Merge pull request #417 from mipt-npm/dev

0.3.0-dev-15

.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-
@@ -35,5 +37,7 @@ jobs:
           key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
           restore-keys: |
             ${{ runner.os }}-gradle-
+      - name: Gradle Wrapper Validation
+        uses: gradle/wrapper-validation-action@v1.0.4
       - 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-
@@ -40,22 +38,18 @@ jobs:
           key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
           restore-keys: |
             ${{ runner.os }}-gradle-
+      - name: Gradle Wrapper Validation
+        uses: gradle/wrapper-validation-action@v1.0.4
       - name: Publish Windows Artifacts
         if: matrix.os == 'windows-latest'
+        shell: cmd
         run: >
-          ./gradlew release --no-daemon
+          ./gradlew release --no-daemon --build-cache -Ppublishing.enabled=true
-          -Ppublishing.enabled=true
+          -Ppublishing.space.user=${{ 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 }}

.idea/copyright/kmath.xml

@@ -1,6 +1,6 @@
 <component name="CopyrightManager">
     <copyright>
-        <option name="notice" value="Copyright 2018-2021 KMath contributors.&#10;Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file." />
+        <option name="notice" value="Copyright 2018-2021 KMath contributors.&#10;Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file." />
         <option name="myName" value="kmath" />
     </copyright>
 </component>

CHANGELOG.md

@@ -2,32 +2,49 @@
 
 ## [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
 - Extended operations for ND4J fields
 - Jupyter Notebook integration module (kmath-jupyter)
+- `@PerformancePitfall` annotation to mark possibly slow API
+- Unified architecture for Integration and Optimization using features.
+- `BigInt` operation performance improvement and fixes by @zhelenskiy (#328)
+- Integration between `MST` and Symja `IExpr`
+- Complex power
 
 ### Changed
 - Exponential operations merged with hyperbolic functions
 - Space is replaced by Group. Space is reserved for vector spaces.
 - VectorSpace is now a vector space
 - Buffer factories for primitives moved to MutableBuffer.Companion
-- NDStructure and NDAlgebra to StructureND and AlgebraND respectively
+- Rename `NDStructure` and `NDAlgebra` to `StructureND` and `AlgebraND` respectively
-- Real -> Double
+- `Real` -> `Double`
 - DataSets are moved from functions to core
 - Redesign advanced Chain API
-- Redesign MST. Remove MSTExpression.
+- Redesign `MST`. Remove `MstExpression`.
-- Move MST to core
+- Move `MST` to core
 - Separated benchmarks and examples
-- Rewritten EJML module without ejml-simple
+- Rewrite `kmath-ejml` without `ejml-simple` artifact, support sparse matrices
+- Promote stability of kmath-ast and kmath-kotlingrad to EXPERIMENTAL.
+- ColumnarData returns nullable column
+- `MST` is made sealed interface
+- Replace `MST.Symbolic` by `Symbol`, `Symbol` now implements MST
+- Remove Any restriction on polynomials
+- Add `out` variance to type parameters of `StructureND` and its implementations where possible
+- Rename `DifferentiableMstExpression` to `KotlingradExpression`
+- `FeatureSet` now accepts only `Feature`. It is possible to override keys and use interfaces.
+- Use `Symbol` factory function instead of `StringSymbol`
+- New discoverability pattern: `<Type>.algebra.<nd/etc>`
+- Adjusted commons-math API for linear solvers to match conventions.
 
 ### Deprecated
+- Specialized `DoubleBufferAlgebra`
 
 ### Removed
 - Nearest in Domain. To be implemented in geometry package.
@@ -35,10 +52,13 @@
 - `contentEquals` from Buffer. It moved to the companion.
 - MSTExpression
 - Expression algebra builders
-- Comples and Quaternion no longer are elements.
+- Complex and Quaternion no longer are elements.
+- Second generic from DifferentiableExpression
+- Algebra elements are completely removed. Use algebra contexts instead.
 
 ### Fixed
 - Ring inherits RingOperations, not GroupOperations
+- Univariate histogram filling
 
 ### Security
 

LICENSE

@@ -1,201 +0,0 @@
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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 genrally 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)
 > 
 >
@@ -209,17 +226,22 @@ One can still use generic algebras though.
 * ### [kmath-kotlingrad](kmath-kotlingrad)
 > 
 >
-> **Maturity**: PROTOTYPE
+> **Maturity**: EXPERIMENTAL
+>
+> **Features:**
+> - [differentiable-mst-expression](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/KotlingradExpression.kt) : MST based DifferentiableExpression.
+> - [scalars-adapters](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/scalarsAdapters.kt) : Conversions between Kotlin∇'s SFun and MST
+
 <hr/>
 
 * ### [kmath-memory](kmath-memory)
-> 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
 >
@@ -236,6 +258,12 @@ One can still use generic algebras though.
 > **Maturity**: EXPERIMENTAL
 <hr/>
 
+* ### [kmath-symja](kmath-symja)
+> 
+>
+> **Maturity**: PROTOTYPE
+<hr/>
+
 * ### [kmath-tensors](kmath-tensors)
 > 
 >
@@ -243,7 +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/>
@@ -257,30 +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 {
@@ -288,8 +319,8 @@ repositories {
 }
 
