merge dev

.github/workflows/build.yml

@@ -1,6 +1,9 @@
 name: Gradle build
 
-on: [ push ]
+on:
+  push:
+    branches: [ dev, master ]
+  pull_request:
 
 jobs:
   build:
@@ -8,23 +11,22 @@ jobs:
       matrix:
         os: [ macOS-latest, windows-latest ]
     runs-on: ${{matrix.os}}
-    timeout-minutes: 30
+    timeout-minutes: 40
     steps:
       - name: Checkout the repo
         uses: actions/checkout@v2
       - name: Set up JDK 11
         uses: DeLaGuardo/setup-graalvm@4.0
         with:
-          graalvm: 21.1.0
+          graalvm: 21.2.0
           java: java11
           arch: amd64
-      - name: Add msys to path
-        if: matrix.os == 'windows-latest'
-        run: SETX PATH "%PATH%;C:\msys64\mingw64\bin"
       - name: Cache gradle
         uses: actions/cache@v2
         with:
-          path: ~/.gradle/caches
+          path: |
+            ~/.gradle/caches
+            ~/.gradle/wrapper
           key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
           restore-keys: |
             ${{ runner.os }}-gradle-
@@ -36,4 +38,4 @@ jobs:
           restore-keys: |
             ${{ runner.os }}-gradle-
       - name: Build
-        run: ./gradlew build --no-daemon --stacktrace
+        run: ./gradlew build --build-cache --no-daemon --stacktrace

.github/workflows/pages.yml

@@ -2,23 +2,27 @@ name: Dokka publication
 
 on:
   push:
-    branches:
+    branches: [ master ]
-      - master
 
 jobs:
   build:
     runs-on: ubuntu-20.04
+    timeout-minutes: 40
     steps:
-      - name: Checkout the repo
+      - uses: actions/checkout@v2
-        uses: actions/checkout@v2
+      - uses: DeLaGuardo/setup-graalvm@4.0
-      - name: Set up JDK 11
-        uses: actions/setup-java@v1
         with:
-          java-version: 11
+          graalvm: 21.2.0
-      - name: Build
+          java: java11
-        run: ./gradlew dokkaHtmlMultiModule --no-daemon --no-parallel --stacktrace
+          arch: amd64
-      - name: Deploy to GitHub Pages
+      - uses: actions/cache@v2
-        uses: JamesIves/github-pages-deploy-action@4.1.0
+        with:
+          path: ~/.gradle/caches
+          key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
+          restore-keys: |
+            ${{ runner.os }}-gradle-
+      - run: ./gradlew dokkaHtmlMultiModule --build-cache --no-daemon --no-parallel --stacktrace
+      - uses: JamesIves/github-pages-deploy-action@4.1.0
         with:
           branch: gh-pages
           folder: build/dokka/htmlMultiModule

.github/workflows/publish.yml

@@ -3,8 +3,7 @@ name: Gradle publish
 on:
   workflow_dispatch:
   release:
-    types:
+    types: [ created ]
-      - created
 
 jobs:
   publish:
@@ -20,16 +19,15 @@ jobs:
       - name: Set up JDK 11
         uses: DeLaGuardo/setup-graalvm@4.0
         with:
-          graalvm: 21.1.0
+          graalvm: 21.2.0
           java: java11
           arch: amd64
-      - name: Add msys to path
-        if: matrix.os == 'windows-latest'
-        run: SETX PATH "%PATH%;C:\msys64\mingw64\bin"
       - name: Cache gradle
         uses: actions/cache@v2
         with:
-          path: ~/.gradle/caches
+          path: |
+            ~/.gradle/caches
+            ~/.gradle/wrapper
           key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
           restore-keys: |
             ${{ runner.os }}-gradle-
@@ -42,20 +40,14 @@ jobs:
             ${{ runner.os }}-gradle-
       - name: Publish Windows Artifacts
         if: matrix.os == 'windows-latest'
+        shell: cmd
         run: >
-          ./gradlew release --no-daemon
+          ./gradlew release --no-daemon --build-cache -Ppublishing.enabled=true
-          -Ppublishing.enabled=true
+          -Ppublishing.space.user=${{ secrets.SPACE_APP_ID }}
-          -Ppublishing.github.user=${{ secrets.PUBLISHING_GITHUB_USER }}
+          -Ppublishing.space.token=${{ secrets.SPACE_APP_SECRET }}
-          -Ppublishing.github.token=${{ secrets.PUBLISHING_GITHUB_TOKEN }}
-          -Ppublishing.space.user=${{ secrets.PUBLISHING_SPACE_USER }}
-          -Ppublishing.space.token=${{ secrets.PUBLISHING_SPACE_TOKEN }}
       - name: Publish Mac Artifacts
         if: matrix.os == 'macOS-latest'
         run: >
-          ./gradlew release --no-daemon
+          ./gradlew release --no-daemon --build-cache -Ppublishing.enabled=true -Ppublishing.platform=macosX64
-          -Ppublishing.enabled=true
+          -Ppublishing.space.user=${{ secrets.SPACE_APP_ID }}
-          -Ppublishing.platform=macosX64
+          -Ppublishing.space.token=${{ secrets.SPACE_APP_SECRET }}
-          -Ppublishing.github.user=${{ secrets.PUBLISHING_GITHUB_USER }}
-          -Ppublishing.github.token=${{ secrets.PUBLISHING_GITHUB_TOKEN }}
-          -Ppublishing.space.user=${{ secrets.PUBLISHING_SPACE_USER }}
-          -Ppublishing.space.token=${{ secrets.PUBLISHING_SPACE_TOKEN }}

CHANGELOG.md

@@ -2,11 +2,11 @@
 
 ## [Unreleased]
 ### Added
-- ScaleOperations interface
+- `ScaleOperations` interface
-- Field extends ScaleOperations
+- `Field` extends `ScaleOperations`
 - Basic integration API
 - Basic MPP distributions and samplers
-- bindSymbolOrNull
+- `bindSymbolOrNull`
 - Blocking chains and Statistics
 - Multiplatform integration
 - Integration for any Field element
@@ -15,6 +15,8 @@
 - `@PerformancePitfall` annotation to mark possibly slow API
 - BigInt operation performance improvement and fixes by @zhelenskiy (#328)
 - Unified architecture for Integration and Optimization using features.
+- `BigInt` operation performance improvement and fixes by @zhelenskiy (#328)
+- Integration between `MST` and Symja `IExpr`
 
 ### Changed
 - Exponential operations merged with hyperbolic functions
@@ -53,6 +55,7 @@
 
 ### Fixed
 - Ring inherits RingOperations, not GroupOperations
+- Univariate histogram filling
 
 ### Security
 

README.md

@@ -2,14 +2,14 @@
 [![DOI](https://zenodo.org/badge/129486382.svg)](https://zenodo.org/badge/latestdoi/129486382)
 ![Gradle build](https://github.com/mipt-npm/kmath/workflows/Gradle%20build/badge.svg)
 [![Maven Central](https://img.shields.io/maven-central/v/space.kscience/kmath-core.svg?label=Maven%20Central)](https://search.maven.org/search?q=g:%22space.kscience%22)
-[![Space](https://img.shields.io/maven-metadata/v?label=Space&metadataUrl=https%3A%2F%2Fmaven.pkg.jetbrains.space%2Fmipt-npm%2Fp%2Fsci%2Fmaven%2Fkscience%2Fkmath%2Fkmath-core%2Fmaven-metadata.xml)](https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven/space/kscience/)
+[![Space](https://img.shields.io/badge/dynamic/xml?color=orange&label=Space&query=//metadata/versioning/latest&url=https%3A%2F%2Fmaven.pkg.jetbrains.space%2Fmipt-npm%2Fp%2Fsci%2Fmaven%2Fspace%2Fkscience%2Fkmath-core%2Fmaven-metadata.xml)](https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven/space/kscience/)
 
 # KMath
 
-Could be pronounced as `key-math`. The **K**otlin **Math**ematics library was initially intended as a Kotlin-based analog to
+Could be pronounced as `key-math`. The **K**otlin **Math**ematics library was initially intended as a Kotlin-based
-Python's NumPy library. Later we found that kotlin is much more flexible language and allows superior architecture 
+analog to Python's NumPy library. Later we found that kotlin is much more flexible language and allows superior
-designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like experience could 
+architecture designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like
-be achieved with [kmath-for-real](/kmath-for-real) extension module.
+experience could be achieved with [kmath-for-real](/kmath-for-real) extension module.
 
 [Documentation site (**WIP**)](https://mipt-npm.github.io/kmath/)
 
@@ -21,26 +21,33 @@ be achieved with [kmath-for-real](/kmath-for-real) extension module.
 
 # Goal
 
-* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS and Native). 
+* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS and Native)
+  .
 * Provide basic multiplatform implementations for those abstractions (without significant performance optimization).
 * Provide bindings and wrappers with those abstractions for popular optimized platform libraries.
 
 ## Non-goals
 
-* Be like NumPy. It was the idea at the beginning, but we decided that we can do better in terms of API.
+* Be like NumPy. It was the idea at the beginning, but we decided that we can do better in API.
 * Provide the best performance out of the box. We have specialized libraries for that. Need only API wrappers for them.
 * Cover all cases as immediately and in one bundle. We will modularize everything and add new features gradually.
-* Provide specialized behavior in the core. API is made generic on purpose, so one needs to specialize for types, like 
+* Provide specialized behavior in the core. API is made generic on purpose, so one needs to specialize for types, like
-for `Double` in the core. For that we will have specialization modules like `kmath-for-real`, which will give better
+  for `Double` in the core. For that we will have specialization modules like `kmath-for-real`, which will give better
-experience for those, who want to work with specific types.
+  experience for those, who want to work with specific types.
 
 ## Features and stability
 
-KMath is a modular library. Different modules provide different features with different API stability guarantees. All core modules are released with the same version, but with different API change policy. The features are described in module definitions below. The module stability could have following levels:
+KMath is a modular library. Different modules provide different features with different API stability guarantees. All
+core modules are released with the same version, but with different API change policy. The features are described in
+module definitions below. The module stability could have the following levels:
 
-* **PROTOTYPE**. On this level there are no compatibility guarantees. All methods and classes form those modules could break any moment. You can still use it, but be sure to fix the specific version.
+* **PROTOTYPE**. On this level there are no compatibility guarantees. All methods and classes form those modules could
-* **EXPERIMENTAL**. The general API is decided, but some changes could be made. Volatile API is marked with `@UnstableKmathAPI` or other stability warning annotations.
+  break any moment. You can still use it, but be sure to fix the specific version.
-* **DEVELOPMENT**. API breaking generally follows semantic versioning ideology. There could be changes in minor versions, but not in patch versions. API is protected with [binary-compatibility-validator](https://github.com/Kotlin/binary-compatibility-validator) tool.
+* **EXPERIMENTAL**. The general API is decided, but some changes could be made. Volatile API is marked
+  with `@UnstableKmathAPI` or other stability warning annotations.
+* **DEVELOPMENT**. API breaking generally follows semantic versioning ideology. There could be changes in minor
+  versions, but not in patch versions. API is protected
+  with [binary-compatibility-validator](https://github.com/Kotlin/binary-compatibility-validator) tool.
 * **STABLE**. The API stabilized. Breaking changes are allowed only in major releases.
 
 <!--Current feature list is [here](/docs/features.md)-->
@@ -132,7 +139,7 @@ KMath is a modular library. Different modules provide different features with di
 objects to the expression by providing a context. Expressions can be used for a wide variety of purposes from high 
 performance calculations to code generation.
 > - [domains](kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains) : Domains
-> - [autodif](kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt) : Automatic differentiation
+> - [autodiff](kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt) : Automatic differentiation
 
 <hr/>
 
@@ -161,7 +168,7 @@ performance calculations to code generation.
 <hr/>
 
 * ### [kmath-for-real](kmath-for-real)
-> Extension module that should be used to achieve numpy-like behavior. 
+> 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.
 >
@@ -175,7 +182,7 @@ One can still use generic algebras though.
 <hr/>
 
 * ### [kmath-functions](kmath-functions)
-> Functions, integration and interpolation
+> 
 >
 > **Maturity**: EXPERIMENTAL
 >
@@ -200,6 +207,16 @@ One can still use generic algebras though.
 > **Maturity**: PROTOTYPE
 <hr/>
 
+* ### [kmath-jafama](kmath-jafama)
+> 
+>
+> **Maturity**: PROTOTYPE
+>
+> **Features:**
+> - [jafama-double](kmath-jafama/src/main/kotlin/space/kscience/kmath/jafama/) : Double ExtendedField implementations based on Jafama
+
+<hr/>
+
 * ### [kmath-jupyter](kmath-jupyter)
 > 
 >
@@ -207,24 +224,24 @@ One can still use generic algebras though.
 <hr/>
 
 * ### [kmath-kotlingrad](kmath-kotlingrad)
-> Functions, integration and interpolation
+> 
 >
 > **Maturity**: EXPERIMENTAL
 >
 > **Features:**
-> - [differentiable-mst-expression](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/DifferentiableMstExpression.kt) : MST based DifferentiableExpression.
+> - [differentiable-mst-expression](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/KotlingradExpression.kt) : MST based DifferentiableExpression.
-> - [differentiable-mst-expression](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/DifferentiableMstExpression.kt) : Conversions between Kotlin∇'s SFun and MST
+> - [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)
-> An API and basic implementation for arranging objects in a continous memory block.
+> An API and basic implementation for arranging objects in a continuous memory block.
 >
 > **Maturity**: DEVELOPMENT
 <hr/>
 
 * ### [kmath-nd4j](kmath-nd4j)
-> ND4J NDStructure implementation and according NDAlgebra classes
+> 
 >
 > **Maturity**: EXPERIMENTAL
 >
@@ -241,6 +258,12 @@ One can still use generic algebras though.
 > **Maturity**: EXPERIMENTAL
 <hr/>
 
+* ### [kmath-symja](kmath-symja)
+> 
+>
+> **Maturity**: PROTOTYPE
+<hr/>
+
 * ### [kmath-tensors](kmath-tensors)
 > 
 >
@@ -248,7 +271,7 @@ One can still use generic algebras though.
 >
 > **Features:**
 > - [tensor algebra](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt) : Basic linear algebra operations on tensors (plus, dot, etc.)
-> - [tensor algebra with broadcasting](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt) : Basic linear algebra operations implemented with broadcasting.
+> - [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/>
@@ -262,30 +285,33 @@ One can still use generic algebras though.
 
 ## Multi-platform support
 
-KMath is developed as a multi-platform library, which means that most of the interfaces are declared in the 
+KMath is developed as a multi-platform library, which means that most of the interfaces are declared in the
-[common source sets](/kmath-core/src/commonMain) and implemented there wherever it is possible. In some cases, features 
+[common source sets](/kmath-core/src/commonMain) and implemented there wherever it is possible. In some cases, features
-are delegated to platform-specific implementations even if they could be provided in the common module for performance 
+are delegated to platform-specific implementations even if they could be provided in the common module for performance
-reasons. Currently, the Kotlin/JVM is the primary platform, however Kotlin/Native and Kotlin/JS contributions and 
+reasons. Currently, the Kotlin/JVM is the primary platform, however Kotlin/Native and Kotlin/JS contributions and
 feedback are also welcome.
 
 ## Performance
 
-Calculation performance is one of major goals of KMath in the future, but in some cases it is impossible to achieve 
+Calculation performance is one of major goals of KMath in the future, but in some cases it is impossible to achieve both
-both performance and flexibility. 
+performance and flexibility.
 
-We expect to focus on creating convenient universal API first and then work on increasing performance for specific 
+We expect to focus on creating convenient universal API first and then work on increasing performance for specific
-cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized 
+cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized
-native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be 
+native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be
 better than SciPy.
 
 ## Requirements
 
-KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend to use GraalVM-CE 11 for execution in order to get better performance.
+KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend to use GraalVM-CE 11 for
+execution to get better performance.
 
 ### Repositories
 
-Release and development artifacts are accessible from mipt-npm [Space](https://www.jetbrains.com/space/) repository `https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven` (see documentation of
+Release and development artifacts are accessible from mipt-npm [Space](https://www.jetbrains.com/space/)
-[Kotlin Multiplatform](https://kotlinlang.org/docs/reference/multiplatform.html) for more details). The repository could be reached through [repo.kotlin.link](https://repo.kotlin.link) proxy:
+repository `https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven` (see documentation of
+[Kotlin Multiplatform](https://kotlinlang.org/docs/reference/multiplatform.html) for more details). The repository could
+be reached through [repo.kotlin.link](https://repo.kotlin.link) proxy:
 
 ```kotlin
 repositories {
@@ -293,8 +319,8 @@ repositories {
 }
 
 dependencies {
-    api("space.kscience:kmath-core:0.3.0-dev-11")
+    api("space.kscience:kmath-core:0.3.0-dev-14")
-    // api("space.kscience:kmath-core-jvm:0.3.0-dev-11") for jvm-specific version
+    // api("space.kscience:kmath-core-jvm:0.3.0-dev-14") for jvm-specific version
 }
 ```
 
@@ -302,7 +328,7 @@ Gradle `6.0+` is required for multiplatform artifacts.
 
 ## Contributing
 
-The project requires a lot of additional work. The most important thing we need is a feedback about what features are 
+The project requires a lot of additional work. The most important thing we need is a feedback about what features are
-required the most. Feel free to create feature requests. We are also welcome to code contributions, 
+required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues
-especially in issues marked with 
+marked with
 [waiting for a hero](https://github.com/mipt-npm/kmath/labels/waiting%20for%20a%20hero) label.

benchmarks/build.gradle.kts

@@ -1,3 +1,7 @@
+@file:Suppress("UNUSED_VARIABLE")
+
+import space.kscience.kmath.benchmarks.addBenchmarkProperties
+
 plugins {
     kotlin("multiplatform")
     kotlin("plugin.allopen")
@@ -12,6 +16,7 @@ repositories {
     maven("https://repo.kotlin.link")
     maven("https://clojars.org/repo")
     maven("https://jitpack.io")
+
     maven("http://logicrunch.research.it.uu.se/maven") {
         isAllowInsecureProtocol = true
     }
@@ -30,7 +35,8 @@ kotlin {
                 implementation(project(":kmath-stat"))
                 implementation(project(":kmath-dimensions"))
                 implementation(project(":kmath-for-real"))
-                implementation("org.jetbrains.kotlinx:kotlinx-benchmark-runtime:0.3.0")
+                implementation(project(":kmath-jafama"))
+                implementation("org.jetbrains.kotlinx:kotlinx-benchmark-runtime:0.3.1")
             }
         }
 
@@ -41,8 +47,7 @@ kotlin {
                 implementation(project(":kmath-nd4j"))
                 implementation(project(":kmath-kotlingrad"))
                 implementation(project(":kmath-viktor"))
-                implementation("org.nd4j:nd4j-native:1.0.0-beta7")
+                implementation("org.nd4j:nd4j-native:1.0.0-M1")
-
                 //    uncomment if your system supports AVX2
                 //    val os = System.getProperty("os.name")
                 //
@@ -95,6 +100,11 @@ benchmark {
         commonConfiguration()
         include("BigIntBenchmark")
     }
+
+    configurations.register("jafamaDouble") {
+        commonConfiguration()
+        include("JafamaBenchmark")
+    }
 }
 
 // Fix kotlinx-benchmarks bug
@@ -124,3 +134,5 @@ tasks.withType<org.jetbrains.kotlin.gradle.tasks.KotlinCompile> {
 readme {
     maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL
 }
+
+addBenchmarkProperties()

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

@@ -14,22 +14,51 @@ import space.kscience.kmath.expressions.*
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.operations.bindSymbol
 import space.kscience.kmath.operations.invoke
+import kotlin.math.sin
 import kotlin.random.Random
 
 @State(Scope.Benchmark)
 internal class ExpressionsInterpretersBenchmark {
+    /**
+     * Benchmark case for [Expression] created with [expressionInExtendedField].
+     */
     @Benchmark
     fun functionalExpression(blackhole: Blackhole) = invokeAndSum(functional, blackhole)
 
+    /**
+     * Benchmark case for [Expression] created with [toExpression].
+     */
     @Benchmark
     fun mstExpression(blackhole: Blackhole) = invokeAndSum(mst, blackhole)
 
+    /**
+     * Benchmark case for [Expression] created with [compileToExpression].
+     */
     @Benchmark
     fun asmExpression(blackhole: Blackhole) = invokeAndSum(asm, blackhole)
 
+    /**
+     * Benchmark case for [Expression] implemented manually with `kotlin.math` functions.
+     */
     @Benchmark
     fun rawExpression(blackhole: Blackhole) = invokeAndSum(raw, blackhole)
 
+    /**
+     * Benchmark case for direct computation w/o [Expression].
+     */
+    @Benchmark
+    fun justCalculate(blackhole: Blackhole) {
+        val random = Random(0)
+        var sum = 0.0
+
+        repeat(times) {
+            val x = random.nextDouble()
+            sum += x * 2.0 + 2.0 / x - 16.0 / sin(x)
+        }
+
+        blackhole.consume(sum)
+    }
+
     private fun invokeAndSum(expr: Expression<Double>, blackhole: Blackhole) {
         val random = Random(0)
         var sum = 0.0
@@ -42,23 +71,24 @@ internal class ExpressionsInterpretersBenchmark {
     }
 
     private companion object {
-        private val x: Symbol by symbol
+        private val x by symbol
-        private val algebra: DoubleField = DoubleField
+        private val algebra = DoubleField
         private const val times = 1_000_000
 
-        private val functional: Expression<Double> = DoubleField.expressionInExtendedField {
+        private val functional = DoubleField.expressionInExtendedField {
             bindSymbol(x) * number(2.0) + number(2.0) / bindSymbol(x) - number(16.0) / sin(bindSymbol(x))
         }
 
         private val node = MstExtendedField {
-            bindSymbol(x) * 2.0 + number(2.0) / bindSymbol(x) - number(16.0) / sin(bindSymbol(x))
+            x * 2.0 + number(2.0) / x - number(16.0) / sin(x)
         }
 
-        private val mst: Expression<Double> = node.toExpression(DoubleField)
+        private val mst = node.toExpression(DoubleField)
-        private val asm: Expression<Double> = node.compileToExpression(DoubleField)
+        private val asm = node.compileToExpression(DoubleField)
 
-        private val raw: Expression<Double> = Expression { args ->
+        private val raw = Expression<Double> { args ->
-            args.getValue(x) * 2.0 + 2.0 / args.getValue(x) - 16.0 / kotlin.math.sin(args.getValue(x))
+            val x = args[x]!!
+            x * 2.0 + 2.0 / x - 16.0 / sin(x)
         }
     }
 }

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

@@ -0,0 +1,42 @@
+/*
+ * 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.Blackhole
+import org.openjdk.jmh.annotations.Benchmark
+import org.openjdk.jmh.annotations.Scope
+import org.openjdk.jmh.annotations.State
+import space.kscience.kmath.jafama.JafamaDoubleField
+import space.kscience.kmath.jafama.StrictJafamaDoubleField
+import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.operations.invoke
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
+import kotlin.random.Random
+
+@State(Scope.Benchmark)
+internal class JafamaBenchmark {
+    @Benchmark
+    fun jafama(blackhole: Blackhole) = invokeBenchmarks(blackhole) { x ->
+        JafamaDoubleField { x * power(x, 4) * exp(x) / cos(x) + sin(x) }
+    }
+
+    @Benchmark
+    fun core(blackhole: Blackhole) = invokeBenchmarks(blackhole) { x ->
+        DoubleField { x * power(x, 4) * exp(x) / cos(x) + sin(x) }
+    }
+
+    @Benchmark
+    fun strictJafama(blackhole: Blackhole) = invokeBenchmarks(blackhole) { x ->
+        StrictJafamaDoubleField { x * power(x, 4) * exp(x) / cos(x) + sin(x) }
+    }
+}
+
+private inline fun invokeBenchmarks(blackhole: Blackhole, expr: (Double) -> Double) {
+    contract { callsInPlace(expr, InvocationKind.AT_LEAST_ONCE) }
+    val rng = Random(0)
+    repeat(1000000) { blackhole.consume(expr(rng.nextDouble())) }
+}

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

@@ -14,8 +14,8 @@ import space.kscience.kmath.commons.linear.inverse
 import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
 import space.kscience.kmath.linear.InverseMatrixFeature
 import space.kscience.kmath.linear.LinearSpace
-import space.kscience.kmath.linear.inverseWithLup
 import space.kscience.kmath.linear.invoke
+import space.kscience.kmath.linear.lupSolver
 import space.kscience.kmath.nd.getFeature
 import kotlin.random.Random
 
@@ -35,19 +35,19 @@ internal class MatrixInverseBenchmark {
 
     @Benchmark
     fun kmathLupInversion(blackhole: Blackhole) {
-        blackhole.consume(LinearSpace.double.inverseWithLup(matrix))
+        blackhole.consume(LinearSpace.double.lupSolver().inverse(matrix))
     }
 
     @Benchmark
     fun cmLUPInversion(blackhole: Blackhole) {
-        with(CMLinearSpace) {
+        CMLinearSpace {
             blackhole.consume(inverse(matrix))
         }
     }
 
     @Benchmark
     fun ejmlInverse(blackhole: Blackhole) {
-        with(EjmlLinearSpaceDDRM) {
+        EjmlLinearSpaceDDRM {
             blackhole.consume(matrix.getFeature<InverseMatrixFeature<Double>>()?.inverse)
         }
     }

build.gradle.kts

@@ -1,3 +1,5 @@
+import java.net.URL
+
 plugins {
     id("ru.mipt.npm.gradle.project")
     kotlin("jupyter.api") apply false
@@ -7,15 +9,17 @@ allprojects {
     repositories {
         maven("https://clojars.org/repo")
         maven("https://jitpack.io")
+
         maven("http://logicrunch.research.it.uu.se/maven") {
             isAllowInsecureProtocol = true
         }
+
         maven("https://oss.sonatype.org/content/repositories/snapshots")
         mavenCentral()
     }
 
     group = "space.kscience"
-    version = "0.3.0-dev-13"
+    version = "0.3.0-dev-14"
 }
 
 subprojects {
@@ -23,30 +27,46 @@ subprojects {
 
     afterEvaluate {
         tasks.withType<org.jetbrains.dokka.gradle.DokkaTaskPartial> {
-            dependsOn(tasks.getByName("assemble"))
+            dependsOn(tasks["assemble"])
 
             dokkaSourceSets.all {
-                val readmeFile = File(this@subprojects.projectDir, "README.md")
+                val readmeFile = this@subprojects.projectDir.resolve("README.md")
-                if (readmeFile.exists()) includes.setFrom(includes + readmeFile.absolutePath)
+                if (readmeFile.exists()) includes.from(readmeFile)
-                externalDocumentationLink("http://ejml.org/javadoc/")
+                val kotlinDirPath = "src/$name/kotlin"
+                val kotlinDir = file(kotlinDirPath)
+
+                if (kotlinDir.exists()) sourceLink {
+                    localDirectory.set(kotlinDir)
+
+                    remoteUrl.set(
+                        URL("https://github.com/mipt-npm/${rootProject.name}/tree/master/${this@subprojects.name}/$kotlinDirPath")
+                    )
+                }
+
                 externalDocumentationLink("https://commons.apache.org/proper/commons-math/javadocs/api-3.6.1/")
                 externalDocumentationLink("https://deeplearning4j.org/api/latest/")
-                externalDocumentationLink("https://kotlin.github.io/kotlinx.coroutines/kotlinx-coroutines-core/")
+                externalDocumentationLink("https://axelclk.bitbucket.io/symja/javadoc/")
+
+                externalDocumentationLink(
+                    "https://kotlin.github.io/kotlinx.coroutines/kotlinx-coroutines-core/",
+                    "https://kotlin.github.io/kotlinx.coroutines/package-list",
+                )
+
+                externalDocumentationLink(
+                    "https://breandan.net/kotlingrad/kotlingrad/",
+                    "https://breandan.net/kotlingrad/kotlingrad/kotlingrad/package-list",
+                )
             }
         }
     }
 }
 
-readme {
+readme.readmeTemplate = file("docs/templates/README-TEMPLATE.md")
-    readmeTemplate = file("docs/templates/README-TEMPLATE.md")
-}
 
 ksciencePublish {
-    github("kmath")
+    vcs("https://github.com/mipt-npm/kmath")
-    space()
+    space(publish = true)
-    sonatype()
+    sonatype(publish = true)
 }
 
-apiValidation {
+apiValidation.nonPublicMarkers.add("space.kscience.kmath.misc.UnstableKMathAPI")
-    nonPublicMarkers.add("space.kscience.kmath.misc.UnstableKMathAPI")
-}

buildSrc/build.gradle.kts

@@ -1,5 +1,20 @@
 plugins {
     `kotlin-dsl`
+    kotlin("plugin.serialization") version "1.4.31"
 }
 
