -63,8 +63,8 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.indices.linearSize) { i ->
-            newThis.mutableBuffer.array()[newThis.bufferStart + i] *
+            newThis.mutableBuffer[newThis.bufferStart + i] *
-                    newOther.mutableBuffer.array()[newOther.bufferStart + i]
+                    newOther.mutableBuffer[newOther.bufferStart + i]
         }
         return DoubleTensor(newThis.shape, resBuffer)
     }
@@ -72,8 +72,8 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
     override fun Tensor<Double>.timesAssign(arg: StructureND<Double>) {
         val newOther = broadcastTo(arg.tensor, tensor.shape)
         for (i in 0 until tensor.indices.linearSize) {
-            tensor.mutableBuffer.array()[tensor.bufferStart + i] *=
+            tensor.mutableBuffer[tensor.bufferStart + i] *=
-                newOther.mutableBuffer.array()[tensor.bufferStart + i]
+                newOther.mutableBuffer[tensor.bufferStart + i]
         }
     }
 
@@ -82,8 +82,8 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.indices.linearSize) { i ->
-            newThis.mutableBuffer.array()[newOther.bufferStart + i] /
+            newThis.mutableBuffer[newOther.bufferStart + i] /
-                    newOther.mutableBuffer.array()[newOther.bufferStart + i]
+                    newOther.mutableBuffer[newOther.bufferStart + i]
         }
         return DoubleTensor(newThis.shape, resBuffer)
     }
@@ -91,8 +91,8 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
     override fun Tensor<Double>.divAssign(arg: StructureND<Double>) {
         val newOther = broadcastTo(arg.tensor, tensor.shape)
         for (i in 0 until tensor.indices.linearSize) {
-            tensor.mutableBuffer.array()[tensor.bufferStart + i] /=
+            tensor.mutableBuffer[tensor.bufferStart + i] /=
-                newOther.mutableBuffer.array()[tensor.bufferStart + i]
+                newOther.mutableBuffer[tensor.bufferStart + i]
         }
     }
 }

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt

@@ -89,7 +89,7 @@ public open class DoubleTensorAlgebra :
     }
 
     override fun StructureND<Double>.valueOrNull(): Double? = if (tensor.shape contentEquals intArrayOf(1))
-        tensor.mutableBuffer.array()[tensor.bufferStart] else null
+        tensor.mutableBuffer[tensor.bufferStart] else null
 
     override fun StructureND<Double>.value(): Double = valueOrNull()
         ?: throw IllegalArgumentException("The tensor shape is $shape, but value method is allowed only for shape [1]")
@@ -208,7 +208,7 @@ public open class DoubleTensorAlgebra :
 
     override fun Double.plus(arg: StructureND<Double>): DoubleTensor {
         val resBuffer = DoubleArray(arg.tensor.numElements) { i ->
-            arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i] + this
+            arg.tensor.mutableBuffer[arg.tensor.bufferStart + i] + this
         }
         return DoubleTensor(arg.shape, resBuffer)
     }
@@ -218,35 +218,35 @@ public open class DoubleTensorAlgebra :
     override fun StructureND<Double>.plus(arg: StructureND<Double>): DoubleTensor {
         checkShapesCompatible(tensor, arg.tensor)
         val resBuffer = DoubleArray(tensor.numElements) { i ->
-            tensor.mutableBuffer.array()[i] + arg.tensor.mutableBuffer.array()[i]
+            tensor.mutableBuffer[i] + arg.tensor.mutableBuffer[i]
         }
         return DoubleTensor(tensor.shape, resBuffer)
     }
 
     override fun Tensor<Double>.plusAssign(value: Double) {
         for (i in 0 until tensor.numElements) {
-            tensor.mutableBuffer.array()[tensor.bufferStart + i] += value
+            tensor.mutableBuffer[tensor.bufferStart + i] += value
         }
     }
 
     override fun Tensor<Double>.plusAssign(arg: StructureND<Double>) {
         checkShapesCompatible(tensor, arg.tensor)
         for (i in 0 until tensor.numElements) {
-            tensor.mutableBuffer.array()[tensor.bufferStart + i] +=
+            tensor.mutableBuffer[tensor.bufferStart + i] +=
-                arg.tensor.mutableBuffer.array()[tensor.bufferStart + i]
+                arg.tensor.mutableBuffer[tensor.bufferStart + i]
         }
     }
 
     override fun Double.minus(arg: StructureND<Double>): DoubleTensor {
         val resBuffer = DoubleArray(arg.tensor.numElements) { i ->
-            this - arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i]
+            this - arg.tensor.mutableBuffer[arg.tensor.bufferStart + i]
         }
         return DoubleTensor(arg.shape, resBuffer)
     }
 
     override fun StructureND<Double>.minus(arg: Double): DoubleTensor {
         val resBuffer = DoubleArray(tensor.numElements) { i ->
-            tensor.mutableBuffer.array()[tensor.bufferStart + i] - arg
+            tensor.mutableBuffer[tensor.bufferStart + i] - arg
         }
         return DoubleTensor(tensor.shape, resBuffer)
     }
@@ -254,28 +254,28 @@ public open class DoubleTensorAlgebra :
     override fun StructureND<Double>.minus(arg: StructureND<Double>): DoubleTensor {
         checkShapesCompatible(tensor, arg)
         val resBuffer = DoubleArray(tensor.numElements) { i ->
-            tensor.mutableBuffer.array()[i] - arg.tensor.mutableBuffer.array()[i]
+            tensor.mutableBuffer[i] - arg.tensor.mutableBuffer[i]
         }
         return DoubleTensor(tensor.shape, resBuffer)
     }
 
     override fun Tensor<Double>.minusAssign(value: Double) {
         for (i in 0 until tensor.numElements) {
-            tensor.mutableBuffer.array()[tensor.bufferStart + i] -= value
+            tensor.mutableBuffer[tensor.bufferStart + i] -= value
         }
     }
 
