Merge branch 'dev' into beta/kotlin2.0.0

# Conflicts:
#	gradle.properties
#	kmath-histograms/build.gradle.kts
#	kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/Histogram.kt

.gitignore

@@ -5,6 +5,7 @@ out/
 .idea/
 .vscode/
 .fleet/
+.kotlin/
 
 
 # Avoid ignoring Gradle wrapper jar file (.jar files are usually ignored)

CHANGELOG.md

@@ -3,28 +3,62 @@
 ## Unreleased
 
 ### Added
-- Integer division algebras
-- Float32 geometries
 
 ### Changed
-- Default naming for algebra and buffers now uses IntXX/FloatXX notation instead of Java types.
-- Remove unnecessary inlines in basic algebras.
-- QuaternionField -> QuaternionAlgebra and does not implement `Field` anymore since it is non-commutative
-- kmath-geometry is split into `euclidean2d` and `euclidean3d`
 
 ### Deprecated
 
 ### Removed
-- `asPolynomial` function due to scope pollution
 
 ### Fixed
-- Median statistics
 
 ### Security
 
+## 0.4.0-dev-3 - 2024-02-18
+
+### Added
+
+- Reification. Explicit `SafeType` for algebras.
+- Integer division algebras.
+- Float32 geometries.
+- New Attributes-kt module that could be used as stand-alone. It declares. type-safe attributes containers.
+- Explicit `mutableStructureND` builders for mutable structures.
+- `Buffer.asList()` zero-copy transformation.
+- Wasm support.
+- Parallel implementation of `LinearSpace` for Float64
+- Parallel buffer factories
+
+### Changed
+
+- Buffer copy removed from API (added as an extension).
+- Default naming for algebra and buffers now uses IntXX/FloatXX notation instead of Java types.
+- Remove unnecessary inlines in basic algebras.
+- QuaternionField -> QuaternionAlgebra and does not implement `Field` anymore since it is non-commutative
+- kmath-geometry is split into `euclidean2d` and `euclidean3d`
+- Features replaced with Attributes.
+- Transposed refactored.
+- Kmath-memory is moved on top of core.
+
+### Deprecated
+
+- ND4J engine
+
+### Removed
+
+- `asPolynomial` function due to scope pollution
+- Codegend for ejml (450 lines of codegen for 1000 lines of code is too much)
+
+### Fixed
+
+- Median statistics
+- Complex power of negative real numbers
+- Add proper mutability for MutableBufferND rows and columns
+- Generic Float32 and Float64 vectors are used in geometry algebras.
+
 ## 0.3.1 - 2023-04-09
 
 ### Added
+
 - Wasm support for `memory`, `core`, `complex` and `functions` modules.
 - Generic builders for `BufferND` and `MutableBufferND`
 - `NamedMatrix` - matrix with symbol-based indexing
@@ -34,6 +68,8 @@
 - Algebra now has an obligatory `bufferFactory` (#477).
 
 ### Changed
+
+- Removed marker `Vector` type for geometry
 - Geometry uses type-safe angles
 - Tensor operations switched to prefix notation
 - Row-wise and column-wise ND shapes in the core
@@ -45,12 +81,14 @@
 - Multik went MPP
 
 ### Removed
+
 - Trajectory moved to https://github.com/SciProgCentre/maps-kt
 - Polynomials moved to https://github.com/SciProgCentre/kmath-polynomial
 
 ## 0.3.0
 
 ### Added
+
 - `ScaleOperations` interface
 - `Field` extends `ScaleOperations`
 - Basic integration API
@@ -75,6 +113,7 @@
 - Compilation to TeX for MST: #254
 
 ### Changed
+
 - Annotations moved to `space.kscience.kmath`
 - Exponential operations merged with hyperbolic functions
 - Space is replaced by Group. Space is reserved for vector spaces.
@@ -108,9 +147,11 @@
 - `UnivariateFunction` -> `Function1D`, `MultivariateFunction` -> `FunctionND`
 
 ### Deprecated
+
 - Specialized `DoubleBufferAlgebra`
 
 ### Removed
+
 - Nearest in Domain. To be implemented in geometry package.
 - Number multiplication and division in main Algebra chain
 - `contentEquals` from Buffer. It moved to the companion.
@@ -121,12 +162,14 @@
 - Algebra elements are completely removed. Use algebra contexts instead.
 
 ### Fixed
+
 - Ring inherits RingOperations, not GroupOperations
 - Univariate histogram filling
 
 ## 0.2.0
 
 ### Added
+
 - `fun` annotation for SAM interfaces in library
 - Explicit `public` visibility for all public APIs
 - Better trigonometric and hyperbolic functions for `AutoDiffField` (https://github.com/mipt-npm/kmath/pull/140)
@@ -146,6 +189,7 @@
 - Basic Quaternion vector support in `kmath-complex`.
 