-repositories.mavenCentral()
+repositories {
+    maven("https://repo.kotlin.link")
+    mavenCentral()
+    gradlePluginPortal()
+}
+
+dependencies {
+    api("org.jetbrains.kotlinx:kotlinx-serialization-json:1.1.0")
+    api("ru.mipt.npm:gradle-tools:0.10.2")
+    api("org.jetbrains.kotlinx:kotlinx-benchmark-plugin:0.3.1")
+}
+
+kotlin.sourceSets.all {
+    languageSettings.useExperimentalAnnotation("kotlin.ExperimentalStdlibApi")
+}

buildSrc/src/main/kotlin/space/kscience/kmath/benchmarks/JmhReport.kt

@@ -0,0 +1,60 @@
+/*
+ * 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.serialization.Serializable
+
+@Serializable
+data class JmhReport(
+    val jmhVersion: String,
+    val benchmark: String,
+    val mode: String,
+    val threads: Int,
+    val forks: Int,
+    val jvm: String,
+    val jvmArgs: List<String>,
+    val jdkVersion: String,
+    val vmName: String,
+    val vmVersion: String,
+    val warmupIterations: Int,
+    val warmupTime: String,
+    val warmupBatchSize: Int,
+    val measurementIterations: Int,
+    val measurementTime: String,
+    val measurementBatchSize: Int,
+    val params: Map<String, String> = emptyMap(),
+    val primaryMetric: PrimaryMetric,
+    val secondaryMetrics: Map<String, SecondaryMetric>,
+) {
+    interface Metric {
+        val score: Double
+        val scoreError: Double
+        val scoreConfidence: List<Double>
+        val scorePercentiles: Map<Double, Double>
+        val scoreUnit: String
+    }
+
+    @Serializable
+    data class PrimaryMetric(
+        override val score: Double,
+        override val scoreError: Double,
+        override val scoreConfidence: List<Double>,
+        override val scorePercentiles: Map<Double, Double>,
+        override val scoreUnit: String,
+        val rawDataHistogram: List<List<List<List<Double>>>>? = null,
+        val rawData: List<List<Double>>? = null,
+    ) : Metric
+
+    @Serializable
+    data class SecondaryMetric(
+        override val score: Double,
+        override val scoreError: Double,
+        override val scoreConfidence: List<Double>,
+        override val scorePercentiles: Map<Double, Double>,
+        override val scoreUnit: String,
+        val rawData: List<List<Double>>,
+    ) : Metric
+}

buildSrc/src/main/kotlin/space/kscience/kmath/benchmarks/addBenchmarkProperties.kt

@@ -0,0 +1,100 @@
+/*
+ * 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.gradle.BenchmarksExtension
+import kotlinx.serialization.*
+import kotlinx.serialization.json.*
+import org.gradle.api.Project
+import ru.mipt.npm.gradle.KScienceReadmeExtension
+import java.time.*
+import java.time.format.*
+import java.time.temporal.ChronoField.*
+
+private val ISO_DATE_TIME: DateTimeFormatter = DateTimeFormatterBuilder().run {
+    parseCaseInsensitive()
+    appendValue(YEAR, 4, 10, SignStyle.EXCEEDS_PAD)
+    appendLiteral('-')
+    appendValue(MONTH_OF_YEAR, 2)
+    appendLiteral('-')
+    appendValue(DAY_OF_MONTH, 2)
+    appendLiteral('T')
+    appendValue(HOUR_OF_DAY, 2)
+    appendLiteral('.')
+    appendValue(MINUTE_OF_HOUR, 2)
+    optionalStart()
+    appendLiteral('.')
+    appendValue(SECOND_OF_MINUTE, 2)
+    optionalStart()
+    appendFraction(NANO_OF_SECOND, 0, 9, true)
+    optionalStart()
+    appendOffsetId()
+    optionalStart()
+    appendLiteral('[')
+    parseCaseSensitive()
+    appendZoneRegionId()
+    appendLiteral(']')
+    toFormatter()
+}
+
+private fun noun(number: Number, singular: String, plural: String) = if (number.toLong() == 1L) singular else plural
+
+fun Project.addBenchmarkProperties() {
+    val benchmarksProject = this
+    rootProject.subprojects.forEach { p ->
+        p.extensions.findByType(KScienceReadmeExtension::class.java)?.run {
+            benchmarksProject.extensions.findByType(BenchmarksExtension::class.java)?.configurations?.forEach { cfg ->
+                property("benchmark${cfg.name.replaceFirstChar(Char::uppercase)}") {
+                    val launches = benchmarksProject.buildDir.resolve("reports/benchmarks/${cfg.name}")
+
+                    val resDirectory = launches.listFiles()?.maxByOrNull {
+                        LocalDateTime.parse(it.name, ISO_DATE_TIME).atZone(ZoneId.systemDefault()).toInstant()
+                    }
+
+                    if (resDirectory == null) {
+                        "> **Can't find appropriate benchmark data. Try generating readme files after running benchmarks**."
+                    } else {
+                        val reports =
+                            Json.decodeFromString<List<JmhReport>>(resDirectory.resolve("jvm.json").readText())
+
+                        buildString {
+                            appendLine("<details>")
+                            appendLine("<summary>")
+                            appendLine("Report for benchmark configuration <code>${cfg.name}</code>")
+                            appendLine("</summary>")
+                            appendLine()
+                            val first = reports.first()
+
+                            appendLine("* Run on ${first.vmName} (build ${first.vmVersion}) with Java process:")
+                            appendLine()
+                            appendLine("```")
+                            appendLine("${first.jvm} ${
+                                first.jvmArgs.joinToString(" ")
+                            }")
+                            appendLine("```")
+
+                            appendLine("* JMH ${first.jmhVersion} was used in `${first.mode}` mode with ${first.warmupIterations} warmup ${
+                                noun(first.warmupIterations, "iteration", "iterations")
+                            } by ${first.warmupTime} and ${first.measurementIterations} measurement ${
+                                noun(first.measurementIterations, "iteration", "iterations")
+                            } by ${first.measurementTime}.")
+
+                            appendLine()
+                            appendLine("| Benchmark | Score |")
+                            appendLine("|:---------:|:-----:|")
+
+                            reports.forEach { report ->
+                                appendLine("|`${report.benchmark}`|${report.primaryMetric.score} &plusmn; ${report.primaryMetric.scoreError} ${report.primaryMetric.scoreUnit}|")
+                            }
+
+                            appendLine("</details>")
+                        }
+                    }
+                }
+            }
+        }
+    }
+}

buildSrc/src/main/kotlin/space/kscience/kmath/ejml/codegen/ejmlCodegen.kt

@@ -14,12 +14,12 @@ private fun Appendable.appendEjmlVector(type: String, ejmlMatrixType: String) {
     @Language("kotlin") val text = """/**
  * [EjmlVector] specialization for [$type].
  */
-public class Ejml${type}Vector<out M : $ejmlMatrixType>(public override val origin: M) : EjmlVector<$type, M>(origin) {
+public class Ejml${type}Vector<out M : $ejmlMatrixType>(override val origin: M) : EjmlVector<$type, M>(origin) {
     init {
         require(origin.numRows == 1) { "The origin matrix must have only one row to form a vector" }
     }
 
-    public override operator fun get(index: Int): $type = origin[0, index]
+    override operator fun get(index: Int): $type = origin[0, index]
 }"""
     appendLine(text)
     appendLine()
@@ -29,8 +29,8 @@ private fun Appendable.appendEjmlMatrix(type: String, ejmlMatrixType: String) {
     val text = """/**
  * [EjmlMatrix] specialization for [$type].
  */
-public class Ejml${type}Matrix<out M : $ejmlMatrixType>(public override val origin: M) : EjmlMatrix<$type, M>(origin) {
+public class Ejml${type}Matrix<out M : $ejmlMatrixType>(override val origin: M) : EjmlMatrix<$type, M>(origin) {
-    public override operator fun get(i: Int, j: Int): $type = origin[i, j]
+    override operator fun get(i: Int, j: Int): $type = origin[i, j]
 }"""
     appendLine(text)
     appendLine()
@@ -54,23 +54,23 @@ public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra},
     /**
      * The [${kmathAlgebra}] reference.
      */
-    public override val elementAlgebra: $kmathAlgebra get() = $kmathAlgebra
+    override val elementAlgebra: $kmathAlgebra get() = $kmathAlgebra
 
     @Suppress("UNCHECKED_CAST")
-    public override fun Matrix<${type}>.toEjml(): Ejml${type}Matrix<${ejmlMatrixType}> = when {
+    override fun Matrix<${type}>.toEjml(): Ejml${type}Matrix<${ejmlMatrixType}> = when {
         this is Ejml${type}Matrix<*> && origin is $ejmlMatrixType -> this as Ejml${type}Matrix<${ejmlMatrixType}>
         else -> buildMatrix(rowNum, colNum) { i, j -> get(i, j) }
     }
 
     @Suppress("UNCHECKED_CAST")
-    public override fun Point<${type}>.toEjml(): Ejml${type}Vector<${ejmlMatrixType}> = when {
+    override fun Point<${type}>.toEjml(): Ejml${type}Vector<${ejmlMatrixType}> = when {
         this is Ejml${type}Vector<*> && origin is $ejmlMatrixType -> this as Ejml${type}Vector<${ejmlMatrixType}>
         else -> Ejml${type}Vector(${ejmlMatrixType}(size, 1).also {
             (0 until it.numRows).forEach { row -> it[row, 0] = get(row) }
         })
     }
 
-    public override fun buildMatrix(
+    override fun buildMatrix(
         rows: Int,
         columns: Int,
         initializer: ${kmathAlgebra}.(i: Int, j: Int) -> ${type},
@@ -80,7 +80,7 @@ public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra},
         }
     }.wrapMatrix()
 
-    public override fun buildVector(
+    override fun buildVector(
         size: Int,
         initializer: ${kmathAlgebra}.(Int) -> ${type},
     ): Ejml${type}Vector<${ejmlMatrixType}> = Ejml${type}Vector(${ejmlMatrixType}(size, 1).also {
@@ -90,21 +90,21 @@ public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra},
     private fun <T : ${ejmlMatrixParentTypeMatrix}> T.wrapMatrix() = Ejml${type}Matrix(this)
     private fun <T : ${ejmlMatrixParentTypeMatrix}> T.wrapVector() = Ejml${type}Vector(this)
 
-    public override fun Matrix<${type}>.unaryMinus(): Matrix<${type}> = this * elementAlgebra { -one }
+    override fun Matrix<${type}>.unaryMinus(): Matrix<${type}> = this * elementAlgebra { -one }
 
-    public override fun Matrix<${type}>.dot(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
+    override fun Matrix<${type}>.dot(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
         val out = ${ejmlMatrixType}(1, 1)
         CommonOps_${ops}.mult(toEjml().origin, other.toEjml().origin, out)
         return out.wrapMatrix()
     }
 
-    public override fun Matrix<${type}>.dot(vector: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
+    override fun Matrix<${type}>.dot(vector: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
         val out = ${ejmlMatrixType}(1, 1)
         CommonOps_${ops}.mult(toEjml().origin, vector.toEjml().origin, out)
         return out.wrapVector()
     }
 
-    public override operator fun Matrix<${type}>.minus(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
+    override operator fun Matrix<${type}>.minus(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
         val out = ${ejmlMatrixType}(1, 1)
 
         CommonOps_${ops}.add(
@@ -123,19 +123,19 @@ public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra},
         return out.wrapMatrix()
     }
 
-    public override operator fun Matrix<${type}>.times(value: ${type}): Ejml${type}Matrix<${ejmlMatrixType}> {
+    override operator fun Matrix<${type}>.times(value: ${type}): Ejml${type}Matrix<${ejmlMatrixType}> {
         val res = ${ejmlMatrixType}(1, 1)
         CommonOps_${ops}.scale(value, toEjml().origin, res)
         return res.wrapMatrix()
     }
 
-    public override fun Point<${type}>.unaryMinus(): Ejml${type}Vector<${ejmlMatrixType}> {
+    override fun Point<${type}>.unaryMinus(): Ejml${type}Vector<${ejmlMatrixType}> {
         val res = ${ejmlMatrixType}(1, 1)
         CommonOps_${ops}.changeSign(toEjml().origin, res)
         return res.wrapVector()
     }
 
-    public override fun Matrix<${type}>.plus(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
+    override fun Matrix<${type}>.plus(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
         val out = ${ejmlMatrixType}(1, 1)
         
         CommonOps_${ops}.add(
@@ -154,7 +154,7 @@ public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra},
         return out.wrapMatrix()
     }
 
-    public override fun Point<${type}>.plus(other: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
+    override fun Point<${type}>.plus(other: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
         val out = ${ejmlMatrixType}(1, 1)
 
         CommonOps_${ops}.add(
@@ -173,7 +173,7 @@ public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra},
         return out.wrapVector()
     }
 
-    public override fun Point<${type}>.minus(other: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
+    override fun Point<${type}>.minus(other: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
         val out = ${ejmlMatrixType}(1, 1)
 
         CommonOps_${ops}.add(
@@ -192,18 +192,18 @@ public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra},
         return out.wrapVector()
     }
 
-    public override fun ${type}.times(m: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> = m * this
+    override fun ${type}.times(m: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> = m * this
 
-    public override fun Point<${type}>.times(value: ${type}): Ejml${type}Vector<${ejmlMatrixType}> {
+    override fun Point<${type}>.times(value: ${type}): Ejml${type}Vector<${ejmlMatrixType}> {
         val res = ${ejmlMatrixType}(1, 1)
         CommonOps_${ops}.scale(value, toEjml().origin, res)
         return res.wrapVector()
     }
 
-    public override fun ${type}.times(v: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> = v * this
+    override fun ${type}.times(v: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> = v * this
 
     @UnstableKMathAPI
-    public override fun <F : StructureFeature> computeFeature(structure: Matrix<${type}>, type: KClass<out F>): F? {
+    override fun <F : StructureFeature> computeFeature(structure: Matrix<${type}>, type: KClass<out F>): F? {
         structure.getFeature(type)?.let { return it }
         val origin = structure.toEjml().origin
 

docs/algebra.md

@@ -1,85 +1,45 @@
 # Algebraic Structures and Algebraic Elements
 
-The mathematical operations in KMath are generally separated from mathematical objects. This means that to perform an 
+The mathematical operations in KMath are generally separated from mathematical objects. This means that to perform an
-operation, say `+`, one needs two objects of a type `T` and an algebra context, which draws appropriate operation up, 
+operation, say `+`, one needs two objects of a type `T` and an algebra context, which draws appropriate operation up,
-say `Space<T>`. Next one needs to run the actual operation in the context:
+say `Group<T>`. Next one needs to run the actual operation in the context:
 
 ```kotlin
 import space.kscience.kmath.operations.*
 
 val a: T = ...
 val b: T = ...
-val space: Space<T> = ...
+val group: Group<T> = ...
 
-val c = space { a + b }
+val c = group { a + b }
 ```
 
-At first glance, this distinction seems to be a needless complication, but in fact one needs to remember that in 
+At first glance, this distinction seems to be a needless complication, but in fact one needs to remember that in
-mathematics, one could draw up different operations on same objects. For example, one could use different types of 
+mathematics, one could draw up different operations on same objects. For example, one could use different types of
 geometry for vectors.
 
 ## Algebraic Structures
 
-Mathematical contexts have the following hierarchy:
+Primary mathematical contexts have the following hierarchy:
 
-**Algebra** ← **Space** ← **Ring** ← **Field**
+`Field <: Ring <: Group <: Algebra`
 
 These interfaces follow real algebraic structures:
 
-- [Space](https://mathworld.wolfram.com/VectorSpace.html) defines addition, its neutral element (i.e. 0) and scalar 
+- [Group](https://mathworld.wolfram.com/Group.html) defines addition, its identity element (i.e., 0) and additive
-multiplication;
+  inverse (-x);
-- [Ring](http://mathworld.wolfram.com/Ring.html) adds multiplication and its neutral element (i.e. 1);
+- [Ring](http://mathworld.wolfram.com/Ring.html) adds multiplication and its identity element (i.e., 1);
 - [Field](http://mathworld.wolfram.com/Field.html) adds division operation.
 
 A typical implementation of `Field<T>` is the `DoubleField` which works on doubles, and `VectorSpace` for `Space<T>`.
 
 In some cases algebra context can hold additional operations like `exp` or `sin`, and then it inherits appropriate
-interface. Also, contexts may have operations, which produce elements outside of the context. For example, `Matrix.dot` 
+interface. Also, contexts may have operations, which produce elements outside the context. For example, `Matrix.dot`
-operation produces a matrix with new dimensions, which can be incompatible with initial matrix in terms of linear 
+operation produces a matrix with new dimensions, which can be incompatible with initial matrix in linear operations.
-operations.
-
-## Algebraic Element
-
-To achieve more familiar behavior (where you apply operations directly to mathematical objects), without involving 
-contexts KMath submits special type objects called `MathElement`. A `MathElement` is basically some object coupled to
-a mathematical context. For example `Complex` is the pair of real numbers representing real and imaginary parts,
-but it also holds reference to the `ComplexField` singleton, which allows performing direct operations on `Complex`
-numbers without explicit involving the context like:
-
-```kotlin
-import space.kscience.kmath.operations.*
-
-// Using elements
-val c1 = Complex(1.0, 1.0)
-val c2 = Complex(1.0, -1.0)
-val c3 = c1 + c2 + 3.0.toComplex()
-
-// Using context
-val c4 = ComplexField { c1 + i - 2.0 }
-```
-
-Both notations have their pros and cons.
-
-The hierarchy for algebraic elements follows the hierarchy for the corresponding algebraic structures.
-
-**MathElement** ← **SpaceElement** ← **RingElement** ← **FieldElement**
-
-`MathElement<C>` is the generic common ancestor of the class with context.
-
-One major distinction between algebraic elements and algebraic contexts is that elements have three type 
-parameters:
-
-1. The type of elements, the field operates on.
-2. The self-type of the element returned from operation (which has to be an algebraic element).
-3. The type of the algebra over first type-parameter.
-
-The middle type is needed for of algebra members do not store context. For example, it is impossible to add a context 
-to regular `Double`. The element performs automatic conversions from context types and back. One should use context 
-operations in all performance-critical places. The performance of element operations is not guaranteed.
 
 ## Spaces and Fields
 
-KMath submits both contexts and elements for builtin algebraic structures:
+KMath introduces contexts for builtin algebraic structures:
 
 ```kotlin
 import space.kscience.kmath.operations.*
@@ -102,13 +62,13 @@ val c2 = ComplexField { c1 - 1.0 } // Returns: Complex(re=0.0, im=2.0)
 val c3 = ComplexField { c1 - i * 2.0 }
 ```
 
-**Note**: In theory it is possible to add behaviors directly to the context, but as for now Kotlin does not support 
+**Note**: In theory it is possible to add behaviors directly to the context, but as for now Kotlin does not support
-that. Watch [KT-10468](https://youtrack.jetbrains.com/issue/KT-10468) and 
+that. Watch [KT-10468](https://youtrack.jetbrains.com/issue/KT-10468) and
 [KEEP-176](https://github.com/Kotlin/KEEP/pull/176) for updates.
 
 ## Nested fields
 
-Contexts allow one to build more complex structures. For example, it is possible to create a `Matrix` from complex 
+Contexts allow one to build more complex structures. For example, it is possible to create a `Matrix` from complex
 elements like so:
 
 ```kotlin
@@ -118,8 +78,9 @@ val element = NDElement.complex(shape = intArrayOf(2, 2)) { index: IntArray ->
 ```
 
 The `element` in this example is a member of the `Field` of 2D structures, each element of which is a member of its own
-`ComplexField`. It is important one does not need to create a special n-d class to hold complex
+`ComplexField`. It is important one does not need to create a special n-d class to hold complex numbers and implement
-numbers and implement operations on it, one just needs to provide a field for its elements.
+operations on it, one just needs to provide a field for its elements.
 
-**Note**: Fields themselves do not solve the problem of JVM boxing, but it is possible to solve with special contexts like
+**Note**: Fields themselves do not solve the problem of JVM boxing, but it is possible to solve with special contexts
+like
 `MemorySpec`.

docs/buffers.md

@@ -1,17 +1,20 @@
 # Buffers
 
-Buffer is one of main building blocks of kmath. It is a basic interface allowing random-access read and write (with `MutableBuffer`).
+Buffer is one of main building blocks of kmath. It is a basic interface allowing random-access read and write (
-There are different types of buffers:
+with `MutableBuffer`). There are different types of buffers:
 
-* Primitive buffers wrapping like `RealBuffer` which are wrapping primitive arrays.
+* Primitive buffers wrapping like `DoubleBuffer` which are wrapping primitive arrays.
 * Boxing `ListBuffer` wrapping a list
 * Functionally defined `VirtualBuffer` which does not hold a state itself, but provides a function to calculate value
 * `MemoryBuffer` allows direct allocation of objects in continuous memory block.
 
-Some kmath features require a `BufferFactory` class to operate properly. A general convention is to use functions defined in
+Some kmath features require a `BufferFactory` class to operate properly. A general convention is to use functions
-`Buffer` and `MutableBuffer` companion classes. For example factory `Buffer.Companion::auto` in most cases creates the most suitable
+defined in
-buffer for given reified type (for types with custom memory buffer it still better to use their own `MemoryBuffer.create()` factory).
+`Buffer` and `MutableBuffer` companion classes. For example factory `Buffer.Companion::auto` in most cases creates the
+most suitable buffer for given reified type (for types with custom memory buffer it still better to use their
+own `MemoryBuffer.create()` factory).
 
 ## Buffer performance
 
-One should avoid using default boxing buffer wherever it is possible. Try to use primitive buffers or memory buffers instead  
+One should avoid using default boxing buffer wherever it is possible. Try to use primitive buffers or memory buffers
+instead  .

docs/codestyle.md

@@ -1,26 +1,20 @@
 # Coding Conventions
 
-KMath code follows general [Kotlin conventions](https://kotlinlang.org/docs/reference/coding-conventions.html), but 
+Generally, KMath code follows general [Kotlin coding conventions](https://kotlinlang.org/docs/reference/coding-conventions.html), but with a number of small changes and clarifications.
-with a number of small changes and clarifications.
 
 ## Utility Class Naming
 
-Filename should coincide with a name of one of the classes contained in the file or start with small letter and 
+Filename should coincide with a name of one of the classes contained in the file or start with small letter and describe its contents.
-describe its contents.
 
-The code convention [here](https://kotlinlang.org/docs/reference/coding-conventions.html#source-file-names) says that 
+The code convention [here](https://kotlinlang.org/docs/reference/coding-conventions.html#source-file-names) says that file names should start with a capital letter even if file does not contain classes. Yet starting utility classes and aggregators with a small letter seems to be a good way to visually separate those files.
-file names should start with a capital letter even if file does not contain classes. Yet starting utility classes and 
-aggregators with a small letter seems to be a good way to visually separate those files.
 
 This convention could be changed in future in a non-breaking way.
 
 ## Private Variable Naming
 
-Private variables' names may start with underscore `_` for of the private mutable variable is shadowed by the public 
+Private variables' names may start with underscore `_` for of the private mutable variable is shadowed by the public read-only value with the same meaning.
-read-only value with the same meaning.
 
-This rule does not permit underscores in names, but it is sometimes useful to "underscore" the fact that public and 
+This rule does not permit underscores in names, but it is sometimes useful to "underscore" the fact that public and private versions draw up the same entity. It is allowed only for private variables.
-private versions draw up the same entity. It is allowed only for private variables.
 
 This convention could be changed in future in a non-breaking way.
 
@@ -30,5 +24,4 @@ Use one-liners when they occupy single code window line both for functions and p
 `val b: String get() = "fff"`. The same should be performed with multiline expressions when they could be 
 cleanly separated.
 
-There is no universal consensus whenever use `fun a() = ...` or `fun a() { return ... }`. Yet from reader outlook 
+There is no universal consensus whenever use `fun a() = ...` or `fun a() { return ... }`. Yet from reader outlook one-lines seem to better show that the property or function is easily calculated.
-one-lines seem to better show that the property or function is easily calculated.

docs/contexts.md

@@ -2,18 +2,17 @@
 
 ## The problem
 
-A known problem for implementing mathematics in statically-typed languages (but not only in them) is that different
+A known problem for implementing mathematics in statically-typed languages (but not only in them) is that different sets
-sets of mathematical operators can be defined on the same mathematical objects. Sometimes there is no single way to
+of mathematical operators can be defined on the same mathematical objects. Sometimes there is no single way to treat
-treat some operations, including basic arithmetic operations, on a Java/Kotlin `Number`. Sometimes there are different ways to
+some operations, including basic arithmetic operations, on a Java/Kotlin `Number`. Sometimes there are different ways to
-define the same structure, such as Euclidean and elliptic geometry vector spaces over real vectors. Another problem arises when
+define the same structure, such as Euclidean and elliptic geometry vector spaces over real vectors. Another problem
-one wants to add some kind of behavior to an existing entity. In dynamic languages those problems are usually solved
+arises when one wants to add some kind of behavior to an existing entity. In dynamic languages those problems are
-by adding dynamic context-specific behaviors at runtime, but this solution has a lot of drawbacks.
+usually solved by adding dynamic context-specific behaviors at runtime, but this solution has a lot of drawbacks.
 
 ## Context-oriented approach
 
-One possible solution to these problems is to divorce numerical representations from behaviors.
+One possible solution to these problems is to divorce numerical representations from behaviors. For example in Kotlin
-For example in Kotlin one can define a separate class which represents some entity without any operations,
+one can define a separate class representing some entity without any operations, ex. a complex number:
-ex. a complex number:
 
 ```kotlin
 data class Complex(val re: Double, val im: Double)
@@ -28,9 +27,10 @@ object ComplexOperations {
 }
 ```
 
-In Java, applying such external operations could be very cumbersome, but Kotlin has a unique feature which allows us
+In Java, applying such external operations could be cumbersome, but Kotlin has a unique feature that allows us
-implement this naturally: [extensions with receivers](https://kotlinlang.org/docs/reference/extensions.html#extension-functions).
+implement this
-In Kotlin, an operation on complex number could be implemented as:
+naturally: [extensions with receivers](https://kotlinlang.org/docs/reference/extensions.html#extension-functions). In
+Kotlin, an operation on complex number could be implemented as:
 
 ```kotlin
 with(ComplexOperations) { c1 + c2 - c3 }
@@ -52,20 +52,20 @@ In KMath, contexts are not only responsible for operations, but also for raw obj
 
 ### Type classes
 
-An obvious candidate to get more or less the same functionality is the type class, which allows one to bind a behavior to
+An obvious candidate to get more or less the same functionality is the type class, which allows one to bind a behavior
-a specific type without modifying the type itself. On the plus side, type classes do not require explicit context
+to a specific type without modifying the type itself. On the plus side, type classes do not require explicit context
 declaration, so the code looks cleaner. On the minus side, if there are different sets of behaviors for the same types,
-it is impossible to combine them into one module. Also, unlike type classes, context can have parameters or even
+it is impossible to combine them into one module. Also, unlike type classes, context can have parameters or even state.
-state. For example in KMath, sizes and strides for `NDElement` or `Matrix` could be moved to context to optimize
+For example in KMath, sizes and strides for `NDElement` or `Matrix` could be moved to context to optimize performance in
-performance in case of a large amount of structures.
+case of a large amount of structures.
 
 ### Wildcard imports and importing-on-demand
 
-Sometimes, one may wish to use a single context throughout a file. In this case, is possible to import all members
+Sometimes, one may wish to use a single context throughout a file. In this case, is possible to import all members from
-from a package or file, via `import context.complex.*`. Effectively, this is the same as enclosing an entire file
+a package or file, via `import context.complex.*`. Effectively, this is the same as enclosing an entire file with a
-with a single context. However when using multiple contexts, this technique can introduce operator ambiguity, due to
+single context. However, when using multiple contexts, this technique can introduce operator ambiguity, due to namespace
-namespace pollution. If there are multiple scoped contexts which define the same operation, it is still possible to
+pollution. If there are multiple scoped contexts that define the same operation, it is still possible to import
-to import specific operations as needed, without using an explicit context with extension functions, for example:
+specific operations as needed, without using an explicit context with extension functions, for example:
 
 ```
 import context.complex.op1

docs/expressions.md

@@ -1,26 +1,21 @@
 # Expressions
 
-**Experimental: this API is in early stage and could change any time**
+Expressions is a feature, which allows constructing lazily or immediately calculated parametric mathematical expressions.
-
-Expressions is an experimental feature which allows to construct lazily or immediately calculated parametric mathematical 
-expressions.
 
 The potential use-cases for it (so far) are following:
 
-* Lazy evaluation (in general simple lambda is better, but there are some border cases)
+* lazy evaluation (in general simple lambda is better, but there are some border cases);
+* automatic differentiation in single-dimension and in multiple dimensions;
+* generation of mathematical syntax trees with subsequent code generation for other languages;
+* symbolic computations, especially differentiation (and some other actions with `kmath-symja` integration with Symja's `IExpr`—integration, simplification, and more);
+* visualization with `kmath-jupyter`.
 
-* Automatic differentiation in single-dimension and in multiple dimensions
+The workhorse of this API is `Expression` interface, which exposes single `operator fun invoke(arguments: Map<Symbol, T>): T`
-
+method. `ExpressionAlgebra` is used to generate expressions and introduce variables.
-* Generation of mathematical syntax trees with subsequent code generation for other languages
-
-* Maybe symbolic computations (needs additional research)
-
-The workhorse of this API is `Expression` interface which exposes single `operator fun invoke(arguments: Map<String, T>): T`
-method. `ExpressionContext` is used to generate expressions and introduce variables.
 
 Currently there are two implementations:
 
 * Generic `ExpressionField` in `kmath-core` which allows construction of custom lazy expressions
 
-* Auto-differentiation expression in `kmath-commons` module allows to use full power of `DerivativeStructure` 
+* Auto-differentiation expression in `kmath-commons` module allows using full power of `DerivativeStructure` 
 from commons-math. **TODO: add example**

docs/features.md

@@ -1,14 +0,0 @@
-# Features
-
-* [Algebra](algebra.md) - [Context-based](contexts.md) operations on different primitives and structures.
-
-* [NDStructures](nd-structure.md)
-
-* [Linear algebra](linear.md) - Matrices, operations and linear equations solving. To be moved to separate module. Currently supports basic
-api and multiple library back-ends.
-
-* [Histograms](histograms.md) - Multidimensional histogram calculation and operations.
-
-* [Expressions](expressions.md)
-
-* Commons math integration

docs/linear.md

@@ -1,19 +1,31 @@
 ## Basic linear algebra layout
 
-KMath support for linear algebra organized in a context-oriented way. Meaning that operations are in most cases declared 
+KMath support for linear algebra organized in a context-oriented way, which means that operations are in most cases declared in context classes, and are not the members of classes that store data. This allows more flexible approach to maintain multiple back-ends. The new operations added as extensions to contexts instead of being member functions of data structures.
-in context classes, and are not the members of classes that store data. This allows more flexible approach to maintain multiple 
-back-ends. The new operations added as extensions to contexts instead of being member functions of data structures.
 