 dependencies {
-    api("space.kscience:kmath-core:0.3.0-dev-8")
+    api("space.kscience:kmath-core:0.3.0-dev-14")
-    // api("space.kscience:kmath-core-jvm:0.3.0-dev-8") for jvm-specific version
+    // api("space.kscience:kmath-core-jvm:0.3.0-dev-14") for jvm-specific version
 }
 ```
 
@@ -297,7 +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 +35,9 @@ 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(project(":kmath-tensors"))
+                implementation("org.jetbrains.kotlinx:kotlinx-benchmark-runtime:0.3.1")
             }
         }
 
@@ -41,8 +48,7 @@ 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")
                 //
@@ -76,6 +82,11 @@ benchmark {
         include("BufferBenchmark")
     }
 
+    configurations.register("nd") {
+        commonConfiguration()
+        include("NDFieldBenchmark")
+    }
+
     configurations.register("dot") {
         commonConfiguration()
         include("DotBenchmark")
@@ -95,6 +106,21 @@ benchmark {
         commonConfiguration()
         include("BigIntBenchmark")
     }
+
+    configurations.register("jafamaDouble") {
+        commonConfiguration()
+        include("JafamaBenchmark")
+    }
+
+    configurations.register("viktor") {
+        commonConfiguration()
+        include("ViktorBenchmark")
+    }
+
+    configurations.register("viktorLog") {
+        commonConfiguration()
+        include("ViktorLogBenchmark")
+    }
 }
 
 // Fix kotlinx-benchmarks bug
@@ -124,3 +150,5 @@ 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/ArrayBenchmark.kt

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.benchmarks

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.benchmarks
@@ -10,22 +10,36 @@ 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.operations.BigInt
+import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.operations.BigIntField
 import space.kscience.kmath.operations.JBigIntegerField
 import space.kscience.kmath.operations.invoke
+import space.kscience.kmath.operations.parseBigInteger
+import java.math.BigInteger
 
-private fun BigInt.pow(power: Int): BigInt = modPow(BigIntField.number(power), BigInt.ZERO)
 
+@UnstableKMathAPI
 @State(Scope.Benchmark)
 internal class BigIntBenchmark {
 
+    val kmSmallNumber = BigIntField.number(100)
+    val jvmSmallNumber = JBigIntegerField.number(100)
     val kmNumber = BigIntField.number(Int.MAX_VALUE)
     val jvmNumber = JBigIntegerField.number(Int.MAX_VALUE)
-    val largeKmNumber = BigIntField { number(11).pow(100_000) }
+    val kmLargeNumber = BigIntField { number(11).pow(100_000U) }
-    val largeJvmNumber = JBigIntegerField { number(11).pow(100_000) }
+    val jvmLargeNumber: BigInteger = JBigIntegerField { number(11).pow(100_000) }
     val bigExponent = 50_000
 
+    @Benchmark
+    fun kmSmallAdd(blackhole: Blackhole) = BigIntField {
+        blackhole.consume(kmSmallNumber + kmSmallNumber + kmSmallNumber)
+    }
+
+    @Benchmark
+    fun jvmSmallAdd(blackhole: Blackhole) = JBigIntegerField {
+        blackhole.consume(jvmSmallNumber + jvmSmallNumber + jvmSmallNumber)
+    }
+
     @Benchmark
     fun kmAdd(blackhole: Blackhole) = BigIntField {
         blackhole.consume(kmNumber + kmNumber + kmNumber)
@@ -36,6 +50,16 @@ internal class BigIntBenchmark {
         blackhole.consume(jvmNumber + jvmNumber + jvmNumber)
     }
 
+    @Benchmark
+    fun kmAddLarge(blackhole: Blackhole) = BigIntField {
+        blackhole.consume(kmLargeNumber + kmLargeNumber + kmLargeNumber)
+    }
+
+    @Benchmark
+    fun jvmAddLarge(blackhole: Blackhole) = JBigIntegerField {
+        blackhole.consume(jvmLargeNumber + jvmLargeNumber + jvmLargeNumber)
+    }
+
     @Benchmark
     fun kmMultiply(blackhole: Blackhole) = BigIntField {
         blackhole.consume(kmNumber * kmNumber * kmNumber)
@@ -43,7 +67,7 @@ internal class BigIntBenchmark {
 
     @Benchmark
     fun kmMultiplyLarge(blackhole: Blackhole) = BigIntField {
-        blackhole.consume(largeKmNumber*largeKmNumber)
+        blackhole.consume(kmLargeNumber*kmLargeNumber)
     }
 
     @Benchmark
@@ -53,16 +77,36 @@ internal class BigIntBenchmark {
 
     @Benchmark
     fun jvmMultiplyLarge(blackhole: Blackhole) = JBigIntegerField {
-        blackhole.consume(largeJvmNumber*largeJvmNumber)
+        blackhole.consume(jvmLargeNumber*jvmLargeNumber)
     }
 
-//    @Benchmark
+    @Benchmark
-//    fun kmPower(blackhole: Blackhole) = BigIntField {
+    fun kmPower(blackhole: Blackhole) = BigIntField {
-//        blackhole.consume(kmNumber.pow(bigExponent))
+        blackhole.consume(kmNumber.pow(bigExponent.toUInt()))
-//    }
+    }
-//
+
-//    @Benchmark
+    @Benchmark
-//    fun jvmPower(blackhole: Blackhole) = JBigIntegerField {
+    fun jvmPower(blackhole: Blackhole) = JBigIntegerField {
-//        blackhole.consume(jvmNumber.pow(bigExponent))
+        blackhole.consume(jvmNumber.pow(bigExponent))
-//    }
+    }
+
+    @Benchmark
+    fun kmParsing16(blackhole: Blackhole) = JBigIntegerField {
+        blackhole.consume("0x7f57ed8b89c29a3b9a85c7a5b84ca3929c7b7488593".parseBigInteger())
+    }
+
+    @Benchmark
+    fun kmParsing10(blackhole: Blackhole) = JBigIntegerField {
+        blackhole.consume("236656783929183747565738292847574838922010".parseBigInteger())
+    }
+
+    @Benchmark
+    fun jvmParsing10(blackhole: Blackhole) = JBigIntegerField {
+        blackhole.consume("236656783929183747565738292847574838922010".toBigInteger(10))
+    }
+
+    @Benchmark
+    fun jvmParsing16(blackhole: Blackhole) = JBigIntegerField {
+        blackhole.consume("7f57ed8b89c29a3b9a85c7a5b84ca3929c7b7488593".toBigInteger(16))
+    }
 }

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.benchmarks

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.benchmarks
@@ -11,9 +11,10 @@ import kotlinx.benchmark.Scope
 import kotlinx.benchmark.State
 import space.kscience.kmath.commons.linear.CMLinearSpace
 import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
-import space.kscience.kmath.linear.LinearSpace
 import space.kscience.kmath.linear.invoke
+import space.kscience.kmath.linear.linearSpace
 import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.structures.Buffer
 import kotlin.random.Random
 
 @State(Scope.Benchmark)
@@ -23,8 +24,12 @@ internal class DotBenchmark {
         const val dim = 1000
 
         //creating invertible matrix
-        val matrix1 = LinearSpace.real.buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
+        val matrix1 = DoubleField.linearSpace.buildMatrix(dim, dim) { _, _ ->
-        val matrix2 = LinearSpace.real.buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
+            random.nextDouble()
+        }
+        val matrix2 = DoubleField.linearSpace.buildMatrix(dim, dim) { _, _ ->
+            random.nextDouble()
+        }
 
         val cmMatrix1 = CMLinearSpace { matrix1.toCM() }
         val cmMatrix2 = CMLinearSpace { matrix2.toCM() }
@@ -34,37 +39,32 @@ internal class DotBenchmark {
     }
 
     @Benchmark
-    fun cmDot(blackhole: Blackhole) {
+    fun cmDotWithConversion(blackhole: Blackhole) = CMLinearSpace {
-        CMLinearSpace.run {
+        blackhole.consume(matrix1 dot matrix2)
-            blackhole.consume(cmMatrix1 dot cmMatrix2)
-        }
     }
 
     @Benchmark
-    fun ejmlDot(blackhole: Blackhole) {
+    fun cmDot(blackhole: Blackhole) = CMLinearSpace {
-        EjmlLinearSpaceDDRM {
+        blackhole.consume(cmMatrix1 dot cmMatrix2)
-            blackhole.consume(ejmlMatrix1 dot ejmlMatrix2)
-        }
     }
 
     @Benchmark
-    fun ejmlDotWithConversion(blackhole: Blackhole) {
+    fun ejmlDot(blackhole: Blackhole) = EjmlLinearSpaceDDRM {
-        EjmlLinearSpaceDDRM {
+        blackhole.consume(ejmlMatrix1 dot ejmlMatrix2)
-            blackhole.consume(matrix1 dot matrix2)
-        }
     }
 
     @Benchmark
-    fun bufferedDot(blackhole: Blackhole) {
+    fun ejmlDotWithConversion(blackhole: Blackhole) = EjmlLinearSpaceDDRM {
-        LinearSpace.auto(DoubleField).invoke {
+        blackhole.consume(matrix1 dot matrix2)
-            blackhole.consume(matrix1 dot matrix2)
-        }
     }
 
     @Benchmark
-    fun realDot(blackhole: Blackhole) {
+    fun bufferedDot(blackhole: Blackhole) = with(DoubleField.linearSpace(Buffer.Companion::auto)) {
-        LinearSpace.real {
+        blackhole.consume(matrix1 dot matrix2)
-            blackhole.consume(matrix1 dot matrix2)
+    }
-        }
+
+    @Benchmark
+    fun doubleDot(blackhole: Blackhole) = with(DoubleField.linearSpace) {
+        blackhole.consume(matrix1 dot matrix2)
     }
 }

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.benchmarks
@@ -11,27 +11,54 @@ import kotlinx.benchmark.Scope
 import kotlinx.benchmark.State
 import space.kscience.kmath.asm.compileToExpression
 import space.kscience.kmath.expressions.*
-import space.kscience.kmath.misc.Symbol
-import space.kscience.kmath.misc.symbol
 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
@@ -44,23 +71,25 @@ 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.expression {
-            bindSymbol(x) * number(2.0) + number(2.0) / bindSymbol(x) - number(16.0) / sin(bindSymbol(x))
+            val x = bindSymbol(Symbol.x)
+            x * number(2.0) + 2.0 / x - 16.0 / sin(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 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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.benchmarks
@@ -10,13 +10,12 @@ import kotlinx.benchmark.Blackhole
 import kotlinx.benchmark.Scope
 import kotlinx.benchmark.State
 import space.kscience.kmath.commons.linear.CMLinearSpace
-import space.kscience.kmath.commons.linear.inverse
+import space.kscience.kmath.commons.linear.lupSolver
 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.nd.getFeature
+import space.kscience.kmath.linear.linearSpace
+import space.kscience.kmath.linear.lupSolver
+import space.kscience.kmath.operations.algebra
 import kotlin.random.Random
 
 @State(Scope.Benchmark)
@@ -25,7 +24,7 @@ internal class MatrixInverseBenchmark {
         private val random = Random(1224)
         private const val dim = 100
 
-        private val space = LinearSpace.real
+        private val space = Double.algebra.linearSpace
 
         //creating invertible matrix
         private val u = space.buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
@@ -35,20 +34,20 @@ internal class MatrixInverseBenchmark {
 
     @Benchmark
     fun kmathLupInversion(blackhole: Blackhole) {
-        blackhole.consume(LinearSpace.real.inverseWithLup(matrix))
+        blackhole.consume(Double.algebra.linearSpace.lupSolver().inverse(matrix))
     }
 
     @Benchmark
     fun cmLUPInversion(blackhole: Blackhole) {
-        with(CMLinearSpace) {
+        CMLinearSpace {
-            blackhole.consume(inverse(matrix))
+            blackhole.consume(lupSolver().inverse(matrix))
         }
     }
 
     @Benchmark
     fun ejmlInverse(blackhole: Blackhole) {
-        with(EjmlLinearSpaceDDRM) {
+        EjmlLinearSpaceDDRM {
-            blackhole.consume(matrix.getFeature<InverseMatrixFeature<Double>>()?.inverse)
+            blackhole.consume(matrix.toEjml().inverse())
         }
     }
 }

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.benchmarks
@@ -9,45 +9,66 @@ import kotlinx.benchmark.Benchmark
 import kotlinx.benchmark.Blackhole
 import kotlinx.benchmark.Scope
 import kotlinx.benchmark.State
-import space.kscience.kmath.nd.*
+import space.kscience.kmath.nd.StructureND