 ### Changed
+
 - Package changed from `scientifik` to `space.kscience`
 - Gradle version: 6.6 -> 6.8.2
 - Minor exceptions refactor (throwing `IllegalArgumentException` by argument checks instead of `IllegalStateException`)
@@ -170,6 +214,7 @@
 - Add `out` projection to `Buffer` generic
 
 ### Removed
+
 - `kmath-koma` module because it doesn't support Kotlin 1.4.
 - Support of `legacy` JS backend (we will support only IR)
 - `toGrid` method.
@@ -178,11 +223,13 @@
 - StructureND identity and equals
 
 ### Fixed
+
 - `symbol` method in `MstExtendedField` (https://github.com/mipt-npm/kmath/pull/140)
 
 ## 0.1.4
 
 ### Added
+
 - Functional Expressions API
 - Mathematical Syntax Tree, its interpreter and API
 - String to MST parser (https://github.com/mipt-npm/kmath/pull/120)
@@ -200,6 +247,7 @@
 - Norm support for `Complex`
 
 ### Changed
+
 - `readAsMemory` now has `throws IOException` in JVM signature.
 - Several functions taking functional types were made `inline`.
 - Several functions taking functional types now have `callsInPlace` contracts.
@@ -211,6 +259,7 @@
 - Moved probability distributions to commons-rng and to `kmath-prob`
 
 ### Fixed
+
 - Missing copy method in Memory implementation on JS (https://github.com/mipt-npm/kmath/pull/106)
 - D3.dim value in `kmath-dimensions`
 - Multiplication in integer rings in `kmath-core` (https://github.com/mipt-npm/kmath/pull/101)

README.md

@@ -2,7 +2,7 @@
 [![DOI](https://zenodo.org/badge/129486382.svg)](https://zenodo.org/badge/latestdoi/129486382)
 ![Gradle build](https://github.com/SciProgCentre/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/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/spc/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
 
@@ -11,18 +11,21 @@ analog to Python's NumPy library. Later we found that kotlin is much more flexib
 architecture designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like
 experience could be achieved with [kmath-for-real](/kmath-for-real) extension module.
 
-[Documentation site (**WIP**)](https://SciProgCentre.github.io/kmath/)
+[Documentation site](https://SciProgCentre.github.io/kmath/)
 
 ## Publications and talks
 
 * [A conceptual article about context-oriented design](https://proandroiddev.com/an-introduction-context-oriented-programming-in-kotlin-2e79d316b0a2)
 * [Another article about context-oriented design](https://proandroiddev.com/diving-deeper-into-context-oriented-programming-in-kotlin-3ecb4ec38814)
 * [ACAT 2019 conference paper](https://aip.scitation.org/doi/abs/10.1063/1.5130103)
+* [A talk at KotlinConf 2019 about using kotlin for science](https://youtu.be/LI_5TZ7tnOE?si=4LknX41gl_YeUbIe)
+* [A talk on architecture at Joker-2021 (in Russian)](https://youtu.be/1bZ2doHiRRM?si=9w953ro9yu98X_KJ)
+* [The same talk in English](https://youtu.be/yP5DIc2fVwQ?si=louZzQ1dcXV6gP10)
+* [A seminar on tensor API](https://youtu.be/0H99wUs0xTM?si=6c__04jrByFQtVpo)
 
 # 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, Native and Wasm).
-  .
 * Provide basic multiplatform implementations for those abstractions (without significant performance optimization).
 * Provide bindings and wrappers with those abstractions for popular optimized platform libraries.
 
@@ -53,19 +56,21 @@ module definitions below. The module stability could have the following levels:
 ## Modules
 
 
-### [benchmarks](benchmarks)
+### [attributes-kt](attributes-kt)
+> An API and basic implementation for arranging objects in a continuous memory block.
 >
+> **Maturity**: DEVELOPMENT
+
+### [benchmarks](benchmarks)
 >
 > **Maturity**: EXPERIMENTAL
 
 ### [examples](examples)
 >
->
 > **Maturity**: EXPERIMENTAL
 
 ### [kmath-ast](kmath-ast)
 >
->
 > **Maturity**: EXPERIMENTAL
 >
 > **Features:**
@@ -76,7 +81,7 @@ module definitions below. The module stability could have the following levels:
 
 
 ### [kmath-commons](kmath-commons)
-> 
+> Commons math binding for kmath
 >
 > **Maturity**: EXPERIMENTAL
 
@@ -109,17 +114,15 @@ performance calculations to code generation.
 