-Two major contexts used for linear algebra and hyper-geometry:
+The main context for linear algebra over matrices and vectors is `LinearSpace`, which defines addition and dot products of matrices and vectors:
 
-* `VectorSpace` forms a mathematical space on top of array-like structure (`Buffer` and its type alias `Point` used for geometry). 
+```kotlin
+import space.kscience.kmath.linear.*
 
-* `MatrixContext` forms a space-like context for 2d-structures. It does not store matrix size and therefore does not implement
+LinearSpace.Companion.real {
-`Space` interface (it is impossible to create zero element without knowing the matrix size). 
+    val vec = buildVector(10) { i -> i.toDouble() }
+    val mat = buildMatrix(10, 10) { i, j -> i.toDouble() + j }
 
-## Vector spaces
+    // Addition
+    vec + vec
+    mat + mat
 
+    // Multiplication by scalar
+    vec * 2.0
+    mat * 2.0
 
-## Matrix operations
+    // Dot product
+    mat dot vec
+    mat dot mat
+}
+```
 
-## Back-end overview
+## Backends overview
+
+### EJML
+### Commons Math

docs/nd-structure.md

@@ -11,16 +11,16 @@ Let us consider following contexts:
 ```kotlin
     // automatically build context most suited for given type.
     val autoField = NDField.auto(DoubleField, dim, dim)
-    // specialized nd-field for Double. It works as generic Double field as well
+    // specialized nd-field for Double. It works as generic Double field as well.
     val specializedField = NDField.real(dim, dim)
     //A generic boxing field. It should be used for objects, not primitives.
     val genericField = NDField.buffered(DoubleField, dim, dim)
 ```
-Now let us perform several tests and see which implementation is best suited for each case:
+Now let us perform several tests and see, which implementation is best suited for each case:
 
 ## Test case
 
-In order to test performance we will take 2d-structures with `dim = 1000` and add a structure filled with `1.0`
+To test performance we will take 2d-structures with `dim = 1000` and add a structure filled with `1.0`
 to it `n = 1000` times.
 
 ## Specialized
@@ -35,8 +35,8 @@ The code to run this looks like:
 ```
 The performance of this code is the best of all tests since it inlines all operations and is specialized for operation
 with doubles. We will measure everything else relative to this one, so time for this test will be `1x` (real time
-on my computer is about 4.5 seconds). The only problem with this approach is that it requires to specify type
+on my computer is about 4.5 seconds). The only problem with this approach is that it requires specifying type
-from the beginning. Everyone do so anyway, so it is the recommended approach.
+from the beginning. Everyone does so anyway, so it is the recommended approach.
 
 ## Automatic
 Let's do the same with automatic field inference:
@@ -49,7 +49,7 @@ Let's do the same with automatic field inference:
     }
 ```
 Ths speed of this operation is approximately the same as for specialized case since `NDField.auto` just
-returns the same `RealNDField` in this case. Of course it is usually better to use specialized method to be sure.
+returns the same `RealNDField` in this case. Of course, it is usually better to use specialized method to be sure.
 
 ## Lazy
 Lazy field does not produce a structure when asked, instead it generates an empty structure and fills it on-demand
@@ -63,7 +63,7 @@ When one calls
         }
     }
 ```
-The result will be calculated almost immediately but the result will be empty. In order to get the full result
+The result will be calculated almost immediately but the result will be empty. To get the full result
 structure one needs to call all its elements. In this case computation overhead will be huge. So this field never
 should be used if one expects to use the full result structure. Though if one wants only small fraction, it could
 save a lot of time.
@@ -94,7 +94,7 @@ The boxing field produced by
         }
     }
 ```
-obviously is the slowest one, because it requires to box and unbox the `double` on each operation. It takes about
+is the slowest one, because it requires boxing and unboxing the `double` on each operation. It takes about
 `15x` time (**TODO: there seems to be a problem here, it should be slow, but not that slow**). This field should
 never be used for primitives.
 
@@ -115,12 +115,14 @@ via extension function.
 Usually it is bad idea to compare the direct numerical operation performance in different languages, but it hard to
 work completely without frame of reference. In this case, simple numpy code:
 ```python
+import numpy as np
+
 res = np.ones((1000,1000))
 for i in range(1000):
     res = res + 1.0
 ```
 gives the completion time of about `1.1x`, which means that specialized kotlin code in fact is working faster (I think it is
 because better memory management). Of course if one writes `res += 1.0`, the performance will be different,
-but it would be differenc case, because numpy overrides `+=` with in-place operations. In-place operations are
+but it would be different case, because numpy overrides `+=` with in-place operations. In-place operations are
 available in `kmath` with `MutableNDStructure` but there is no field for it (one can still work with mapping
 functions).

docs/readme.md

@@ -0,0 +1,14 @@
+# Documentation
+
+* [Algebra](algebra.md): [context-based](contexts.md) operations on different primitives and structures.
+
+* [NDStructures](nd-structure.md)
+
+* [Linear algebra](linear.md): matrices, operations and linear equations solving. To be moved to separate module.
+  Currently, supports basic API and multiple library back-ends.
+
+* [Histograms](histograms.md): multidimensional histogram calculation and operations.
+
+* [Expressions](expressions.md)
+
+* Commons math integration

docs/templates/README-TEMPLATE.md

@@ -2,14 +2,14 @@
 [![DOI](https://zenodo.org/badge/129486382.svg)](https://zenodo.org/badge/latestdoi/129486382)
 ![Gradle build](https://github.com/mipt-npm/kmath/workflows/Gradle%20build/badge.svg)
 [![Maven Central](https://img.shields.io/maven-central/v/space.kscience/kmath-core.svg?label=Maven%20Central)](https://search.maven.org/search?q=g:%22space.kscience%22)
-[![Space](https://img.shields.io/maven-metadata/v?label=Space&metadataUrl=https%3A%2F%2Fmaven.pkg.jetbrains.space%2Fmipt-npm%2Fp%2Fsci%2Fmaven%2Fkscience%2Fkmath%2Fkmath-core%2Fmaven-metadata.xml)](https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven/space/kscience/)
+[![Space](https://img.shields.io/badge/dynamic/xml?color=orange&label=Space&query=//metadata/versioning/latest&url=https%3A%2F%2Fmaven.pkg.jetbrains.space%2Fmipt-npm%2Fp%2Fsci%2Fmaven%2Fspace%2Fkscience%2Fkmath-core%2Fmaven-metadata.xml)](https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven/space/kscience/)
 
 # KMath
 
-Could be pronounced as `key-math`. The **K**otlin **Math**ematics library was initially intended as a Kotlin-based analog to
+Could be pronounced as `key-math`. The **K**otlin **Math**ematics library was initially intended as a Kotlin-based
-Python's NumPy library. Later we found that kotlin is much more flexible language and allows superior architecture 
+analog to Python's NumPy library. Later we found that kotlin is much more flexible language and allows superior
-designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like experience could 
+architecture designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like
-be achieved with [kmath-for-real](/kmath-for-real) extension module.
+experience could be achieved with [kmath-for-real](/kmath-for-real) extension module.
 
 [Documentation site (**WIP**)](https://mipt-npm.github.io/kmath/)
 
@@ -21,26 +21,33 @@ be achieved with [kmath-for-real](/kmath-for-real) extension module.
 
 # Goal
 
-* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS and Native). 
+* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS and Native)
+  .
 * Provide basic multiplatform implementations for those abstractions (without significant performance optimization).
 * Provide bindings and wrappers with those abstractions for popular optimized platform libraries.
 
 ## Non-goals
 
-* Be like NumPy. It was the idea at the beginning, but we decided that we can do better in terms of API.
+* Be like NumPy. It was the idea at the beginning, but we decided that we can do better in API.
 * Provide the best performance out of the box. We have specialized libraries for that. Need only API wrappers for them.
 * Cover all cases as immediately and in one bundle. We will modularize everything and add new features gradually.
-* Provide specialized behavior in the core. API is made generic on purpose, so one needs to specialize for types, like 
+* Provide specialized behavior in the core. API is made generic on purpose, so one needs to specialize for types, like
-for `Double` in the core. For that we will have specialization modules like `kmath-for-real`, which will give better
+  for `Double` in the core. For that we will have specialization modules like `kmath-for-real`, which will give better
-experience for those, who want to work with specific types.
+  experience for those, who want to work with specific types.
 
 ## Features and stability
 
-KMath is a modular library. Different modules provide different features with different API stability guarantees. All core modules are released with the same version, but with different API change policy. The features are described in module definitions below. The module stability could have following levels:
+KMath is a modular library. Different modules provide different features with different API stability guarantees. All
+core modules are released with the same version, but with different API change policy. The features are described in
+module definitions below. The module stability could have the following levels:
 
-* **PROTOTYPE**. On this level there are no compatibility guarantees. All methods and classes form those modules could break any moment. You can still use it, but be sure to fix the specific version.
+* **PROTOTYPE**. On this level there are no compatibility guarantees. All methods and classes form those modules could
-* **EXPERIMENTAL**. The general API is decided, but some changes could be made. Volatile API is marked with `@UnstableKmathAPI` or other stability warning annotations.
+  break any moment. You can still use it, but be sure to fix the specific version.
-* **DEVELOPMENT**. API breaking generally follows semantic versioning ideology. There could be changes in minor versions, but not in patch versions. API is protected with [binary-compatibility-validator](https://github.com/Kotlin/binary-compatibility-validator) tool.
+* **EXPERIMENTAL**. The general API is decided, but some changes could be made. Volatile API is marked
+  with `@UnstableKmathAPI` or other stability warning annotations.
+* **DEVELOPMENT**. API breaking generally follows semantic versioning ideology. There could be changes in minor
+  versions, but not in patch versions. API is protected
+  with [binary-compatibility-validator](https://github.com/Kotlin/binary-compatibility-validator) tool.
 * **STABLE**. The API stabilized. Breaking changes are allowed only in major releases.
 
 <!--Current feature list is [here](/docs/features.md)-->
@@ -78,30 +85,33 @@ $modules
 
 ## Multi-platform support
 
-KMath is developed as a multi-platform library, which means that most of the interfaces are declared in the 
+KMath is developed as a multi-platform library, which means that most of the interfaces are declared in the
-[common source sets](/kmath-core/src/commonMain) and implemented there wherever it is possible. In some cases, features 
+[common source sets](/kmath-core/src/commonMain) and implemented there wherever it is possible. In some cases, features
-are delegated to platform-specific implementations even if they could be provided in the common module for performance 
+are delegated to platform-specific implementations even if they could be provided in the common module for performance
-reasons. Currently, the Kotlin/JVM is the primary platform, however Kotlin/Native and Kotlin/JS contributions and 
+reasons. Currently, the Kotlin/JVM is the primary platform, however Kotlin/Native and Kotlin/JS contributions and
 feedback are also welcome.
 
 ## Performance
 
-Calculation performance is one of major goals of KMath in the future, but in some cases it is impossible to achieve 
+Calculation performance is one of major goals of KMath in the future, but in some cases it is impossible to achieve both
-both performance and flexibility. 
+performance and flexibility.
 
-We expect to focus on creating convenient universal API first and then work on increasing performance for specific 
+We expect to focus on creating convenient universal API first and then work on increasing performance for specific
-cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized 
+cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized
-native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be 
+native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be
 better than SciPy.
 
 ## Requirements
 
-KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend to use GraalVM-CE 11 for execution in order to get better performance.
+KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend to use GraalVM-CE 11 for
+execution to get better performance.
 
 ### Repositories
 
-Release and development artifacts are accessible from mipt-npm [Space](https://www.jetbrains.com/space/) repository `https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven` (see documentation of
+Release and development artifacts are accessible from mipt-npm [Space](https://www.jetbrains.com/space/)
-[Kotlin Multiplatform](https://kotlinlang.org/docs/reference/multiplatform.html) for more details). The repository could be reached through [repo.kotlin.link](https://repo.kotlin.link) proxy:
+repository `https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven` (see documentation of
+[Kotlin Multiplatform](https://kotlinlang.org/docs/reference/multiplatform.html) for more details). The repository could
+be reached through [repo.kotlin.link](https://repo.kotlin.link) proxy:
 
 ```kotlin
 repositories {
@@ -118,7 +128,7 @@ Gradle `6.0+` is required for multiplatform artifacts.
 
 ## Contributing
 
-The project requires a lot of additional work. The most important thing we need is a feedback about what features are 
+The project requires a lot of additional work. The most important thing we need is a feedback about what features are
-required the most. Feel free to create feature requests. We are also welcome to code contributions, 
+required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues
-especially in issues marked with 
+marked with
 [waiting for a hero](https://github.com/mipt-npm/kmath/labels/waiting%20for%20a%20hero) label.

examples/build.gradle.kts

@@ -26,7 +26,7 @@ dependencies {
     implementation(project(":kmath-ejml"))
     implementation(project(":kmath-nd4j"))
     implementation(project(":kmath-tensors"))
-
+    implementation(project(":kmath-symja"))
     implementation(project(":kmath-for-real"))
 
     implementation("org.nd4j:nd4j-native:1.0.0-beta7")
@@ -41,9 +41,11 @@ dependencies {
 //    } else
     implementation("org.nd4j:nd4j-native-platform:1.0.0-beta7")
 
-    implementation("org.slf4j:slf4j-simple:1.7.30")
+    implementation("org.slf4j:slf4j-simple:1.7.31")
     // plotting
-    implementation("space.kscience:plotlykt-server:0.4.0")
+    implementation("space.kscience:plotlykt-server:0.4.2")
+    //jafama
+    implementation(project(":kmath-jafama"))
 }
 
 kotlin.sourceSets.all {

examples/src/main/kotlin/space/kscience/kmath/ast/astRendering.kt

@@ -10,7 +10,7 @@ import space.kscience.kmath.ast.rendering.LatexSyntaxRenderer
 import space.kscience.kmath.ast.rendering.MathMLSyntaxRenderer
 import space.kscience.kmath.ast.rendering.renderWithStringBuilder
 
-public fun main() {
+fun main() {
     val mst = "exp(sqrt(x))-asin(2*x)/(2e10+x^3)/(-12)".parseMath()
     val syntax = FeaturedMathRendererWithPostProcess.Default.render(mst)
     println("MathSyntax:")

examples/src/main/kotlin/space/kscience/kmath/ast/expressions.kt

@@ -9,12 +9,10 @@ import space.kscience.kmath.expressions.MstField
 import space.kscience.kmath.expressions.Symbol.Companion.x
 import space.kscience.kmath.expressions.interpret
 import space.kscience.kmath.operations.DoubleField
-import space.kscience.kmath.operations.bindSymbol
 import space.kscience.kmath.operations.invoke
 
 fun main() {
     val expr = MstField {
-        val x = bindSymbol(x)
         x * 2.0 + number(2.0) / x - 16.0
     }
 

examples/src/main/kotlin/space/kscience/kmath/ast/kotlingradSupport.kt

@@ -5,25 +5,23 @@
 
 package space.kscience.kmath.ast
 
-import space.kscience.kmath.asm.compileToExpression
 import space.kscience.kmath.expressions.derivative
 import space.kscience.kmath.expressions.invoke
-import space.kscience.kmath.expressions.symbol
+import space.kscience.kmath.expressions.Symbol.Companion.x
+import space.kscience.kmath.expressions.toExpression
 import space.kscience.kmath.kotlingrad.toKotlingradExpression
 import space.kscience.kmath.operations.DoubleField
 
 /**
- * In this example, x^2-4*x-44 function is differentiated with Kotlin∇, and the autodiff result is compared with
+ * In this example, *x<sup>2</sup> &minus; 4 x &minus; 44* function is differentiated with Kotlin∇, and the
- * valid derivative.
+ * derivation result is compared with valid derivative in a certain point.
  */
 fun main() {
-    val x by symbol
+    val actualDerivative = "x^2-4*x-44"
-
+        .parseMath()
-    val actualDerivative = "x^2-4*x-44".parseMath()
         .toKotlingradExpression(DoubleField)
         .derivative(x)
 
-
+    val expectedDerivative = "2*x-4".parseMath().toExpression(DoubleField)
-    val expectedDerivative = "2*x-4".parseMath().compileToExpression(DoubleField)
+    check(actualDerivative(x to 123.0) == expectedDerivative(x to 123.0))
-    assert(actualDerivative(x to 123.0) == expectedDerivative(x to 123.0))
 }

examples/src/main/kotlin/space/kscience/kmath/ast/symjaSupport.kt

@@ -0,0 +1,27 @@
+/*
+ * 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.ast
+
+import space.kscience.kmath.expressions.Symbol.Companion.x
+import space.kscience.kmath.expressions.derivative
+import space.kscience.kmath.expressions.invoke
+import space.kscience.kmath.expressions.toExpression
+import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.symja.toSymjaExpression
+
+/**
+ * In this example, *x<sup>2</sup> &minus; 4 x &minus; 44* function is differentiated with Symja, and the
+ * derivation result is compared with valid derivative in a certain point.
+ */
+fun main() {
+    val actualDerivative = "x^2-4*x-44"
+        .parseMath()
+        .toSymjaExpression(DoubleField)
+        .derivative(x)
+
+    val expectedDerivative = "2*x-4".parseMath().toExpression(DoubleField)
+    check(actualDerivative(x to 123.0) == expectedDerivative(x to 123.0))
+}

examples/src/main/kotlin/space/kscience/kmath/commons/fit/fitWithAutoDiff.kt

@@ -7,11 +7,15 @@ package space.kscience.kmath.commons.fit
 
 import kotlinx.html.br
 import kotlinx.html.h3
-import space.kscience.kmath.commons.optimization.chiSquared
+import space.kscience.kmath.commons.expressions.DSProcessor
+import space.kscience.kmath.commons.optimization.CMOptimizer
 import space.kscience.kmath.distributions.NormalDistribution
+import space.kscience.kmath.expressions.chiSquaredExpression
 import space.kscience.kmath.expressions.symbol
-import space.kscience.kmath.optimization.FunctionOptimization
+import space.kscience.kmath.optimization.FunctionOptimizationTarget
-import space.kscience.kmath.optimization.OptimizationResult
+import space.kscience.kmath.optimization.optimizeWith
+import space.kscience.kmath.optimization.resultPoint
+import space.kscience.kmath.optimization.resultValue
 import space.kscience.kmath.real.DoubleVector
 import space.kscience.kmath.real.map
 import space.kscience.kmath.real.step
@@ -24,8 +28,7 @@ import space.kscience.plotly.models.TraceValues
 import kotlin.math.pow
 import kotlin.math.sqrt
 
-//Forward declaration of symbols that will be used in expressions.
+// Forward declaration of symbols that will be used in expressions.
-// This declaration is required for
 private val a by symbol
 private val b by symbol
 private val c by symbol
@@ -64,17 +67,21 @@ suspend fun main() {
     val yErr = y.map { sqrt(it) }//RealVector.same(x.size, sigma)
 
     // compute differentiable chi^2 sum for given model ax^2 + bx + c
-    val chi2 = FunctionOptimization.chiSquared(x, y, yErr) { x1 ->
+    val chi2 = DSProcessor.chiSquaredExpression(x, y, yErr) { arg ->
         //bind variables to autodiff context
         val a = bindSymbol(a)
         val b = bindSymbol(b)
         //Include default value for c if it is not provided as a parameter
         val c = bindSymbolOrNull(c) ?: one
-        a * x1.pow(2) + b * x1 + c
+        a * arg.pow(2) + b * arg + c
     }
 
     //minimize the chi^2 in given starting point. Derivatives are not required, they are already included.
-    val result: OptimizationResult<Double> = chi2.minimize(a to 1.5, b to 0.9, c to 1.0)
+    val result = chi2.optimizeWith(
+        CMOptimizer,
+        mapOf(a to 1.5, b to 0.9, c to 1.0),
+        FunctionOptimizationTarget.MINIMIZE
+    )
 
     //display a page with plot and numerical results
     val page = Plotly.page {
@@ -91,7 +98,7 @@ suspend fun main() {
             scatter {
                 mode = ScatterMode.lines
                 x(x)
-                y(x.map { result.point[a]!! * it.pow(2) + result.point[b]!! * it + 1 })
+                y(x.map { result.resultPoint[a]!! * it.pow(2) + result.resultPoint[b]!! * it + 1 })
                 name = "fit"
             }
         }
@@ -100,7 +107,7 @@ suspend fun main() {
             +"Fit result: $result"
         }
         h3 {
-            +"Chi2/dof = ${result.value / (x.size - 3)}"
+            +"Chi2/dof = ${result.resultValue / (x.size - 3)}"
         }
     }
 

examples/src/main/kotlin/space/kscience/kmath/jafama/JafamaDemo.kt

@@ -0,0 +1,15 @@
+/*
+ * 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.jafama
+
+import space.kscience.kmath.operations.invoke
+
+fun main() {
+    val a = 2.0
+    val b = StrictJafamaDoubleField { exp(a) }
+    println(JafamaDoubleField { b + a })
+    println(StrictJafamaDoubleField { ln(b) })
+}

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

@@ -10,9 +10,6 @@ import space.kscience.kmath.chains.Chain
 import space.kscience.kmath.chains.collectWithState
 import space.kscience.kmath.distributions.NormalDistribution
 
-/**
- * The state of distribution averager.
- */
 private data class AveragingChainState(var num: Int = 0, var value: Double = 0.0)
 
 /**

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

@@ -32,7 +32,7 @@ fun main() {
 
     // automatically build context most suited for given type.
     val autoField = AlgebraND.auto(DoubleField, dim, dim)
-    // specialized nd-field for Double. It works as generic Double field as well
+    // specialized nd-field for Double. It works as generic Double field as well.
     val realField = AlgebraND.real(dim, dim)
     //A generic boxing field. It should be used for objects, not primitives.
     val boxingField = AlgebraND.field(DoubleField, Buffer.Companion::boxing, dim, dim)

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

@@ -19,7 +19,7 @@ private fun DMatrixContext<Double, *>.simple() {
 }
 
 private object D5 : Dimension {
-    override val dim: UInt = 5u
+    override val dim: Int = 5
 }
 
 private fun DMatrixContext<Double, *>.custom() {

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

@@ -17,7 +17,7 @@ fun main() = BroadcastDoubleTensorAlgebra {  // work in context with broadcast m
 
     dataset += fromArray(
         intArrayOf(5),
-        doubleArrayOf(0.0, 1.0, 1.5, 3.0, 5.0) // rows means
+        doubleArrayOf(0.0, 1.0, 1.5, 3.0, 5.0) // row means
     )
 
 
@@ -28,7 +28,7 @@ fun main() = BroadcastDoubleTensorAlgebra {  // work in context with broadcast m
     println("Mean:\n$mean")
     println("Standard deviation:\n$std")
 
-    // also we can calculate other statistic as minimum and maximum of rows
+    // also, we can calculate other statistic as minimum and maximum of rows
     println("Minimum:\n${dataset.min(0, false)}")
     println("Maximum:\n${dataset.max(0, false)}")
 

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

@@ -42,7 +42,7 @@ fun main() = BroadcastDoubleTensorAlgebra {// work in context with linear operat
     // get P, L, U such that PA = LU
     val (p, l, u) = a.lu()
 
-    // check that P is permutation matrix
+    // check P is permutation matrix
     println("P:\n$p")
     // L is lower triangular matrix and U is upper triangular matrix
     println("L:\n$l")

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

@@ -186,7 +186,7 @@ fun main() = BroadcastDoubleTensorAlgebra {
 
     x += fromArray(
         intArrayOf(5),
-        doubleArrayOf(0.0, -1.0, -2.5, -3.0, 5.5) // rows means
+        doubleArrayOf(0.0, -1.0, -2.5, -3.0, 5.5) // row means
     )
 
 

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

@@ -65,8 +65,8 @@ fun main(): Unit = BroadcastDoubleTensorAlgebra {  // work in context with broad
     val datasetReduced  = v dot stack(listOf(xScaled, yScaled))
     println("Reduced data:\n$datasetReduced")
 
-    // we can restore original data from reduced data.
+    // we can restore original data from reduced data;
-    // for example, find 7th element of dataset
+    // for example, find 7th element of dataset.
     val n = 7
     val restored = (datasetReduced[n] dot v.view(intArrayOf(1, 2))) * std + mean
     println("Original value:\n${dataset[n]}")

gradle.properties

@@ -7,7 +7,6 @@ kotlin.code.style=official
 kotlin.mpp.enableGranularSourceSetsMetadata=true
 kotlin.mpp.stability.nowarn=true
 kotlin.native.enableDependencyPropagation=false
-kotlin.parallel.tasks.in.project=true
 org.gradle.configureondemand=true
-org.gradle.jvmargs=-XX:MaxMetaspaceSize=1G
+org.gradle.jvmargs=-XX:MaxMetaspaceSize=2G
 org.gradle.parallel=true

gradle/wrapper/gradle-wrapper.properties

@@ -1,5 +1,5 @@
 distributionBase=GRADLE_USER_HOME
 distributionPath=wrapper/dists
-distributionUrl=https\://services.gradle.org/distributions/gradle-7.0-bin.zip
+distributionUrl=https\://services.gradle.org/distributions/gradle-7.1.1-bin.zip
 zipStoreBase=GRADLE_USER_HOME
 zipStorePath=wrapper/dists

gradlew

@@ -72,7 +72,7 @@ case "`uname`" in
   Darwin* )
     darwin=true
     ;;
-  MINGW* )
+  MSYS* | MINGW* )
     msys=true
     ;;
   NONSTOP* )

kmath-ast/README.md

@@ -10,7 +10,7 @@ Performance and visualization extensions to MST API.
 
 ## Artifact:
 
-The Maven coordinates of this project are `space.kscience:kmath-ast:0.3.0-dev-11`.
+The Maven coordinates of this project are `space.kscience:kmath-ast:0.3.0-dev-14`.
 
 **Gradle:**
 ```gradle
@@ -20,7 +20,7 @@ repositories {
 }
 
 dependencies {
-    implementation 'space.kscience:kmath-ast:0.3.0-dev-11'
+    implementation 'space.kscience:kmath-ast:0.3.0-dev-14'
 }
 ```
 **Gradle Kotlin DSL:**
@@ -31,7 +31,7 @@ repositories {
 }
 
 dependencies {
-    implementation("space.kscience:kmath-ast:0.3.0-dev-11")
+    implementation("space.kscience:kmath-ast:0.3.0-dev-14")
 }
 ```
 
@@ -45,11 +45,12 @@ special implementation of `Expression<T>` with implemented `invoke` function.
 For example, the following builder:
 
 ```kotlin
+import space.kscience.kmath.expressions.Symbol.Companion.x
 import space.kscience.kmath.expressions.*
 import space.kscience.kmath.operations.*
 import space.kscience.kmath.asm.*
 
-MstField { bindSymbol("x") + 2 }.compileToExpression(DoubleField)
+MstField { x + 2 }.compileToExpression(DoubleField)
 ``` 
 
 ... leads to generation of bytecode, which can be decompiled to the following Java class:
@@ -89,11 +90,12 @@ public final class AsmCompiledExpression_45045_0 implements Expression<Double> {
 A similar feature is also available on JS.
 
 ```kotlin
+import space.kscience.kmath.expressions.Symbol.Companion.x
 import space.kscience.kmath.expressions.*
 import space.kscience.kmath.operations.*
 import space.kscience.kmath.estree.*
 
-MstField { bindSymbol("x") + 2 }.compileToExpression(DoubleField)
+MstField { x + 2 }.compileToExpression(DoubleField)
 ``` 
 
 The code above returns expression implemented with such a JS function:
@@ -104,15 +106,16 @@ var executable = function (constants, arguments) {
 };
 ```
 
-JS also supports very experimental expression optimization with [WebAssembly](https://webassembly.org/) IR generation.
+JS also supports experimental expression optimization with [WebAssembly](https://webassembly.org/) IR generation.
 Currently, only expressions inside `DoubleField` and `IntRing` are supported.
 