+import space.kscience.kmath.nd.autoNdAlgebra
+import space.kscience.kmath.nd.ndAlgebra
+import space.kscience.kmath.nd4j.nd4j
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.tensors.core.DoubleTensor
+import space.kscience.kmath.tensors.core.ones
+import space.kscience.kmath.tensors.core.tensorAlgebra
 
 @State(Scope.Benchmark)
 internal class NDFieldBenchmark {
     @Benchmark
-    fun autoFieldAdd(blackhole: Blackhole) {
+    fun autoFieldAdd(blackhole: Blackhole) = with(autoField) {
-        with(autoField) {
+        var res: StructureND<Double> = one
-            var res: StructureND<Double> = one
+        repeat(n) { res += one }
-            repeat(n) { res += one }
+        blackhole.consume(res)
-            blackhole.consume(res)
-        }
     }
 
     @Benchmark
-    fun specializedFieldAdd(blackhole: Blackhole) {
+    fun specializedFieldAdd(blackhole: Blackhole) = with(specializedField) {
-        with(specializedField) {
+        var res: StructureND<Double> = one
-            var res: StructureND<Double> = one
+        repeat(n) { res += 1.0 }
-            repeat(n) { res += 1.0 }
+        blackhole.consume(res)
-            blackhole.consume(res)
-        }
     }
 
-
     @Benchmark
-    fun boxingFieldAdd(blackhole: Blackhole) {
+    fun boxingFieldAdd(blackhole: Blackhole) = with(genericField) {
-        with(genericField) {
+        var res: StructureND<Double> = one
-            var res: StructureND<Double> = one
+        repeat(n) { res += 1.0 }
-            repeat(n) { res += 1.0 }
+        blackhole.consume(res)
-            blackhole.consume(res)
-        }
     }
 
+    @Benchmark
+    fun tensorAdd(blackhole: Blackhole) = with(Double.tensorAlgebra) {
+        var res: DoubleTensor = ones(dim, dim)
+        repeat(n) { res = res + 1.0 }
+        blackhole.consume(res)
+    }
+
+    @Benchmark
+    fun tensorInPlaceAdd(blackhole: Blackhole) = with(Double.tensorAlgebra) {
+        val res: DoubleTensor = ones(dim, dim)
+        repeat(n) { res += 1.0 }
+        blackhole.consume(res)
+    }
+
+//    @Benchmark
+//    fun nd4jAdd(blackhole: Blackhole) = with(nd4jField) {
+//        var res: StructureND<Double> = one
+//        repeat(n) { res += 1.0 }
+//        blackhole.consume(res)
+//    }
+
     private companion object {
         private const val dim = 1000
         private const val n = 100
-        private val autoField = AlgebraND.auto(DoubleField, dim, dim)
+        private val autoField = DoubleField.autoNdAlgebra(dim, dim)
-        private val specializedField = AlgebraND.real(dim, dim)
+        private val specializedField = DoubleField.ndAlgebra(dim, dim)
-        private val genericField = AlgebraND.field(DoubleField, Buffer.Companion::boxing, dim, dim)
+        private val genericField = DoubleField.ndAlgebra(Buffer.Companion::boxing, dim, dim)
+        private val nd4jField = DoubleField.nd4j(dim, dim)
     }
 }

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.benchmarks
@@ -10,10 +10,9 @@ import kotlinx.benchmark.Blackhole
 import kotlinx.benchmark.Scope
 import kotlinx.benchmark.State
 import org.jetbrains.bio.viktor.F64Array
-import space.kscience.kmath.nd.AlgebraND
 import space.kscience.kmath.nd.StructureND
-import space.kscience.kmath.nd.auto
+import space.kscience.kmath.nd.autoNdAlgebra
-import space.kscience.kmath.nd.real
+import space.kscience.kmath.nd.ndAlgebra
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.viktor.ViktorNDField
 
@@ -59,8 +58,8 @@ internal class ViktorBenchmark {
         private const val n = 100
 
         // automatically build context most suited for given type.
-        private val autoField = AlgebraND.auto(DoubleField, dim, dim)
+        private val autoField = DoubleField.autoNdAlgebra(dim, dim)
-        private val realField = AlgebraND.real(dim, dim)
+        private val realField = DoubleField.ndAlgebra(dim, dim)
         private val viktorField = ViktorNDField(dim, dim)
     }
 }

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.benchmarks
@@ -10,9 +10,8 @@ import kotlinx.benchmark.Blackhole
 import kotlinx.benchmark.Scope
 import kotlinx.benchmark.State
 import org.jetbrains.bio.viktor.F64Array
-import space.kscience.kmath.nd.AlgebraND
+import space.kscience.kmath.nd.autoNdAlgebra
-import space.kscience.kmath.nd.auto
+import space.kscience.kmath.nd.ndAlgebra
-import space.kscience.kmath.nd.real
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.viktor.ViktorFieldND
 
@@ -51,8 +50,8 @@ internal class ViktorLogBenchmark {
         private const val n = 100
 
         // automatically build context most suited for given type.
-        private val autoField = AlgebraND.auto(DoubleField, dim, dim)
+        private val autoField = DoubleField.autoNdAlgebra(dim, dim)
-        private val realNdField = AlgebraND.real(dim, dim)
+        private val realNdField = DoubleField.ndAlgebra(dim, dim)
         private val viktorField = ViktorFieldND(intArrayOf(dim, dim))
     }
 }

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://maven.pkg.jetbrains.space/public/p/kotlinx-html/maven")
+
+        maven("https://oss.sonatype.org/content/repositories/snapshots")
         mavenCentral()
     }
 
     group = "space.kscience"
-    version = "0.3.0-dev-8"
+    version = "0.3.0-dev-16"
 }
 
 subprojects {
@@ -23,31 +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.from(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://breandan.net/kotlingrad/kotlingrad/", "https://breandan.net/kotlingrad/kotlingrad/kotlingrad/package-list")
+
+                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

@@ -0,0 +1,20 @@
+plugins {
+    `kotlin-dsl`
+    kotlin("plugin.serialization") version "1.4.31"
+}
+
+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 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 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

@@ -0,0 +1,425 @@
+/*
+ * 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 file.
+ */
+
+@file:Suppress("KDocUnresolvedReference")
+
+package space.kscience.kmath.ejml.codegen
+
+import org.intellij.lang.annotations.Language
+import java.io.File
+
+private fun Appendable.appendEjmlVector(type: String, ejmlMatrixType: String) {
+    @Language("kotlin") val text = """/**
+ * [EjmlVector] specialization for [$type].
+ */
+public class Ejml${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" }
+    }
+
+    override operator fun get(index: Int): $type = origin[0, index]
+}"""
+    appendLine(text)
+    appendLine()
+}
+
+private fun Appendable.appendEjmlMatrix(type: String, ejmlMatrixType: String) {
+    val text = """/**
+ * [EjmlMatrix] specialization for [$type].
+ */
+public class Ejml${type}Matrix<out M : $ejmlMatrixType>(override val origin: M) : EjmlMatrix<$type, M>(origin) {
+    override operator fun get(i: Int, j: Int): $type = origin[i, j]
+}"""
+    appendLine(text)
+    appendLine()
+}
+
+private fun Appendable.appendEjmlLinearSpace(
+    type: String,
+    kmathAlgebra: String,
+    ejmlMatrixParentTypeMatrix: String,
+    ejmlMatrixType: String,
+    ejmlMatrixDenseType: String,
+    ops: String,
+    denseOps: String,
+    isDense: Boolean,
+) {
+    @Language("kotlin") val text = """/**
+ * [EjmlLinearSpace] implementation based on [CommonOps_$ops], [DecompositionFactory_${ops}] operations and
+ * [${ejmlMatrixType}] matrices.
+ */
+public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra}, $ejmlMatrixType>() {
+    /**
+     * The [${kmathAlgebra}] reference.
+     */
+    override val elementAlgebra: $kmathAlgebra get() = $kmathAlgebra
+
+    @Suppress("UNCHECKED_CAST")
+    override fun Matrix<${type}>.toEjml(): Ejml${type}Matrix<${ejmlMatrixType}> = when {
+        this is Ejml${type}Matrix<*> && origin is $ejmlMatrixType -> this as Ejml${type}Matrix<${ejmlMatrixType}>
+        else -> buildMatrix(rowNum, colNum) { i, j -> get(i, j) }
+    }
+
+    @Suppress("UNCHECKED_CAST")
+    override fun Point<${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) }
+        })
+    }
+
+    override fun buildMatrix(
+        rows: Int,
+        columns: Int,
+        initializer: ${kmathAlgebra}.(i: Int, j: Int) -> ${type},
+    ): Ejml${type}Matrix<${ejmlMatrixType}> = ${ejmlMatrixType}(rows, columns).also {
+        (0 until rows).forEach { row ->
+            (0 until columns).forEach { col -> it[row, col] = elementAlgebra.initializer(row, col) }
+        }
+    }.wrapMatrix()
+
+    override fun buildVector(
+        size: Int,
+        initializer: ${kmathAlgebra}.(Int) -> ${type},
+    ): Ejml${type}Vector<${ejmlMatrixType}> = Ejml${type}Vector(${ejmlMatrixType}(size, 1).also {
+        (0 until it.numRows).forEach { row -> it[row, 0] = elementAlgebra.initializer(row) }
+    })
+
+    private fun <T : ${ejmlMatrixParentTypeMatrix}> T.wrapMatrix() = Ejml${type}Matrix(this)
+    private fun <T : ${ejmlMatrixParentTypeMatrix}> T.wrapVector() = Ejml${type}Vector(this)
+
+    override fun Matrix<${type}>.unaryMinus(): Matrix<${type}> = this * elementAlgebra { -one }
+
+    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()
+    }
+
+    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()
+    }
+
+    override operator fun Matrix<${type}>.minus(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
+        val out = ${ejmlMatrixType}(1, 1)
+
+        CommonOps_${ops}.add(
+            elementAlgebra.one, 
+            toEjml().origin, 
+            elementAlgebra { -one },
+            other.toEjml().origin, 
+            out,${
+        if (isDense) "" else
+            """
+            null, 
+            null,"""
+    }
+        )
+
+        return out.wrapMatrix()
+    }
+
+    override operator fun Matrix<${type}>.times(value: ${type}): Ejml${type}Matrix<${ejmlMatrixType}> {
+        val res = ${ejmlMatrixType}(1, 1)
+        CommonOps_${ops}.scale(value, toEjml().origin, res)
+        return res.wrapMatrix()
+    }
+
+    override fun Point<${type}>.unaryMinus(): Ejml${type}Vector<${ejmlMatrixType}> {
+        val res = ${ejmlMatrixType}(1, 1)
+        CommonOps_${ops}.changeSign(toEjml().origin, res)
+        return res.wrapVector()
+    }
+
+    override fun Matrix<${type}>.plus(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
+        val out = ${ejmlMatrixType}(1, 1)
+        
+        CommonOps_${ops}.add(
+            elementAlgebra.one,
+            toEjml().origin,
+            elementAlgebra.one,
+            other.toEjml().origin, 
+            out,${
+        if (isDense) "" else
+            """
+            null, 
+            null,"""
+    }
+        )
+
+        return out.wrapMatrix()
+    }
+
+    override fun Point<${type}>.plus(other: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
+        val out = ${ejmlMatrixType}(1, 1)
+
+        CommonOps_${ops}.add(
+            elementAlgebra.one,
+            toEjml().origin,
+            elementAlgebra.one,
+            other.toEjml().origin,
+            out,${
+        if (isDense) "" else
+            """
+            null, 
+            null,"""
+    }
+        )
+
+        return out.wrapVector()
+    }
+
+    override fun Point<${type}>.minus(other: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
+        val out = ${ejmlMatrixType}(1, 1)
+
+        CommonOps_${ops}.add(
+            elementAlgebra.one,
+            toEjml().origin,
+            elementAlgebra { -one },
+            other.toEjml().origin,
+            out,${
+        if (isDense) "" else
+            """
+            null, 
+            null,"""