     override fun Tensor<Double>.minusAssign(arg: StructureND<Double>) {
         checkShapesCompatible(tensor, arg)
         for (i in 0 until tensor.numElements) {
-            tensor.mutableBuffer.array()[tensor.bufferStart + i] -=
+            tensor.mutableBuffer[tensor.bufferStart + i] -=
-                arg.tensor.mutableBuffer.array()[tensor.bufferStart + i]
+                arg.tensor.mutableBuffer[tensor.bufferStart + i]
         }
     }
 
     override fun Double.times(arg: StructureND<Double>): DoubleTensor {
         val resBuffer = DoubleArray(arg.tensor.numElements) { i ->
-            arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i] * this
+            arg.tensor.mutableBuffer[arg.tensor.bufferStart + i] * this
         }
         return DoubleTensor(arg.shape, resBuffer)
     }
@@ -285,36 +285,36 @@ public open class DoubleTensorAlgebra :
     override fun StructureND<Double>.times(arg: StructureND<Double>): DoubleTensor {
         checkShapesCompatible(tensor, arg)
         val resBuffer = DoubleArray(tensor.numElements) { i ->
-            tensor.mutableBuffer.array()[tensor.bufferStart + i] *
+            tensor.mutableBuffer[tensor.bufferStart + i] *
-                    arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i]
+                    arg.tensor.mutableBuffer[arg.tensor.bufferStart + i]
         }
         return DoubleTensor(tensor.shape, resBuffer)
     }
 
     override fun Tensor<Double>.timesAssign(value: Double) {
         for (i in 0 until tensor.numElements) {
-            tensor.mutableBuffer.array()[tensor.bufferStart + i] *= value
+            tensor.mutableBuffer[tensor.bufferStart + i] *= value
         }
     }
 
     override fun Tensor<Double>.timesAssign(arg: StructureND<Double>) {
         checkShapesCompatible(tensor, arg)
         for (i in 0 until tensor.numElements) {
-            tensor.mutableBuffer.array()[tensor.bufferStart + i] *=
+            tensor.mutableBuffer[tensor.bufferStart + i] *=
-                arg.tensor.mutableBuffer.array()[tensor.bufferStart + i]
+                arg.tensor.mutableBuffer[tensor.bufferStart + i]
         }
     }
 
     override fun Double.div(arg: StructureND<Double>): DoubleTensor {
         val resBuffer = DoubleArray(arg.tensor.numElements) { i ->
-            this / arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i]
+            this / arg.tensor.mutableBuffer[arg.tensor.bufferStart + i]
         }
         return DoubleTensor(arg.shape, resBuffer)
     }
 
     override fun StructureND<Double>.div(arg: Double): DoubleTensor {
         val resBuffer = DoubleArray(tensor.numElements) { i ->
-            tensor.mutableBuffer.array()[tensor.bufferStart + i] / arg
+            tensor.mutableBuffer[tensor.bufferStart + i] / arg
         }
         return DoubleTensor(shape, resBuffer)
     }
@@ -322,29 +322,29 @@ public open class DoubleTensorAlgebra :
     override fun StructureND<Double>.div(arg: StructureND<Double>): DoubleTensor {
         checkShapesCompatible(tensor, arg)
         val resBuffer = DoubleArray(tensor.numElements) { i ->
-            tensor.mutableBuffer.array()[arg.tensor.bufferStart + i] /
+            tensor.mutableBuffer[arg.tensor.bufferStart + i] /
-                    arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i]
+                    arg.tensor.mutableBuffer[arg.tensor.bufferStart + i]
         }
         return DoubleTensor(tensor.shape, resBuffer)
     }
 
     override fun Tensor<Double>.divAssign(value: Double) {
         for (i in 0 until tensor.numElements) {
-            tensor.mutableBuffer.array()[tensor.bufferStart + i] /= value
+            tensor.mutableBuffer[tensor.bufferStart + i] /= value
         }
     }
 
     override fun Tensor<Double>.divAssign(arg: StructureND<Double>) {
         checkShapesCompatible(tensor, arg)
         for (i in 0 until tensor.numElements) {
-            tensor.mutableBuffer.array()[tensor.bufferStart + i] /=
+            tensor.mutableBuffer[tensor.bufferStart + i] /=
-                arg.tensor.mutableBuffer.array()[tensor.bufferStart + i]
+                arg.tensor.mutableBuffer[tensor.bufferStart + i]
         }
     }
 
     override fun StructureND<Double>.unaryMinus(): DoubleTensor {
         val resBuffer = DoubleArray(tensor.numElements) { i ->
-            tensor.mutableBuffer.array()[tensor.bufferStart + i].unaryMinus()
+            tensor.mutableBuffer[tensor.bufferStart + i].unaryMinus()
         }
         return DoubleTensor(tensor.shape, resBuffer)
     }
@@ -367,8 +367,8 @@ public open class DoubleTensorAlgebra :
             newMultiIndex[ii] = newMultiIndex[jj].also { newMultiIndex[jj] = newMultiIndex[ii] }
 
             val linearIndex = resTensor.indices.offset(newMultiIndex)
-            resTensor.mutableBuffer.array()[linearIndex] =
+            resTensor.mutableBuffer[linearIndex] =
-                tensor.mutableBuffer.array()[tensor.bufferStart + offset]
+                tensor.mutableBuffer[tensor.bufferStart + offset]
         }
         return resTensor
     }
@@ -833,8 +833,38 @@ public open class DoubleTensorAlgebra :
         return qTensor to rTensor
     }
 