 ```kotlin
+import space.kscience.kmath.expressions.Symbol.Companion.x
 import space.kscience.kmath.expressions.*
 import space.kscience.kmath.operations.*
 import space.kscience.kmath.wasm.*
 
-MstField { bindSymbol("x") + 2 }.compileToExpression(DoubleField)
+MstField { x + 2 }.compileToExpression(DoubleField)
 ```
 
 An example of emitted Wasm IR in the form of WAT:
@@ -158,7 +161,10 @@ public fun main() {
 
 Result LaTeX:
 
+<div style="background-color:white;">
+
 ![](https://latex.codecogs.com/gif.latex?%5Coperatorname{exp}%5C,%5Cleft(%5Csqrt{x}%5Cright)-%5Cfrac{%5Cfrac{%5Coperatorname{arcsin}%5C,%5Cleft(2%5C,x%5Cright)}{2%5Ctimes10^{10}%2Bx^{3}}}{12}+x^{2/3})
+</div>
 
 Result MathML (can be used with MathJax or other renderers):
 

kmath-ast/build.gradle.kts

@@ -37,17 +37,15 @@ kotlin.sourceSets {
 
     jsMain {
         dependencies {
-            implementation(npm("astring", "1.7.4"))
+            implementation(npm("astring", "1.7.5"))
-            implementation(npm("binaryen", "100.0"))
+            implementation(npm("binaryen", "101.0.0"))
-            implementation(npm("js-base64", "3.6.0"))
+            implementation(npm("js-base64", "3.6.1"))
-            implementation(npm("webassembly", "0.11.0"))
         }
     }
 
     jvmMain {
         dependencies {
-            implementation("org.ow2.asm:asm:9.1")
+            implementation("org.ow2.asm:asm-commons:9.2")
-            implementation("org.ow2.asm:asm-commons:9.1")
         }
     }
 }
@@ -63,25 +61,21 @@ readme {
 
     feature(
         id = "expression-language",
-        description = "Expression language and its parser",
         ref = "src/commonMain/kotlin/space/kscience/kmath/ast/parser.kt"
-    )
+    ) { "Expression language and its parser" }
 
     feature(
         id = "mst-jvm-codegen",
-        description = "Dynamic MST to JVM bytecode compiler",
         ref = "src/jvmMain/kotlin/space/kscience/kmath/asm/asm.kt"
-    )
+    ) { "Dynamic MST to JVM bytecode compiler" }
 
     feature(
         id = "mst-js-codegen",
-        description = "Dynamic MST to JS compiler",
         ref = "src/jsMain/kotlin/space/kscience/kmath/estree/estree.kt"
-    )
+    ) { "Dynamic MST to JS compiler" }
 
     feature(
         id = "rendering",
-        description = "Extendable MST rendering",
         ref = "src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt"
-    )
+    ) { "Extendable MST rendering" }
 }

kmath-ast/docs/README-TEMPLATE.md

@@ -16,11 +16,12 @@ special implementation of `Expression<T>` with implemented `invoke` function.
 For example, the following builder:
 
 ```kotlin
+import space.kscience.kmath.expressions.Symbol.Companion.x
 import space.kscience.kmath.expressions.*
 import space.kscience.kmath.operations.*
 import space.kscience.kmath.asm.*
 
-MstField { bindSymbol("x") + 2 }.compileToExpression(DoubleField)
+MstField { x + 2 }.compileToExpression(DoubleField)
 ``` 
 
 ... leads to generation of bytecode, which can be decompiled to the following Java class:
@@ -60,11 +61,12 @@ public final class AsmCompiledExpression_45045_0 implements Expression<Double> {
 A similar feature is also available on JS.
 
 ```kotlin
+import space.kscience.kmath.expressions.Symbol.Companion.x
 import space.kscience.kmath.expressions.*
 import space.kscience.kmath.operations.*
 import space.kscience.kmath.estree.*
 
-MstField { bindSymbol("x") + 2 }.compileToExpression(DoubleField)
+MstField { x + 2 }.compileToExpression(DoubleField)
 ``` 
 
 The code above returns expression implemented with such a JS function:
@@ -75,15 +77,16 @@ var executable = function (constants, arguments) {
 };
 ```
 
-JS also supports very experimental expression optimization with [WebAssembly](https://webassembly.org/) IR generation.
+JS also supports experimental expression optimization with [WebAssembly](https://webassembly.org/) IR generation.
 Currently, only expressions inside `DoubleField` and `IntRing` are supported.
 
 ```kotlin
+import space.kscience.kmath.expressions.Symbol.Companion.x
 import space.kscience.kmath.expressions.*
 import space.kscience.kmath.operations.*
 import space.kscience.kmath.wasm.*
 
-MstField { bindSymbol("x") + 2 }.compileToExpression(DoubleField)
+MstField { x + 2 }.compileToExpression(DoubleField)
 ```
 
 An example of emitted Wasm IR in the form of WAT:
@@ -129,7 +132,10 @@ public fun main() {
 
 Result LaTeX:
 
+<div style="background-color:white;">
+
 ![](https://latex.codecogs.com/gif.latex?%5Coperatorname{exp}%5C,%5Cleft(%5Csqrt{x}%5Cright)-%5Cfrac{%5Cfrac{%5Coperatorname{arcsin}%5C,%5Cleft(2%5C,x%5Cright)}{2%5Ctimes10^{10}%2Bx^{3}}}{12}+x^{2/3})
+</div>
 
 Result MathML (can be used with MathJax or other renderers):
 

kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/LatexSyntaxRenderer.kt

@@ -27,7 +27,7 @@ import space.kscience.kmath.misc.UnstableKMathAPI
  */
 @UnstableKMathAPI
 public object LatexSyntaxRenderer : SyntaxRenderer {
-    public override fun render(node: MathSyntax, output: Appendable): Unit = output.run {
+    override fun render(node: MathSyntax, output: Appendable): Unit = output.run {
         fun render(syntax: MathSyntax) = render(syntax, output)
 
         when (node) {

kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathMLSyntaxRenderer.kt

@@ -16,7 +16,7 @@ import space.kscience.kmath.misc.UnstableKMathAPI
  */
 @UnstableKMathAPI
 public object MathMLSyntaxRenderer : SyntaxRenderer {
-    public override fun render(node: MathSyntax, output: Appendable) {
+    override fun render(node: MathSyntax, output: Appendable) {
         output.append("<math xmlns=\"https://www.w3.org/1998/Math/MathML\"><mrow>")
         renderPart(node, output)
         output.append("</mrow></math>")

kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt

@@ -29,7 +29,7 @@ public fun interface MathRenderer {
  */
 @UnstableKMathAPI
 public open class FeaturedMathRenderer(public val features: List<RenderFeature>) : MathRenderer {
-    public override fun render(mst: MST): MathSyntax {
+    override fun render(mst: MST): MathSyntax {
         for (feature in features) feature.render(this, mst)?.let { return it }
         throw UnsupportedOperationException("Renderer $this has no appropriate feature to render node $mst.")
     }
@@ -54,9 +54,9 @@ public open class FeaturedMathRenderer(public val features: List<RenderFeature>)
 @UnstableKMathAPI
 public open class FeaturedMathRendererWithPostProcess(
     features: List<RenderFeature>,
-    public val stages: List<PostProcessStage>,
+    public val stages: List<PostProcessPhase>,
 ) : FeaturedMathRenderer(features) {
-    public override fun render(mst: MST): MathSyntax {
+    override fun render(mst: MST): MathSyntax {
         val res = super.render(mst)
         for (stage in stages) stage.perform(res)
         return res
@@ -65,7 +65,7 @@ public open class FeaturedMathRendererWithPostProcess(
     /**
      * Logical unit of [MathSyntax] post-processing.
      */
-    public fun interface PostProcessStage {
+    public fun interface PostProcessPhase {
         /**
          * Performs the specified action over [MathSyntax].
          */
@@ -102,7 +102,7 @@ public open class FeaturedMathRendererWithPostProcess(
 
                 // Printing terminal nodes as string
                 PrintNumeric,
-                PrintSymbolic,
+                PrintSymbol,
             ),
             listOf(
                 BetterExponent,

kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathSyntax.kt

@@ -8,7 +8,7 @@ package space.kscience.kmath.ast.rendering
 import space.kscience.kmath.misc.UnstableKMathAPI
 
 /**
- * Mathematical typography syntax node.
+ * Syntax node for mathematical typography.
  *
  * @author Iaroslav Postovalov
  */
@@ -150,9 +150,9 @@ public data class OperandSyntax(
  */
 @UnstableKMathAPI
 public data class UnaryOperatorSyntax(
-    public override val operation: String,
+    override val operation: String,
     public var prefix: MathSyntax,
-    public override val operand: OperandSyntax,
+    override val operand: OperandSyntax,
 ) : UnarySyntax() {
     init {
         operand.parent = this
@@ -166,8 +166,8 @@ public data class UnaryOperatorSyntax(
  */
 @UnstableKMathAPI
 public data class UnaryPlusSyntax(
-    public override val operation: String,
+    override val operation: String,
-    public override val operand: OperandSyntax,
+    override val operand: OperandSyntax,
 ) : UnarySyntax() {
     init {
         operand.parent = this
@@ -181,8 +181,8 @@ public data class UnaryPlusSyntax(
  */
 @UnstableKMathAPI
 public data class UnaryMinusSyntax(
-    public override val operation: String,
+    override val operation: String,
-    public override val operand: OperandSyntax,
+    override val operand: OperandSyntax,
 ) : UnarySyntax() {
     init {
         operand.parent = this
@@ -197,8 +197,8 @@ public data class UnaryMinusSyntax(
  */
 @UnstableKMathAPI
 public data class RadicalSyntax(
-    public override val operation: String,
+    override val operation: String,
-    public override val operand: MathSyntax,
+    override val operand: MathSyntax,
 ) : UnarySyntax() {
     init {
         operand.parent = this
@@ -215,8 +215,8 @@ public data class RadicalSyntax(
  */
 @UnstableKMathAPI
 public data class ExponentSyntax(
-    public override val operation: String,
+    override val operation: String,
-    public override val operand: OperandSyntax,
+    override val operand: OperandSyntax,
     public var useOperatorForm: Boolean,
 ) : UnarySyntax() {
     init {
@@ -233,9 +233,9 @@ public data class ExponentSyntax(
  */
 @UnstableKMathAPI
 public data class SuperscriptSyntax(
-    public override val operation: String,
+    override val operation: String,
-    public override val left: MathSyntax,
+    override val left: MathSyntax,
-    public override val right: MathSyntax,
+    override val right: MathSyntax,
 ) : BinarySyntax() {
     init {
         left.parent = this
@@ -252,9 +252,9 @@ public data class SuperscriptSyntax(
  */
 @UnstableKMathAPI
 public data class SubscriptSyntax(
-    public override val operation: String,
+    override val operation: String,
-    public override val left: MathSyntax,
+    override val left: MathSyntax,
-    public override val right: MathSyntax,
+    override val right: MathSyntax,
 ) : BinarySyntax() {
     init {
         left.parent = this
@@ -270,10 +270,10 @@ public data class SubscriptSyntax(
  */
 @UnstableKMathAPI
 public data class BinaryOperatorSyntax(
-    public override val operation: String,
+    override val operation: String,
     public var prefix: MathSyntax,
-    public override val left: MathSyntax,
+    override val left: MathSyntax,
-    public override val right: MathSyntax,
+    override val right: MathSyntax,
 ) : BinarySyntax() {
     init {
         left.parent = this
@@ -290,9 +290,9 @@ public data class BinaryOperatorSyntax(
  */
 @UnstableKMathAPI
 public data class BinaryPlusSyntax(
-    public override val operation: String,
+    override val operation: String,
-    public override val left: OperandSyntax,
+    override val left: OperandSyntax,
-    public override val right: OperandSyntax,
+    override val right: OperandSyntax,
 ) : BinarySyntax() {
     init {
         left.parent = this
@@ -301,7 +301,7 @@ public data class BinaryPlusSyntax(
 }
 
 /**
- * Represents binary, infix subtraction (*42 - 42*).
+ * Represents binary, infix subtraction (*42 − 42*).
  *
  * @param left The minuend.
  * @param right The subtrahend.
@@ -309,9 +309,9 @@ public data class BinaryPlusSyntax(
  */
 @UnstableKMathAPI
 public data class BinaryMinusSyntax(
-    public override val operation: String,
+    override val operation: String,
-    public override val left: OperandSyntax,
+    override val left: OperandSyntax,
-    public override val right: OperandSyntax,
+    override val right: OperandSyntax,
 ) : BinarySyntax() {
     init {
         left.parent = this
@@ -329,9 +329,9 @@ public data class BinaryMinusSyntax(
  */
 @UnstableKMathAPI
 public data class FractionSyntax(
-    public override val operation: String,
+    override val operation: String,
-    public override val left: OperandSyntax,
+    override val left: OperandSyntax,
-    public override val right: OperandSyntax,
+    override val right: OperandSyntax,
     public var infix: Boolean,
 ) : BinarySyntax() {
     init {
@@ -349,9 +349,9 @@ public data class FractionSyntax(
  */
 @UnstableKMathAPI
 public data class RadicalWithIndexSyntax(
-    public override val operation: String,
+    override val operation: String,
-    public override val left: MathSyntax,
+    override val left: MathSyntax,
-    public override val right: MathSyntax,
+    override val right: MathSyntax,
 ) : BinarySyntax() {
     init {
         left.parent = this
@@ -369,9 +369,9 @@ public data class RadicalWithIndexSyntax(
  */
 @UnstableKMathAPI
 public data class MultiplicationSyntax(
-    public override val operation: String,
+    override val operation: String,
-    public override val left: OperandSyntax,
+    override val left: OperandSyntax,
-    public override val right: OperandSyntax,
+    override val right: OperandSyntax,
     public var times: Boolean,
 ) : BinarySyntax() {
     init {

kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/SyntaxRenderer.kt

@@ -9,7 +9,7 @@ import space.kscience.kmath.misc.UnstableKMathAPI
 
 /**
  * Abstraction of writing [MathSyntax] as a string of an actual markup language. Typical implementation should
- * involve traversal of MathSyntax with handling each its subtype.
+ * involve traversal of MathSyntax with handling each subtype.
  *
  * @author Iaroslav Postovalov
  */

kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/features.kt

@@ -13,15 +13,14 @@ import space.kscience.kmath.operations.*
 import kotlin.reflect.KClass
 
 /**
- * Prints any [MST.Symbolic] as a [SymbolSyntax] containing the [MST.Symbolic.value] of it.
+ * Prints any [Symbol] as a [SymbolSyntax] containing the [Symbol.identity] of it.
  *
  * @author Iaroslav Postovalov
  */
 @UnstableKMathAPI
-public object PrintSymbolic : RenderFeature {
+public val PrintSymbol: RenderFeature = RenderFeature { _, node ->
-    public override fun render(renderer: FeaturedMathRenderer, node: MST): SymbolSyntax? =
+    if (node !is Symbol) null
-        if (node !is Symbol) null
+    else SymbolSyntax(string = node.identity)
-        else SymbolSyntax(string = node.identity)
 }
 
 /**
@@ -30,8 +29,8 @@ public object PrintSymbolic : RenderFeature {
  * @author Iaroslav Postovalov
  */
 @UnstableKMathAPI
-public object PrintNumeric : RenderFeature {
+public val PrintNumeric: RenderFeature = RenderFeature { _, node ->
-    public override fun render(renderer: FeaturedMathRenderer, node: MST): NumberSyntax? = if (node !is MST.Numeric)
+    if (node !is MST.Numeric)
         null
     else
         NumberSyntax(string = node.value.toString())
@@ -50,7 +49,7 @@ else
     NumberSyntax(string = s)
 
 /**
- * Special printing for numeric types which are printed in form of
+ * Special printing for numeric types that are printed in form of
  * *('-'? (DIGIT+ ('.' DIGIT+)? ('E' '-'? DIGIT+)? | 'Infinity')) | 'NaN'*.
  *
  * @property types The suitable types.
@@ -58,7 +57,7 @@ else
  */
 @UnstableKMathAPI
 public class PrettyPrintFloats(public val types: Set<KClass<out Number>>) : RenderFeature {
-    public override fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? {
+    override fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? {
         if (node !is MST.Numeric || node.value::class !in types) return null
 
         val toString = when (val v = node.value) {
@@ -111,14 +110,14 @@ public class PrettyPrintFloats(public val types: Set<KClass<out Number>>) : Rend
 }
 
 /**
- * Special printing for numeric types which are printed in form of *'-'? DIGIT+*.
+ * Special printing for numeric types that are printed in form of *'-'? DIGIT+*.
  *
  * @property types The suitable types.
  * @author Iaroslav Postovalov
  */
 @UnstableKMathAPI
 public class PrettyPrintIntegers(public val types: Set<KClass<out Number>>) : RenderFeature {
-    public override fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? =
+    override fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? =
         if (node !is MST.Numeric || node.value::class !in types)
             null
         else
@@ -141,7 +140,7 @@ public class PrettyPrintIntegers(public val types: Set<KClass<out Number>>) : Re
  */
 @UnstableKMathAPI
 public class PrettyPrintPi(public val symbols: Set<String>) : RenderFeature {
-    public override fun render(renderer: FeaturedMathRenderer, node: MST): SpecialSymbolSyntax? =
+    override fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? =
         if (node !is Symbol || node.identity !in symbols)
             null
         else
@@ -156,7 +155,7 @@ public class PrettyPrintPi(public val symbols: Set<String>) : RenderFeature {
 }
 
 /**
- * Abstract printing of unary operations which discards [MST] if their operation is not in [operations] or its type is
+ * Abstract printing of unary operations that discards [MST] if their operation is not in [operations] or its type is
  * not [MST.Unary].
  *
  * @param operations the allowed operations. If `null`, any operation is accepted.
@@ -177,7 +176,7 @@ public abstract class Unary(public val operations: Collection<String>?) : Render
 }
 
 /**
- * Abstract printing of unary operations which discards [MST] if their operation is not in [operations] or its type is
+ * Abstract printing of unary operations that discards [MST] if their operation is not in [operations] or its type is
  * not [MST.Binary].
  *
  * @property operations the allowed operations. If `null`, any operation is accepted.
@@ -203,7 +202,7 @@ public abstract class Binary(public val operations: Collection<String>?) : Rende
  */
 @UnstableKMathAPI
 public class BinaryPlus(operations: Collection<String>?) : Binary(operations) {
-    public override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): BinaryPlusSyntax =
+    override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax =
         BinaryPlusSyntax(
             operation = node.operation,
             left = OperandSyntax(parent.render(node.left), true),
@@ -225,7 +224,7 @@ public class BinaryPlus(operations: Collection<String>?) : Binary(operations) {
  */
 @UnstableKMathAPI
 public class BinaryMinus(operations: Collection<String>?) : Binary(operations) {
-    public override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): BinaryMinusSyntax =
+    override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax =
         BinaryMinusSyntax(
             operation = node.operation,
             left = OperandSyntax(operand = parent.render(node.left), parentheses = true),
@@ -247,7 +246,7 @@ public class BinaryMinus(operations: Collection<String>?) : Binary(operations) {
  */
 @UnstableKMathAPI
 public class UnaryPlus(operations: Collection<String>?) : Unary(operations) {
-    public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): UnaryPlusSyntax = UnaryPlusSyntax(
+    override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax = UnaryPlusSyntax(
         operation = node.operation,
         operand = OperandSyntax(operand = parent.render(node.value), parentheses = true),
     )
@@ -267,7 +266,7 @@ public class UnaryPlus(operations: Collection<String>?) : Unary(operations) {
  */
 @UnstableKMathAPI
 public class UnaryMinus(operations: Collection<String>?) : Unary(operations) {
-    public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): UnaryMinusSyntax = UnaryMinusSyntax(
+    override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax = UnaryMinusSyntax(
         operation = node.operation,
         operand = OperandSyntax(operand = parent.render(node.value), parentheses = true),
     )
@@ -287,7 +286,7 @@ public class UnaryMinus(operations: Collection<String>?) : Unary(operations) {
  */
 @UnstableKMathAPI
 public class Fraction(operations: Collection<String>?) : Binary(operations) {
-    public override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): FractionSyntax = FractionSyntax(
+    override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax = FractionSyntax(
         operation = node.operation,
         left = OperandSyntax(operand = parent.render(node.left), parentheses = true),
         right = OperandSyntax(operand = parent.render(node.right), parentheses = true),
@@ -309,7 +308,7 @@ public class Fraction(operations: Collection<String>?) : Binary(operations) {
  */
 @UnstableKMathAPI
 public class BinaryOperator(operations: Collection<String>?) : Binary(operations) {
-    public override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): BinaryOperatorSyntax =
+    override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax =
         BinaryOperatorSyntax(
             operation = node.operation,
             prefix = OperatorNameSyntax(name = node.operation),
@@ -332,7 +331,7 @@ public class BinaryOperator(operations: Collection<String>?) : Binary(operations
  */
 @UnstableKMathAPI
 public class UnaryOperator(operations: Collection<String>?) : Unary(operations) {
-    public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): UnaryOperatorSyntax =
+    override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax =
         UnaryOperatorSyntax(
             operation = node.operation,
             prefix = OperatorNameSyntax(node.operation),
@@ -354,7 +353,7 @@ public class UnaryOperator(operations: Collection<String>?) : Unary(operations)
  */
 @UnstableKMathAPI
 public class Power(operations: Collection<String>?) : Binary(operations) {
-    public override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): SuperscriptSyntax =
+    override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax =
         SuperscriptSyntax(
             operation = node.operation,
             left = OperandSyntax(parent.render(node.left), true),
@@ -374,7 +373,7 @@ public class Power(operations: Collection<String>?) : Binary(operations) {
  */
 @UnstableKMathAPI
 public class SquareRoot(operations: Collection<String>?) : Unary(operations) {
-    public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): RadicalSyntax =
+    override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax =
         RadicalSyntax(operation = node.operation, operand = parent.render(node.value))
 
     public companion object {
@@ -392,7 +391,7 @@ public class SquareRoot(operations: Collection<String>?) : Unary(operations) {
  */
 @UnstableKMathAPI
 public class Exponent(operations: Collection<String>?) : Unary(operations) {
-    public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): ExponentSyntax = ExponentSyntax(
+    override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax = ExponentSyntax(
         operation = node.operation,
         operand = OperandSyntax(operand = parent.render(node.value), parentheses = true),
         useOperatorForm = true,
@@ -413,7 +412,7 @@ public class Exponent(operations: Collection<String>?) : Unary(operations) {
  */
 @UnstableKMathAPI
 public class Multiplication(operations: Collection<String>?) : Binary(operations) {
-    public override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MultiplicationSyntax =
+    override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax =
         MultiplicationSyntax(
             operation = node.operation,
             left = OperandSyntax(operand = parent.render(node.left), parentheses = true),
@@ -436,7 +435,7 @@ public class Multiplication(operations: Collection<String>?) : Binary(operations
  */
 @UnstableKMathAPI
 public class InverseTrigonometricOperations(operations: Collection<String>?) : Unary(operations) {
-    public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): UnaryOperatorSyntax =
+    override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax =
         UnaryOperatorSyntax(
             operation = node.operation,
             prefix = OperatorNameSyntax(name = node.operation.replaceFirst("a", "arc")),
@@ -463,7 +462,7 @@ public class InverseTrigonometricOperations(operations: Collection<String>?) : U
  */
 @UnstableKMathAPI
 public class InverseHyperbolicOperations(operations: Collection<String>?) : Unary(operations) {
-    public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): UnaryOperatorSyntax =
+    override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax =
         UnaryOperatorSyntax(
             operation = node.operation,
             prefix = OperatorNameSyntax(name = node.operation.replaceFirst("a", "ar")),

kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/phases.kt

@@ -5,6 +5,7 @@
 
 package space.kscience.kmath.ast.rendering
 
+import space.kscience.kmath.ast.rendering.FeaturedMathRendererWithPostProcess.PostProcessPhase
 import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.operations.FieldOperations
 import space.kscience.kmath.operations.GroupOperations
@@ -17,8 +18,8 @@ import space.kscience.kmath.operations.RingOperations
  * @author Iaroslav Postovalov
  */
 @UnstableKMathAPI
-public object BetterMultiplication : FeaturedMathRendererWithPostProcess.PostProcessStage {
+public val BetterMultiplication: PostProcessPhase = PostProcessPhase { node ->
-    public override fun perform(node: MathSyntax): Unit = when (node) {
+    fun perform(node: MathSyntax): Unit = when (node) {
         is NumberSyntax -> Unit
         is SymbolSyntax -> Unit
         is OperatorNameSyntax -> Unit
@@ -81,6 +82,8 @@ public object BetterMultiplication : FeaturedMathRendererWithPostProcess.PostPro
             perform(node.right)
         }
     }
+
+    perform(node)
 }
 
 /**
@@ -89,68 +92,68 @@ public object BetterMultiplication : FeaturedMathRendererWithPostProcess.PostPro
  * @author Iaroslav Postovalov
  */
 @UnstableKMathAPI
-public object BetterFraction : FeaturedMathRendererWithPostProcess.PostProcessStage {
+public val BetterFraction: PostProcessPhase = PostProcessPhase { node ->
-    private fun perform0(node: MathSyntax, infix: Boolean = false): Unit = when (node) {
+    fun perform(node: MathSyntax, infix: Boolean = false): Unit = when (node) {
         is NumberSyntax -> Unit
         is SymbolSyntax -> Unit
         is OperatorNameSyntax -> Unit
         is SpecialSymbolSyntax -> Unit
-        is OperandSyntax -> perform0(node.operand, infix)
+        is OperandSyntax -> perform(node.operand, infix)
 
         is UnaryOperatorSyntax -> {
-            perform0(node.prefix, infix)
+            perform(node.prefix, infix)
-            perform0(node.operand, infix)
+            perform(node.operand, infix)
         }
 
-        is UnaryPlusSyntax -> perform0(node.operand, infix)
+        is UnaryPlusSyntax -> perform(node.operand, infix)
-        is UnaryMinusSyntax -> perform0(node.operand, infix)
+        is UnaryMinusSyntax -> perform(node.operand, infix)
-        is RadicalSyntax -> perform0(node.operand, infix)
+        is RadicalSyntax -> perform(node.operand, infix)
-        is ExponentSyntax -> perform0(node.operand, infix)
+        is ExponentSyntax -> perform(node.operand, infix)
 
         is SuperscriptSyntax -> {
-            perform0(node.left, true)
+            perform(node.left, true)
-            perform0(node.right, true)
+            perform(node.right, true)
         }
 
         is SubscriptSyntax -> {
-            perform0(node.left, true)
+            perform(node.left, true)
-            perform0(node.right, true)
+            perform(node.right, true)
         }
 
         is BinaryOperatorSyntax -> {
-            perform0(node.prefix, infix)
+            perform(node.prefix, infix)
-            perform0(node.left, infix)
+            perform(node.left, infix)
-            perform0(node.right, infix)
+            perform(node.right, infix)
         }
 
         is BinaryPlusSyntax -> {
-            perform0(node.left, infix)
+            perform(node.left, infix)
-            perform0(node.right, infix)
+            perform(node.right, infix)
         }
 
         is BinaryMinusSyntax -> {
-            perform0(node.left, infix)
+            perform(node.left, infix)
-            perform0(node.right, infix)
+            perform(node.right, infix)
         }
 
         is FractionSyntax -> {
             node.infix = infix
-            perform0(node.left, infix)
+            perform(node.left, infix)
-            perform0(node.right, infix)
+            perform(node.right, infix)
         }
 
         is RadicalWithIndexSyntax -> {
-            perform0(node.left, true)
+            perform(node.left, true)
-            perform0(node.right, true)
+            perform(node.right, true)
         }
 
         is MultiplicationSyntax -> {
-            perform0(node.left, infix)
+            perform(node.left, infix)
-            perform0(node.right, infix)
+            perform(node.right, infix)
         }
     }
 
-    public override fun perform(node: MathSyntax): Unit = perform0(node)
+    perform(node)
 }
 
 /**
@@ -160,39 +163,37 @@ public object BetterFraction : FeaturedMathRendererWithPostProcess.PostProcessSt
  * @author Iaroslav Postovalov
  */
 @UnstableKMathAPI
-public object BetterExponent : FeaturedMathRendererWithPostProcess.PostProcessStage {
+public val BetterExponent: PostProcessPhase = PostProcessPhase { node ->
-    private fun perform0(node: MathSyntax): Boolean {
+    fun perform(node: MathSyntax): Boolean {
         return when (node) {
             is NumberSyntax -> false
             is SymbolSyntax -> false
             is OperatorNameSyntax -> false
             is SpecialSymbolSyntax -> false
-            is OperandSyntax -> perform0(node.operand)
+            is OperandSyntax -> perform(node.operand)
-            is UnaryOperatorSyntax -> perform0(node.prefix) || perform0(node.operand)
+            is UnaryOperatorSyntax -> perform(node.prefix) || perform(node.operand)
-            is UnaryPlusSyntax -> perform0(node.operand)
+            is UnaryPlusSyntax -> perform(node.operand)
-            is UnaryMinusSyntax -> perform0(node.operand)
+            is UnaryMinusSyntax -> perform(node.operand)
             is RadicalSyntax -> true
 
             is ExponentSyntax -> {
-                val r = perform0(node.operand)
+                val r = perform(node.operand)
                 node.useOperatorForm = r
                 r
             }
 
             is SuperscriptSyntax -> true
             is SubscriptSyntax -> true
-            is BinaryOperatorSyntax -> perform0(node.prefix) || perform0(node.left) || perform0(node.right)
+            is BinaryOperatorSyntax -> perform(node.prefix) || perform(node.left) || perform(node.right)
-            is BinaryPlusSyntax -> perform0(node.left) || perform0(node.right)
+            is BinaryPlusSyntax -> perform(node.left) || perform(node.right)
-            is BinaryMinusSyntax -> perform0(node.left) || perform0(node.right)
+            is BinaryMinusSyntax -> perform(node.left) || perform(node.right)
             is FractionSyntax -> true
             is RadicalWithIndexSyntax -> true
-            is MultiplicationSyntax -> perform0(node.left) || perform0(node.right)
+            is MultiplicationSyntax -> perform(node.left) || perform(node.right)
         }
     }
 
-    public override fun perform(node: MathSyntax) {
+    perform(node)
-        perform0(node)
-    }
 }
 
 /**
@@ -203,8 +204,8 @@ public object BetterExponent : FeaturedMathRendererWithPostProcess.PostProcessSt
  */
 @UnstableKMathAPI
 public class SimplifyParentheses(public val precedenceFunction: (MathSyntax) -> Int) :
-    FeaturedMathRendererWithPostProcess.PostProcessStage {
+    PostProcessPhase {
-    public override fun perform(node: MathSyntax): Unit = when (node) {
+    override fun perform(node: MathSyntax): Unit = when (node) {
         is NumberSyntax -> Unit
         is SymbolSyntax -> Unit
         is OperatorNameSyntax -> Unit