+    }
+        )
+
+        return out.wrapVector()
+    }
+
+    override fun ${type}.times(m: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> = m * this
+
+    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()
+    }
+
+    override fun ${type}.times(v: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> = v * this
+
+    @UnstableKMathAPI
+    override fun <F : StructureFeature> computeFeature(structure: Matrix<${type}>, type: KClass<out F>): F? {
+        structure.getFeature(type)?.let { return it }
+        val origin = structure.toEjml().origin
+
+        return when (type) {
+        ${
+        if (isDense)
+            """    InverseMatrixFeature::class -> object : InverseMatrixFeature<${type}> {
+                override val inverse: Matrix<${type}> by lazy {
+                    val res = origin.copy()
+                    CommonOps_${ops}.invert(res)
+                    res.wrapMatrix()
+                }
+            }
+
+            DeterminantFeature::class -> object : DeterminantFeature<${type}> {
+                override val determinant: $type by lazy { CommonOps_${ops}.det(origin) }
+            }
+
+            SingularValueDecompositionFeature::class -> object : SingularValueDecompositionFeature<${type}> {
+                private val svd by lazy {
+                    DecompositionFactory_${ops}.svd(origin.numRows, origin.numCols, true, true, false)
+                        .apply { decompose(origin.copy()) }
+                }
+
+                override val u: Matrix<${type}> by lazy { svd.getU(null, false).wrapMatrix() }
+                override val s: Matrix<${type}> by lazy { svd.getW(null).wrapMatrix() }
+                override val v: Matrix<${type}> by lazy { svd.getV(null, false).wrapMatrix() }
+                override val singularValues: Point<${type}> by lazy { ${type}Buffer(svd.singularValues) }
+            }
+
+            QRDecompositionFeature::class -> object : QRDecompositionFeature<${type}> {
+                private val qr by lazy {
+                    DecompositionFactory_${ops}.qr().apply { decompose(origin.copy()) }
+                }
+
+                override val q: Matrix<${type}> by lazy {
+                    qr.getQ(null, false).wrapMatrix().withFeature(OrthogonalFeature)
+                }
+
+                override val r: Matrix<${type}> by lazy { qr.getR(null, false).wrapMatrix().withFeature(UFeature) }
+            }
+
+            CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<${type}> {
+                override val l: Matrix<${type}> by lazy {
+                    val cholesky =
+                        DecompositionFactory_${ops}.chol(structure.rowNum, true).apply { decompose(origin.copy()) }
+
+                    cholesky.getT(null).wrapMatrix().withFeature(LFeature)
+                }
+            }
+
+            LupDecompositionFeature::class -> object : LupDecompositionFeature<${type}> {
+                private val lup by lazy {
+                    DecompositionFactory_${ops}.lu(origin.numRows, origin.numCols).apply { decompose(origin.copy()) }
+                }
+
+                override val l: Matrix<${type}> by lazy {
+                    lup.getLower(null).wrapMatrix().withFeature(LFeature)
+                }
+
+                override val u: Matrix<${type}> by lazy {
+                    lup.getUpper(null).wrapMatrix().withFeature(UFeature)
+                }
+
+                override val p: Matrix<${type}> by lazy { lup.getRowPivot(null).wrapMatrix() }
+            }""" else """    QRDecompositionFeature::class -> object : QRDecompositionFeature<$type> {
+                private val qr by lazy {
+                    DecompositionFactory_${ops}.qr(FillReducing.NONE).apply { decompose(origin.copy()) }
+                }
+
+                override val q: Matrix<${type}> by lazy {
+                    qr.getQ(null, false).wrapMatrix().withFeature(OrthogonalFeature)
+                }
+
+                override val r: Matrix<${type}> by lazy { qr.getR(null, false).wrapMatrix().withFeature(UFeature) }
+            }
+
+            CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<${type}> {
+                override val l: Matrix<${type}> by lazy {
+                    val cholesky =
+                        DecompositionFactory_${ops}.cholesky().apply { decompose(origin.copy()) }
+
+                    (cholesky.getT(null) as ${ejmlMatrixParentTypeMatrix}).wrapMatrix().withFeature(LFeature)
+                }
+            }
+
+            LUDecompositionFeature::class, DeterminantFeature::class, InverseMatrixFeature::class -> object :
+                LUDecompositionFeature<${type}>, DeterminantFeature<${type}>, InverseMatrixFeature<${type}> {
+                private val lu by lazy {
+                    DecompositionFactory_${ops}.lu(FillReducing.NONE).apply { decompose(origin.copy()) }
+                }
+
+                override val l: Matrix<${type}> by lazy {
+                    lu.getLower(null).wrapMatrix().withFeature(LFeature)
+                }
+
+                override val u: Matrix<${type}> by lazy {
+                    lu.getUpper(null).wrapMatrix().withFeature(UFeature)
+                }
+
+                override val inverse: Matrix<${type}> by lazy {
+                    var a = origin
+                    val inverse = ${ejmlMatrixDenseType}(1, 1)
+                    val solver = LinearSolverFactory_${ops}.lu(FillReducing.NONE)
+                    if (solver.modifiesA()) a = a.copy()
+                    val i = CommonOps_${denseOps}.identity(a.numRows)
+                    solver.solve(i, inverse)
+                    inverse.wrapMatrix()
+                }
+
+                override val determinant: $type by lazy { elementAlgebra.number(lu.computeDeterminant().real) }
+            }"""
+    }
+
+            else -> null
+        }?.let(type::cast)
+    }
+
+    /**
+     * Solves for *x* in the following equation: *x = [a] <sup>-1</sup> &middot; [b]*.
+     *
+     * @param a the base matrix.
+     * @param b n by p matrix.
+     * @return the solution for *x* that is n by p.
+     */
+    public fun solve(a: Matrix<${type}>, b: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
+        val res = ${ejmlMatrixType}(1, 1)
+        CommonOps_${ops}.solve(${ejmlMatrixType}(a.toEjml().origin), ${ejmlMatrixType}(b.toEjml().origin), res)
+        return res.wrapMatrix()
+    }
+
+    /**
+     * Solves for *x* in the following equation: *x = [a] <sup>-1</sup> &middot; [b]*.
+     *
+     * @param a the base matrix.
+     * @param b n by p vector.
+     * @return the solution for *x* that is n by p.
+     */
+    public fun solve(a: Matrix<${type}>, b: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
+        val res = ${ejmlMatrixType}(1, 1)
+        CommonOps_${ops}.solve(${ejmlMatrixType}(a.toEjml().origin), ${ejmlMatrixType}(b.toEjml().origin), res)
+        return Ejml${type}Vector(res)
+    }
+}"""
+    appendLine(text)
+    appendLine()
+}
+
+
+/**
+ * Generates routine EJML classes.
+ */
+fun ejmlCodegen(outputFile: String): Unit = File(outputFile).run {
+    parentFile.mkdirs()
+
+    writer().use {
+        it.appendLine("/*")
+        it.appendLine(" * Copyright 2018-2021 KMath contributors.")
+        it.appendLine(" * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.")
+        it.appendLine(" */")
+        it.appendLine()
+        it.appendLine("/* This file is generated with buildSrc/src/main/kotlin/space/kscience/kmath/ejml/codegen/ejmlCodegen.kt */")
+        it.appendLine()
+        it.appendLine("package space.kscience.kmath.ejml")
+        it.appendLine()
+        it.appendLine("""import org.ejml.data.*
+import org.ejml.dense.row.CommonOps_DDRM
+import org.ejml.dense.row.CommonOps_FDRM
+import org.ejml.dense.row.factory.DecompositionFactory_DDRM
+import org.ejml.dense.row.factory.DecompositionFactory_FDRM
+import org.ejml.sparse.FillReducing
+import org.ejml.sparse.csc.CommonOps_DSCC
+import org.ejml.sparse.csc.CommonOps_FSCC
+import org.ejml.sparse.csc.factory.DecompositionFactory_DSCC
+import org.ejml.sparse.csc.factory.DecompositionFactory_FSCC
+import org.ejml.sparse.csc.factory.LinearSolverFactory_DSCC
+import org.ejml.sparse.csc.factory.LinearSolverFactory_FSCC
+import space.kscience.kmath.linear.*
+import space.kscience.kmath.linear.Matrix
+import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.nd.StructureFeature
+import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.operations.FloatField
+import space.kscience.kmath.operations.invoke
+import space.kscience.kmath.structures.DoubleBuffer
+import space.kscience.kmath.structures.FloatBuffer
+import kotlin.reflect.KClass
+import kotlin.reflect.cast""")
+        it.appendLine()
+        it.appendEjmlVector("Double", "DMatrix")
+        it.appendEjmlVector("Float", "FMatrix")
+        it.appendEjmlMatrix("Double", "DMatrix")
+        it.appendEjmlMatrix("Float", "FMatrix")
+        it.appendEjmlLinearSpace("Double", "DoubleField", "DMatrix", "DMatrixRMaj", "DMatrixRMaj", "DDRM", "DDRM", true)
+        it.appendEjmlLinearSpace("Float", "FloatField", "FMatrix", "FMatrixRMaj", "FMatrixRMaj", "FDRM", "FDRM", true)
+
+        it.appendEjmlLinearSpace(
+            type = "Double",
+            kmathAlgebra = "DoubleField",
+            ejmlMatrixParentTypeMatrix = "DMatrix",
+            ejmlMatrixType = "DMatrixSparseCSC",
+            ejmlMatrixDenseType = "DMatrixRMaj",
+            ops = "DSCC",
+            denseOps = "DDRM",
+            isDense = false,
+        )
+
+        it.appendEjmlLinearSpace(
+            type = "Float",
+            kmathAlgebra = "FloatField",
+            ejmlMatrixParentTypeMatrix = "FMatrix",
+            ejmlMatrixType = "FMatrixSparseCSC",
+            ejmlMatrixDenseType = "FMatrixRMaj",
+            ops = "FSCC",
+            denseOps = "FDRM",
+            isDense = false,
+        )
+    }
+}

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/images/KM.svg

@@ -1,4 +1,9 @@
 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!--
+  - 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 file.
+  -->
+
 <svg
         xmlns:dc="http://purl.org/dc/elements/1.1/"
         xmlns:cc="http://creativecommons.org/ns#"

docs/images/KM_mono.svg

@@ -1,4 +1,9 @@
 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!--
+  - 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 file.
+  -->
+
 <svg
         xmlns:dc="http://purl.org/dc/elements/1.1/"
         xmlns:cc="http://creativecommons.org/ns#"

docs/images/KMath.svg

@@ -1,4 +1,9 @@
 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!--
+  - 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 file.
+  -->
+
 <svg
         xmlns:dc="http://purl.org/dc/elements/1.1/"
         xmlns:cc="http://creativecommons.org/ns#"

docs/images/KMath_mono.svg

@@ -1,4 +1,9 @@
 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!--
+  - 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 file.
+  -->
+
 <svg
         xmlns:dc="http://purl.org/dc/elements/1.1/"
         xmlns:cc="http://creativecommons.org/ns#"

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 genrally 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

@@ -20,13 +20,14 @@ dependencies {
     implementation(project(":kmath-coroutines"))
     implementation(project(":kmath-commons"))
     implementation(project(":kmath-complex"))
+    implementation(project(":kmath-optimization"))