-    override fun StructureND<Double>.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> =
+    override fun StructureND<Double>.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
-        svd(epsilon = 1e-10)
+        return this.svdGolubKahan()
+    }
+
+    public fun StructureND<Double>.svdGolubKahan(iterations: Int = 30, epsilon: Double = 1e-10): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
+        val size = tensor.dimension
+        val commonShape = tensor.shape.sliceArray(0 until size - 2)
+        val (n, m) = tensor.shape.sliceArray(size - 2 until size)
+        val uTensor = zeros(commonShape + intArrayOf(n, m))
+        val sTensor = zeros(commonShape + intArrayOf(m))
+        val vTensor = zeros(commonShape + intArrayOf(m, m))
+
+        val matrices = tensor.matrices
+        val uTensors = uTensor.matrices
+        val sTensorVectors = sTensor.vectors
+        val vTensors = vTensor.matrices
+
+        for (index in matrices.indices) {
+            val matrix = matrices[index]
+            val matrixSize = matrix.shape.reduce { acc, i -> acc * i }
+            val curMatrix = DoubleTensor(
+                matrix.shape,
+                matrix.mutableBuffer.array()
+                    .slice(matrix.bufferStart until matrix.bufferStart + matrixSize)
+                    .toDoubleArray()
+            )
+            curMatrix.as2D().svdGolubKahanHelper(uTensors[index].as2D(), sTensorVectors[index], vTensors[index].as2D(),
+                iterations, epsilon)
+        }
+
+        return Triple(uTensor, sTensor, vTensor)
+    }
 
     /**
      * Singular Value Decomposition.
@@ -849,7 +879,7 @@ public open class DoubleTensorAlgebra :
      * i.e., the precision with which the cosine approaches 1 in an iterative algorithm.
      * @return a triple `Triple(U, S, V)`.
      */
-    public fun StructureND<Double>.svd(epsilon: Double): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
+    public fun StructureND<Double>.svdPowerMethod(epsilon: Double = 1e-10): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         val size = tensor.dimension
         val commonShape = tensor.shape.sliceArray(0 until size - 2)
         val (n, m) = tensor.shape.sliceArray(size - 2 until size)
@@ -876,7 +906,7 @@ public open class DoubleTensorAlgebra :
                     .slice(matrix.bufferStart until matrix.bufferStart + matrixSize)
                     .toDoubleArray()
             )
-            svdHelper(curMatrix, usv, m, n, epsilon)
+            svdPowerMethodHelper(curMatrix, usv, m, n, epsilon)
         }
 
         return Triple(uTensor.transpose(), sTensor, vTensor.transpose())
@@ -907,7 +937,7 @@ public open class DoubleTensorAlgebra :
             }
         }
 
-        val (u, s, v) = tensor.svd(epsilon)
+        val (u, s, v) = tensor.svd()
         val shp = s.shape + intArrayOf(1)
         val utv = u.transpose() dot v
         val n = s.shape.last()
@@ -928,18 +958,21 @@ public open class DoubleTensorAlgebra :
 
         var eigenvalueStart = 0
         var eigenvectorStart = 0
+        val eigenvaluesBuffer = eigenvalues.mutableBuffer
+        val eigenvectorsBuffer = eigenvectors.mutableBuffer
+
         for (matrix in tensor.matrixSequence()) {
             val matrix2D = matrix.as2D()
             val (d, v) = matrix2D.jacobiHelper(maxIteration, epsilon)
 
             for (i in 0 until matrix2D.rowNum) {
                 for (j in 0 until matrix2D.colNum) {
-                    eigenvectors.mutableBuffer.array()[eigenvectorStart + i * matrix2D.rowNum + j] = v[i, j]
+                    eigenvectorsBuffer[eigenvectorStart + i * matrix2D.rowNum + j] = v[i, j]
                 }
             }
 
             for (i in 0 until matrix2D.rowNum) {
-                eigenvalues.mutableBuffer.array()[eigenvalueStart + i] = d[i]
+                eigenvaluesBuffer[eigenvalueStart + i] = d[i]
             }
 
             eigenvalueStart += this.shape.last()
@@ -962,11 +995,11 @@ public open class DoubleTensorAlgebra :
 
         // assume that buffered tensor is square matrix
         operator fun BufferedTensor<Double>.get(i: Int, j: Int): Double {
-            return this.mutableBuffer.array()[bufferStart + i * this.shape[0] + j]
+            return this.mutableBuffer[bufferStart + i * this.shape[0] + j]
         }
 
         operator fun BufferedTensor<Double>.set(i: Int, j: Int, value: Double) {
-            this.mutableBuffer.array()[bufferStart + i * this.shape[0] + j] = value
+            this.mutableBuffer[bufferStart + i * this.shape[0] + j] = value
         }
 
         fun maxOffDiagonal(matrix: BufferedTensor<Double>): Double {

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt

@@ -17,6 +17,7 @@ import space.kscience.kmath.tensors.core.DoubleTensor
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.IntTensor
 import kotlin.math.abs
+import kotlin.math.max
 import kotlin.math.min
 import kotlin.math.sqrt
 
@@ -299,7 +300,7 @@ internal fun DoubleTensorAlgebra.svd1d(a: DoubleTensor, epsilon: Double = 1e-10)
     }
 }
 
-internal fun DoubleTensorAlgebra.svdHelper(
+internal fun DoubleTensorAlgebra.svdPowerMethodHelper(
     matrix: DoubleTensor,
     USV: Triple<BufferedTensor<Double>, BufferedTensor<Double>, BufferedTensor<Double>>,
     m: Int, n: Int, epsilon: Double,
@@ -307,6 +308,14 @@ internal fun DoubleTensorAlgebra.svdHelper(
     val res = ArrayList<Triple<Double, DoubleTensor, DoubleTensor>>(0)
     val (matrixU, matrixS, matrixV) = USV
 