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

@@ -11,7 +11,6 @@ import space.kscience.kmath.expressions.Symbol.Companion.x
 import space.kscience.kmath.expressions.interpret
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.operations.IntRing
-import space.kscience.kmath.operations.bindSymbol
 import space.kscience.kmath.operations.invoke
 import kotlin.test.Test
 import kotlin.test.assertEquals
@@ -22,7 +21,7 @@ internal class TestCompilerConsistencyWithInterpreter {
         val mst = MstRing {
             binaryOperationFunction("+")(
                 unaryOperationFunction("+")(
-                    (bindSymbol(x) - (2.toByte() + (scale(
+                    (x - (2.toByte() + (scale(
                         add(number(1), number(1)),
                         2.0,
                     ) + 1.toByte()))) * 3.0 - 1.toByte()
@@ -42,7 +41,7 @@ internal class TestCompilerConsistencyWithInterpreter {
     fun doubleField() = runCompilerTest {
         val mst = MstField {
             +(3 - 2 + 2 * number(1) + 1.0) + binaryOperationFunction("+")(
-                (3.0 - (bindSymbol(x) + (scale(add(number(1.0), number(1.0)), 2.0) + 1.0))) * 3 - 1.0
+                (3.0 - (x + (scale(add(number(1.0), number(1.0)), 2.0) + 1.0))) * 3 - 1.0
                         + number(1),
                 number(1) / 2 + number(2.0) * one,
             ) + zero

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

@@ -9,7 +9,6 @@ import space.kscience.kmath.expressions.MstExtendedField
 import space.kscience.kmath.expressions.Symbol.Companion.x
 import space.kscience.kmath.expressions.invoke
 import space.kscience.kmath.operations.DoubleField
-import space.kscience.kmath.operations.bindSymbol
 import space.kscience.kmath.operations.invoke
 import kotlin.test.Test
 import kotlin.test.assertEquals
@@ -47,19 +46,19 @@ internal class TestCompilerOperations {
 
     @Test
     fun testSubtract() = runCompilerTest {
-        val expr = MstExtendedField { bindSymbol(x) - bindSymbol(x) }.compileToExpression(DoubleField)
+        val expr = MstExtendedField { x - x }.compileToExpression(DoubleField)
         assertEquals(0.0, expr(x to 2.0))
     }
 
     @Test
     fun testDivide() = runCompilerTest {
-        val expr = MstExtendedField { bindSymbol(x) / bindSymbol(x) }.compileToExpression(DoubleField)
+        val expr = MstExtendedField { x / x }.compileToExpression(DoubleField)
         assertEquals(1.0, expr(x to 2.0))
     }
 
     @Test
     fun testPower() = runCompilerTest {
-        val expr = MstExtendedField { bindSymbol(x) pow 2 }.compileToExpression(DoubleField)
+        val expr = MstExtendedField { x pow 2 }.compileToExpression(DoubleField)
         assertEquals(4.0, expr(x to 2.0))
     }
 }

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

@@ -9,7 +9,6 @@ import space.kscience.kmath.expressions.MstRing
 import space.kscience.kmath.expressions.Symbol.Companion.x
 import space.kscience.kmath.expressions.invoke
 import space.kscience.kmath.operations.IntRing
-import space.kscience.kmath.operations.bindSymbol
 import space.kscience.kmath.operations.invoke
 import kotlin.test.Test
 import kotlin.test.assertEquals
@@ -18,13 +17,13 @@ import kotlin.test.assertFailsWith
 internal class TestCompilerVariables {
     @Test
     fun testVariable() = runCompilerTest {
-        val expr = MstRing { bindSymbol(x) }.compileToExpression(IntRing)
+        val expr = MstRing { x }.compileToExpression(IntRing)
         assertEquals(1, expr(x to 1))
     }
 
     @Test
     fun testUndefinedVariableFails() = runCompilerTest {
-        val expr = MstRing { bindSymbol(x) }.compileToExpression(IntRing)
+        val expr = MstRing { x }.compileToExpression(IntRing)
         assertFailsWith<NoSuchElementException> { expr() }
     }
 }

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

@@ -3,6 +3,8 @@
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
+@file:Suppress("ClassName")
+
 package space.kscience.kmath.internal.estree
 
 import kotlin.js.RegExp

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

@@ -27,11 +27,7 @@ internal sealed class WasmBuilder<T>(
     lateinit var ctx: BinaryenModule
 
     open fun visitSymbolic(mst: Symbol): ExpressionRef {
-        try {
+        algebra.bindSymbolOrNull(mst)?.let { return visitNumeric(Numeric(it)) }
-            algebra.bindSymbol(mst)
-        } catch (ignored: Throwable) {
-            null
-        }?.let { return visitNumeric(Numeric(it)) }
 
         var idx = keys.indexOf(mst)
 

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

@@ -7,7 +7,6 @@ package space.kscience.kmath.ast
 
 import space.kscience.kmath.expressions.*
 import space.kscience.kmath.operations.DoubleField
-import space.kscience.kmath.operations.ExtendedField
 import space.kscience.kmath.operations.bindSymbol
 import space.kscience.kmath.operations.invoke
 import kotlin.math.sin
@@ -17,18 +16,19 @@ import kotlin.time.measureTime
 import space.kscience.kmath.estree.compileToExpression as estreeCompileToExpression
 import space.kscience.kmath.wasm.compileToExpression as wasmCompileToExpression
 
+// TODO move to benchmarks when https://github.com/Kotlin/kotlinx-benchmark/pull/38 or similar feature is merged
 internal class TestExecutionTime {
     private companion object {
         private const val times = 1_000_000
         private val x by symbol
-        private val algebra: ExtendedField<Double> = DoubleField
+        private val algebra = DoubleField
 
         private val functional = DoubleField.expressionInExtendedField {
             bindSymbol(x) * const(2.0) + const(2.0) / bindSymbol(x) - const(16.0) / sin(bindSymbol(x))
         }
 
         private val node = MstExtendedField {
-            bindSymbol(x) * number(2.0) + number(2.0) / bindSymbol(x) - number(16.0) / sin(bindSymbol(x))
+            x * number(2.0) + number(2.0) / x - number(16.0) / sin(x)
         }
 
         private val mst = node.toExpression(DoubleField)
@@ -43,7 +43,13 @@ internal class TestExecutionTime {
         //  };
 
         private val raw = Expression<Double> { args ->
-            args.getValue(x) * 2.0 + 2.0 / args.getValue(x) - 16.0 / sin(args.getValue(x))
+            val x = args[x]!!
+            x * 2.0 + 2.0 / x - 16.0 / sin(x)
+        }
+
+        private val justCalculate = { args: dynamic ->
+            val x = args[x].unsafeCast<Double>()
+            x * 2.0 + 2.0 / x - 16.0 / sin(x)
         }
     }
 
@@ -51,21 +57,56 @@ internal class TestExecutionTime {
         println(name)
         val rng = Random(0)
         var sum = 0.0
-        measureTime { repeat(times) { sum += expr(x to rng.nextDouble()) } }.also(::println)
+        measureTime {
+            repeat(times) { sum += expr(x to rng.nextDouble()) }
+        }.also(::println)
     }
 
+    /**
+     * [Expression] created with [expressionInExtendedField].
+     */
     @Test
     fun functionalExpression() = invokeAndSum("functional", functional)
 
+    /**
+     * [Expression] created with [mstExpression].
+     */
     @Test
     fun mstExpression() = invokeAndSum("mst", mst)
 
+    /**
+     * [Expression] created with [wasmCompileToExpression].
+     */
     @Test
     fun wasmExpression() = invokeAndSum("wasm", wasm)
 
+    /**
+     * [Expression] created with [estreeCompileToExpression].
+     */
     @Test
     fun estreeExpression() = invokeAndSum("estree", wasm)
 
+    /**
+     * [Expression] implemented manually with `kotlin.math`.
+     */
     @Test
     fun rawExpression() = invokeAndSum("raw", raw)
+
+    /**
+     * Direct computation w/o [Expression].
+     */
+    @Test
+    fun justCalculateExpression() {
+        println("justCalculate")
+        val rng = Random(0)
+        var sum = 0.0
+        measureTime {
+            repeat(times) {
+                val arg = rng.nextDouble()
+                val o = js("{}")
+                o["x"] = arg
+                sum += justCalculate(o)
+            }
+        }.also(::println)
+    }
 }

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

@@ -10,6 +10,8 @@ import space.kscience.kmath.expressions.MST
 import space.kscience.kmath.expressions.Symbol
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.operations.IntRing
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
 import space.kscience.kmath.estree.compile as estreeCompile
 import space.kscience.kmath.estree.compileToExpression as estreeCompileToExpression
 import space.kscience.kmath.wasm.compile as wasmCompile
@@ -34,6 +36,7 @@ private object ESTreeCompilerTestContext : CompilerTestContext {
 }
 
 internal actual inline fun runCompilerTest(action: CompilerTestContext.() -> Unit) {
+    contract { callsInPlace(action, InvocationKind.AT_LEAST_ONCE) }
     action(WasmCompilerTestContext)
     action(ESTreeCompilerTestContext)
 }

kmath-ast/src/jsTest/kotlin/space/kscience/kmath/wasm/TestWasmSpecific.kt

@@ -11,7 +11,6 @@ import space.kscience.kmath.expressions.invoke
 import space.kscience.kmath.expressions.symbol
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.operations.IntRing
-import space.kscience.kmath.operations.bindSymbol
 import space.kscience.kmath.operations.invoke
 import kotlin.test.Test
 import kotlin.test.assertEquals
@@ -31,7 +30,7 @@ internal class TestWasmSpecific {
 
     @Test
     fun argsPassing() {
-        val res = MstExtendedField { bindSymbol(y) + bindSymbol(x).pow(10) }.compile(
+        val res = MstExtendedField { y + x.pow(10) }.compile(
             DoubleField,
             x to 2.0,
             y to 100000000.0,
@@ -42,7 +41,7 @@ internal class TestWasmSpecific {
 
     @Test
     fun powFunction() {
-        val expr = MstExtendedField { bindSymbol(x).pow(1.0 / 6.0) }.compileToExpression(DoubleField)
+        val expr = MstExtendedField { x.pow(1.0 / 6.0) }.compileToExpression(DoubleField)
         assertEquals(0.9730585187140817, expr(x to 0.8488554755054833))
     }
 

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

@@ -57,13 +57,13 @@ internal fun MethodVisitor.label(): Label = Label().also(::visitLabel)
 /**
  * Creates a class name for [Expression] subclassed to implement [mst] provided.
  *
- * This methods helps to avoid collisions of class name to prevent loading several classes with the same name. If there
+ * These methods help to avoid collisions of class name to prevent loading several classes with the same name. If there
  * is a colliding class, change [collision] parameter or leave it `0` to check existing classes recursively.
  *
  * @author Iaroslav Postovalov
  */
 internal tailrec fun buildName(mst: MST, collision: Int = 0): String {
-    val name = "kscience.kmath.asm.generated.AsmCompiledExpression_${mst.hashCode()}_$collision"
+    val name = "space.kscience.kmath.asm.generated.CompiledExpression_${mst.hashCode()}_$collision"
 
     try {
         Class.forName(name)

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

@@ -10,6 +10,8 @@ import space.kscience.kmath.expressions.MST
 import space.kscience.kmath.expressions.Symbol
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.operations.IntRing
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
 import space.kscience.kmath.asm.compile as asmCompile
 import space.kscience.kmath.asm.compileToExpression as asmCompileToExpression
 
@@ -22,4 +24,7 @@ private object AsmCompilerTestContext : CompilerTestContext {
         asmCompile(algebra, arguments)
 }
 
-internal actual inline fun runCompilerTest(action: CompilerTestContext.() -> Unit) = action(AsmCompilerTestContext)
+internal actual inline fun runCompilerTest(action: CompilerTestContext.() -> Unit) {
+    contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) }
+    action(AsmCompilerTestContext)
+}

kmath-commons/src/main/kotlin/space/kscience/kmath/commons/expressions/DerivativeStructureExpression.kt

@@ -15,7 +15,7 @@ import space.kscience.kmath.operations.NumbersAddOperations
  * A field over commons-math [DerivativeStructure].
  *
  * @property order The derivation order.
- * @property bindings The map of bindings values. All bindings are considered free parameters
+ * @param bindings The map of bindings values. All bindings are considered free parameters
  */
 @OptIn(UnstableKMathAPI::class)
 public class DerivativeStructureField(
@@ -25,13 +25,13 @@ public class DerivativeStructureField(
     NumbersAddOperations<DerivativeStructure> {
     public val numberOfVariables: Int = bindings.size
 
-    public override val zero: DerivativeStructure by lazy { DerivativeStructure(numberOfVariables, order) }
+    override val zero: DerivativeStructure by lazy { DerivativeStructure(numberOfVariables, order) }
-    public override val one: DerivativeStructure by lazy { DerivativeStructure(numberOfVariables, order, 1.0) }
+    override val one: DerivativeStructure by lazy { DerivativeStructure(numberOfVariables, order, 1.0) }
 
-    public override fun number(value: Number): DerivativeStructure = const(value.toDouble())
+    override fun number(value: Number): DerivativeStructure = const(value.toDouble())
 
     /**
-     * A class that implements both [DerivativeStructure] and a [Symbol]
+     * A class implementing both [DerivativeStructure] and [Symbol].
      */
     public inner class DerivativeStructureSymbol(
         size: Int,
@@ -39,10 +39,10 @@ public class DerivativeStructureField(
         symbol: Symbol,
         value: Double,
     ) : DerivativeStructure(size, order, index, value), Symbol {
-        public override val identity: String = symbol.identity
+        override val identity: String = symbol.identity
-        public override fun toString(): String = identity
+        override fun toString(): String = identity
-        public override fun equals(other: Any?): Boolean = this.identity == (other as? Symbol)?.identity
+        override fun equals(other: Any?): Boolean = this.identity == (other as? Symbol)?.identity
-        public override fun hashCode(): Int = identity.hashCode()
+        override fun hashCode(): Int = identity.hashCode()
     }
 
     /**
@@ -52,10 +52,10 @@ public class DerivativeStructureField(
         key.identity to DerivativeStructureSymbol(numberOfVariables, index, key, value)
     }.toMap()
 
-    public override fun const(value: Double): DerivativeStructure = DerivativeStructure(numberOfVariables, order, value)
+    override fun const(value: Double): DerivativeStructure = DerivativeStructure(numberOfVariables, order, value)
 
-    public override fun bindSymbolOrNull(value: String): DerivativeStructureSymbol? = variables[value]
+    override fun bindSymbolOrNull(value: String): DerivativeStructureSymbol? = variables[value]
-    public override fun bindSymbol(value: String): DerivativeStructureSymbol = variables.getValue(value)
+    override fun bindSymbol(value: String): DerivativeStructureSymbol = variables.getValue(value)
 
     public fun bindSymbolOrNull(symbol: Symbol): DerivativeStructureSymbol? = variables[symbol.identity]
     public fun bindSymbol(symbol: Symbol): DerivativeStructureSymbol = variables.getValue(symbol.identity)
@@ -68,45 +68,48 @@ public class DerivativeStructureField(
 
     public fun DerivativeStructure.derivative(vararg symbols: Symbol): Double = derivative(symbols.toList())
 
-    public override fun DerivativeStructure.unaryMinus(): DerivativeStructure = negate()
+    override fun DerivativeStructure.unaryMinus(): DerivativeStructure = negate()
 
-    public override fun add(a: DerivativeStructure, b: DerivativeStructure): DerivativeStructure = a.add(b)
+    override fun add(a: DerivativeStructure, b: DerivativeStructure): DerivativeStructure = a.add(b)
 
-    public override fun scale(a: DerivativeStructure, value: Double): DerivativeStructure = a.multiply(value)
+    override fun scale(a: DerivativeStructure, value: Double): DerivativeStructure = a.multiply(value)
 
-    public override fun multiply(a: DerivativeStructure, b: DerivativeStructure): DerivativeStructure = a.multiply(b)
+    override fun multiply(a: DerivativeStructure, b: DerivativeStructure): DerivativeStructure = a.multiply(b)
-    public override fun divide(a: DerivativeStructure, b: DerivativeStructure): DerivativeStructure = a.divide(b)
+    override fun divide(a: DerivativeStructure, b: DerivativeStructure): DerivativeStructure = a.divide(b)
-    public override fun sin(arg: DerivativeStructure): DerivativeStructure = arg.sin()
+    override fun sin(arg: DerivativeStructure): DerivativeStructure = arg.sin()
-    public override fun cos(arg: DerivativeStructure): DerivativeStructure = arg.cos()
+    override fun cos(arg: DerivativeStructure): DerivativeStructure = arg.cos()
-    public override fun tan(arg: DerivativeStructure): DerivativeStructure = arg.tan()
+    override fun tan(arg: DerivativeStructure): DerivativeStructure = arg.tan()
-    public override fun asin(arg: DerivativeStructure): DerivativeStructure = arg.asin()
+    override fun asin(arg: DerivativeStructure): DerivativeStructure = arg.asin()
-    public override fun acos(arg: DerivativeStructure): DerivativeStructure = arg.acos()
+    override fun acos(arg: DerivativeStructure): DerivativeStructure = arg.acos()
-    public override fun atan(arg: DerivativeStructure): DerivativeStructure = arg.atan()
+    override fun atan(arg: DerivativeStructure): DerivativeStructure = arg.atan()
-    public override fun sinh(arg: DerivativeStructure): DerivativeStructure = arg.sinh()
+    override fun sinh(arg: DerivativeStructure): DerivativeStructure = arg.sinh()
-    public override fun cosh(arg: DerivativeStructure): DerivativeStructure = arg.cosh()
+    override fun cosh(arg: DerivativeStructure): DerivativeStructure = arg.cosh()
-    public override fun tanh(arg: DerivativeStructure): DerivativeStructure = arg.tanh()
+    override fun tanh(arg: DerivativeStructure): DerivativeStructure = arg.tanh()
-    public override fun asinh(arg: DerivativeStructure): DerivativeStructure = arg.asinh()
+    override fun asinh(arg: DerivativeStructure): DerivativeStructure = arg.asinh()
-    public override fun acosh(arg: DerivativeStructure): DerivativeStructure = arg.acosh()
+    override fun acosh(arg: DerivativeStructure): DerivativeStructure = arg.acosh()
-    public override fun atanh(arg: DerivativeStructure): DerivativeStructure = arg.atanh()
+    override fun atanh(arg: DerivativeStructure): DerivativeStructure = arg.atanh()
 
-    public override fun power(arg: DerivativeStructure, pow: Number): DerivativeStructure = when (pow) {
+    override fun power(arg: DerivativeStructure, pow: Number): DerivativeStructure = when (pow) {
         is Double -> arg.pow(pow)
         is Int -> arg.pow(pow)
         else -> arg.pow(pow.toDouble())
     }
 
     public fun power(arg: DerivativeStructure, pow: DerivativeStructure): DerivativeStructure = arg.pow(pow)
-    public override fun exp(arg: DerivativeStructure): DerivativeStructure = arg.exp()
+    override fun exp(arg: DerivativeStructure): DerivativeStructure = arg.exp()
-    public override fun ln(arg: DerivativeStructure): DerivativeStructure = arg.log()
+    override fun ln(arg: DerivativeStructure): DerivativeStructure = arg.log()
 
-    public override operator fun DerivativeStructure.plus(b: Number): DerivativeStructure = add(b.toDouble())
+    override operator fun DerivativeStructure.plus(b: Number): DerivativeStructure = add(b.toDouble())
-    public override operator fun DerivativeStructure.minus(b: Number): DerivativeStructure = subtract(b.toDouble())
+    override operator fun DerivativeStructure.minus(b: Number): DerivativeStructure = subtract(b.toDouble())
-    public override operator fun Number.plus(b: DerivativeStructure): DerivativeStructure = b + this
+    override operator fun Number.plus(b: DerivativeStructure): DerivativeStructure = b + this
-    public override operator fun Number.minus(b: DerivativeStructure): DerivativeStructure = b - this
+    override operator fun Number.minus(b: DerivativeStructure): DerivativeStructure = b - this
 }
 
+/**
+ * Auto-diff processor based on Commons-math [DerivativeStructure]
+ */
 public object DSProcessor : AutoDiffProcessor<Double, DerivativeStructure, DerivativeStructureField> {
-    public override fun differentiate(
+     override fun differentiate(
         function: DerivativeStructureField.() -> DerivativeStructure,
     ): DerivativeStructureExpression = DerivativeStructureExpression(function)
 }
@@ -117,13 +120,13 @@ public object DSProcessor : AutoDiffProcessor<Double, DerivativeStructure, Deriv
 public class DerivativeStructureExpression(
     public val function: DerivativeStructureField.() -> DerivativeStructure,
 ) : DifferentiableExpression<Double> {
-    public override operator fun invoke(arguments: Map<Symbol, Double>): Double =
+    override operator fun invoke(arguments: Map<Symbol, Double>): Double =
         DerivativeStructureField(0, arguments).function().value
 
     /**
      * Get the derivative expression with given orders
      */
-    public override fun derivativeOrNull(symbols: List<Symbol>): Expression<Double> = Expression { arguments ->
+    override fun derivativeOrNull(symbols: List<Symbol>): Expression<Double> = Expression { arguments ->
         with(DerivativeStructureField(symbols.size, arguments)) { function().derivative(symbols) }
     }
 }

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

@@ -15,18 +15,18 @@ import kotlin.reflect.KClass
 import kotlin.reflect.cast
 
 public class CMMatrix(public val origin: RealMatrix) : Matrix<Double> {
-    public override val rowNum: Int get() = origin.rowDimension
+    override val rowNum: Int get() = origin.rowDimension
-    public override val colNum: Int get() = origin.columnDimension
+    override val colNum: Int get() = origin.columnDimension
 
-    public override operator fun get(i: Int, j: Int): Double = origin.getEntry(i, j)
+    override operator fun get(i: Int, j: Int): Double = origin.getEntry(i, j)
 }
 
 public class CMVector(public val origin: RealVector) : Point<Double> {
-    public override val size: Int get() = origin.dimension
+    override val size: Int get() = origin.dimension
 
-    public override operator fun get(index: Int): Double = origin.getEntry(index)
+    override operator fun get(index: Int): Double = origin.getEntry(index)
 
-    public override operator fun iterator(): Iterator<Double> = origin.toArray().iterator()
+    override operator fun iterator(): Iterator<Double> = origin.toArray().iterator()
 }
 
 public fun RealVector.toPoint(): CMVector = CMVector(this)
@@ -34,7 +34,7 @@ public fun RealVector.toPoint(): CMVector = CMVector(this)
 public object CMLinearSpace : LinearSpace<Double, DoubleField> {
     override val elementAlgebra: DoubleField get() = DoubleField
 
-    public override fun buildMatrix(
+    override fun buildMatrix(
         rows: Int,
         columns: Int,
         initializer: DoubleField.(i: Int, j: Int) -> Double,
@@ -73,16 +73,16 @@ public object CMLinearSpace : LinearSpace<Double, DoubleField> {
     override fun Point<Double>.minus(other: Point<Double>): CMVector =
         toCM().origin.subtract(other.toCM().origin).wrap()
 
-    public override fun Matrix<Double>.dot(other: Matrix<Double>): CMMatrix =
+    override fun Matrix<Double>.dot(other: Matrix<Double>): CMMatrix =
         toCM().origin.multiply(other.toCM().origin).wrap()
 
-    public override fun Matrix<Double>.dot(vector: Point<Double>): CMVector =
+    override fun Matrix<Double>.dot(vector: Point<Double>): CMVector =
         toCM().origin.preMultiply(vector.toCM().origin).wrap()
 
-    public override operator fun Matrix<Double>.minus(other: Matrix<Double>): CMMatrix =
+    override operator fun Matrix<Double>.minus(other: Matrix<Double>): CMMatrix =
         toCM().origin.subtract(other.toCM().origin).wrap()
 
-    public override operator fun Matrix<Double>.times(value: Double): CMMatrix =
+    override operator fun Matrix<Double>.times(value: Double): CMMatrix =
         toCM().origin.scalarMultiply(value).wrap()
 
     override fun Double.times(m: Matrix<Double>): CMMatrix =

kmath-commons/src/main/kotlin/space/kscience/kmath/commons/random/CMRandomGeneratorWrapper.kt

@@ -5,35 +5,43 @@
 
 package space.kscience.kmath.commons.random
 
+import kotlinx.coroutines.runBlocking
+import space.kscience.kmath.misc.PerformancePitfall
+import space.kscience.kmath.samplers.GaussianSampler
+import space.kscience.kmath.misc.toIntExact
 import space.kscience.kmath.stat.RandomGenerator
+import space.kscience.kmath.stat.next
 
 public class CMRandomGeneratorWrapper(
     public val factory: (IntArray) -> RandomGenerator,
 ) : org.apache.commons.math3.random.RandomGenerator {
     private var generator: RandomGenerator = factory(intArrayOf())
 
-    public override fun nextBoolean(): Boolean = generator.nextBoolean()
+    override fun nextBoolean(): Boolean = generator.nextBoolean()
-    public override fun nextFloat(): Float = generator.nextDouble().toFloat()
+    override fun nextFloat(): Float = generator.nextDouble().toFloat()
 
-    public override fun setSeed(seed: Int) {
+    override fun setSeed(seed: Int) {
         generator = factory(intArrayOf(seed))
     }
 
-    public override fun setSeed(seed: IntArray) {
+    override fun setSeed(seed: IntArray) {
         generator = factory(seed)
     }
 
-    public override fun setSeed(seed: Long) {
+    override fun setSeed(seed: Long) {
-        setSeed(seed.toInt())
+        setSeed(seed.toIntExact())
     }
 
-    public override fun nextBytes(bytes: ByteArray) {
+    override fun nextBytes(bytes: ByteArray) {
         generator.fillBytes(bytes)
     }
 
-    public override fun nextInt(): Int = generator.nextInt()
+    override fun nextInt(): Int = generator.nextInt()
-    public override fun nextInt(n: Int): Int = generator.nextInt(n)
+    override fun nextInt(n: Int): Int = generator.nextInt(n)
-    public override fun nextGaussian(): Double = TODO()
+
-    public override fun nextDouble(): Double = generator.nextDouble()
+    @PerformancePitfall
-    public override fun nextLong(): Long = generator.nextLong()
+    override fun nextGaussian(): Double = runBlocking { GaussianSampler(0.0, 1.0).next(generator) }
+
+    override fun nextDouble(): Double = generator.nextDouble()
+    override fun nextLong(): Long = generator.nextLong()
 }

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

@@ -32,7 +32,7 @@ public object Transformations {
     /**
      * Create a virtual buffer on top of array
      */
-    private fun Array<org.apache.commons.math3.complex.Complex>.asBuffer() = VirtualBuffer<Complex>(size) {
+    private fun Array<org.apache.commons.math3.complex.Complex>.asBuffer() = VirtualBuffer(size) {
         val value = get(it)
         Complex(value.real, value.imaginary)
     }

kmath-commons/src/test/kotlin/space/kscience/kmath/commons/expressions/DerivativeStructureExpressionTest.kt

@@ -16,7 +16,7 @@ internal inline fun diff(
     order: Int,
     vararg parameters: Pair<Symbol, Double>,
     block: DerivativeStructureField.() -> Unit,
-): Unit {
+) {
     contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
     DerivativeStructureField(order, mapOf(*parameters)).run(block)
 }
@@ -34,7 +34,7 @@ internal class AutoDiffTest {
             println(z.derivative(x))
             println(z.derivative(y, x))
             assertEquals(z.derivative(x, y), z.derivative(y, x))
-            //check that improper order cause failure
+            // check improper order cause failure
             assertFails { z.derivative(x, x, y) }
         }
     }

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

@@ -11,9 +11,12 @@ import space.kscience.kmath.commons.expressions.DerivativeStructureExpression
 import space.kscience.kmath.distributions.NormalDistribution
 import space.kscience.kmath.expressions.Symbol.Companion.x
 import space.kscience.kmath.expressions.Symbol.Companion.y
+import space.kscience.kmath.expressions.chiSquaredExpression
 import space.kscience.kmath.expressions.symbol
 import space.kscience.kmath.optimization.*
 import space.kscience.kmath.stat.RandomGenerator
+import space.kscience.kmath.structures.asBuffer
+import space.kscience.kmath.structures.map
 import kotlin.math.pow
 import kotlin.test.Test
 
@@ -49,26 +52,27 @@ internal class OptimizeTest {
         val sigma = 1.0
         val generator = NormalDistribution(0.0, sigma)
         val chain = generator.sample(RandomGenerator.default(112667))
-        val x = (1..100).map(Int::toDouble)
+        val x = (1..100).map(Int::toDouble).asBuffer()
 
         val y = x.map {
             it.pow(2) + it + 1 + chain.next()
         }
 
-        val yErr = List(x.size) { sigma }
+        val yErr = List(x.size) { sigma }.asBuffer()
 
-        val model = DSProcessor.differentiate { x1 ->
+        val chi2 = DSProcessor.chiSquaredExpression(
+            x, y, yErr
+        ) { arg ->
             val cWithDefault = bindSymbolOrNull(c) ?: one
-            bindSymbol(a) * x1.pow(2) + bindSymbol(b) * x1 + cWithDefault
+            bindSymbol(a) * arg.pow(2) + bindSymbol(b) * arg + cWithDefault
         }
 
-        val chi2 = FunctionOptimization.chiSquared(x, y, yErr) { x1 ->
+        val result: FunctionOptimization<Double> = chi2.optimizeWith(
-            val cWithDefault = bindSymbolOrNull(c) ?: one
+            CMOptimizer,
-            bindSymbol(a) * x1.pow(2) + bindSymbol(b) * x1 + cWithDefault
+            mapOf(a to 1.5, b to 0.9, c to 1.0),
-        }
+            FunctionOptimizationTarget.MINIMIZE
-
+        )
-        val result = chi2.minimize(a to 1.5, b to 0.9, c to 1.0)
         println(result)
-        println("Chi2/dof = ${result.value / (x.size - 3)}")
+        println("Chi2/dof = ${result.resultValue / (x.size - 3)}")
     }
 }

kmath-complex/README.md

@@ -8,7 +8,7 @@ Complex and hypercomplex number systems in KMath.
 