     implementation(project(":kmath-stat"))
     implementation(project(":kmath-viktor"))
     implementation(project(":kmath-dimensions"))
     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 +42,11 @@ 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-dev-2")
+    implementation("space.kscience:plotlykt-server:0.4.2")
+    //jafama
+    implementation(project(":kmath-jafama"))
 }
 
 kotlin.sourceSets.all {
@@ -57,7 +60,7 @@ kotlin.sourceSets.all {
 tasks.withType<org.jetbrains.kotlin.gradle.tasks.KotlinCompile> {
     kotlinOptions{
         jvmTarget = "11"
-        freeCompilerArgs = freeCompilerArgs + "-Xjvm-default=all"
+        freeCompilerArgs = freeCompilerArgs + "-Xjvm-default=all" + "-Xopt-in=kotlin.RequiresOptIn"
     }
 }
 

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast
@@ -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

@@ -1,20 +1,18 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast
 
 import space.kscience.kmath.expressions.MstField
+import space.kscience.kmath.expressions.Symbol.Companion.x
 import space.kscience.kmath.expressions.interpret
-import space.kscience.kmath.misc.Symbol.Companion.x
 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

@@ -1,29 +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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast
 
-import space.kscience.kmath.asm.compileToExpression
 import space.kscience.kmath.expressions.derivative
 import space.kscience.kmath.expressions.invoke
-import space.kscience.kmath.kotlingrad.toDiffExpression
+import space.kscience.kmath.expressions.Symbol.Companion.x
-import space.kscience.kmath.misc.symbol
+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)
-        .toDiffExpression(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 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/fit/chiSquared.kt

@@ -1,32 +1,34 @@
 /*
  * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
-package space.kscience.kmath.commons.fit
+package space.kscience.kmath.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.minimize
+import space.kscience.kmath.commons.optimization.CMOptimizer
 import space.kscience.kmath.distributions.NormalDistribution
-import space.kscience.kmath.misc.symbol
+import space.kscience.kmath.expressions.chiSquaredExpression
-import space.kscience.kmath.optimization.FunctionOptimization
+import space.kscience.kmath.expressions.symbol
-import space.kscience.kmath.optimization.OptimizationResult
+import space.kscience.kmath.operations.asIterable
+import space.kscience.kmath.operations.toList
+import space.kscience.kmath.optimization.FunctionOptimizationTarget
+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
 import space.kscience.kmath.stat.RandomGenerator
-import space.kscience.kmath.structures.asIterable
-import space.kscience.kmath.structures.toList
 import space.kscience.plotly.*
 import space.kscience.plotly.models.ScatterMode
 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
@@ -43,7 +45,7 @@ operator fun TraceValues.invoke(vector: DoubleVector) {
  */
 suspend fun main() {
     //A generator for a normally distributed values
-    val generator = NormalDistribution(2.0, 7.0)
+    val generator = NormalDistribution(0.0, 1.0)
 
     //A chain/flow of random values with the given seed
     val chain = generator.sample(RandomGenerator.default(112667))
@@ -54,7 +56,7 @@ suspend fun main() {
 
 
     //Perform an operation on each x value (much more effective, than numpy)
-    val y = x.map {
+    val y = x.map { it ->
         val value = it.pow(2) + it + 1
         value + chain.next() * sqrt(value)
     }
@@ -65,17 +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 {
@@ -92,7 +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"
             }
         }
@@ -101,7 +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/fit/qowFit.kt

@@ -0,0 +1,106 @@
+/*
+ * 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.fit
+
+import kotlinx.html.br
+import kotlinx.html.h3
+import space.kscience.kmath.commons.expressions.DSProcessor
+import space.kscience.kmath.data.XYErrorColumnarData
+import space.kscience.kmath.distributions.NormalDistribution
+import space.kscience.kmath.expressions.Symbol
+import space.kscience.kmath.expressions.binding
+import space.kscience.kmath.expressions.symbol
+import space.kscience.kmath.operations.asIterable
+import space.kscience.kmath.operations.toList
+import space.kscience.kmath.optimization.QowOptimizer
+import space.kscience.kmath.optimization.chiSquaredOrNull
+import space.kscience.kmath.optimization.fitWith
+import space.kscience.kmath.optimization.resultPoint
+import space.kscience.kmath.real.map
+import space.kscience.kmath.real.step
+import space.kscience.kmath.stat.RandomGenerator
+import space.kscience.plotly.*
+import space.kscience.plotly.models.ScatterMode
+import kotlin.math.abs
+import kotlin.math.pow
+import kotlin.math.sqrt
+
+// Forward declaration of symbols that will be used in expressions.
+private val a by symbol
+private val b by symbol
+private val c by symbol
+
+
+/**
+ * Least squares fie with auto-differentiation. Uses `kmath-commons` and `kmath-for-real` modules.
+ */
+suspend fun main() {
+    //A generator for a normally distributed values
+    val generator = NormalDistribution(0.0, 1.0)
+
+    //A chain/flow of random values with the given seed
+    val chain = generator.sample(RandomGenerator.default(112667))
+
+
+    //Create a uniformly distributed x values like numpy.arrange
+    val x = 1.0..100.0 step 1.0
+
+
+    //Perform an operation on each x value (much more effective, than numpy)
+    val y = x.map { it ->
+        val value = it.pow(2) + it + 1
+        value + chain.next() * sqrt(value)
+    }
+    // this will also work, but less effective:
+    // val y = x.pow(2)+ x + 1 + chain.nextDouble()
+
+    // create same errors for all xs
+    val yErr = y.map { sqrt(abs(it)) }
+    require(yErr.asIterable().all { it > 0 }) { "All errors must be strictly positive" }
+
+    val result = XYErrorColumnarData.of(x, y, yErr).fitWith(
+        QowOptimizer,
+        DSProcessor,
+        mapOf(a to 0.9, b to 1.2, c to 2.0)
+    ) { arg ->
+        //bind variables to autodiff context
+        val a by binding
+        val b by binding
+        //Include default value for c if it is not provided as a parameter
+        val c = bindSymbolOrNull(c) ?: one
+        a * arg.pow(2) + b * arg + c
+    }
+
+    //display a page with plot and numerical results
+    val page = Plotly.page {
+        plot {
+            scatter {
+                mode = ScatterMode.markers
+                x(x)
+                y(y)
+                error_y {
+                    array = yErr.toList()
+                }
+                name = "data"
+            }
+            scatter {
+                mode = ScatterMode.lines
+                x(x)
+                y(x.map { result.model(result.resultPoint + (Symbol.x to it)) })
+                name = "fit"
+            }
+        }
+        br()
+        h3 {
+            +"Fit result: ${result.resultPoint}"
+        }
+        h3 {
+            +"Chi2/dof = ${result.chiSquaredOrNull!! / (x.size - 3)}"
+        }
+    }
+
+    page.makeFile()
+}

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

@@ -1,10 +1,11 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.functions
 
+import space.kscience.kmath.integration.gaussIntegrator
 import space.kscience.kmath.integration.integrate
 import space.kscience.kmath.integration.value
 import space.kscience.kmath.operations.DoubleField
@@ -15,7 +16,7 @@ fun main() {
     val function: UnivariateFunction<Double> = { x -> 3 * x.pow(2) + 2 * x + 1 }
 
     //get the result of the integration
-    val result = DoubleField.integrate(0.0..10.0, function = function)
+    val result = DoubleField.gaussIntegrator.integrate(0.0..10.0, function = function)
 
     //the value is nullable because in some cases the integration could not succeed
     println(result.value)

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

@@ -0,0 +1,54 @@
+/*
+ * 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 file.
+ */
+
+package space.kscience.kmath.functions
+
+import space.kscience.kmath.interpolation.SplineInterpolator
+import space.kscience.kmath.interpolation.interpolatePolynomials
+import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.structures.DoubleBuffer
+import space.kscience.plotly.Plotly
+import space.kscience.plotly.UnstablePlotlyAPI
+import space.kscience.plotly.makeFile
+import space.kscience.plotly.models.functionXY
+import space.kscience.plotly.scatter
+import kotlin.math.PI
+import kotlin.math.sin
+
+@OptIn(UnstablePlotlyAPI::class)
+fun main() {
+    val data = (0..10).map {
+        val x = it.toDouble() / 5 * PI
+        x to sin(x)
+    }
+
+    val polynomial: PiecewisePolynomial<Double> = SplineInterpolator(
+        DoubleField, ::DoubleBuffer
+    ).interpolatePolynomials(data)
+
+    val function = polynomial.asFunction(DoubleField, 0.0)
+
+    val cmInterpolate = org.apache.commons.math3.analysis.interpolation.SplineInterpolator().interpolate(
+        data.map { it.first }.toDoubleArray(),
+        data.map { it.second }.toDoubleArray()
+    )
+
+    Plotly.plot {
+        scatter {
+            name = "interpolated"
+            x.numbers = data.map { it.first }
+            y.numbers = x.doubles.map { function(it) }
+        }
+        scatter {
+            name = "original"
+            functionXY(0.0..(2 * PI), 0.1) { sin(it) }
+        }
+        scatter {
+            name = "cm"
+            x.numbers = data.map { it.first }
+            y.numbers = x.doubles.map { cmInterpolate.value(it) }
+        }
+    }.makeFile()
+}

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

@@ -0,0 +1,45 @@
+/*
+ * 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 file.
+ */
+
+package space.kscience.kmath.functions
+
+import space.kscience.kmath.interpolation.SplineInterpolator
+import space.kscience.kmath.interpolation.interpolatePolynomials
+import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.real.map
+import space.kscience.kmath.real.step
+import space.kscience.plotly.Plotly
+import space.kscience.plotly.UnstablePlotlyAPI
+import space.kscience.plotly.makeFile
+import space.kscience.plotly.models.functionXY
+import space.kscience.plotly.scatter
+
+@OptIn(UnstablePlotlyAPI::class)
+fun main() {
+    val function: UnivariateFunction<Double> = { x ->
+        if (x in 30.0..50.0) {
+            1.0
+        } else {
+            0.0
+        }
+    }
+    val xs = 0.0..100.0 step 0.5
+    val ys = xs.map(function)
+
+    val polynomial: PiecewisePolynomial<Double> = SplineInterpolator.double.interpolatePolynomials(xs, ys)
+
+    val polyFunction = polynomial.asFunction(DoubleField, 0.0)
+
+    Plotly.plot {
+        scatter {
+            name = "interpolated"
+            functionXY(25.0..55.0, 0.1) { polyFunction(it) }
+        }
+        scatter {
+            name = "original"
+            functionXY(25.0..55.0, 0.1) { function(it) }
+        }
+    }.makeFile()
+}

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

@@ -1,19 +1,20 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.functions
 
+import space.kscience.kmath.integration.gaussIntegrator
 import space.kscience.kmath.integration.integrate
 import space.kscience.kmath.integration.value
 import space.kscience.kmath.nd.StructureND
-import space.kscience.kmath.nd.nd