+    val matrixUStart = matrixU.bufferStart
+    val matrixSStart = matrixS.bufferStart
+    val matrixVStart = matrixV.bufferStart
+
+    val matrixUBuffer = matrixU.mutableBuffer
+    val matrixSBuffer = matrixS.mutableBuffer
+    val matrixVBuffer = matrixV.mutableBuffer
+
     for (k in 0 until min(n, m)) {
         var a = matrix.copy()
         for ((singularValue, u, v) in res.slice(0 until k)) {
@@ -340,12 +349,307 @@ internal fun DoubleTensorAlgebra.svdHelper(
     val uBuffer = res.map { it.second }.flatMap { it.mutableBuffer.array().toList() }.toDoubleArray()
     val vBuffer = res.map { it.third }.flatMap { it.mutableBuffer.array().toList() }.toDoubleArray()
     for (i in uBuffer.indices) {
-        matrixU.mutableBuffer.array()[matrixU.bufferStart + i] = uBuffer[i]
+        matrixUBuffer[matrixUStart + i] = uBuffer[i]
     }
     for (i in s.indices) {
-        matrixS.mutableBuffer.array()[matrixS.bufferStart + i] = s[i]
+        matrixSBuffer[matrixSStart + i] = s[i]
     }
     for (i in vBuffer.indices) {
-        matrixV.mutableBuffer.array()[matrixV.bufferStart + i] = vBuffer[i]
+        matrixVBuffer[matrixVStart + i] = vBuffer[i]
+    }
+}
+
+private fun pythag(a: Double, b: Double): Double {
+    val at: Double = abs(a)
+    val bt: Double = abs(b)
+    val ct: Double
+    val result: Double
+    if (at > bt) {
+        ct = bt / at
+        result = at * sqrt(1.0 + ct * ct)
+    } else if (bt > 0.0) {
+        ct = at / bt
+        result = bt * sqrt(1.0 + ct * ct)
+    } else result = 0.0
+    return result
+}
+
+private fun SIGN(a: Double, b: Double): Double {
+    if (b >= 0.0)
+        return abs(a)
+    else
+        return -abs(a)
+}
+internal fun MutableStructure2D<Double>.svdGolubKahanHelper(u: MutableStructure2D<Double>, w: BufferedTensor<Double>,
+                                                            v: MutableStructure2D<Double>, iterations: Int, epsilon: Double) {
+    val shape = this.shape
+    val m = shape.component1()
+    val n = shape.component2()
+    var f = 0.0
+    val rv1 = DoubleArray(n)
+    var s = 0.0
+    var scale = 0.0
+    var anorm = 0.0
+    var g = 0.0
+    var l = 0
+
+    val wStart = w.bufferStart
+    val wBuffer = w.mutableBuffer
+
+    for (i in 0 until n) {
+        /* left-hand reduction */
+        l = i + 1
+        rv1[i] = scale * g
+        g = 0.0
+        s = 0.0
+        scale = 0.0
+        if (i < m) {
+            for (k in i until m) {
+                scale += abs(this[k, i]);
+            }
+            if (abs(scale) > epsilon) {
+                for (k in i until m) {
+                    this[k, i] = (this[k, i] / scale)
+                    s += this[k, i] * this[k, i]
+                }
+                f = this[i, i]
+                if (f >= 0) {
+                    g = (-1) * abs(sqrt(s))
+                } else {
+                    g = abs(sqrt(s))
+                }
+                val h = f * g - s
+                this[i, i] = f - g
+                if (i != n - 1) {
+                    for (j in l until n) {
+                        s = 0.0
+                        for (k in i until m) {
+                            s += this[k, i] * this[k, j]
+                        }
+                        f = s / h
+                        for (k in i until m) {
+                            this[k, j] += f * this[k, i]
+                        }
+                    }
+                }
+                for (k in i until m) {
+                    this[k, i] = this[k, i] * scale
+                }
+            }
+        }
+
+        wBuffer[wStart + i] = scale * g
+        /* right-hand reduction */
+        g = 0.0
+        s = 0.0
+        scale = 0.0
+        if (i < m && i != n - 1) {
+            for (k in l until n) {
+                scale += abs(this[i, k])
+            }
+            if (abs(scale) > epsilon) {
+                for (k in l until n) {
+                    this[i, k] = this[i, k] / scale
+                    s += this[i, k] * this[i, k]
+                }
+                f = this[i, l]
+                if (f >= 0) {
+                    g = (-1) * abs(sqrt(s))
+                } else {
+                    g = abs(sqrt(s))
+                }
+                val h = f * g - s
+                this[i, l] = f - g
+                for (k in l until n) {
+                    rv1[k] = this[i, k] / h
+                }
+                if (i != m - 1) {
+                    for (j in l until m) {
+                        s = 0.0
+                        for (k in l until n) {
+                            s += this[j, k] * this[i, k]
+                        }
+                        for (k in l until n) {
+                            this[j, k] += s * rv1[k]
+                        }
+                    }
+                }
+                for (k in l until n) {
+                    this[i, k] = this[i, k] * scale
+                }
+            }
+        }
+        anorm = max(anorm, (abs(wBuffer[wStart + i]) + abs(rv1[i])));
+    }
+
+    for (i in n - 1 downTo 0) {
+        if (i < n - 1) {
+            if (abs(g) > epsilon) {
+                for (j in l until n) {
+                    v[j, i] = (this[i, j] / this[i, l]) / g
+                }
+                for (j in l until n) {
+                    s = 0.0
+                    for (k in l until n)
+                        s += this[i, k] * v[k, j]
+                    for (k in l until n)
+                        v[k, j] += s * v[k, i]
+                }
+            }
+            for (j in l until n) {
+                v[i, j] = 0.0
+                v[j, i] = 0.0
+            }
+        }
+        v[i, i] = 1.0
+        g = rv1[i]
+        l = i
+    }
+
+    for (i in min(n, m) - 1 downTo 0) {
+        l = i + 1
+        g =  wBuffer[wStart + i]
+        for (j in l until n) {
+            this[i, j] = 0.0
+        }
+        if (abs(g) > epsilon) {
+            g = 1.0 / g
+            for (j in l until n) {
+                s = 0.0
+                for (k in l until m) {
+                    s += this[k, i] * this[k, j]
+                }
+                f = (s / this[i, i]) * g
+                for (k in i until m) {
+                    this[k, j] += f * this[k, i]
+                }
+            }
+            for (j in i until m) {
+                this[j, i] *= g
+            }
+        } else {
+            for (j in i until m) {
+                this[j, i] = 0.0
+            }
+        }
+        this[i, i] += 1.0
+    }
+
+    var flag = 0
+    var nm = 0
+    var c = 0.0
+    var h = 0.0
+    var y = 0.0
+    var z = 0.0
+    var x = 0.0
+    for (k in n - 1 downTo 0) {
+        for (its in 1 until iterations) {
+            flag = 1
+            for (newl in k downTo 0) {
+                nm = newl - 1
+                if (abs(rv1[newl]) + anorm == anorm) {
+                    flag = 0
+                    l = newl
+                    break
+                }
+                if (abs(wBuffer[wStart + nm]) + anorm == anorm) {
+                    l = newl
+                    break
+                }
+            }
+
+            if (flag != 0) {
+                c = 0.0
+                s = 1.0
+                for (i in l until k + 1) {
+                    f = s * rv1[i]
+                    rv1[i] = c * rv1[i]
+                    if (abs(f) + anorm == anorm) {
+                        break
+                    }
+                    g = wBuffer[wStart + i]
+                    h = pythag(f, g)
+                    wBuffer[wStart + i] = h
+                    h = 1.0 / h
+                    c = g * h
+                    s = (-f) * h
+                    for (j in 0 until m) {
+                        y = this[j, nm]
+                        z = this[j, i]
+                        this[j, nm] = y * c + z * s
+                        this[j, i] = z * c - y * s
+                    }
+                }
+            }
+
+            z =  wBuffer[wStart + k]
+            if (l == k) {
+                if (z < 0.0) {
+                    wBuffer[wStart + k] = -z
+                    for (j in 0 until n)
+                        v[j, k] = -v[j, k]
+                }
+                break
+            }
+
+            x =  wBuffer[wStart + l]
+            nm = k - 1
+            y =  wBuffer[wStart + nm]
+            g = rv1[nm]
+            h = rv1[k]
+            f = ((y - z) * (y + z) + (g - h) * (g + h)) / (2.0 * h * y)
+            g = pythag(f, 1.0)
+            f = ((x - z) * (x + z) + h * ((y / (f + SIGN(g, f))) - h)) / x
+            c = 1.0
+            s = 1.0
+
+            var i = 0
+            for (j in l until nm + 1) {
+                i = j + 1
+                g = rv1[i]
+                y = wBuffer[wStart + i]
+                h = s * g
+                g = c * g
+                z = pythag(f, h)
+                rv1[j] = z
+                c = f / z
+                s = h / z
+                f = x * c + g * s
+                g = g * c - x * s
+                h = y * s
+                y *= c
+
+                for (jj in 0 until n) {
+                    x = v[jj, j];
+                    z = v[jj, i];
+                    v[jj, j] = x * c + z * s;
+                    v[jj, i] = z * c - x * s;
+                }
+                z = pythag(f, h)
+                wBuffer[wStart + j] = z
+                if (abs(z) > epsilon) {
+                    z = 1.0 / z
+                    c = f * z
+                    s = h * z
+                }
+                f = c * g + s * y
+                x = c * y - s * g
+                for (jj in 0 until m) {
+                    y = this[jj, j]
+                    z = this[jj, i]
+                    this[jj, j] = y * c + z * s
+                    this[jj, i] = z * c - y * s
+                }
+            }
+            rv1[l] = 0.0
+            rv1[k] = f
+            wBuffer[wStart + k] = x
+        }
+    }
+
+    for (i in 0 until n) {
+        for (j in 0 until m) {
+            u[j, i] = this[j, i]
+        }
     }
 }

kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt

@@ -156,23 +156,12 @@ internal class TestDoubleLinearOpsTensorAlgebra {
 
         val res = svd1d(tensor2)
 
+        val resBuffer = res.mutableBuffer
+        val resStart = res.bufferStart
+
         assertTrue(res.shape contentEquals intArrayOf(2))
-        assertTrue { abs(abs(res.mutableBuffer.array()[res.bufferStart]) - 0.386) < 0.01 }
+        assertTrue { abs(abs(resBuffer[resStart]) - 0.386) < 0.01 }
-        assertTrue { abs(abs(res.mutableBuffer.array()[res.bufferStart + 1]) - 0.922) < 0.01 }
+        assertTrue { abs(abs(resBuffer[resStart + 1]) - 0.922) < 0.01 }
-    }
-
-    @Test
-    fun testSVD() = DoubleTensorAlgebra{
-        testSVDFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)))
-        testSVDFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
-    }
-
-    @Test
-    fun testBatchedSVD() = DoubleTensorAlgebra {
-        val tensor = randomNormal(intArrayOf(2, 5, 3), 0)
-        val (tensorU, tensorS, tensorV) = tensor.svd()
-        val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV.transpose())
-        assertTrue(tensor.eq(tensorSVD))
     }
 
     @Test
@@ -184,13 +173,118 @@ internal class TestDoubleLinearOpsTensorAlgebra {
         assertTrue(tensorSigma.eq(tensorSigmaCalc))
     }
 
+    @Test
+    fun testSVD() = DoubleTensorAlgebra{
+        testSVDFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)))
+        testSVDFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
+        val buffer1 = doubleArrayOf(
+            1.000000, 2.000000, 3.000000,
+            2.000000, 3.000000, 4.000000,
+            3.000000, 4.000000, 5.000000,
+            4.000000, 5.000000, 6.000000,
+            5.000000, 6.000000, 9.000000
+        )
+        testSVDFor(fromArray(intArrayOf(5, 3), buffer1))
+        val buffer2 = doubleArrayOf(
+            1.0, 2.0, 3.0, 2.0, 3.0,
+            4.0, 3.0, 4.0, 5.0, 4.0,
+            5.0, 6.0, 5.0, 6.0, 7.0
+        )
+        testSVDFor(fromArray(intArrayOf(3, 5), buffer2))
+    }
 