 ## Artifact:
 
-The Maven coordinates of this project are `space.kscience:kmath-complex:0.3.0-dev-11`.
+The Maven coordinates of this project are `space.kscience:kmath-complex:0.3.0-dev-14`.
 
 **Gradle:**
 ```gradle
@@ -18,7 +18,7 @@ repositories {
 }
 
 dependencies {
-    implementation 'space.kscience:kmath-complex:0.3.0-dev-11'
+    implementation 'space.kscience:kmath-complex:0.3.0-dev-14'
 }
 ```
 **Gradle Kotlin DSL:**
@@ -29,6 +29,6 @@ repositories {
 }
 
 dependencies {
-    implementation("space.kscience:kmath-complex:0.3.0-dev-11")
+    implementation("space.kscience:kmath-complex:0.3.0-dev-14")
 }
 ```

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

@@ -54,8 +54,8 @@ private val PI_DIV_2 = Complex(PI / 2, 0)
 @OptIn(UnstableKMathAPI::class)
 public object ComplexField : ExtendedField<Complex>, Norm<Complex, Complex>, NumbersAddOperations<Complex>,
     ScaleOperations<Complex> {
-    public override val zero: Complex = 0.0.toComplex()
+    override val zero: Complex = 0.0.toComplex()
-    public override val one: Complex = 1.0.toComplex()
+    override val one: Complex = 1.0.toComplex()
 
     /**
      * The imaginary unit.
@@ -68,13 +68,13 @@ public object ComplexField : ExtendedField<Complex>, Norm<Complex, Complex>, Num
 
     override fun scale(a: Complex, value: Double): Complex = Complex(a.re * value, a.im * value)
 
-    public override fun add(a: Complex, b: Complex): Complex = Complex(a.re + b.re, a.im + b.im)
+    override fun add(a: Complex, b: Complex): Complex = Complex(a.re + b.re, a.im + b.im)
-//    public override fun multiply(a: Complex, k: Number): Complex = Complex(a.re * k.toDouble(), a.im * k.toDouble())
+//    override fun multiply(a: Complex, k: Number): Complex = Complex(a.re * k.toDouble(), a.im * k.toDouble())
 
-    public override fun multiply(a: Complex, b: Complex): Complex =
+    override fun multiply(a: Complex, b: Complex): Complex =
         Complex(a.re * b.re - a.im * b.im, a.re * b.im + a.im * b.re)
 
-    public override fun divide(a: Complex, b: Complex): Complex = when {
+    override fun divide(a: Complex, b: Complex): Complex = when {
         abs(b.im) < abs(b.re) -> {
             val wr = b.im / b.re
             val wd = b.re + wr * b.im
@@ -100,31 +100,31 @@ public object ComplexField : ExtendedField<Complex>, Norm<Complex, Complex>, Num
 
     override operator fun Complex.div(k: Number): Complex = Complex(re / k.toDouble(), im / k.toDouble())
 
-    public override fun sin(arg: Complex): Complex = i * (exp(-i * arg) - exp(i * arg)) / 2.0
+    override fun sin(arg: Complex): Complex = i * (exp(-i * arg) - exp(i * arg)) / 2.0
-    public override fun cos(arg: Complex): Complex = (exp(-i * arg) + exp(i * arg)) / 2.0
+    override fun cos(arg: Complex): Complex = (exp(-i * arg) + exp(i * arg)) / 2.0
 
-    public override fun tan(arg: Complex): Complex {
+    override fun tan(arg: Complex): Complex {
         val e1 = exp(-i * arg)
         val e2 = exp(i * arg)
         return i * (e1 - e2) / (e1 + e2)
     }
 
-    public override fun asin(arg: Complex): Complex = -i * ln(sqrt(1 - (arg * arg)) + i * arg)
+    override fun asin(arg: Complex): Complex = -i * ln(sqrt(1 - (arg * arg)) + i * arg)
-    public override fun acos(arg: Complex): Complex = PI_DIV_2 + i * ln(sqrt(1 - (arg * arg)) + i * arg)
+    override fun acos(arg: Complex): Complex = PI_DIV_2 + i * ln(sqrt(1 - (arg * arg)) + i * arg)
 
-    public override fun atan(arg: Complex): Complex {
+    override fun atan(arg: Complex): Complex {
         val iArg = i * arg
         return i * (ln(1 - iArg) - ln(1 + iArg)) / 2
     }
 
-    public override fun power(arg: Complex, pow: Number): Complex = if (arg.im == 0.0)
+    override fun power(arg: Complex, pow: Number): Complex = if (arg.im == 0.0)
         arg.re.pow(pow.toDouble()).toComplex()
     else
         exp(pow * ln(arg))
 
-    public override fun exp(arg: Complex): Complex = exp(arg.re) * (cos(arg.im) + i * sin(arg.im))
+    override fun exp(arg: Complex): Complex = exp(arg.re) * (cos(arg.im) + i * sin(arg.im))
 
-    public override fun ln(arg: Complex): Complex = ln(arg.r) + i * atan2(arg.im, arg.re)
+    override fun ln(arg: Complex): Complex = ln(arg.r) + i * atan2(arg.im, arg.re)
 
     /**
      * Adds complex number to real one.
@@ -171,9 +171,9 @@ public object ComplexField : ExtendedField<Complex>, Norm<Complex, Complex>, Num
      */
     public operator fun Double.times(c: Complex): Complex = Complex(c.re * this, c.im * this)
 
-    public override fun norm(arg: Complex): Complex = sqrt(arg.conjugate * arg)
+    override fun norm(arg: Complex): Complex = sqrt(arg.conjugate * arg)
 
-    public override fun bindSymbolOrNull(value: String): Complex? = if (value == "i") i else null
+    override fun bindSymbolOrNull(value: String): Complex? = if (value == "i") i else null
 }
 
 /**
@@ -187,16 +187,16 @@ public data class Complex(val re: Double, val im: Double) {
     public constructor(re: Number, im: Number) : this(re.toDouble(), im.toDouble())
     public constructor(re: Number) : this(re.toDouble(), 0.0)
 
-    public override fun toString(): String = "($re + i * $im)"
+    override fun toString(): String = "($re + i * $im)"
 
     public companion object : MemorySpec<Complex> {
-        public override val objectSize: Int
+        override val objectSize: Int
             get() = 16
 
-        public override fun MemoryReader.read(offset: Int): Complex =
+        override fun MemoryReader.read(offset: Int): Complex =
             Complex(readDouble(offset), readDouble(offset + 8))
 
-        public override fun MemoryWriter.write(offset: Int, value: Complex) {
+        override fun MemoryWriter.write(offset: Int, value: Complex) {
             writeDouble(offset, value.re)
             writeDouble(offset + 8, value.im)
         }

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

@@ -27,10 +27,10 @@ public class ComplexFieldND(
     NumbersAddOperations<StructureND<Complex>>,
     ExtendedField<StructureND<Complex>> {
 
-    public override val zero: BufferND<Complex> by lazy { produce { zero } }
+    override val zero: BufferND<Complex> by lazy { produce { zero } }
-    public override val one: BufferND<Complex> by lazy { produce { one } }
+    override val one: BufferND<Complex> by lazy { produce { one } }
 
-    public override fun number(value: Number): BufferND<Complex> {
+    override fun number(value: Number): BufferND<Complex> {
         val d = value.toComplex() // minimize conversions
         return produce { d }
     }
@@ -81,25 +81,25 @@ public class ComplexFieldND(
 //        return BufferedNDFieldElement(this, buffer)
 //    }
 
-    public override fun power(arg: StructureND<Complex>, pow: Number): BufferND<Complex> = arg.map { power(it, pow) }
+    override fun power(arg: StructureND<Complex>, pow: Number): BufferND<Complex> = arg.map { power(it, pow) }
 
-    public override fun exp(arg: StructureND<Complex>): BufferND<Complex> = arg.map { exp(it) }
+    override fun exp(arg: StructureND<Complex>): BufferND<Complex> = arg.map { exp(it) }
 
-    public override fun ln(arg: StructureND<Complex>): BufferND<Complex> = arg.map { ln(it) }
+    override fun ln(arg: StructureND<Complex>): BufferND<Complex> = arg.map { ln(it) }
 
-    public override fun sin(arg: StructureND<Complex>): BufferND<Complex> = arg.map { sin(it) }
+    override fun sin(arg: StructureND<Complex>): BufferND<Complex> = arg.map { sin(it) }
-    public override fun cos(arg: StructureND<Complex>): BufferND<Complex> = arg.map { cos(it) }
+    override fun cos(arg: StructureND<Complex>): BufferND<Complex> = arg.map { cos(it) }
-    public override fun tan(arg: StructureND<Complex>): BufferND<Complex> = arg.map { tan(it) }
+    override fun tan(arg: StructureND<Complex>): BufferND<Complex> = arg.map { tan(it) }
-    public override fun asin(arg: StructureND<Complex>): BufferND<Complex> = arg.map { asin(it) }
+    override fun asin(arg: StructureND<Complex>): BufferND<Complex> = arg.map { asin(it) }
-    public override fun acos(arg: StructureND<Complex>): BufferND<Complex> = arg.map { acos(it) }
+    override fun acos(arg: StructureND<Complex>): BufferND<Complex> = arg.map { acos(it) }
-    public override fun atan(arg: StructureND<Complex>): BufferND<Complex> = arg.map { atan(it) }
+    override fun atan(arg: StructureND<Complex>): BufferND<Complex> = arg.map { atan(it) }
 
-    public override fun sinh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { sinh(it) }
+    override fun sinh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { sinh(it) }
-    public override fun cosh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { cosh(it) }
+    override fun cosh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { cosh(it) }
-    public override fun tanh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { tanh(it) }
+    override fun tanh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { tanh(it) }
-    public override fun asinh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { asinh(it) }
+    override fun asinh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { asinh(it) }
-    public override fun acosh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { acosh(it) }
+    override fun acosh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { acosh(it) }
-    public override fun atanh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { atanh(it) }
+    override fun atanh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { atanh(it) }
 }
 
 

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

@@ -63,20 +63,20 @@ public object QuaternionField : Field<Quaternion>, Norm<Quaternion, Quaternion>,
      */
     public val k: Quaternion = Quaternion(0, 0, 0, 1)
 
-    public override fun add(a: Quaternion, b: Quaternion): Quaternion =
+    override fun add(a: Quaternion, b: Quaternion): Quaternion =
         Quaternion(a.w + b.w, a.x + b.x, a.y + b.y, a.z + b.z)
 
-    public override fun scale(a: Quaternion, value: Double): Quaternion =
+    override fun scale(a: Quaternion, value: Double): Quaternion =
         Quaternion(a.w * value, a.x * value, a.y * value, a.z * value)
 
-    public override fun multiply(a: Quaternion, b: Quaternion): Quaternion = Quaternion(
+    override fun multiply(a: Quaternion, b: Quaternion): Quaternion = Quaternion(
         a.w * b.w - a.x * b.x - a.y * b.y - a.z * b.z,
         a.w * b.x + a.x * b.w + a.y * b.z - a.z * b.y,
         a.w * b.y - a.x * b.z + a.y * b.w + a.z * b.x,
         a.w * b.z + a.x * b.y - a.y * b.x + a.z * b.w,
     )
 
-    public override fun divide(a: Quaternion, b: Quaternion): Quaternion {
+    override fun divide(a: Quaternion, b: Quaternion): Quaternion {
         val s = b.w * b.w + b.x * b.x + b.y * b.y + b.z * b.z
 
         return Quaternion(
@@ -87,7 +87,7 @@ public object QuaternionField : Field<Quaternion>, Norm<Quaternion, Quaternion>,
         )
     }
 
-    public override fun power(arg: Quaternion, pow: Number): Quaternion {
+    override fun power(arg: Quaternion, pow: Number): Quaternion {
         if (pow is Int) return pwr(arg, pow)
         if (floor(pow.toDouble()) == pow.toDouble()) return pwr(arg, pow.toInt())
         return exp(pow * ln(arg))
@@ -131,7 +131,7 @@ public object QuaternionField : Field<Quaternion>, Norm<Quaternion, Quaternion>,
         return Quaternion(a2 * a2 - 6 * a2 * n1 + n1 * n1, x.x * n2, x.y * n2, x.z * n2)
     }
 
-    public override fun exp(arg: Quaternion): Quaternion {
+    override fun exp(arg: Quaternion): Quaternion {
         val un = arg.x * arg.x + arg.y * arg.y + arg.z * arg.z
         if (un == 0.0) return exp(arg.w).toQuaternion()
         val n1 = sqrt(un)
@@ -140,14 +140,14 @@ public object QuaternionField : Field<Quaternion>, Norm<Quaternion, Quaternion>,
         return Quaternion(ea * cos(n1), n2 * arg.x, n2 * arg.y, n2 * arg.z)
     }
 
-    public override fun ln(arg: Quaternion): Quaternion {
+    override fun ln(arg: Quaternion): Quaternion {
         val nu2 = arg.x * arg.x + arg.y * arg.y + arg.z * arg.z
 
         if (nu2 == 0.0)
             return if (arg.w > 0)
                 Quaternion(ln(arg.w), 0, 0, 0)
             else {
-                val l = ComplexField { ComplexField.ln(arg.w.toComplex()) }
+                val l = ComplexField { ln(arg.w.toComplex()) }
                 Quaternion(l.re, l.im, 0, 0)
             }
 
@@ -158,21 +158,21 @@ public object QuaternionField : Field<Quaternion>, Norm<Quaternion, Quaternion>,
         return Quaternion(ln(n), th * arg.x, th * arg.y, th * arg.z)
     }
 
-    public override operator fun Number.plus(b: Quaternion): Quaternion = Quaternion(toDouble() + b.w, b.x, b.y, b.z)
+    override operator fun Number.plus(b: Quaternion): Quaternion = Quaternion(toDouble() + b.w, b.x, b.y, b.z)
 
-    public override operator fun Number.minus(b: Quaternion): Quaternion =
+    override operator fun Number.minus(b: Quaternion): Quaternion =
         Quaternion(toDouble() - b.w, -b.x, -b.y, -b.z)
 
-    public override operator fun Quaternion.plus(b: Number): Quaternion = Quaternion(w + b.toDouble(), x, y, z)
+    override operator fun Quaternion.plus(b: Number): Quaternion = Quaternion(w + b.toDouble(), x, y, z)
-    public override operator fun Quaternion.minus(b: Number): Quaternion = Quaternion(w - b.toDouble(), x, y, z)
+    override operator fun Quaternion.minus(b: Number): Quaternion = Quaternion(w - b.toDouble(), x, y, z)
 
-    public override operator fun Number.times(b: Quaternion): Quaternion =
+    override operator fun Number.times(b: Quaternion): Quaternion =
         Quaternion(toDouble() * b.w, toDouble() * b.x, toDouble() * b.y, toDouble() * b.z)
 
-    public override fun Quaternion.unaryMinus(): Quaternion = Quaternion(-w, -x, -y, -z)
+    override fun Quaternion.unaryMinus(): Quaternion = Quaternion(-w, -x, -y, -z)
-    public override fun norm(arg: Quaternion): Quaternion = sqrt(arg.conjugate * arg)
+    override fun norm(arg: Quaternion): Quaternion = sqrt(arg.conjugate * arg)
 
-    public override fun bindSymbolOrNull(value: String): Quaternion? = when (value) {
+    override fun bindSymbolOrNull(value: String): Quaternion? = when (value) {
         "i" -> i
         "j" -> j
         "k" -> k
@@ -181,12 +181,12 @@ public object QuaternionField : Field<Quaternion>, Norm<Quaternion, Quaternion>,
 
     override fun number(value: Number): Quaternion = value.toQuaternion()
 
-    public override fun sinh(arg: Quaternion): Quaternion = (exp(arg) - exp(-arg)) / 2.0
+    override fun sinh(arg: Quaternion): Quaternion = (exp(arg) - exp(-arg)) / 2.0
-    public override fun cosh(arg: Quaternion): Quaternion = (exp(arg) + exp(-arg)) / 2.0
+    override fun cosh(arg: Quaternion): Quaternion = (exp(arg) + exp(-arg)) / 2.0
-    public override fun tanh(arg: Quaternion): Quaternion = (exp(arg) - exp(-arg)) / (exp(-arg) + exp(arg))
+    override fun tanh(arg: Quaternion): Quaternion = (exp(arg) - exp(-arg)) / (exp(-arg) + exp(arg))
-    public override fun asinh(arg: Quaternion): Quaternion = ln(sqrt(arg * arg + one) + arg)
+    override fun asinh(arg: Quaternion): Quaternion = ln(sqrt(arg * arg + one) + arg)
-    public override fun acosh(arg: Quaternion): Quaternion = ln(arg + sqrt((arg - one) * (arg + one)))
+    override fun acosh(arg: Quaternion): Quaternion = ln(arg + sqrt((arg - one) * (arg + one)))
-    public override fun atanh(arg: Quaternion): Quaternion = (ln(arg + one) - ln(one - arg)) / 2.0
+    override fun atanh(arg: Quaternion): Quaternion = (ln(arg + one) - ln(one - arg)) / 2.0
 }
 
 /**
@@ -224,16 +224,16 @@ public data class Quaternion(
     /**
      * Returns a string representation of this quaternion.
      */
-    public override fun toString(): String = "($w + $x * i + $y * j + $z * k)"
+    override fun toString(): String = "($w + $x * i + $y * j + $z * k)"
 
     public companion object : MemorySpec<Quaternion> {
-        public override val objectSize: Int
+        override val objectSize: Int
             get() = 32
 
-        public override fun MemoryReader.read(offset: Int): Quaternion =
+        override fun MemoryReader.read(offset: Int): Quaternion =
             Quaternion(readDouble(offset), readDouble(offset + 8), readDouble(offset + 16), readDouble(offset + 24))
 
-        public override fun MemoryWriter.write(offset: Int, value: Quaternion) {
+        override fun MemoryWriter.write(offset: Int, value: Quaternion) {
             writeDouble(offset, value.w)
             writeDouble(offset + 8, value.x)
             writeDouble(offset + 16, value.y)

kmath-core/README.md

@@ -10,12 +10,12 @@ The core interfaces of KMath.
 objects to the expression by providing a context. Expressions can be used for a wide variety of purposes from high 
 performance calculations to code generation.
  - [domains](src/commonMain/kotlin/space/kscience/kmath/domains) : Domains
- - [autodif](src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt) : Automatic differentiation
+ - [autodiff](src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt) : Automatic differentiation
 
 
 ## Artifact:
 
-The Maven coordinates of this project are `space.kscience:kmath-core:0.3.0-dev-11`.
+The Maven coordinates of this project are `space.kscience:kmath-core:0.3.0-dev-14`.
 
 **Gradle:**
 ```gradle
@@ -25,7 +25,7 @@ repositories {
 }
 
 dependencies {
-    implementation 'space.kscience:kmath-core:0.3.0-dev-11'
+    implementation 'space.kscience:kmath-core:0.3.0-dev-14'
 }
 ```
 **Gradle Kotlin DSL:**
@@ -36,6 +36,6 @@ repositories {
 }
 
 dependencies {
-    implementation("space.kscience:kmath-core:0.3.0-dev-11")
+    implementation("space.kscience:kmath-core:0.3.0-dev-14")
 }
 ```

kmath-core/build.gradle.kts

@@ -19,51 +19,42 @@ readme {
 
     feature(
         id = "algebras",
-        description = """
+        ref = "src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt",
-            Algebraic structures like rings, spaces and fields.
+    ) { "Algebraic structures like rings, spaces and fields." }
-        """.trimIndent(),
-        ref = "src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt"
-    )
 
     feature(
         id = "nd",
-        description = "Many-dimensional structures and operations on them.",
+        ref = "src/commonMain/kotlin/space/kscience/kmath/structures/StructureND.kt",
-        ref = "src/commonMain/kotlin/space/kscience/kmath/structures/StructureND.kt"
+    ) { "Many-dimensional structures and operations on them." }
-    )
 
     feature(
         id = "linear",
-        description = """
+        ref = "src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt",
-            Basic linear algebra operations (sums, products, etc.), backed by the `Space` API. Advanced linear algebra operations like matrix inversion and LU decomposition.
+    ) { "Basic linear algebra operations (sums, products, etc.), backed by the `Space` API. Advanced linear algebra operations like matrix inversion and LU decomposition." }
-        """.trimIndent(),
-        ref = "src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt"
-    )
 
     feature(
         id = "buffers",
-        description = "One-dimensional structure",
+        ref = "src/commonMain/kotlin/space/kscience/kmath/structures/Buffers.kt",
-        ref = "src/commonMain/kotlin/space/kscience/kmath/structures/Buffers.kt"
+    ) { "One-dimensional structure" }
-    )
 
     feature(
         id = "expressions",
-        description = """
+        ref = "src/commonMain/kotlin/space/kscience/kmath/expressions"
+    ) {
+        """
             By writing a single mathematical expression once, users will be able to apply different types of 
             objects to the expression by providing a context. Expressions can be used for a wide variety of purposes from high 
             performance calculations to code generation.
-        """.trimIndent(),
+        """.trimIndent()
-        ref = "src/commonMain/kotlin/space/kscience/kmath/expressions"
+    }
-    )
 
     feature(
         id = "domains",
-        description = "Domains",
+        ref = "src/commonMain/kotlin/space/kscience/kmath/domains",
-        ref = "src/commonMain/kotlin/space/kscience/kmath/domains"
+    ) { "Domains" }
-    )
 
     feature(
-        id = "autodif",
+        id = "autodiff",
-        description = "Automatic differentiation",
         ref = "src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt"
-    )
+    ) { "Automatic differentiation" }
 }

kmath-core/src/commonMain/kotlin/space/kscience/kmath/data/XYColumnarData.kt

@@ -16,7 +16,7 @@ import kotlin.math.max
  * The buffer of X values.
  */
 @UnstableKMathAPI
-public interface XYColumnarData<T, out X : T, out Y : T> : ColumnarData<T> {
+public interface XYColumnarData<out T, out X : T, out Y : T> : ColumnarData<T> {
     /**
      * The buffer of X values
      */

kmath-core/src/commonMain/kotlin/space/kscience/kmath/data/XYZColumnarData.kt

@@ -14,7 +14,7 @@ import space.kscience.kmath.structures.Buffer
  * Inherits [XYColumnarData].
  */
 @UnstableKMathAPI
-public interface XYZColumnarData<T, out X : T, out Y : T, out Z : T> : XYColumnarData<T, X, Y> {
+public interface XYZColumnarData<out T, out X : T, out Y : T, out Z : T> : XYColumnarData<T, X, Y> {
     public val z: Buffer<Z>
 
     override fun get(symbol: Symbol): Buffer<T>? = when (symbol) {
@@ -23,4 +23,4 @@ public interface XYZColumnarData<T, out X : T, out Y : T, out Z : T> : XYColumna
         Symbol.z -> z
         else -> null
     }
-}
+}

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

@@ -12,7 +12,7 @@ import space.kscience.kmath.linear.Point
  *
  * @param T the type of element of this domain.
  */
-public interface Domain<T : Any> {
+public interface Domain<in T : Any> {
     /**
      * Checks if the specified point is contained in this domain.
      */

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

@@ -17,17 +17,17 @@ import space.kscience.kmath.structures.indices
  */
 @UnstableKMathAPI
 public class HyperSquareDomain(private val lower: Buffer<Double>, private val upper: Buffer<Double>) : DoubleDomain {
-    public override val dimension: Int get() = lower.size
+    override val dimension: Int get() = lower.size
 
-    public override operator fun contains(point: Point<Double>): Boolean = point.indices.all { i ->
+    override operator fun contains(point: Point<Double>): Boolean = point.indices.all { i ->
         point[i] in lower[i]..upper[i]
     }
 
-    public override fun getLowerBound(num: Int): Double = lower[num]
+    override fun getLowerBound(num: Int): Double = lower[num]
 
-    public override fun getUpperBound(num: Int): Double = upper[num]
+    override fun getUpperBound(num: Int): Double = upper[num]
 
-    public override fun volume(): Double {
+    override fun volume(): Double {
         var res = 1.0
 
         for (i in 0 until dimension) {

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

@@ -8,12 +8,12 @@ import space.kscience.kmath.linear.Point
 import space.kscience.kmath.misc.UnstableKMathAPI
 
 @UnstableKMathAPI
-public class UnconstrainedDomain(public override val dimension: Int) : DoubleDomain {
+public class UnconstrainedDomain(override val dimension: Int) : DoubleDomain {
-    public override operator fun contains(point: Point<Double>): Boolean = true
+    override operator fun contains(point: Point<Double>): Boolean = true
 
-    public override fun getLowerBound(num: Int): Double = Double.NEGATIVE_INFINITY
+    override fun getLowerBound(num: Int): Double = Double.NEGATIVE_INFINITY
 
-    public override fun getUpperBound(num: Int): Double = Double.POSITIVE_INFINITY
+    override fun getUpperBound(num: Int): Double = Double.POSITIVE_INFINITY
 
-    public override fun volume(): Double = Double.POSITIVE_INFINITY
+    override fun volume(): Double = Double.POSITIVE_INFINITY
 }

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

@@ -10,24 +10,24 @@ import space.kscience.kmath.misc.UnstableKMathAPI
 
 @UnstableKMathAPI
 public class UnivariateDomain(public val range: ClosedFloatingPointRange<Double>) : DoubleDomain {
-    public override val dimension: Int get() = 1
+    override val dimension: Int get() = 1
 
     public operator fun contains(d: Double): Boolean = range.contains(d)
 
-    public override operator fun contains(point: Point<Double>): Boolean {
+    override operator fun contains(point: Point<Double>): Boolean {
         require(point.size == 0)
         return contains(point[0])
     }
 
-    public override fun getLowerBound(num: Int): Double {
+    override fun getLowerBound(num: Int): Double {
         require(num == 0)
         return range.start
     }
 
-    public override fun getUpperBound(num: Int): Double {
+    override fun getUpperBound(num: Int): Double {
         require(num == 0)
         return range.endInclusive
     }
 
-    public override fun volume(): Double = range.endInclusive - range.start
+    override fun volume(): Double = range.endInclusive - range.start
 }

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

@@ -8,10 +8,9 @@ package space.kscience.kmath.expressions
 import space.kscience.kmath.operations.Algebra
 
 /**
- * Represents expression which structure can be differentiated.
+ * Represents expression, which structure can be differentiated.
  *
  * @param T the type this expression takes as argument and returns.
- * @param R the type of expression this expression can be differentiated to.
  */
 public interface DifferentiableExpression<T> : Expression<T> {
     /**
@@ -26,16 +25,18 @@ public interface DifferentiableExpression<T> : Expression<T> {
 public fun <T> DifferentiableExpression<T>.derivative(symbols: List<Symbol>): Expression<T> =
     derivativeOrNull(symbols) ?: error("Derivative by symbols $symbols not provided")
 
-public fun <T> DifferentiableExpression<T>.derivative(vararg symbols: Symbol):  Expression<T> =
+public fun <T> DifferentiableExpression<T>.derivative(vararg symbols: Symbol): Expression<T> =
     derivative(symbols.toList())
 
-public fun <T> DifferentiableExpression<T>.derivative(name: String):  Expression<T> =
+public fun <T> DifferentiableExpression<T>.derivative(name: String): Expression<T> =
     derivative(StringSymbol(name))
 
 /**
- * A special type of [DifferentiableExpression] which returns typed expressions as derivatives
+ * A special type of [DifferentiableExpression] which returns typed expressions as derivatives.
+ *
+ * @param R the type of expression this expression can be differentiated to.
  */
-public interface SpecialDifferentiableExpression<T, R: Expression<T>>: DifferentiableExpression<T> {
+public interface SpecialDifferentiableExpression<T, out R : Expression<T>> : DifferentiableExpression<T> {
     override fun derivativeOrNull(symbols: List<Symbol>): R?
 }
 
@@ -55,9 +56,9 @@ public abstract class FirstDerivativeExpression<T> : DifferentiableExpression<T>
     /**
      * Returns first derivative of this expression by given [symbol].
      */
-    public abstract fun derivativeOrNull(symbol: Symbol):  Expression<T>?
+    public abstract fun derivativeOrNull(symbol: Symbol): Expression<T>?
 