+import space.kscience.kmath.nd.withNdAlgebra
-import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.operations.algebra
 import space.kscience.kmath.operations.invoke
 
-fun main(): Unit = DoubleField {
+fun main(): Unit = Double.algebra {
-    nd(2, 2) {
+    withNdAlgebra(2, 2) {
 
         //Produce a diagonal StructureND
         fun diagonal(v: Double) = produce { (i, j) ->
@@ -21,10 +22,10 @@ fun main(): Unit = DoubleField {
         }
 
         //Define a function in a nd space
-        val function: (Double) -> StructureND<Double> = { x: Double -> 3 * number(x).pow(2) + 2 * diagonal(x) + 1 }
+        val function: (Double) -> StructureND<Double> = { x: Double -> 3 * x.pow(2) + 2 * diagonal(x) + 1 }
 
         //get the result of the integration
-        val result = integrate(0.0..10.0, function = function)
+        val result = gaussIntegrator.integrate(0.0..10.0, function = function)
 
         //the value is nullable because in some cases the integration could not succeed
         println(result.value)

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 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/linear/dotPerformance.kt

@@ -0,0 +1,34 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
+ */
+
+package space.kscience.kmath.linear
+
+import space.kscience.kmath.operations.algebra
+import kotlin.random.Random
+import kotlin.system.measureTimeMillis
+
+fun main() {
+    val random = Random(12224)
+    val dim = 1000
+
+    //creating invertible matrix
+    val matrix1 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
+        if (i <= j) random.nextDouble() else 0.0
+    }
+    val matrix2 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
+        if (i <= j) random.nextDouble() else 0.0
+    }
+
+    val time = measureTimeMillis {
+        with(Double.algebra.linearSpace) {
+            repeat(10) {
+                matrix1 dot matrix2
+            }
+        }
+    }
+
+    println(time)
+
+}

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.linear

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.operations

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

@@ -1,29 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.operations
-
-import space.kscience.kmath.complex.Complex
-import space.kscience.kmath.complex.complex
-import space.kscience.kmath.nd.AlgebraND
-
-fun main() {
-    // 2d element
-    val element = AlgebraND.complex(2, 2).produce { (i, j) ->
-        Complex(i.toDouble() - j.toDouble(), i.toDouble() + j.toDouble())
-    }
-    println(element)
-
-    // 1d element operation
-    val result = with(AlgebraND.complex(8)) {
-        val a = produce { (it) -> i * it - it.toDouble() }
-        val b = 3
-        val c = Complex(1.0, 1.0)
-
-        (a pow b) + c
-    }
-
-    println(result)
-}

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

@@ -0,0 +1,41 @@
+/*
+ * 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 file.
+ */
+
+package space.kscience.kmath.operations
+
+import space.kscience.kmath.complex.Complex
+import space.kscience.kmath.complex.algebra
+import space.kscience.kmath.complex.bufferAlgebra
+import space.kscience.kmath.complex.ndAlgebra
+import space.kscience.kmath.nd.BufferND
+import space.kscience.kmath.nd.StructureND
+
+fun main() = Complex.algebra {
+    val complex = 2 + 2 * i
+    println(complex * 8 - 5 * i)
+
+    //flat buffer
+    val buffer = bufferAlgebra(8).run {
+        buffer { Complex(it, -it) }.map { Complex(it.im, it.re) }
+    }
+    println(buffer)
+
+
+    // 2d element
+    val element: BufferND<Complex> = ndAlgebra(2, 2).produce { (i, j) ->
+        Complex(i - j, i + j)
+    }
+    println(element)
+
+    // 1d element operation
+    val result: StructureND<Complex> = ndAlgebra(8).run {
+        val a = produce { (it) -> i * it - it.toDouble() }
+        val b = 3
+        val c = Complex(1.0, 1.0)
+
+        (a pow b) + c
+    }
+    println(result)
+}

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.stat

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.stat
@@ -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/ComplexND.kt

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 @file:Suppress("unused")
@@ -9,10 +9,10 @@ package space.kscience.kmath.structures
 
 import space.kscience.kmath.complex.*
 import space.kscience.kmath.linear.transpose
-import space.kscience.kmath.nd.AlgebraND
 import space.kscience.kmath.nd.StructureND
 import space.kscience.kmath.nd.as2D
-import space.kscience.kmath.nd.real
+import space.kscience.kmath.nd.ndAlgebra
+import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.operations.invoke
 import kotlin.system.measureTimeMillis
 
@@ -20,8 +20,8 @@ fun main() {
     val dim = 1000
     val n = 1000
 
-    val realField = AlgebraND.real(dim, dim)
+    val realField = DoubleField.ndAlgebra(dim, dim)
-    val complexField: ComplexFieldND = AlgebraND.complex(dim, dim)
+    val complexField: ComplexFieldND = ComplexField.ndAlgebra(dim, dim)
 
     val realTime = measureTimeMillis {
         realField {
@@ -49,7 +49,7 @@ fun main() {
 fun complexExample() {
     //Create a context for 2-d structure with complex values
     ComplexField {
-        nd(4, 8) {
+        withNdAlgebra(4, 8) {
             //a constant real-valued structure
             val x = one * 2.5
             operator fun Number.plus(other: Complex) = Complex(this.toDouble() + other.re, other.im)

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.structures
@@ -8,7 +8,9 @@ package space.kscience.kmath.structures
 import kotlinx.coroutines.DelicateCoroutinesApi
 import kotlinx.coroutines.GlobalScope
 import org.nd4j.linalg.factory.Nd4j
-import space.kscience.kmath.nd.*
+import space.kscience.kmath.nd.StructureND
+import space.kscience.kmath.nd.autoNdAlgebra
+import space.kscience.kmath.nd.ndAlgebra
 import space.kscience.kmath.nd4j.Nd4jArrayField
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.operations.invoke
@@ -31,17 +33,17 @@ fun main() {
     val n = 1000
 
     // automatically build context most suited for given type.
-    val autoField = AlgebraND.auto(DoubleField, dim, dim)
+    val autoField = DoubleField.autoNdAlgebra(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)
+    val realField = DoubleField.ndAlgebra(dim, dim)
     //A generic boxing field. It should be used for objects, not primitives.
-    val boxingField = AlgebraND.field(DoubleField, Buffer.Companion::boxing, dim, dim)
+    val boxingField = DoubleField.ndAlgebra(Buffer.Companion::boxing, dim, dim)
     // Nd4j specialized field.
     val nd4jField = Nd4jArrayField.real(dim, dim)
     //viktor field
     val viktorField = ViktorNDField(dim, dim)
     //parallel processing based on Java Streams
-    val parallelField = AlgebraND.realWithStream(dim, dim)
+    val parallelField = DoubleField.ndStreaming(dim, dim)
 
     measureAndPrint("Boxing addition") {
         boxingField {

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.structures
@@ -105,4 +105,4 @@ class StreamDoubleFieldND(override val shape: IntArray) : FieldND<Double, Double
     override fun atanh(arg: StructureND<Double>): BufferND<Double> = arg.map { atanh(it) }
 }
 
-fun AlgebraND.Companion.realWithStream(vararg shape: Int): StreamDoubleFieldND = StreamDoubleFieldND(shape)
+fun DoubleField.ndStreaming(vararg shape: Int): StreamDoubleFieldND = StreamDoubleFieldND(shape)

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.structures

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.structures

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

@@ -0,0 +1,22 @@
+/*
+ * 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.structures
+
+import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.operations.buffer
+import space.kscience.kmath.operations.bufferAlgebra
+
+inline fun <reified R : Any> MutableBuffer.Companion.same(
+    n: Int,
+    value: R
+): MutableBuffer<R> = auto(n) { value }
+
+
+fun main() {
+    with(DoubleField.bufferAlgebra(5)) {
+        println(number(2.0) + buffer(1, 2, 3, 4, 5))
+    }
+}

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.structures
@@ -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

@@ -1,46 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.tensors
-
-import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
-
-
-// Dataset normalization
-
-fun main() {
-
-    // work in context with broadcast methods
-    BroadcastDoubleTensorAlgebra {
-        // take dataset of 5-element vectors from normal distribution
-        val dataset = randomNormal(intArrayOf(100, 5)) * 1.5 // all elements from N(0, 1.5)
-
-        dataset += fromArray(
-            intArrayOf(5),
-            doubleArrayOf(0.0, 1.0, 1.5, 3.0, 5.0) // rows means
-        )
-
-
-        // find out mean and standard deviation of each column
-        val mean = dataset.mean(0, false)
-        val std = dataset.std(0, false)
-
-        println("Mean:\n$mean")
-        println("Standard deviation:\n$std")
-
-        // 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)}")
-
-        // now we can scale dataset with mean normalization
-        val datasetScaled = (dataset - mean) / std
-
-        // find out mean and std of scaled dataset
-
-        println("Mean of scaled:\n${datasetScaled.mean(0, false)}")
-        println("Mean of scaled:\n${datasetScaled.std(0, false)}")
-    }
-}

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

@@ -1,97 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.tensors
-
-import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.tensors.core.DoubleTensor
-import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
-
-// solving linear system with LUP decomposition
-
-fun main () {
-
-    // work in context with linear operations
-    BroadcastDoubleTensorAlgebra {
-
-        // set true value of x
-        val trueX = fromArray(
-            intArrayOf(4),
-            doubleArrayOf(-2.0, 1.5, 6.8, -2.4)
-        )
-
-        // and A matrix
-        val a = fromArray(
-            intArrayOf(4, 4),
-            doubleArrayOf(
-                0.5, 10.5, 4.5, 1.0,
-                8.5, 0.9, 12.8, 0.1,
-                5.56, 9.19, 7.62, 5.45,
-                1.0, 2.0, -3.0, -2.5
-            )
-        )
-
-        // calculate y value
-        val b = a dot trueX
-
-        // check out A and b
-        println("A:\n$a")
-        println("b:\n$b")
-
-        // solve `Ax = b` system using LUP decomposition
-
-        // get P, L, U such that PA = LU
-        val (p, l, u) = a.lu()
-
-        // check that P is permutation matrix
-        println("P:\n$p")
-        // L is lower triangular matrix and U is upper triangular matrix
-        println("L:\n$l")
-        println("U:\n$u")
-        // and PA = LU
-        println("PA:\n${p dot a}")
-        println("LU:\n${l dot u}")
-
-     /* Ax = b;
-        PAx = Pb;
-        LUx = Pb;
-        let y = Ux, then
-        Ly = Pb -- this system can be easily solved, since the matrix L is lower triangular;
-        Ux = y can be solved the same way, since the matrix L is upper triangular
-         */
-
-
-
-        // this function returns solution x of a system lx = b, l should be lower triangular
-        fun solveLT(l: DoubleTensor, b: DoubleTensor): DoubleTensor {
-            val n = l.shape[0]
-            val x = zeros(intArrayOf(n))
-            for (i in 0 until n){
-                x[intArrayOf(i)] = (b[intArrayOf(i)] -  l[i].dot(x).value()) / l[intArrayOf(i, i)]
-            }