+    @Test
+    fun testBatchedSVD() = DoubleTensorAlgebra{
+        val tensor1 = randomNormal(intArrayOf(2, 5, 3), 0)
+        testSVDFor(tensor1)
+        val tensor2 = DoubleTensorAlgebra.randomNormal(intArrayOf(30, 30, 30), 0)
+        testSVDFor(tensor2)
+    }
+
+    @Test
+    fun testSVDGolubKahan() = DoubleTensorAlgebra{
+        testSVDGolubKahanFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)))
+        testSVDGolubKahanFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
+        val buffer1 = doubleArrayOf(
+            1.000000, 2.000000, 3.000000,
+            2.000000, 3.000000, 4.000000,
+            3.000000, 4.000000, 5.000000,
+            4.000000, 5.000000, 6.000000,
+            5.000000, 6.000000, 9.000000
+        )
+        testSVDGolubKahanFor(fromArray(intArrayOf(5, 3), buffer1))
+        val buffer2 = doubleArrayOf(
+            1.0, 2.0, 3.0, 2.0, 3.0,
+            4.0, 3.0, 4.0, 5.0, 4.0,
+            5.0, 6.0, 5.0, 6.0, 7.0
+        )
+        testSVDGolubKahanFor(fromArray(intArrayOf(3, 5), buffer2))
+    }
+
+    @Test
+    fun testBatchedSVDGolubKahan() = DoubleTensorAlgebra{
+        val tensor1 = randomNormal(intArrayOf(2, 5, 3), 0)
+        testSVDGolubKahanFor(tensor1)
+        val tensor2 = DoubleTensorAlgebra.randomNormal(intArrayOf(30, 30, 30), 0)
+        testSVDGolubKahanFor(tensor2)
+    }
+
+    @Test
+    fun testSVDPowerMethod() = DoubleTensorAlgebra{
+        testSVDPowerMethodFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)))
+        testSVDPowerMethodFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
+        val buffer1 = doubleArrayOf(
+            1.000000, 2.000000, 3.000000,
+            2.000000, 3.000000, 4.000000,
+            3.000000, 4.000000, 5.000000,
+            4.000000, 5.000000, 6.000000,
+            5.000000, 6.000000, 9.000000
+        )
+        testSVDPowerMethodFor(fromArray(intArrayOf(5, 3), buffer1))
+        val buffer2 = doubleArrayOf(
+            1.0, 2.0, 3.0, 2.0, 3.0,
+            4.0, 3.0, 4.0, 5.0, 4.0,
+            5.0, 6.0, 5.0, 6.0, 7.0
+        )
+        testSVDPowerMethodFor(fromArray(intArrayOf(3, 5), buffer2))
+    }
+
+    @Test
+    fun testBatchedSVDPowerMethod() = DoubleTensorAlgebra {
+        val tensor1 = randomNormal(intArrayOf(2, 5, 3), 0)
+        testSVDPowerMethodFor(tensor1)
+        val tensor2 = DoubleTensorAlgebra.randomNormal(intArrayOf(30, 30, 30), 0)
+        testSVDPowerMethodFor(tensor2)
+    }
+
+//    @Test
+//    fun testSVDPowerMethodError() = DoubleTensorAlgebra{
+//       val buffer = doubleArrayOf(
+//            1.000000, 2.000000, 3.000000,
+//            2.000000, 3.000000, 4.000000,
+//            3.000000, 4.000000, 5.000000,
+//            4.000000, 5.000000, 6.000000,
+//            5.000000, 6.000000, 7.000000
+//        )
+//        testSVDPowerMethodFor(fromArray(intArrayOf(5, 3), buffer))
+//    }
 }
 
-
+private fun DoubleTensorAlgebra.testSVDFor(tensor: DoubleTensor) {
-private fun DoubleTensorAlgebra.testSVDFor(tensor: DoubleTensor, epsilon: Double = 1e-10) {
     val svd = tensor.svd()
 
+    val tensorSVD = svd.first
+        .dot(
+            diagonalEmbedding(svd.second)
+                .dot(svd.third.transpose())
+        )
+
+    assertTrue(tensor.eq(tensorSVD))
+}
+
+private fun DoubleTensorAlgebra.testSVDGolubKahanFor(tensor: DoubleTensor, epsilon: Double = 1e-10) {
+    val svd = tensor.svdGolubKahan()
+
     val tensorSVD = svd.first
         .dot(
             diagonalEmbedding(svd.second)
@@ -199,3 +293,15 @@ private fun DoubleTensorAlgebra.testSVDFor(tensor: DoubleTensor, epsilon: Double
 
     assertTrue(tensor.eq(tensorSVD, epsilon))
 }
+
+private fun DoubleTensorAlgebra.testSVDPowerMethodFor(tensor: DoubleTensor, epsilon: Double = 1e-10) {
+    val svd = tensor.svdPowerMethod()
+
+    val tensorSVD = svd.first
+        .dot(
+            diagonalEmbedding(svd.second)
+                .dot(svd.third.transpose())
+        )
+
+    assertTrue(tensor.eq(tensorSVD, epsilon))
+}

kmath-viktor/README.md

@@ -0,0 +1,32 @@
+# Module kmath-viktor
+
+Binding for https://github.com/JetBrains-Research/viktor
+
+## Usage
+
+## Artifact:
+
+The Maven coordinates of this project are `space.kscience:kmath-viktor:0.3.0`.
+
+**Gradle Groovy:**
+```groovy
+repositories {
+    maven { url 'https://repo.kotlin.link' }
+    mavenCentral()
+}
+
+dependencies {
+    implementation 'space.kscience:kmath-viktor:0.3.0'
+}
+```
+**Gradle Kotlin DSL:**
+```kotlin
+repositories {
+    maven("https://repo.kotlin.link")
+    mavenCentral()
+}
+
+dependencies {
+    implementation("space.kscience:kmath-viktor:0.3.0")
+}
+```