-    public final override fun derivativeOrNull(symbols: List<Symbol>):  Expression<T>? {
+    public final override fun derivativeOrNull(symbols: List<Symbol>): Expression<T>? {
         val dSymbol = symbols.firstOrNull() ?: return null
         return derivativeOrNull(dSymbol)
     }
@@ -69,6 +70,6 @@ public abstract class FirstDerivativeExpression<T> : DifferentiableExpression<T>
  * @param I type of the actual expression state
  * @param A type of expression algebra
  */
-public fun interface AutoDiffProcessor<T, I, A : Algebra<I>> {
+public fun interface AutoDiffProcessor<T, I, out A : Algebra<I>> {
     public fun differentiate(function: A.() -> I): DifferentiableExpression<T>
 }

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

@@ -43,7 +43,7 @@ public operator fun <T> Expression<T>.invoke(vararg pairs: Pair<Symbol, T>): T =
 /**
  * Calls this expression from arguments.
  *
- * @param pairs the pairs of arguments' names to values.
+ * @param pairs the pairs of arguments' names to value.
  * @return a value.
  */
 @JvmName("callByString")
@@ -60,7 +60,7 @@ public operator fun <T> Expression<T>.invoke(vararg pairs: Pair<String, T>): T =
 public interface ExpressionAlgebra<in T, E> : Algebra<E> {
 
     /**
-     * A constant expression which does not depend on arguments
+     * A constant expression that does not depend on arguments.
      */
     public fun const(value: T): E
 }

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

@@ -6,43 +6,39 @@
 package space.kscience.kmath.expressions
 
 import space.kscience.kmath.operations.*
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
 
 /**
  * A context class for [Expression] construction.
  *
  * @param algebra The algebra to provide for Expressions built.
  */
-public abstract class FunctionalExpressionAlgebra<T, A : Algebra<T>>(
+public abstract class FunctionalExpressionAlgebra<T, out A : Algebra<T>>(
     public val algebra: A,
 ) : ExpressionAlgebra<T, Expression<T>> {
     /**
-     * Builds an Expression of constant expression which does not depend on arguments.
+     * Builds an Expression of constant expression that does not depend on arguments.
      */
-    public override fun const(value: T): Expression<T> = Expression { value }
+    override fun const(value: T): Expression<T> = Expression { value }
 
     /**
      * Builds an Expression to access a variable.
      */
-    public override fun bindSymbolOrNull(value: String): Expression<T>? = Expression { arguments ->
+    override fun bindSymbolOrNull(value: String): Expression<T>? = Expression { arguments ->
         algebra.bindSymbolOrNull(value)
             ?: arguments[StringSymbol(value)]
             ?: error("Symbol '$value' is not supported in $this")
     }
 
-    /**
+    override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
-     * Builds an Expression of dynamic call of binary operation [operation] on [left] and [right].
-     */
-    public override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
         { left, right ->
             Expression { arguments ->
                 algebra.binaryOperationFunction(operation)(left.invoke(arguments), right.invoke(arguments))
             }
         }
 
-    /**
+    override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> = { arg ->
-     * Builds an Expression of dynamic call of unary operation with name [operation] on [arg].
-     */
-    public override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> = { arg ->
         Expression { arguments -> algebra.unaryOperationFunction(operation)(arg.invoke(arguments)) }
     }
 }
@@ -50,24 +46,24 @@ public abstract class FunctionalExpressionAlgebra<T, A : Algebra<T>>(
 /**
  * A context class for [Expression] construction for [Ring] algebras.
  */
-public open class FunctionalExpressionGroup<T, A : Group<T>>(
+public open class FunctionalExpressionGroup<T, out A : Group<T>>(
     algebra: A,
 ) : FunctionalExpressionAlgebra<T, A>(algebra), Group<Expression<T>> {
-    public override val zero: Expression<T> get() = const(algebra.zero)
+    override val zero: Expression<T> get() = const(algebra.zero)
 
-    public override fun Expression<T>.unaryMinus(): Expression<T> =
+    override fun Expression<T>.unaryMinus(): Expression<T> =
         unaryOperation(GroupOperations.MINUS_OPERATION, this)
 
     /**
      * Builds an Expression of addition of two another expressions.
      */
-    public override fun add(a: Expression<T>, b: Expression<T>): Expression<T> =
+    override fun add(a: Expression<T>, b: Expression<T>): Expression<T> =
         binaryOperation(GroupOperations.PLUS_OPERATION, a, b)
 
 //    /**
 //     * Builds an Expression of multiplication of expression by number.
 //     */
-//    public override fun multiply(a: Expression<T>, k: Number): Expression<T> = Expression { arguments ->
+//    override fun multiply(a: Expression<T>, k: Number): Expression<T> = Expression { arguments ->
 //        algebra.multiply(a.invoke(arguments), k)
 //    }
 
@@ -76,110 +72,122 @@ public open class FunctionalExpressionGroup<T, A : Group<T>>(
     public operator fun T.plus(arg: Expression<T>): Expression<T> = arg + this
     public operator fun T.minus(arg: Expression<T>): Expression<T> = arg - this
 
-    public override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> =
+    override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> =
         super<FunctionalExpressionAlgebra>.unaryOperationFunction(operation)
 
-    public override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
+    override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
         super<FunctionalExpressionAlgebra>.binaryOperationFunction(operation)
 
 }
 
-public open class FunctionalExpressionRing<T, A : Ring<T>>(
+public open class FunctionalExpressionRing<T, out A : Ring<T>>(
     algebra: A,
 ) : FunctionalExpressionGroup<T, A>(algebra), Ring<Expression<T>> {
-    public override val one: Expression<T> get() = const(algebra.one)
+    override val one: Expression<T> get() = const(algebra.one)
 
     /**
      * Builds an Expression of multiplication of two expressions.
      */
-    public override fun multiply(a: Expression<T>, b: Expression<T>): Expression<T> =
+    override fun multiply(a: Expression<T>, b: Expression<T>): Expression<T> =
         binaryOperationFunction(RingOperations.TIMES_OPERATION)(a, b)
 
     public operator fun Expression<T>.times(arg: T): Expression<T> = this * const(arg)
     public operator fun T.times(arg: Expression<T>): Expression<T> = arg * this
 
-    public override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> =
+    override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> =
         super<FunctionalExpressionGroup>.unaryOperationFunction(operation)
 
-    public override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
+    override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
         super<FunctionalExpressionGroup>.binaryOperationFunction(operation)
 }
 
-public open class FunctionalExpressionField<T, A : Field<T>>(
+public open class FunctionalExpressionField<T, out A : Field<T>>(
     algebra: A,
 ) : FunctionalExpressionRing<T, A>(algebra), Field<Expression<T>>, ScaleOperations<Expression<T>> {
     /**
      * Builds an Expression of division an expression by another one.
      */
-    public override fun divide(a: Expression<T>, b: Expression<T>): Expression<T> =
+    override fun divide(a: Expression<T>, b: Expression<T>): Expression<T> =
         binaryOperationFunction(FieldOperations.DIV_OPERATION)(a, b)
 
     public operator fun Expression<T>.div(arg: T): Expression<T> = this / const(arg)
     public operator fun T.div(arg: Expression<T>): Expression<T> = arg / this
 
-    public override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> =
+    override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> =
         super<FunctionalExpressionRing>.unaryOperationFunction(operation)
 
-    public override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
+    override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
         super<FunctionalExpressionRing>.binaryOperationFunction(operation)
 
-    public override fun scale(a: Expression<T>, value: Double): Expression<T> = algebra {
+    override fun scale(a: Expression<T>, value: Double): Expression<T> = algebra {
         Expression { args -> a(args) * value }
     }
 
-    public override fun bindSymbolOrNull(value: String): Expression<T>? =
+    override fun bindSymbolOrNull(value: String): Expression<T>? =
         super<FunctionalExpressionRing>.bindSymbolOrNull(value)
 }
 
-public open class FunctionalExpressionExtendedField<T, A : ExtendedField<T>>(
+public open class FunctionalExpressionExtendedField<T, out A : ExtendedField<T>>(
     algebra: A,
 ) : FunctionalExpressionField<T, A>(algebra), ExtendedField<Expression<T>> {
-    public override fun number(value: Number): Expression<T> = const(algebra.number(value))
+    override fun number(value: Number): Expression<T> = const(algebra.number(value))
 
-    public override fun sqrt(arg: Expression<T>): Expression<T> =
+    override fun sqrt(arg: Expression<T>): Expression<T> =
         unaryOperationFunction(PowerOperations.SQRT_OPERATION)(arg)
 
-    public override fun sin(arg: Expression<T>): Expression<T> =
+    override fun sin(arg: Expression<T>): Expression<T> =
         unaryOperationFunction(TrigonometricOperations.SIN_OPERATION)(arg)
 
-    public override fun cos(arg: Expression<T>): Expression<T> =
+    override fun cos(arg: Expression<T>): Expression<T> =
         unaryOperationFunction(TrigonometricOperations.COS_OPERATION)(arg)
 
-    public override fun asin(arg: Expression<T>): Expression<T> =
+    override fun asin(arg: Expression<T>): Expression<T> =
         unaryOperationFunction(TrigonometricOperations.ASIN_OPERATION)(arg)
 
-    public override fun acos(arg: Expression<T>): Expression<T> =
+    override fun acos(arg: Expression<T>): Expression<T> =
         unaryOperationFunction(TrigonometricOperations.ACOS_OPERATION)(arg)
 
-    public override fun atan(arg: Expression<T>): Expression<T> =
+    override fun atan(arg: Expression<T>): Expression<T> =
         unaryOperationFunction(TrigonometricOperations.ATAN_OPERATION)(arg)
 
-    public override fun power(arg: Expression<T>, pow: Number): Expression<T> =
+    override fun power(arg: Expression<T>, pow: Number): Expression<T> =
         binaryOperationFunction(PowerOperations.POW_OPERATION)(arg, number(pow))
 
-    public override fun exp(arg: Expression<T>): Expression<T> =
+    override fun exp(arg: Expression<T>): Expression<T> =
         unaryOperationFunction(ExponentialOperations.EXP_OPERATION)(arg)
 
-    public override fun ln(arg: Expression<T>): Expression<T> =
+    override fun ln(arg: Expression<T>): Expression<T> =
         unaryOperationFunction(ExponentialOperations.LN_OPERATION)(arg)
 
-    public override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> =
+    override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> =
         super<FunctionalExpressionField>.unaryOperationFunction(operation)
 
-    public override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
+    override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
         super<FunctionalExpressionField>.binaryOperationFunction(operation)
 
-    public override fun bindSymbol(value: String): Expression<T> = super<FunctionalExpressionField>.bindSymbol(value)
+    override fun bindSymbol(value: String): Expression<T> = super<FunctionalExpressionField>.bindSymbol(value)
 }
 
-public inline fun <T, A : Ring<T>> A.expressionInSpace(block: FunctionalExpressionGroup<T, A>.() -> Expression<T>): Expression<T> =
+public inline fun <T, A : Group<T>> A.expressionInGroup(
-    FunctionalExpressionGroup(this).block()
+    block: FunctionalExpressionGroup<T, A>.() -> Expression<T>,
+): Expression<T> {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return FunctionalExpressionGroup(this).block()
+}
 
-public inline fun <T, A : Ring<T>> A.expressionInRing(block: FunctionalExpressionRing<T, A>.() -> Expression<T>): Expression<T> =
+public inline fun <T, A : Ring<T>> A.expressionInRing(
-    FunctionalExpressionRing(this).block()
+    block: FunctionalExpressionRing<T, A>.() -> Expression<T>,
+): Expression<T> {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return FunctionalExpressionRing(this).block()
+}
 
-public inline fun <T, A : Field<T>> A.expressionInField(block: FunctionalExpressionField<T, A>.() -> Expression<T>): Expression<T> =
+public inline fun <T, A : Field<T>> A.expressionInField(
-    FunctionalExpressionField(this).block()
+    block: FunctionalExpressionField<T, A>.() -> Expression<T>,
+): Expression<T> {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return FunctionalExpressionField(this).block()
+}
 
 public inline fun <T, A : ExtendedField<T>> A.expressionInExtendedField(
     block: FunctionalExpressionExtendedField<T, A>.() -> Expression<T>,

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

@@ -12,14 +12,14 @@ import space.kscience.kmath.operations.*
  * [Algebra] over [MST] nodes.
  */
 public object MstNumericAlgebra : NumericAlgebra<MST> {
-    public override fun number(value: Number): MST.Numeric = MST.Numeric(value)
+    override fun number(value: Number): MST.Numeric = MST.Numeric(value)
-    public override fun bindSymbolOrNull(value: String): Symbol = StringSymbol(value)
+    override fun bindSymbolOrNull(value: String): Symbol = StringSymbol(value)
     override fun bindSymbol(value: String): Symbol = bindSymbolOrNull(value)
 
-    public override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
+    override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
         { arg -> MST.Unary(operation, arg) }
 
-    public override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
+    override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
         { left, right -> MST.Binary(operation, left, right) }
 }
 
@@ -27,27 +27,27 @@ public object MstNumericAlgebra : NumericAlgebra<MST> {
  * [Group] over [MST] nodes.
  */
 public object MstGroup : Group<MST>, NumericAlgebra<MST>, ScaleOperations<MST> {
-    public override val zero: MST.Numeric = number(0.0)
+    override val zero: MST.Numeric = number(0.0)
 
-    public override fun number(value: Number): MST.Numeric = MstNumericAlgebra.number(value)
+    override fun number(value: Number): MST.Numeric = MstNumericAlgebra.number(value)
-    public override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value)
+    override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value)
-    public override fun add(a: MST, b: MST): MST.Binary = binaryOperationFunction(GroupOperations.PLUS_OPERATION)(a, b)
+    override fun add(a: MST, b: MST): MST.Binary = binaryOperationFunction(GroupOperations.PLUS_OPERATION)(a, b)
-    public override operator fun MST.unaryPlus(): MST.Unary =
+    override operator fun MST.unaryPlus(): MST.Unary =
         unaryOperationFunction(GroupOperations.PLUS_OPERATION)(this)
 
-    public override operator fun MST.unaryMinus(): MST.Unary =
+    override operator fun MST.unaryMinus(): MST.Unary =
         unaryOperationFunction(GroupOperations.MINUS_OPERATION)(this)
 
-    public override operator fun MST.minus(b: MST): MST.Binary =
+    override operator fun MST.minus(b: MST): MST.Binary =
         binaryOperationFunction(GroupOperations.MINUS_OPERATION)(this, b)
 
-    public override fun scale(a: MST, value: Double): MST.Binary =
+    override fun scale(a: MST, value: Double): MST.Binary =
         binaryOperationFunction(RingOperations.TIMES_OPERATION)(a, number(value))
 
-    public override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
+    override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
         MstNumericAlgebra.binaryOperationFunction(operation)
 
-    public override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
+    override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
         MstNumericAlgebra.unaryOperationFunction(operation)
 }
 
@@ -57,27 +57,27 @@ public object MstGroup : Group<MST>, NumericAlgebra<MST>, ScaleOperations<MST> {
 @Suppress("OVERRIDE_BY_INLINE")
 @OptIn(UnstableKMathAPI::class)
 public object MstRing : Ring<MST>, NumbersAddOperations<MST>, ScaleOperations<MST> {
-    public override inline val zero: MST.Numeric get() = MstGroup.zero
+    override inline val zero: MST.Numeric get() = MstGroup.zero
-    public override val one: MST.Numeric = number(1.0)
+    override val one: MST.Numeric = number(1.0)
 
-    public override fun number(value: Number): MST.Numeric = MstGroup.number(value)
+    override fun number(value: Number): MST.Numeric = MstGroup.number(value)
-    public override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value)
+    override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value)
-    public override fun add(a: MST, b: MST): MST.Binary = MstGroup.add(a, b)
+    override fun add(a: MST, b: MST): MST.Binary = MstGroup.add(a, b)
 
-    public override fun scale(a: MST, value: Double): MST.Binary =
+    override fun scale(a: MST, value: Double): MST.Binary =
         MstGroup.binaryOperationFunction(RingOperations.TIMES_OPERATION)(a, MstGroup.number(value))
 
-    public override fun multiply(a: MST, b: MST): MST.Binary =
+    override fun multiply(a: MST, b: MST): MST.Binary =
         binaryOperationFunction(RingOperations.TIMES_OPERATION)(a, b)
 
-    public override operator fun MST.unaryPlus(): MST.Unary = MstGroup { +this@unaryPlus }
+    override operator fun MST.unaryPlus(): MST.Unary = MstGroup { +this@unaryPlus }
-    public override operator fun MST.unaryMinus(): MST.Unary = MstGroup { -this@unaryMinus }
+    override operator fun MST.unaryMinus(): MST.Unary = MstGroup { -this@unaryMinus }
-    public override operator fun MST.minus(b: MST): MST.Binary = MstGroup { this@minus - b }
+    override operator fun MST.minus(b: MST): MST.Binary = MstGroup { this@minus - b }
 
-    public override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
+    override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
         MstGroup.binaryOperationFunction(operation)
 
-    public override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
+    override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
         MstNumericAlgebra.unaryOperationFunction(operation)
 }
 
@@ -87,28 +87,28 @@ public object MstRing : Ring<MST>, NumbersAddOperations<MST>, ScaleOperations<MS
 @Suppress("OVERRIDE_BY_INLINE")
 @OptIn(UnstableKMathAPI::class)
 public object MstField : Field<MST>, NumbersAddOperations<MST>, ScaleOperations<MST> {
-    public override inline val zero: MST.Numeric get() = MstRing.zero
+    override inline val zero: MST.Numeric get() = MstRing.zero
-    public override inline val one: MST.Numeric get() = MstRing.one
+    override inline val one: MST.Numeric get() = MstRing.one
 
-    public override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value)
+    override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value)
-    public override fun number(value: Number): MST.Numeric = MstRing.number(value)
+    override fun number(value: Number): MST.Numeric = MstRing.number(value)
-    public override fun add(a: MST, b: MST): MST.Binary = MstRing.add(a, b)
+    override fun add(a: MST, b: MST): MST.Binary = MstRing.add(a, b)
 
-    public override fun scale(a: MST, value: Double): MST.Binary =
+    override fun scale(a: MST, value: Double): MST.Binary =
         MstGroup.binaryOperationFunction(RingOperations.TIMES_OPERATION)(a, MstGroup.number(value))
 
-    public override fun multiply(a: MST, b: MST): MST.Binary = MstRing.multiply(a, b)
+    override fun multiply(a: MST, b: MST): MST.Binary = MstRing.multiply(a, b)
-    public override fun divide(a: MST, b: MST): MST.Binary =
+    override fun divide(a: MST, b: MST): MST.Binary =
         binaryOperationFunction(FieldOperations.DIV_OPERATION)(a, b)
 
-    public override operator fun MST.unaryPlus(): MST.Unary = MstRing { +this@unaryPlus }
+    override operator fun MST.unaryPlus(): MST.Unary = MstRing { +this@unaryPlus }
-    public override operator fun MST.unaryMinus(): MST.Unary = MstRing { -this@unaryMinus }
+    override operator fun MST.unaryMinus(): MST.Unary = MstRing { -this@unaryMinus }
-    public override operator fun MST.minus(b: MST): MST.Binary = MstRing { this@minus - b }
+    override operator fun MST.minus(b: MST): MST.Binary = MstRing { this@minus - b }
 
-    public override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
+    override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
         MstRing.binaryOperationFunction(operation)
 
-    public override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
+    override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
         MstRing.unaryOperationFunction(operation)
 }
 
@@ -117,45 +117,45 @@ public object MstField : Field<MST>, NumbersAddOperations<MST>, ScaleOperations<
  */
 @Suppress("OVERRIDE_BY_INLINE")
 public object MstExtendedField : ExtendedField<MST>, NumericAlgebra<MST> {
-    public override inline val zero: MST.Numeric get() = MstField.zero
+    override inline val zero: MST.Numeric get() = MstField.zero
-    public override inline val one: MST.Numeric get() = MstField.one
+    override inline val one: MST.Numeric get() = MstField.one
 
-    public override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value)
+    override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value)
-    public override fun number(value: Number): MST.Numeric = MstRing.number(value)
+    override fun number(value: Number): MST.Numeric = MstRing.number(value)
-    public override fun sin(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.SIN_OPERATION)(arg)
+    override fun sin(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.SIN_OPERATION)(arg)
-    public override fun cos(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.COS_OPERATION)(arg)
+    override fun cos(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.COS_OPERATION)(arg)
-    public override fun tan(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.TAN_OPERATION)(arg)
+    override fun tan(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.TAN_OPERATION)(arg)
-    public override fun asin(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.ASIN_OPERATION)(arg)
+    override fun asin(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.ASIN_OPERATION)(arg)
-    public override fun acos(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.ACOS_OPERATION)(arg)
+    override fun acos(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.ACOS_OPERATION)(arg)
-    public override fun atan(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.ATAN_OPERATION)(arg)
+    override fun atan(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.ATAN_OPERATION)(arg)
-    public override fun sinh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.SINH_OPERATION)(arg)
+    override fun sinh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.SINH_OPERATION)(arg)
-    public override fun cosh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.COSH_OPERATION)(arg)
+    override fun cosh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.COSH_OPERATION)(arg)
-    public override fun tanh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.TANH_OPERATION)(arg)
+    override fun tanh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.TANH_OPERATION)(arg)
-    public override fun asinh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.ASINH_OPERATION)(arg)
+    override fun asinh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.ASINH_OPERATION)(arg)
-    public override fun acosh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.ACOSH_OPERATION)(arg)
+    override fun acosh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.ACOSH_OPERATION)(arg)
-    public override fun atanh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.ATANH_OPERATION)(arg)
+    override fun atanh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.ATANH_OPERATION)(arg)
-    public override fun add(a: MST, b: MST): MST.Binary = MstField.add(a, b)
+    override fun add(a: MST, b: MST): MST.Binary = MstField.add(a, b)
-    public override fun sqrt(arg: MST): MST = unaryOperationFunction(PowerOperations.SQRT_OPERATION)(arg)
+    override fun sqrt(arg: MST): MST = unaryOperationFunction(PowerOperations.SQRT_OPERATION)(arg)
 
-    public override fun scale(a: MST, value: Double): MST =
+    override fun scale(a: MST, value: Double): MST =
         binaryOperation(GroupOperations.PLUS_OPERATION, a, number(value))
 
-    public override fun multiply(a: MST, b: MST): MST.Binary = MstField.multiply(a, b)
+    override fun multiply(a: MST, b: MST): MST.Binary = MstField.multiply(a, b)
-    public override fun divide(a: MST, b: MST): MST.Binary = MstField.divide(a, b)
+    override fun divide(a: MST, b: MST): MST.Binary = MstField.divide(a, b)
-    public override operator fun MST.unaryPlus(): MST.Unary = MstField { +this@unaryPlus }
+    override operator fun MST.unaryPlus(): MST.Unary = MstField { +this@unaryPlus }
-    public override operator fun MST.unaryMinus(): MST.Unary = MstField { -this@unaryMinus }
+    override operator fun MST.unaryMinus(): MST.Unary = MstField { -this@unaryMinus }
-    public override operator fun MST.minus(b: MST): MST.Binary = MstField { this@minus - b }
+    override operator fun MST.minus(b: MST): MST.Binary = MstField { this@minus - b }
 
-    public override fun power(arg: MST, pow: Number): MST.Binary =
+    override fun power(arg: MST, pow: Number): MST.Binary =
         binaryOperationFunction(PowerOperations.POW_OPERATION)(arg, number(pow))
 
-    public override fun exp(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.EXP_OPERATION)(arg)
+    override fun exp(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.EXP_OPERATION)(arg)
-    public override fun ln(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.LN_OPERATION)(arg)
+    override fun ln(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.LN_OPERATION)(arg)
 
-    public override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
+    override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
         MstField.binaryOperationFunction(operation)
 
-    public override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
+    override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
         MstField.unaryOperationFunction(operation)
 }
 
@@ -164,7 +164,7 @@ public object MstExtendedField : ExtendedField<MST>, NumericAlgebra<MST> {
  */
 @UnstableKMathAPI
 public object MstLogicAlgebra : LogicAlgebra<MST> {
-    public override fun bindSymbolOrNull(value: String): MST = super.bindSymbolOrNull(value) ?: StringSymbol(value)
+    override fun bindSymbolOrNull(value: String): MST = super.bindSymbolOrNull(value) ?: StringSymbol(value)
 
     override fun const(boolean: Boolean): Symbol = if (boolean) {
         LogicAlgebra.TRUE

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

@@ -60,8 +60,8 @@ public open class SimpleAutoDiffField<T : Any, F : Field<T>>(
     public val context: F,
     bindings: Map<Symbol, T>,
 ) : Field<AutoDiffValue<T>>, ExpressionAlgebra<T, AutoDiffValue<T>>, NumbersAddOperations<AutoDiffValue<T>> {
-    public override val zero: AutoDiffValue<T> get() = const(context.zero)
+    override val zero: AutoDiffValue<T> get() = const(context.zero)
-    public override val one: AutoDiffValue<T> get() = const(context.one)
+    override val one: AutoDiffValue<T> get() = const(context.one)
 
     // this stack contains pairs of blocks and values to apply them to
     private var stack: Array<Any?> = arrayOfNulls<Any?>(8)
@@ -119,8 +119,6 @@ public open class SimpleAutoDiffField<T : Any, F : Field<T>>(
         get() = getDerivative(this)
         set(value) = setDerivative(this, value)
 
-    public inline fun const(block: F.() -> T): AutoDiffValue<T> = const(context.block())
-
     /**
      * Performs update of derivative after the rest of the formula in the back-pass.
      *
@@ -151,17 +149,17 @@ public open class SimpleAutoDiffField<T : Any, F : Field<T>>(
 
 //    // Overloads for Double constants
 //
-//    public override operator fun Number.plus(b: AutoDiffValue<T>): AutoDiffValue<T> =
+//    override operator fun Number.plus(b: AutoDiffValue<T>): AutoDiffValue<T> =
 //        derive(const { this@plus.toDouble() * one + b.value }) { z ->
 //            b.d += z.d
 //        }
 //
-//    public override operator fun AutoDiffValue<T>.plus(b: Number): AutoDiffValue<T> = b.plus(this)
+//    override operator fun AutoDiffValue<T>.plus(b: Number): AutoDiffValue<T> = b.plus(this)
 //
-//    public override operator fun Number.minus(b: AutoDiffValue<T>): AutoDiffValue<T> =
+//    override operator fun Number.minus(b: AutoDiffValue<T>): AutoDiffValue<T> =
 //        derive(const { this@minus.toDouble() * one - b.value }) { z -> b.d -= z.d }
 //
-//    public override operator fun AutoDiffValue<T>.minus(b: Number): AutoDiffValue<T> =
+//    override operator fun AutoDiffValue<T>.minus(b: Number): AutoDiffValue<T> =
 //        derive(const { this@minus.value - one * b.toDouble() }) { z -> d += z.d }
 
 
@@ -170,30 +168,35 @@ public open class SimpleAutoDiffField<T : Any, F : Field<T>>(
 
     // Basic math (+, -, *, /)
 
-    public override fun add(a: AutoDiffValue<T>, b: AutoDiffValue<T>): AutoDiffValue<T> =
+    override fun add(a: AutoDiffValue<T>, b: AutoDiffValue<T>): AutoDiffValue<T> =
         derive(const { a.value + b.value }) { z ->
             a.d += z.d
             b.d += z.d
         }
 
-    public override fun multiply(a: AutoDiffValue<T>, b: AutoDiffValue<T>): AutoDiffValue<T> =
+    override fun multiply(a: AutoDiffValue<T>, b: AutoDiffValue<T>): AutoDiffValue<T> =
         derive(const { a.value * b.value }) { z ->
             a.d += z.d * b.value
             b.d += z.d * a.value
         }
 
-    public override fun divide(a: AutoDiffValue<T>, b: AutoDiffValue<T>): AutoDiffValue<T> =
+    override fun divide(a: AutoDiffValue<T>, b: AutoDiffValue<T>): AutoDiffValue<T> =
         derive(const { a.value / b.value }) { z ->
             a.d += z.d / b.value
             b.d -= z.d * a.value / (b.value * b.value)
         }
 
-    public override fun scale(a: AutoDiffValue<T>, value: Double): AutoDiffValue<T> =
+    override fun scale(a: AutoDiffValue<T>, value: Double): AutoDiffValue<T> =
         derive(const { value * a.value }) { z ->
             a.d += z.d * value
         }
 }
 
+public inline fun <T : Any, F : Field<T>> SimpleAutoDiffField<T, F>.const(block: F.() -> T): AutoDiffValue<T> {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return const(context.block())
+}
+
 