-            return x
-        }
-
-        val y = solveLT(l, p dot b)
-
-        // solveLT(l, b) function can be easily adapted for upper triangular matrix by the permutation matrix revMat
-        // create it by placing ones on side diagonal
-        val revMat = u.zeroesLike()
-        val n = revMat.shape[0]
-        for (i in 0 until n) {
-            revMat[intArrayOf(i, n - 1 - i)] = 1.0
-        }
-
-        // solution of system ux = b, u should be upper triangular
-        fun solveUT(u: DoubleTensor, b: DoubleTensor): DoubleTensor = revMat dot solveLT(
-            revMat dot u dot revMat, revMat dot b
-        )
-
-        val x = solveUT(u, y)
-
-        println("True x:\n$trueX")
-        println("x founded with LU method:\n$x")
-    }
-}

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.tensors

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

@@ -1,78 +1,74 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.operations.invoke
+import space.kscience.kmath.tensors.core.tensorAlgebra
-import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.withBroadcast
 
 
 // simple PCA
 
-fun main(){
+fun main(): Unit = Double.tensorAlgebra.withBroadcast {  // work in context with broadcast methods
     val seed = 100500L
 
-    // work in context with broadcast methods
+    // assume x is range from 0 until 10
-    BroadcastDoubleTensorAlgebra {
+    val x = fromArray(
+        intArrayOf(10),
+        DoubleArray(10) { it.toDouble() }
+    )
 
-        // assume x is range from 0 until 10
+    // take y dependent on x with noise
-        val x = fromArray(
+    val y = 2.0 * x + (3.0 + x.randomNormalLike(seed) * 1.5)
-            intArrayOf(10),
-            (0 until 10).toList().map { it.toDouble() }.toDoubleArray()
-        )
 
-        // take y dependent on x with noise
+    println("x:\n$x")
-        val y = 2.0 * x + (3.0 + x.randomNormalLike(seed) * 1.5)
+    println("y:\n$y")
 
-        println("x:\n$x")
+    // stack them into single dataset
-        println("y:\n$y")
+    val dataset = stack(listOf(x, y)).transpose()
 
-        // stack them into single dataset
+    // normalize both x and y
-        val dataset = stack(listOf(x, y)).transpose()
+    val xMean = x.mean()
+    val yMean = y.mean()
 
-        // normalize both x and y
+    val xStd = x.std()
-        val xMean = x.mean()
+    val yStd = y.std()
-        val yMean = y.mean()
 
-        val xStd = x.std()
+    val xScaled = (x - xMean) / xStd
-        val yStd = y.std()
+    val yScaled = (y - yMean) / yStd
 
-        val xScaled = (x - xMean) / xStd
+    // save means ans standard deviations for further recovery
-        val yScaled = (y - yMean) / yStd
+    val mean = fromArray(
+        intArrayOf(2),
+        doubleArrayOf(xMean, yMean)
+    )
+    println("Means:\n$mean")
 
-        // save means ans standard deviations for further recovery
+    val std = fromArray(
-        val mean = fromArray(
+        intArrayOf(2),
-            intArrayOf(2),
+        doubleArrayOf(xStd, yStd)
-            doubleArrayOf(xMean, yMean)
+    )
-        )
+    println("Standard deviations:\n$std")
-        println("Means:\n$mean")
 
-        val std = fromArray(
+    // calculate the covariance matrix of scaled x and y
-            intArrayOf(2),
+    val covMatrix = cov(listOf(xScaled, yScaled))
-            doubleArrayOf(xStd, yStd)
+    println("Covariance matrix:\n$covMatrix")
-        )
-        println("Standard deviations:\n$std")
 
-        // calculate the covariance matrix of scaled x and y
+    // and find out eigenvector of it
-        val covMatrix = cov(listOf(xScaled, yScaled))
+    val (_, evecs) = covMatrix.symEig()
-        println("Covariance matrix:\n$covMatrix")
+    val v = evecs[0]
+    println("Eigenvector:\n$v")
 
-        // and find out eigenvector of it
+    // reduce dimension of dataset
-        val (_, evecs) = covMatrix.symEig()
+    val datasetReduced = v dot stack(listOf(xScaled, yScaled))
-        val v = evecs[0]
+    println("Reduced data:\n$datasetReduced")
-        println("Eigenvector:\n$v")
 
-        // reduce dimension of dataset
+    // we can restore original data from reduced data;
-        val datasetReduced  = v dot stack(listOf(xScaled, yScaled))
+    // for example, find 7th element of dataset.
-        println("Reduced data:\n$datasetReduced")
+    val n = 7
-
+    val restored = (datasetReduced[n] dot v.view(intArrayOf(1, 2))) * std + mean
-        // we can restore original data from reduced data.
+    println("Original value:\n${dataset[n]}")
-        // for example, find 7th element of dataset
+    println("Restored value:\n$restored")
-        val n = 7
-        val restored = (datasetReduced[n] dot v.view(intArrayOf(1, 2))) * std + mean
-        println("Original value:\n${dataset[n]}")
-        println("Restored value:\n$restored")
-    }
 }

examples/src/main/kotlin/space/kscience/kmath/tensors/dataSetNormalization.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 file.
+ */
+
+package space.kscience.kmath.tensors
+
+import space.kscience.kmath.tensors.core.tensorAlgebra
+import space.kscience.kmath.tensors.core.withBroadcast
+
+
+// Dataset normalization
+
+fun main() = Double.tensorAlgebra.withBroadcast {  // work in context with broadcast methods
+    // take dataset of 5-element vectors from normal distribution
+    val dataset = randomNormal(intArrayOf(100, 5)) * 1.5 // all elements from N(0, 1.5)
+
+    dataset += fromArray(
+        intArrayOf(5),
+        doubleArrayOf(0.0, 1.0, 1.5, 3.0, 5.0) // row means
+    )
+
+
+    // find out mean and standard deviation of each column
+    val mean = dataset.mean(0, false)
+    val std = dataset.std(0, false)
+
+    println("Mean:\n$mean")
+    println("Standard deviation:\n$std")
+
+    // 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)}")
+
+    // now we can scale dataset with mean normalization
+    val datasetScaled = (dataset - mean) / std
+
+    // find out mean and std of scaled dataset
+
+    println("Mean of scaled:\n${datasetScaled.mean(0, false)}")
+    println("Mean of scaled:\n${datasetScaled.std(0, false)}")
+}

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

@@ -0,0 +1,93 @@
+/*
+ * 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 file.
+ */
+
+package space.kscience.kmath.tensors
+
+import space.kscience.kmath.tensors.core.DoubleTensor
+import space.kscience.kmath.tensors.core.tensorAlgebra
+import space.kscience.kmath.tensors.core.withBroadcast
+
+// solving linear system with LUP decomposition
+
+fun main() = Double.tensorAlgebra.withBroadcast {// work in context with linear operations
+
+    // set true value of x
+    val trueX = fromArray(
+        intArrayOf(4),
+        doubleArrayOf(-2.0, 1.5, 6.8, -2.4)
+    )
+
+    // and A matrix
+    val a = fromArray(
+        intArrayOf(4, 4),
+        doubleArrayOf(
+            0.5, 10.5, 4.5, 1.0,
+            8.5, 0.9, 12.8, 0.1,
+            5.56, 9.19, 7.62, 5.45,
+            1.0, 2.0, -3.0, -2.5
+        )
+    )
+
+    // calculate y value
+    val b = a dot trueX
+
+    // check out A and b
+    println("A:\n$a")
+    println("b:\n$b")
+
+    // solve `Ax = b` system using LUP decomposition
+
+    // get P, L, U such that PA = LU
+    val (p, l, u) = a.lu()
+
+    // check P is permutation matrix
+    println("P:\n$p")
+    // L is lower triangular matrix and U is upper triangular matrix
+    println("L:\n$l")
+    println("U:\n$u")
+    // and PA = LU
+    println("PA:\n${p dot a}")
+    println("LU:\n${l dot u}")
+
+    /* Ax = b;
+       PAx = Pb;
+       LUx = Pb;
+       let y = Ux, then
+       Ly = Pb -- this system can be easily solved, since the matrix L is lower triangular;
+       Ux = y can be solved the same way, since the matrix L is upper triangular
+        */
+
+
+
+    // this function returns solution x of a system lx = b, l should be lower triangular
+    fun solveLT(l: DoubleTensor, b: DoubleTensor): DoubleTensor {
+        val n = l.shape[0]
+        val x = zeros(intArrayOf(n))
+        for (i in 0 until n) {
+            x[intArrayOf(i)] = (b[intArrayOf(i)] - l[i].dot(x).value()) / l[intArrayOf(i, i)]
+        }
+        return x
+    }
+
+    val y = solveLT(l, p dot b)
+
+    // solveLT(l, b) function can be easily adapted for upper triangular matrix by the permutation matrix revMat
+    // create it by placing ones on side diagonal
+    val revMat = u.zeroesLike()
+    val n = revMat.shape[0]
+    for (i in 0 until n) {
+        revMat[intArrayOf(i, n - 1 - i)] = 1.0
+    }
+
+    // solution of system ux = b, u should be upper triangular
+    fun solveUT(u: DoubleTensor, b: DoubleTensor): DoubleTensor = revMat dot solveLT(
+        revMat dot u dot revMat, revMat dot b
+    )
+
+    val x = solveUT(u, y)
+
+    println("True x:\n$trueX")
+    println("x founded with LU method:\n$x")
+}

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.tensors
@@ -25,7 +25,7 @@ interface Layer {
 // activation layer
 open class Activation(
     val activation: (DoubleTensor) -> DoubleTensor,
-    val activationDer: (DoubleTensor) -> DoubleTensor
+    val activationDer: (DoubleTensor) -> DoubleTensor,
 ) : Layer {
     override fun forward(input: DoubleTensor): DoubleTensor {
         return activation(input)
@@ -62,7 +62,7 @@ class Sigmoid : Activation(::sigmoid, ::sigmoidDer)
 class Dense(
     private val inputUnits: Int,
     private val outputUnits: Int,
-    private val learningRate: Double = 0.1
+    private val learningRate: Double = 0.1,
 ) : Layer {
 
     private val weights: DoubleTensor = DoubleTensorAlgebra {
@@ -74,8 +74,8 @@ class Dense(
 
     private val bias: DoubleTensor = DoubleTensorAlgebra { zeros(intArrayOf(outputUnits)) }
 
-    override fun forward(input: DoubleTensor): DoubleTensor {
+    override fun forward(input: DoubleTensor): DoubleTensor = BroadcastDoubleTensorAlgebra {
-        return BroadcastDoubleTensorAlgebra { (input dot weights) + bias }
+        (input dot weights) + bias
     }
 
     override fun backward(input: DoubleTensor, outputError: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
@@ -116,7 +116,7 @@ class NeuralNetwork(private val layers: List<Layer>) {
             onesForAnswers[intArrayOf(index, label)] = 1.0
         }
 
-        val softmaxValue =  yPred.exp() / yPred.exp().sum(dim = 1, keepDim = true)
+        val softmaxValue = yPred.exp() / yPred.exp().sum(dim = 1, keepDim = true)
 
         (-onesForAnswers + softmaxValue) / (yPred.shape[0].toDouble())
     }
@@ -175,67 +175,65 @@ class NeuralNetwork(private val layers: List<Layer>) {
 
 
 @OptIn(ExperimentalStdlibApi::class)
-fun main() {
+fun main() = BroadcastDoubleTensorAlgebra {
-    BroadcastDoubleTensorAlgebra {
+    val features = 5
-        val features = 5
+    val sampleSize = 250
-        val sampleSize = 250
+    val trainSize = 180
-        val trainSize = 180
+    //val testSize = sampleSize - trainSize
-        //val testSize = sampleSize - trainSize
 
-        // take sample of features from normal distribution
+    // take sample of features from normal distribution
-        val x = randomNormal(intArrayOf(sampleSize, features), seed) * 2.5
+    val x = randomNormal(intArrayOf(sampleSize, features), seed) * 2.5
 
-        x += fromArray(
+    x += fromArray(
-            intArrayOf(5),
+        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
-        )
+    )
 
 
-        // define class like '1' if the sum of features > 0 and '0' otherwise