kmath-viktor/api/kmath-viktor.api

@@ -1,40 +1,69 @@
 public final class space/kscience/kmath/viktor/ViktorBuffer : space/kscience/kmath/structures/MutableBuffer {
-	public fun <init> (Lorg/jetbrains/bio/viktor/F64FlatArray;)V
+	public static final synthetic fun box-impl (Lorg/jetbrains/bio/viktor/F64FlatArray;)Lspace/kscience/kmath/viktor/ViktorBuffer;
+	public static fun constructor-impl (Lorg/jetbrains/bio/viktor/F64FlatArray;)Lorg/jetbrains/bio/viktor/F64FlatArray;
 	public fun copy ()Lspace/kscience/kmath/structures/MutableBuffer;
+	public static fun copy-impl (Lorg/jetbrains/bio/viktor/F64FlatArray;)Lspace/kscience/kmath/structures/MutableBuffer;
+	public fun equals (Ljava/lang/Object;)Z
+	public static fun equals-impl (Lorg/jetbrains/bio/viktor/F64FlatArray;Ljava/lang/Object;)Z
+	public static final fun equals-impl0 (Lorg/jetbrains/bio/viktor/F64FlatArray;Lorg/jetbrains/bio/viktor/F64FlatArray;)Z
 	public fun get (I)Ljava/lang/Double;
 	public synthetic fun get (I)Ljava/lang/Object;
+	public static fun get-impl (Lorg/jetbrains/bio/viktor/F64FlatArray;I)Ljava/lang/Double;
 	public final fun getFlatArray ()Lorg/jetbrains/bio/viktor/F64FlatArray;
 	public fun getSize ()I
+	public static fun getSize-impl (Lorg/jetbrains/bio/viktor/F64FlatArray;)I
+	public fun hashCode ()I
+	public static fun hashCode-impl (Lorg/jetbrains/bio/viktor/F64FlatArray;)I
 	public fun iterator ()Ljava/util/Iterator;
+	public static fun iterator-impl (Lorg/jetbrains/bio/viktor/F64FlatArray;)Ljava/util/Iterator;
 	public fun set (ID)V
 	public synthetic fun set (ILjava/lang/Object;)V
+	public static fun set-impl (Lorg/jetbrains/bio/viktor/F64FlatArray;ID)V
+	public fun toString ()Ljava/lang/String;
+	public static fun toString-impl (Lorg/jetbrains/bio/viktor/F64FlatArray;)Ljava/lang/String;
+	public final synthetic fun unbox-impl ()Lorg/jetbrains/bio/viktor/F64FlatArray;
 }
 