 /**
  * Runs differentiation and establishes [SimpleAutoDiffField] context inside the block of code.
@@ -208,7 +211,7 @@ public open class SimpleAutoDiffField<T : Any, F : Field<T>>(
  * assertEquals(9.0, x.d)  // dy/dx
  * ```
  *
- * @param body the action in [SimpleAutoDiffField] context returning [AutoDiffVariable] to differentiate with respect to.
+ * @param body the action in [SimpleAutoDiffField] context returning [AutoDiffValue] to differentiate with respect to.
  * @return the result of differentiation.
  */
 public fun <T : Any, F : Field<T>> F.simpleAutoDiff(
@@ -233,12 +236,12 @@ public class SimpleAutoDiffExpression<T : Any, F : Field<T>>(
     public val field: F,
     public val function: SimpleAutoDiffField<T, F>.() -> AutoDiffValue<T>,
 ) : FirstDerivativeExpression<T>() {
-    public override operator fun invoke(arguments: Map<Symbol, T>): T {
+    override operator fun invoke(arguments: Map<Symbol, T>): T {
         //val bindings = arguments.entries.map { it.key.bind(it.value) }
         return SimpleAutoDiffField(field, arguments).function().value
     }
 
-    public override fun derivativeOrNull(symbol: Symbol): Expression<T> = Expression { arguments ->
+    override fun derivativeOrNull(symbol: Symbol): Expression<T> = Expression { arguments ->
         //val bindings = arguments.entries.map { it.key.bind(it.value) }
         val derivationResult = SimpleAutoDiffField(field, arguments).differentiate(function)
         derivationResult.derivative(symbol)
@@ -248,7 +251,9 @@ public class SimpleAutoDiffExpression<T : Any, F : Field<T>>(
 /**
  * Generate [AutoDiffProcessor] for [SimpleAutoDiffExpression]
  */
-public fun <T : Any, F : Field<T>> simpleAutoDiff(field: F): AutoDiffProcessor<T, AutoDiffValue<T>, SimpleAutoDiffField<T, F>, Expression<T>> =
+public fun <T : Any, F : Field<T>> simpleAutoDiff(
+    field: F
+): AutoDiffProcessor<T, AutoDiffValue<T>, SimpleAutoDiffField<T, F>> =
     AutoDiffProcessor { function ->
         SimpleAutoDiffExpression(field, function)
     }
@@ -343,28 +348,28 @@ public class SimpleAutoDiffExtendedField<T : Any, F : ExtendedField<T>>(
 
     override fun bindSymbol(value: String): AutoDiffValue<T> = super<SimpleAutoDiffField>.bindSymbol(value)
 
-    public override fun number(value: Number): AutoDiffValue<T> = const { number(value) }
+    override fun number(value: Number): AutoDiffValue<T> = const { number(value) }
 
-    public override fun scale(a: AutoDiffValue<T>, value: Double): AutoDiffValue<T> = a * number(value)
+    override fun scale(a: AutoDiffValue<T>, value: Double): AutoDiffValue<T> = a * number(value)
 
     // x ^ 2
     public fun sqr(x: AutoDiffValue<T>): AutoDiffValue<T> =
         (this as SimpleAutoDiffField<T, F>).sqr(x)
 
     // x ^ 1/2
-    public override fun sqrt(arg: AutoDiffValue<T>): AutoDiffValue<T> =
+    override fun sqrt(arg: AutoDiffValue<T>): AutoDiffValue<T> =
         (this as SimpleAutoDiffField<T, F>).sqrt(arg)
 
     // x ^ y (const)
-    public override fun power(arg: AutoDiffValue<T>, pow: Number): AutoDiffValue<T> =
+    override fun power(arg: AutoDiffValue<T>, pow: Number): AutoDiffValue<T> =
         (this as SimpleAutoDiffField<T, F>).pow(arg, pow.toDouble())
 
     // exp(x)
-    public override fun exp(arg: AutoDiffValue<T>): AutoDiffValue<T> =
+    override fun exp(arg: AutoDiffValue<T>): AutoDiffValue<T> =
         (this as SimpleAutoDiffField<T, F>).exp(arg)
 
     // ln(x)
-    public override fun ln(arg: AutoDiffValue<T>): AutoDiffValue<T> =
+    override fun ln(arg: AutoDiffValue<T>): AutoDiffValue<T> =
         (this as SimpleAutoDiffField<T, F>).ln(arg)
 
     // x ^ y (any)
@@ -374,40 +379,40 @@ public class SimpleAutoDiffExtendedField<T : Any, F : ExtendedField<T>>(
     ): AutoDiffValue<T> = exp(y * ln(x))
 
     // sin(x)
-    public override fun sin(arg: AutoDiffValue<T>): AutoDiffValue<T> =
+    override fun sin(arg: AutoDiffValue<T>): AutoDiffValue<T> =
         (this as SimpleAutoDiffField<T, F>).sin(arg)
 
     // cos(x)
-    public override fun cos(arg: AutoDiffValue<T>): AutoDiffValue<T> =
+    override fun cos(arg: AutoDiffValue<T>): AutoDiffValue<T> =
         (this as SimpleAutoDiffField<T, F>).cos(arg)
 
-    public override fun tan(arg: AutoDiffValue<T>): AutoDiffValue<T> =
+    override fun tan(arg: AutoDiffValue<T>): AutoDiffValue<T> =
         (this as SimpleAutoDiffField<T, F>).tan(arg)
 
-    public override fun asin(arg: AutoDiffValue<T>): AutoDiffValue<T> =
+    override fun asin(arg: AutoDiffValue<T>): AutoDiffValue<T> =
         (this as SimpleAutoDiffField<T, F>).asin(arg)
 
-    public override fun acos(arg: AutoDiffValue<T>): AutoDiffValue<T> =
+    override fun acos(arg: AutoDiffValue<T>): AutoDiffValue<T> =
         (this as SimpleAutoDiffField<T, F>).acos(arg)
 
-    public override fun atan(arg: AutoDiffValue<T>): AutoDiffValue<T> =
+    override fun atan(arg: AutoDiffValue<T>): AutoDiffValue<T> =
         (this as SimpleAutoDiffField<T, F>).atan(arg)
 
-    public override fun sinh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
+    override fun sinh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
         (this as SimpleAutoDiffField<T, F>).sinh(arg)
 
-    public override fun cosh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
+    override fun cosh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
         (this as SimpleAutoDiffField<T, F>).cosh(arg)
 
-    public override fun tanh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
+    override fun tanh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
         (this as SimpleAutoDiffField<T, F>).tanh(arg)
 
-    public override fun asinh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
+    override fun asinh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
         (this as SimpleAutoDiffField<T, F>).asinh(arg)
 
-    public override fun acosh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
+    override fun acosh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
         (this as SimpleAutoDiffField<T, F>).acosh(arg)
 
-    public override fun atanh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
+    override fun atanh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
         (this as SimpleAutoDiffField<T, F>).atanh(arg)
 }

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

@@ -10,6 +10,8 @@ import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.nd.Structure2D
 import space.kscience.kmath.structures.BufferFactory
 import space.kscience.kmath.structures.DoubleBuffer
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
 import kotlin.jvm.JvmInline
 
 /**
@@ -75,9 +77,13 @@ public value class SimpleSymbolIndexer(override val symbols: List<Symbol>) : Sym
  * Execute the block with symbol indexer based on given symbol order
  */
 @UnstableKMathAPI
-public inline fun <R> withSymbols(vararg symbols: Symbol, block: SymbolIndexer.() -> R): R =
+public inline fun <R> withSymbols(vararg symbols: Symbol, block: SymbolIndexer.() -> R): R {
-    with(SimpleSymbolIndexer(symbols.toList()), block)
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return with(SimpleSymbolIndexer(symbols.toList()), block)
+}
 
 @UnstableKMathAPI
-public inline fun <R> withSymbols(symbols: Collection<Symbol>, block: SymbolIndexer.() -> R): R =
+public inline fun <R> withSymbols(symbols: Collection<Symbol>, block: SymbolIndexer.() -> R): R {
-    with(SimpleSymbolIndexer(symbols.toList()), block)
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return with(SimpleSymbolIndexer(symbols.toList()), block)
+}

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

@@ -0,0 +1,38 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.expressions
+
+import space.kscience.kmath.operations.ExtendedField
+import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.indices
+
+/**
+ * Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic
+ * differentiation.
+ */
+public fun <T : Any, I : Any, A> AutoDiffProcessor<T, I, A>.chiSquaredExpression(
+    x: Buffer<T>,
+    y: Buffer<T>,
+    yErr: Buffer<T>,
+    model: A.(I) -> I,
+): DifferentiableExpression<T> where A : ExtendedField<I>, A : ExpressionAlgebra<T, I> {
+    require(x.size == y.size) { "X and y buffers should be of the same size" }
+    require(y.size == yErr.size) { "Y and yErr buffer should of the same size" }
+
+    return differentiate {
+        var sum = zero
+
+        x.indices.forEach {
+            val xValue = const(x[it])
+            val yValue = const(y[it])
+            val yErrValue = const(yErr[it])
+            val modelValue = model(xValue)
+            sum += ((yValue - modelValue) / yErrValue).pow(2)
+        }
+
+        sum
+    }
+}

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

@@ -15,7 +15,7 @@ import space.kscience.kmath.structures.VirtualBuffer
 import space.kscience.kmath.structures.indices
 
 
-public class BufferedLinearSpace<T : Any, A : Ring<T>>(
+public class BufferedLinearSpace<T : Any, out A : Ring<T>>(
     override val elementAlgebra: A,
     private val bufferFactory: BufferFactory<T>,
 ) : LinearSpace<T, A> {
@@ -88,4 +88,4 @@ public class BufferedLinearSpace<T : Any, A : Ring<T>>(
     override fun Matrix<T>.times(value: T): Matrix<T> = ndRing(rowNum, colNum).run {
         unwrap().map { it * value }.as2D()
     }
-}
+}

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

@@ -6,8 +6,8 @@
 package space.kscience.kmath.linear
 
 /**
- * A group of methods to solve for *X* in equation *X = A <sup>-1</sup> &middot; B*, where *A* and *B* are matrices or
+ * A group of methods to solve for *X* in equation *X = A<sup>&minus;1</sup> &middot; B*, where *A* and *B* are
- * vectors.
+ * matrices or vectors.
  *
  * @param T the type of items.
  */

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

@@ -29,10 +29,10 @@ public typealias MutableMatrix<T> = MutableStructure2D<T>
 public typealias Point<T> = Buffer<T>
 
 /**
- * Basic operations on matrices and vectors. Operates on [Matrix].
+ * Basic operations on matrices and vectors.
  *
  * @param T the type of items in the matrices.
- * @param M the type of operated matrices.
+ * @param A the type of ring over [T].
  */
 public interface LinearSpace<T : Any, out A : Ring<T>> {
     public val elementAlgebra: A
@@ -164,7 +164,7 @@ public interface LinearSpace<T : Any, out A : Ring<T>> {
     public operator fun T.times(v: Point<T>): Point<T> = v * this
 
     /**
-     * Compute a feature of the structure in this scope. Structure features take precedence other context features
+     * Compute a feature of the structure in this scope. Structure features take precedence other context features.
      *
      * @param F the type of feature.
      * @param structure the structure.
@@ -195,7 +195,7 @@ public interface LinearSpace<T : Any, out A : Ring<T>> {
 }
 
 /**
- * Get a feature of the structure in this scope. Structure features take precedence other context features
+ * Get a feature of the structure in this scope. Structure features take precedence other context features.
  *
  * @param T the type of items in the matrices.
  * @param F the type of feature.

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

@@ -115,7 +115,7 @@ public fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lup(
                     for (i in 0 until col) sum -= luRow[i] * lu[i, col]
                     luRow[col] = sum
 
-                    // maintain best permutation choice
+                    // maintain the best permutation choice
                     if (abs(sum) > largest) {
                         largest = abs(sum)
                         max = row

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

@@ -31,11 +31,11 @@ public object ZeroFeature : DiagonalFeature
 public object UnitFeature : DiagonalFeature
 
 /**
- * Matrices with this feature can be inverted: [inverse] = `a`<sup>-1</sup> where `a` is the owning matrix.
+ * Matrices with this feature can be inverted: *[inverse] = a<sup>&minus;1</sup>* where *a* is the owning matrix.
  *
  * @param T the type of matrices' items.
  */
-public interface InverseMatrixFeature<T : Any> : MatrixFeature {
+public interface InverseMatrixFeature<out T : Any> : MatrixFeature {
     /**
      * The inverse matrix of the matrix that owns this feature.
      */
@@ -47,7 +47,7 @@ public interface InverseMatrixFeature<T : Any> : MatrixFeature {
  *
  * @param T the type of matrices' items.
  */
-public interface DeterminantFeature<T : Any> : MatrixFeature {
+public interface DeterminantFeature<out T : Any> : MatrixFeature {
     /**
      * The determinant of the matrix that owns this feature.
      */
@@ -80,7 +80,7 @@ public object UFeature : MatrixFeature
  *
  * @param T the type of matrices' items.
  */
-public interface LUDecompositionFeature<T : Any> : MatrixFeature {
+public interface LUDecompositionFeature<out T : Any> : MatrixFeature {
     /**
      * The lower triangular matrix in this decomposition. It may have [LFeature].
      */
@@ -98,7 +98,7 @@ public interface LUDecompositionFeature<T : Any> : MatrixFeature {
  *
  * @param T the type of matrices' items.
  */
-public interface LupDecompositionFeature<T : Any> : MatrixFeature {
+public interface LupDecompositionFeature<out T : Any> : MatrixFeature {
     /**
      * The lower triangular matrix in this decomposition. It may have [LFeature].
      */
@@ -126,7 +126,7 @@ public object OrthogonalFeature : MatrixFeature
  *
  * @param T the type of matrices' items.
  */
-public interface QRDecompositionFeature<T : Any> : MatrixFeature {
+public interface QRDecompositionFeature<out T : Any> : MatrixFeature {
     /**
      * The orthogonal matrix in this decomposition. It may have [OrthogonalFeature].
      */
@@ -144,7 +144,7 @@ public interface QRDecompositionFeature<T : Any> : MatrixFeature {
  *
  * @param T the type of matrices' items.
  */
-public interface CholeskyDecompositionFeature<T : Any> : MatrixFeature {
+public interface CholeskyDecompositionFeature<out T : Any> : MatrixFeature {
     /**
      * The triangular matrix in this decomposition. It may have either [UFeature] or [LFeature].
      */
@@ -157,7 +157,7 @@ public interface CholeskyDecompositionFeature<T : Any> : MatrixFeature {
  *
  * @param T the type of matrices' items.
  */
-public interface SingularValueDecompositionFeature<T : Any> : MatrixFeature {
+public interface SingularValueDecompositionFeature<out T : Any> : MatrixFeature {
     /**
      * The matrix in this decomposition. It is unitary, and it consists from left singular vectors.
      */

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

@@ -23,14 +23,15 @@ public class MatrixWrapper<out T : Any> internal constructor(
 ) : Matrix<T> by origin {
 
     /**
-     * Get the first feature matching given class. Does not guarantee that matrix has only one feature matching the criteria
+     * Get the first feature matching given class. Does not guarantee that matrix has only one feature matching the
+     * criteria.
      */
     @UnstableKMathAPI
     @Suppress("UNCHECKED_CAST")
-    public override fun <F : StructureFeature> getFeature(type: KClass<out F>): F? =
+    override fun <F : StructureFeature> getFeature(type: KClass<out F>): F? =
         features.getFeature(type) ?: origin.getFeature(type)
 
-    public override fun toString(): String = "MatrixWrapper(matrix=$origin, features=$features)"
+    override fun toString(): String = "MatrixWrapper(matrix=$origin, features=$features)"
 }
 
 /**
@@ -64,7 +65,7 @@ public fun <T : Any> Matrix<T>.withFeatures(newFeatures: Iterable<MatrixFeature>
     }
 
 /**
- * Diagonal matrix of ones. The matrix is virtual no actual matrix is created
+ * Diagonal matrix of ones. The matrix is virtual no actual matrix is created.
  */
 public fun <T : Any> LinearSpace<T, Ring<T>>.one(
     rows: Int,
@@ -93,4 +94,4 @@ public class TransposedFeature<out T : Any>(public val original: Matrix<T>) : Ma
 public fun <T : Any> Matrix<T>.transpose(): Matrix<T> = getFeature<TransposedFeature<T>>()?.original ?: VirtualMatrix(
     colNum,
     rowNum,
-) { i, j -> get(j, i) }.withFeature(TransposedFeature(this))
+) { i, j -> get(j, i) }.withFeature(TransposedFeature(this))

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

@@ -7,8 +7,8 @@ package space.kscience.kmath.misc
 
 /**
  * Marks declarations that are still experimental in the KMath APIs, which means that the design of the corresponding
- * declarations has open issues which may (or may not) lead to their changes in the future. Roughly speaking, there is
+ * declarations has open issues that may (or may not) lead to their changes in the future. Roughly speaking, there is
- * a chance that those declarations will be deprecated in the near future or the semantics of their behavior may change
+ * a chance of those declarations will be deprecated in the near future or the semantics of their behavior may change
  * in some way that may break some code.
  */
 @MustBeDocumented
@@ -17,7 +17,7 @@ package space.kscience.kmath.misc
 public annotation class UnstableKMathAPI
 
 /**
- * Marks API which could cause performance problems. The code, marked by this API is not necessary slow, but could cause
+ * Marks API that could cause performance problems. The code marked by this API is unnecessary slow but could cause
  * slow-down in some cases. Refer to the documentation and benchmark it to be sure.
  */
 @MustBeDocumented

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

@@ -34,7 +34,7 @@ public inline fun <T, R> Iterable<T>.cumulative(initial: R, crossinline operatio
 public inline fun <T, R> Sequence<T>.cumulative(initial: R, crossinline operation: (R, T) -> R): Sequence<R> =
     Sequence { this@cumulative.iterator().cumulative(initial, operation) }
 
-public fun <T, R> List<T>.cumulative(initial: R, operation: (R, T) -> R): List<R> =
+public inline fun <T, R> List<T>.cumulative(initial: R, crossinline operation: (R, T) -> R): List<R> =
     iterator().cumulative(initial, operation).asSequence().toList()
 
 //Cumulative sum

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

@@ -3,3 +3,6 @@
  * 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.misc
+
+public expect fun Long.toIntExact(): Int

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

@@ -11,7 +11,7 @@ import space.kscience.kmath.structures.*
 import kotlin.reflect.KClass
 
 /**
- * An exception is thrown when the expected ans actual shape of NDArray differs.
+ * An exception is thrown when the expected and actual shape of NDArray differ.
  *
  * @property expected the expected shape.
  * @property actual the actual shape.
@@ -24,7 +24,6 @@ public class ShapeMismatchException(public val expected: IntArray, public val ac
  *
  * @param T the type of ND-structure element.
  * @param C the type of the element context.
- * @param N the type of the structure.
  */
 public interface AlgebraND<T, out C : Algebra<T>> {
     /**
@@ -64,7 +63,7 @@ public interface AlgebraND<T, out C : Algebra<T>> {
         structure.map { value -> this@invoke(value) }
 
     /**
-     * Get a feature of the structure in this scope. Structure features take precedence other context features
+     * Get a feature of the structure in this scope. Structure features take precedence other context features.
      *
      * @param F the type of feature.
      * @param structure the structure.
@@ -80,7 +79,7 @@ public interface AlgebraND<T, out C : Algebra<T>> {
 
 
 /**
- * Get a feature of the structure in this scope. Structure features take precedence other context features
+ * Get a feature of the structure in this scope. Structure features take precedence other context features.
  *
  * @param T the type of items in the matrices.
  * @param F the type of feature.
@@ -118,8 +117,7 @@ internal fun <T, C : Algebra<T>> AlgebraND<T, C>.checkShape(element: StructureND
  * Space of [StructureND].
  *
  * @param T the type of the element contained in ND structure.
- * @param N the type of ND structure.
+ * @param S the type of group over structure elements.
- * @param S the type of space of structure elements.
  */
 public interface GroupND<T, out S : Group<T>> : Group<StructureND<T>>, AlgebraND<T, S> {
     /**
@@ -129,18 +127,9 @@ public interface GroupND<T, out S : Group<T>> : Group<StructureND<T>>, AlgebraND
      * @param b the addend.
      * @return the sum.
      */
-    public override fun add(a: StructureND<T>, b: StructureND<T>): StructureND<T> =
+    override fun add(a: StructureND<T>, b: StructureND<T>): StructureND<T> =
         combine(a, b) { aValue, bValue -> add(aValue, bValue) }
 
-//    /**
-//     * Element-wise multiplication by scalar.
-//     *
-//     * @param a the multiplicand.
-//     * @param k the multiplier.
-//     * @return the product.
-//     */
-//    public override fun multiply(a: NDStructure<T>, k: Number): NDStructure<T> =  a.map { multiply(it, k) }
-
     // TODO move to extensions after KEEP-176
 
     /**
@@ -186,8 +175,7 @@ public interface GroupND<T, out S : Group<T>> : Group<StructureND<T>>, AlgebraND
  * Ring of [StructureND].
  *
  * @param T the type of the element contained in ND structure.
- * @param N the type of ND structure.
+ * @param R the type of ring over structure elements.
- * @param R the type of ring of structure elements.
  */
 public interface RingND<T, out R : Ring<T>> : Ring<StructureND<T>>, GroupND<T, R> {
     /**
@@ -197,7 +185,7 @@ public interface RingND<T, out R : Ring<T>> : Ring<StructureND<T>>, GroupND<T, R
      * @param b the multiplier.
      * @return the product.
      */
-    public override fun multiply(a: StructureND<T>, b: StructureND<T>): StructureND<T> =
+    override fun multiply(a: StructureND<T>, b: StructureND<T>): StructureND<T> =
         combine(a, b) { aValue, bValue -> multiply(aValue, bValue) }
 
     //TODO move to extensions after KEEP-176
@@ -227,9 +215,9 @@ public interface RingND<T, out R : Ring<T>> : Ring<StructureND<T>>, GroupND<T, R
  * Field of [StructureND].
  *
  * @param T the type of the element contained in ND structure.
- * @param F the type field of structure elements.
+ * @param F the type field over structure elements.
  */
-public interface FieldND<T, out F : Field<T>> : Field<StructureND<T>>, RingND<T, F>, ScaleOperations<StructureND<T>> {
+public interface FieldND<T, out F : Field<T>> : Field<StructureND<T>>, RingND<T, F> {
     /**
      * Element-wise division.
      *
@@ -237,7 +225,7 @@ public interface FieldND<T, out F : Field<T>> : Field<StructureND<T>>, RingND<T,
      * @param b the divisor.
      * @return the quotient.
      */
-    public override fun divide(a: StructureND<T>, b: StructureND<T>): StructureND<T> =
+    override fun divide(a: StructureND<T>, b: StructureND<T>): StructureND<T> =
         combine(a, b) { aValue, bValue -> divide(aValue, bValue) }
 
     //TODO move to extensions after KEEP-176
@@ -259,6 +247,15 @@ public interface FieldND<T, out F : Field<T>> : Field<StructureND<T>>, RingND<T,
      */
     public operator fun T.div(arg: StructureND<T>): StructureND<T> = arg.map { divide(it, this@div) }
 
+    /**
+     * Element-wise scaling.
+     *
+     * @param a the multiplicand.
+     * @param value the multiplier.
+     * @return the product.
+     */
+    override fun scale(a: StructureND<T>, value: Double): StructureND<T> = a.map { scale(it, value) }
+
 //    @ThreadLocal
 //    public companion object {
 //        private val realNDFieldCache: MutableMap<IntArray, RealNDField> = hashMapOf()

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

@@ -57,7 +57,7 @@ public interface BufferAlgebraND<T, out A : Algebra<T>> : AlgebraND<T, A> {
     }
 }
 
-public open class BufferedGroupND<T, A : Group<T>>(
+public open class BufferedGroupND<T, out A : Group<T>>(
     final override val shape: IntArray,
     final override val elementContext: A,
     final override val bufferFactory: BufferFactory<T>,
@@ -67,7 +67,7 @@ public open class BufferedGroupND<T, A : Group<T>>(
     override fun StructureND<T>.unaryMinus(): StructureND<T> = produce { -get(it) }
 }
 
-public open class BufferedRingND<T, R : Ring<T>>(
+public open class BufferedRingND<T, out R : Ring<T>>(
     shape: IntArray,
     elementContext: R,
     bufferFactory: BufferFactory<T>,
@@ -75,7 +75,7 @@ public open class BufferedRingND<T, R : Ring<T>>(
     override val one: BufferND<T> by lazy { produce { one } }
 }
 
-public open class BufferedFieldND<T, R : Field<T>>(
+public open class BufferedFieldND<T, out R : Field<T>>(
     shape: IntArray,
     elementContext: R,
     bufferFactory: BufferFactory<T>,

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

@@ -22,15 +22,15 @@ public class DoubleFieldND(
     ScaleOperations<StructureND<Double>>,
     ExtendedField<StructureND<Double>> {
 
-    public override val zero: BufferND<Double> by lazy { produce { zero } }
+    override val zero: BufferND<Double> by lazy { produce { zero } }
-    public override val one: BufferND<Double> by lazy { produce { one } }
+    override val one: BufferND<Double> by lazy { produce { one } }
 
-    public override fun number(value: Number): BufferND<Double> {
+    override fun number(value: Number): BufferND<Double> {
         val d = value.toDouble() // minimize conversions
         return produce { d }
     }
 
-    public override val StructureND<Double>.buffer: DoubleBuffer
+    override val StructureND<Double>.buffer: DoubleBuffer
         get() = when {
             !shape.contentEquals(this@DoubleFieldND.shape) -> throw ShapeMismatchException(
                 this@DoubleFieldND.shape,
@@ -41,7 +41,7 @@ public class DoubleFieldND(
         }
 
     @Suppress("OVERRIDE_BY_INLINE")
-    public override inline fun StructureND<Double>.map(
+    override inline fun StructureND<Double>.map(
         transform: DoubleField.(Double) -> Double,
     ): BufferND<Double> {
         val buffer = DoubleBuffer(strides.linearSize) { offset -> DoubleField.transform(buffer.array[offset]) }
@@ -49,7 +49,7 @@ public class DoubleFieldND(
     }
 
     @Suppress("OVERRIDE_BY_INLINE")
-    public override inline fun produce(initializer: DoubleField.(IntArray) -> Double): BufferND<Double> {
+    override inline fun produce(initializer: DoubleField.(IntArray) -> Double): BufferND<Double> {
         val array = DoubleArray(strides.linearSize) { offset ->
             val index = strides.index(offset)
             DoubleField.initializer(index)
@@ -58,7 +58,7 @@ public class DoubleFieldND(
     }
 
     @Suppress("OVERRIDE_BY_INLINE")
-    public override inline fun StructureND<Double>.mapIndexed(
+    override inline fun StructureND<Double>.mapIndexed(
         transform: DoubleField.(index: IntArray, Double) -> Double,
     ): BufferND<Double> = BufferND(
         strides,
@@ -70,7 +70,7 @@ public class DoubleFieldND(
         })
 
     @Suppress("OVERRIDE_BY_INLINE")
-    public override inline fun combine(
+    override inline fun combine(
         a: StructureND<Double>,
         b: StructureND<Double>,
         transform: DoubleField.(Double, Double) -> Double,
@@ -81,26 +81,26 @@ public class DoubleFieldND(
         return BufferND(strides, buffer)
     }
 
-    public override fun scale(a: StructureND<Double>, value: Double): StructureND<Double> = a.map { it * value }
+    override fun scale(a: StructureND<Double>, value: Double): StructureND<Double> = a.map { it * value }
 
-    public override fun power(arg: StructureND<Double>, pow: Number): BufferND<Double> = arg.map { power(it, pow) }
+    override fun power(arg: StructureND<Double>, pow: Number): BufferND<Double> = arg.map { power(it, pow) }
 
-    public override fun exp(arg: StructureND<Double>): BufferND<Double> = arg.map { exp(it) }
+    override fun exp(arg: StructureND<Double>): BufferND<Double> = arg.map { exp(it) }
-    public override fun ln(arg: StructureND<Double>): BufferND<Double> = arg.map { ln(it) }
+    override fun ln(arg: StructureND<Double>): BufferND<Double> = arg.map { ln(it) }
 
-    public override fun sin(arg: StructureND<Double>): BufferND<Double> = arg.map { sin(it) }
+    override fun sin(arg: StructureND<Double>): BufferND<Double> = arg.map { sin(it) }
-    public override fun cos(arg: StructureND<Double>): BufferND<Double> = arg.map { cos(it) }
+    override fun cos(arg: StructureND<Double>): BufferND<Double> = arg.map { cos(it) }
-    public override fun tan(arg: StructureND<Double>): BufferND<Double> = arg.map { tan(it) }
+    override fun tan(arg: StructureND<Double>): BufferND<Double> = arg.map { tan(it) }
-    public override fun asin(arg: StructureND<Double>): BufferND<Double> = arg.map { asin(it) }
+    override fun asin(arg: StructureND<Double>): BufferND<Double> = arg.map { asin(it) }
-    public override fun acos(arg: StructureND<Double>): BufferND<Double> = arg.map { acos(it) }
+    override fun acos(arg: StructureND<Double>): BufferND<Double> = arg.map { acos(it) }
-    public override fun atan(arg: StructureND<Double>): BufferND<Double> = arg.map { atan(it) }
+    override fun atan(arg: StructureND<Double>): BufferND<Double> = arg.map { atan(it) }
 
-    public override fun sinh(arg: StructureND<Double>): BufferND<Double> = arg.map { sinh(it) }
+    override fun sinh(arg: StructureND<Double>): BufferND<Double> = arg.map { sinh(it) }
-    public override fun cosh(arg: StructureND<Double>): BufferND<Double> = arg.map { cosh(it) }
+    override fun cosh(arg: StructureND<Double>): BufferND<Double> = arg.map { cosh(it) }
-    public override fun tanh(arg: StructureND<Double>): BufferND<Double> = arg.map { tanh(it) }
+    override fun tanh(arg: StructureND<Double>): BufferND<Double> = arg.map { tanh(it) }
-    public override fun asinh(arg: StructureND<Double>): BufferND<Double> = arg.map { asinh(it) }
+    override fun asinh(arg: StructureND<Double>): BufferND<Double> = arg.map { asinh(it) }
-    public override fun acosh(arg: StructureND<Double>): BufferND<Double> = arg.map { acosh(it) }
+    override fun acosh(arg: StructureND<Double>): BufferND<Double> = arg.map { acosh(it) }
-    public override fun atanh(arg: StructureND<Double>): BufferND<Double> = arg.map { atanh(it) }
+    override fun atanh(arg: StructureND<Double>): BufferND<Double> = arg.map { atanh(it) }
 }
 
 public fun AlgebraND.Companion.real(vararg shape: Int): DoubleFieldND = DoubleFieldND(shape)

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

@@ -31,11 +31,10 @@ public class ShortRingND(
 /**
  * Fast element production using function inlining.
  */
-public inline fun BufferedRingND<Short, ShortRing>.produceInline(crossinline initializer: ShortRing.(Int) -> Short): BufferND<Short> {
+public inline fun BufferedRingND<Short, ShortRing>.produceInline(crossinline initializer: ShortRing.(Int) -> Short): BufferND<Short> =
-    return BufferND(strides, ShortBuffer(ShortArray(strides.linearSize) { offset -> ShortRing.initializer(offset) }))
+    BufferND(strides, ShortBuffer(ShortArray(strides.linearSize) { offset -> ShortRing.initializer(offset) }))
-}
 
 public inline fun <R> ShortRing.nd(vararg shape: Int, action: ShortRingND.() -> R): R {
     contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) }
     return ShortRingND(shape).run(action)
-}
+}