+    // define class like '1' if the sum of features > 0 and '0' otherwise
-        val y = fromArray(
+    val y = fromArray(
-            intArrayOf(sampleSize, 1),
+        intArrayOf(sampleSize, 1),
-            DoubleArray(sampleSize) { i ->
+        DoubleArray(sampleSize) { i ->
-                if (x[i].sum() > 0.0) {
+            if (x[i].sum() > 0.0) {
-                    1.0
+                1.0
-                } else {
+            } else {
-                    0.0
+                0.0
-                }
             }
-        )
-
-        // split train ans test
-        val trainIndices = (0 until trainSize).toList().toIntArray()
-        val testIndices = (trainSize until sampleSize).toList().toIntArray()
-
-        val xTrain = x.rowsByIndices(trainIndices)
-        val yTrain = y.rowsByIndices(trainIndices)
-
-        val xTest = x.rowsByIndices(testIndices)
-        val yTest = y.rowsByIndices(testIndices)
-
-        // build model
-        val layers = buildList {
-            add(Dense(features, 64))
-            add(ReLU())
-            add(Dense(64, 16))
-            add(ReLU())
-            add(Dense(16, 2))
-            add(Sigmoid())
         }
-        val model = NeuralNetwork(layers)
+    )
 
-        // fit it with train data
+    // split train ans test
-        model.fit(xTrain, yTrain, batchSize = 20, epochs = 10)
+    val trainIndices = (0 until trainSize).toList().toIntArray()
+    val testIndices = (trainSize until sampleSize).toList().toIntArray()
 
-        // make prediction
+    val xTrain = x.rowsByIndices(trainIndices)
-        val prediction = model.predict(xTest)
+    val yTrain = y.rowsByIndices(trainIndices)
 
-        // process raw prediction via argMax
+    val xTest = x.rowsByIndices(testIndices)
-        val predictionLabels = prediction.argMax(1, true)
+    val yTest = y.rowsByIndices(testIndices)
-
-        // find out accuracy
-        val acc = accuracy(yTest, predictionLabels)
-        println("Test accuracy:$acc")
 
+    // build model
+    val layers = buildList {
+        add(Dense(features, 64))
+        add(ReLU())
+        add(Dense(64, 16))
+        add(ReLU())
+        add(Dense(16, 2))
+        add(Sigmoid())
     }
+    val model = NeuralNetwork(layers)
+
+    // fit it with train data
+    model.fit(xTrain, yTrain, batchSize = 20, epochs = 10)
+
+    // make prediction
+    val prediction = model.predict(xTest)
+
+    // process raw prediction via argMax
+    val predictionLabels = prediction.argMax(1, true)
+
+    // find out accuracy
+    val acc = accuracy(yTest, predictionLabels)
+    println("Test accuracy:$acc")
+
 }

gradle.properties

@@ -4,10 +4,13 @@
 #
 
 kotlin.code.style=official
-kotlin.mpp.enableGranularSourceSetsMetadata=true
 kotlin.mpp.stability.nowarn=true
-kotlin.native.enableDependencyPropagation=false
+
-kotlin.parallel.tasks.in.project=true
+#kotlin.mpp.enableGranularSourceSetsMetadata=true
+#kotlin.native.enableDependencyPropagation=false
+
+kotlin.jupyter.add.scanner=false
+
 org.gradle.configureondemand=true
 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-8`.
+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-8'
+    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-8")
+    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/parser.kt

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast
@@ -17,6 +17,7 @@ import com.github.h0tk3y.betterParse.lexer.regexToken
 import com.github.h0tk3y.betterParse.parser.ParseResult
 import com.github.h0tk3y.betterParse.parser.Parser
 import space.kscience.kmath.expressions.MST
+import space.kscience.kmath.expressions.Symbol
 import space.kscience.kmath.operations.FieldOperations
 import space.kscience.kmath.operations.GroupOperations
 import space.kscience.kmath.operations.PowerOperations
@@ -42,7 +43,7 @@ public object ArithmeticsEvaluator : Grammar<MST>() {
     private val ws: Token by regexToken("\\s+".toRegex(), ignore = true)
 
     private val number: Parser<MST> by num use { MST.Numeric(text.toDouble()) }
-    private val singular: Parser<MST> by id use { MST.Symbolic(text) }
+    private val singular: Parser<MST> by id use { Symbol(text) }
 
     private val unaryFunction: Parser<MST> by (id and -lpar and parser(ArithmeticsEvaluator::subSumChain) and -rpar)
         .map { (id, term) -> MST.Unary(id.text, term) }

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast.rendering
@@ -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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast.rendering
@@ -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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast.rendering
@@ -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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast.rendering
@@ -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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast.rendering
@@ -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

@@ -1,27 +1,26 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast.rendering
 
 import space.kscience.kmath.ast.rendering.FeaturedMathRenderer.RenderFeature
 import space.kscience.kmath.expressions.MST
+import space.kscience.kmath.expressions.Symbol
 import space.kscience.kmath.misc.UnstableKMathAPI
 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 MST.Symbolic) null
+    else SymbolSyntax(string = node.identity)
-        else
-            SymbolSyntax(string = node.value)
 }
 
 /**
@@ -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
@@ -141,8 +140,8 @@ 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 MST.Symbolic || node.value !in symbols)
+        if (node !is Symbol || node.identity !in symbols)
             null
         else
             SpecialSymbolSyntax(kind = SpecialSymbolSyntax.Kind.SMALL_PI)
@@ -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/multiplatformToString.kt

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast.rendering

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

@@ -1,10 +1,11 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.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

@@ -1,17 +1,16 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast
 
 import space.kscience.kmath.expressions.MstField
 import space.kscience.kmath.expressions.MstRing
+import space.kscience.kmath.expressions.Symbol.Companion.x
 import space.kscience.kmath.expressions.interpret
-import space.kscience.kmath.misc.Symbol.Companion.x
 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

@@ -1,15 +1,14 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast
 
 import space.kscience.kmath.expressions.MstExtendedField
+import space.kscience.kmath.expressions.Symbol.Companion.x
 import space.kscience.kmath.expressions.invoke
-import space.kscience.kmath.misc.Symbol.Companion.x
 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
@@ -17,49 +16,49 @@ import kotlin.test.assertEquals
 internal class TestCompilerOperations {
     @Test
     fun testUnaryPlus() = runCompilerTest {
-        val expr = MstExtendedField { +bindSymbol(x) }.compileToExpression(DoubleField)
+        val expr = MstExtendedField { +x }.compileToExpression(DoubleField)
         assertEquals(2.0, expr(x to 2.0))
     }
 
     @Test
     fun testUnaryMinus() = runCompilerTest {
-        val expr = MstExtendedField { -bindSymbol(x) }.compileToExpression(DoubleField)
+        val expr = MstExtendedField { -x }.compileToExpression(DoubleField)
         assertEquals(-2.0, expr(x to 2.0))
     }
 
     @Test
     fun testAdd() = runCompilerTest {
-        val expr = MstExtendedField { bindSymbol(x) + bindSymbol(x) }.compileToExpression(DoubleField)
+        val expr = MstExtendedField { x + x }.compileToExpression(DoubleField)
         assertEquals(4.0, expr(x to 2.0))
     }
 
     @Test
     fun testSine() = runCompilerTest {
-        val expr = MstExtendedField { sin(bindSymbol(x)) }.compileToExpression(DoubleField)
+        val expr = MstExtendedField { sin(x) }.compileToExpression(DoubleField)
         assertEquals(0.0, expr(x to 0.0))
     }
 
     @Test
     fun testCosine() = runCompilerTest {
-        val expr = MstExtendedField { cos(bindSymbol(x)) }.compileToExpression(DoubleField)
+        val expr = MstExtendedField { cos(x) }.compileToExpression(DoubleField)
         assertEquals(1.0, expr(x to 0.0))
     }
 
     @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

@@ -1,15 +1,14 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast
 
 import space.kscience.kmath.expressions.MstRing
+import space.kscience.kmath.expressions.Symbol.Companion.x
 import space.kscience.kmath.expressions.invoke
-import space.kscience.kmath.misc.Symbol.Companion.x
 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/commonTest/kotlin/space/kscience/kmath/ast/TestParser.kt

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast

kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestFeatures.kt

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast.rendering

kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestLatex.kt

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast.rendering

kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestMathML.kt

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast.rendering

kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestStages.kt

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast.rendering

kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestUtils.kt

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast.rendering

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

@@ -1,13 +1,13 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast
 
 import space.kscience.kmath.expressions.Expression
 import space.kscience.kmath.expressions.MST
-import space.kscience.kmath.misc.Symbol
+import space.kscience.kmath.expressions.Symbol
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.operations.IntRing
 

kmath-ast/src/jsMain/kotlin/space/kscience/kmath/ast/rendering/multiplatformToString.kt

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.ast.rendering

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

@@ -1,6 +1,6 @@
 /*
  * 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.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
 package space.kscience.kmath.estree
@@ -9,22 +9,23 @@ import space.kscience.kmath.estree.internal.ESTreeBuilder
 import space.kscience.kmath.expressions.Expression
 import space.kscience.kmath.expressions.MST
 import space.kscience.kmath.expressions.MST.*
+import space.kscience.kmath.expressions.Symbol
 import space.kscience.kmath.expressions.invoke
 import space.kscience.kmath.internal.estree.BaseExpression
-import space.kscience.kmath.misc.Symbol
 import space.kscience.kmath.operations.Algebra
 import space.kscience.kmath.operations.NumericAlgebra
+import space.kscience.kmath.operations.bindSymbolOrNull
 
 @PublishedApi
 internal fun <T> MST.compileWith(algebra: Algebra<T>): Expression<T> {
     fun ESTreeBuilder<T>.visit(node: MST): BaseExpression = when (node) {
-        is Symbolic -> {
+        is Symbol -> {
-            val symbol = algebra.bindSymbolOrNull(node.value)
+            val symbol = algebra.bindSymbolOrNull(node)
 
             if (symbol != null)
                 constant(symbol)
             else
-                variable(node.value)
+                variable(node.identity)
         }
 
         is Numeric -> constant(node.value)