-public final class space/kscience/kmath/viktor/ViktorFieldND : space/kscience/kmath/nd/FieldND, space/kscience/kmath/operations/ExtendedField, space/kscience/kmath/operations/NumbersAddOperations, space/kscience/kmath/operations/ScaleOperations {
+public class space/kscience/kmath/viktor/ViktorFieldND : space/kscience/kmath/viktor/ViktorFieldOpsND, space/kscience/kmath/nd/FieldND, space/kscience/kmath/operations/NumbersAddOps {
 	public fun <init> ([I)V
+	public synthetic fun getOne ()Ljava/lang/Object;
+	public synthetic fun getOne ()Lspace/kscience/kmath/nd/StructureND;
+	public fun getOne ()Lspace/kscience/kmath/viktor/ViktorStructureND;
+	public fun getShape ()[I
+	public synthetic fun getZero ()Ljava/lang/Object;
+	public synthetic fun getZero ()Lspace/kscience/kmath/nd/StructureND;
+	public fun getZero ()Lspace/kscience/kmath/viktor/ViktorStructureND;
+	public synthetic fun number (Ljava/lang/Number;)Ljava/lang/Object;
+	public fun number (Ljava/lang/Number;)Lspace/kscience/kmath/viktor/ViktorStructureND;
+}
+
+public class space/kscience/kmath/viktor/ViktorFieldOpsND : space/kscience/kmath/nd/FieldOpsND, space/kscience/kmath/operations/ExtendedFieldOps, space/kscience/kmath/operations/PowerOperations {
+	public static final field Companion Lspace/kscience/kmath/viktor/ViktorFieldOpsND$Companion;
+	public fun <init> ()V
 	public synthetic fun acos (Ljava/lang/Object;)Ljava/lang/Object;
 	public fun acos (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND;
+	public synthetic fun acosh (Ljava/lang/Object;)Ljava/lang/Object;
+	public fun acosh (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND;
 	public synthetic fun add (Ljava/lang/Object;Ljava/lang/Object;)Ljava/lang/Object;
 	public synthetic fun add (Lspace/kscience/kmath/nd/StructureND;Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/nd/StructureND;
 	public fun add (Lspace/kscience/kmath/nd/StructureND;Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND;
 	public synthetic fun asin (Ljava/lang/Object;)Ljava/lang/Object;
 	public fun asin (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND;
+	public synthetic fun asinh (Ljava/lang/Object;)Ljava/lang/Object;
+	public fun asinh (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND;
 	public synthetic fun atan (Ljava/lang/Object;)Ljava/lang/Object;
 	public fun atan (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND;
-	public synthetic fun combine (Lspace/kscience/kmath/nd/StructureND;Lspace/kscience/kmath/nd/StructureND;Lkotlin/jvm/functions/Function3;)Lspace/kscience/kmath/nd/StructureND;
+	public synthetic fun atanh (Ljava/lang/Object;)Ljava/lang/Object;
-	public fun combine (Lspace/kscience/kmath/nd/StructureND;Lspace/kscience/kmath/nd/StructureND;Lkotlin/jvm/functions/Function3;)Lspace/kscience/kmath/viktor/ViktorStructureND;
+	public fun atanh (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND;
 	public synthetic fun cos (Ljava/lang/Object;)Ljava/lang/Object;
 	public fun cos (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND;
+	public synthetic fun cosh (Ljava/lang/Object;)Ljava/lang/Object;
+	public fun cosh (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND;
 	public synthetic fun exp (Ljava/lang/Object;)Ljava/lang/Object;
 	public fun exp (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND;
-	public synthetic fun getElementContext ()Lspace/kscience/kmath/operations/Algebra;
+	public synthetic fun getElementAlgebra ()Lspace/kscience/kmath/operations/Algebra;
-	public fun getElementContext ()Lspace/kscience/kmath/operations/DoubleField;
+	public fun getElementAlgebra ()Lspace/kscience/kmath/operations/DoubleField;
 	public final fun getF64Buffer (Lspace/kscience/kmath/nd/StructureND;)Lorg/jetbrains/bio/viktor/F64Array;
-	public synthetic fun getOne ()Ljava/lang/Object;
-	public fun getOne ()Lspace/kscience/kmath/viktor/ViktorStructureND;
-	public fun getShape ()[I
-	public synthetic fun getZero ()Ljava/lang/Object;
-	public fun getZero ()Lspace/kscience/kmath/viktor/ViktorStructureND;
 	public synthetic fun ln (Ljava/lang/Object;)Ljava/lang/Object;
 	public fun ln (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND;
 	public synthetic fun map (Lspace/kscience/kmath/nd/StructureND;Lkotlin/jvm/functions/Function2;)Lspace/kscience/kmath/nd/StructureND;
@@ -43,26 +72,38 @@ public final class space/kscience/kmath/viktor/ViktorFieldND : space/kscience/km
 	public fun mapIndexed (Lspace/kscience/kmath/nd/StructureND;Lkotlin/jvm/functions/Function3;)Lspace/kscience/kmath/viktor/ViktorStructureND;
 	public synthetic fun minus (Ljava/lang/Object;Ljava/lang/Object;)Ljava/lang/Object;
 	public fun minus (Lspace/kscience/kmath/nd/StructureND;Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND;
-	public synthetic fun number (Ljava/lang/Number;)Ljava/lang/Object;
-	public fun number (Ljava/lang/Number;)Lspace/kscience/kmath/viktor/ViktorStructureND;
 	public synthetic fun plus (Ljava/lang/Object;Ljava/lang/Object;)Ljava/lang/Object;
 	public fun plus (Lspace/kscience/kmath/nd/StructureND;D)Lspace/kscience/kmath/viktor/ViktorStructureND;
 	public synthetic fun plus (Lspace/kscience/kmath/nd/StructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/StructureND;
 	public fun plus (Lspace/kscience/kmath/nd/StructureND;Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND;
 	public synthetic fun power (Ljava/lang/Object;Ljava/lang/Number;)Ljava/lang/Object;
 	public fun power (Lspace/kscience/kmath/nd/StructureND;Ljava/lang/Number;)Lspace/kscience/kmath/viktor/ViktorStructureND;
-	public synthetic fun produce (Lkotlin/jvm/functions/Function2;)Lspace/kscience/kmath/nd/StructureND;
-	public fun produce (Lkotlin/jvm/functions/Function2;)Lspace/kscience/kmath/viktor/ViktorStructureND;
 	public synthetic fun scale (Ljava/lang/Object;D)Ljava/lang/Object;
+	public synthetic fun scale (Lspace/kscience/kmath/nd/StructureND;D)Lspace/kscience/kmath/nd/StructureND;
 	public fun scale (Lspace/kscience/kmath/nd/StructureND;D)Lspace/kscience/kmath/viktor/ViktorStructureND;
 	public synthetic fun sin (Ljava/lang/Object;)Ljava/lang/Object;
 	public fun sin (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND;
+	public synthetic fun sinh (Ljava/lang/Object;)Ljava/lang/Object;
+	public fun sinh (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND;
+	public synthetic fun structureND ([ILkotlin/jvm/functions/Function2;)Lspace/kscience/kmath/nd/StructureND;
+	public fun structureND ([ILkotlin/jvm/functions/Function2;)Lspace/kscience/kmath/viktor/ViktorStructureND;
 	public synthetic fun tan (Ljava/lang/Object;)Ljava/lang/Object;
 	public fun tan (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND;
 	public synthetic fun times (Ljava/lang/Object;Ljava/lang/Number;)Ljava/lang/Object;
 	public fun times (Lspace/kscience/kmath/nd/StructureND;Ljava/lang/Number;)Lspace/kscience/kmath/viktor/ViktorStructureND;
 	public synthetic fun unaryMinus (Ljava/lang/Object;)Ljava/lang/Object;
 	public fun unaryMinus (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/nd/StructureND;
+	public synthetic fun zip (Lspace/kscience/kmath/nd/StructureND;Lspace/kscience/kmath/nd/StructureND;Lkotlin/jvm/functions/Function3;)Lspace/kscience/kmath/nd/StructureND;
+	public fun zip (Lspace/kscience/kmath/nd/StructureND;Lspace/kscience/kmath/nd/StructureND;Lkotlin/jvm/functions/Function3;)Lspace/kscience/kmath/viktor/ViktorStructureND;
+}
+
+public final class space/kscience/kmath/viktor/ViktorFieldOpsND$Companion : space/kscience/kmath/viktor/ViktorFieldOpsND {
+}
+
+public final class space/kscience/kmath/viktor/ViktorFieldOpsNDKt {
+	public static final fun ViktorFieldND ([I)Lspace/kscience/kmath/viktor/ViktorFieldND;
+	public static final fun getViktorAlgebra (Lspace/kscience/kmath/operations/DoubleField;)Lspace/kscience/kmath/viktor/ViktorFieldOpsND;
+	public static final fun viktorAlgebra (Lspace/kscience/kmath/operations/DoubleField;[I)Lspace/kscience/kmath/viktor/ViktorFieldND;
 }
 
 public final class space/kscience/kmath/viktor/ViktorStructureND : space/kscience/kmath/nd/MutableStructureND {
@@ -77,7 +118,6 @@ public final class space/kscience/kmath/viktor/ViktorStructureND : space/kscienc
 }
 
 public final class space/kscience/kmath/viktor/ViktorStructureNDKt {
-	public static final fun ViktorNDField ([I)Lspace/kscience/kmath/viktor/ViktorFieldND;
 	public static final fun asStructure (Lorg/jetbrains/bio/viktor/F64Array;)Lspace/kscience/kmath/viktor/ViktorStructureND;
 }
 

settings.gradle.kts

@@ -1,24 +1,6 @@
 rootProject.name = "kmath"
 
 enableFeaturePreview("TYPESAFE_PROJECT_ACCESSORS")
-enableFeaturePreview("VERSION_CATALOGS")
-
-dependencyResolutionManagement {
-
-    val toolsVersion: String by extra
-
-    repositories {
-        maven("https://repo.kotlin.link")
-        mavenCentral()
-    }
-
-    versionCatalogs {
-        create("npmlibs") {
-            from("ru.mipt.npm:version-catalog:$toolsVersion")
-        }
-    }
-}
-
 include(
     ":kmath-memory",
     ":kmath-complex",