 ### [kmath-coroutines](kmath-coroutines)
 >
->
 > **Maturity**: EXPERIMENTAL
 
 ### [kmath-dimensions](kmath-dimensions)
-> 
+> A proof of concept module for adding type-safe dimensions to structures
 >
 > **Maturity**: PROTOTYPE
 
 ### [kmath-ejml](kmath-ejml)
 >
->
 > **Maturity**: PROTOTYPE
 >
 > **Features:**
@@ -142,7 +145,7 @@ One can still use generic algebras though.
 
 
 ### [kmath-functions](kmath-functions)
-> 
+> Functions, integration and interpolation
 >
 > **Maturity**: EXPERIMENTAL
 >
@@ -156,18 +159,16 @@ One can still use generic algebras though.
 
 ### [kmath-geometry](kmath-geometry)
 >
->
 > **Maturity**: PROTOTYPE
 
 ### [kmath-histograms](kmath-histograms)
 >
->
 > **Maturity**: PROTOTYPE
 
 ### [kmath-jafama](kmath-jafama)
+> Jafama integration module
 >
->
+> **Maturity**: DEPRECATED
-> **Maturity**: PROTOTYPE
 >
 > **Features:**
 > - [jafama-double](kmath-jafama/src/main/kotlin/space/kscience/kmath/jafama/) : Double ExtendedField implementations based on Jafama
@@ -175,11 +176,10 @@ One can still use generic algebras though.
 
 ### [kmath-jupyter](kmath-jupyter)
 >
->
 > **Maturity**: PROTOTYPE
 
 ### [kmath-kotlingrad](kmath-kotlingrad)
-> 
+> Kotlin∇ integration module
 >
 > **Maturity**: EXPERIMENTAL
 >
@@ -194,14 +194,14 @@ One can still use generic algebras though.
 > **Maturity**: DEVELOPMENT
 
 ### [kmath-multik](kmath-multik)
-> 
+> JetBrains Multik connector
 >
 > **Maturity**: PROTOTYPE
 
 ### [kmath-nd4j](kmath-nd4j)
+> ND4J NDStructure implementation and according NDAlgebra classes
 >
->
+> **Maturity**: DEPRECATED
-> **Maturity**: EXPERIMENTAL
 >
 > **Features:**
 > - [nd4jarraystructure](kmath-nd4j/#) : NDStructure wrapper for INDArray
@@ -211,27 +211,24 @@ One can still use generic algebras though.
 
 ### [kmath-optimization](kmath-optimization)
 >
->
 > **Maturity**: EXPERIMENTAL
 
 ### [kmath-stat](kmath-stat)
 >
->
 > **Maturity**: EXPERIMENTAL
 
 ### [kmath-symja](kmath-symja)
-> 
+> Symja integration module
 >
 > **Maturity**: PROTOTYPE
 
 ### [kmath-tensorflow](kmath-tensorflow)
-> 
+> Google tensorflow connector
 >
 > **Maturity**: PROTOTYPE
 
 ### [kmath-tensors](kmath-tensors)
 >
->
 > **Maturity**: PROTOTYPE
 >
 > **Features:**
@@ -241,13 +238,12 @@ One can still use generic algebras though.
 
 
 ### [kmath-viktor](kmath-viktor)
-> 
+> Binding for https://github.com/JetBrains-Research/viktor
 >
 > **Maturity**: DEVELOPMENT
 
 ### [test-utils](test-utils)
 >
->
 > **Maturity**: EXPERIMENTAL
 
 
@@ -256,22 +252,21 @@ One can still use generic algebras though.
 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
 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, 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 both
+Calculation of performance is one of the major goals of KMath in the future, but in some cases it is impossible to achieve both
 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 a 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
 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
+KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend using GraalVM-CE or Oracle GraalVM for execution to get better performance.
-execution to get better performance.
 
 ### Repositories
 
@@ -291,10 +286,7 @@ dependencies {
 }
 ```
 
-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 feedback about what features are
-required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues
+required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues marked with [good first issue](hhttps://github.com/SciProgCentre/kmath/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) label.
-marked with [waiting for a hero](https://github.com/SciProgCentre/kmath/labels/waiting%20for%20a%20hero) label.

attributes-kt/README.md

@@ -0,0 +1,4 @@
+# Module attributes-kt
+
+
+

attributes-kt/api/attributes-kt.api

@@ -0,0 +1,104 @@
+public abstract interface class space/kscience/attributes/Attribute {
+}
+
+public abstract interface class space/kscience/attributes/AttributeContainer {
+	public abstract fun getAttributes ()Lspace/kscience/attributes/Attributes;
+}
+
+public abstract interface class space/kscience/attributes/AttributeScope {
+}
+
+public abstract interface class space/kscience/attributes/AttributeWithDefault : space/kscience/attributes/Attribute {
+	public abstract fun getDefault ()Ljava/lang/Object;
+}
+
+public abstract interface class space/kscience/attributes/Attributes {
+	public static final field Companion Lspace/kscience/attributes/Attributes$Companion;
+	public abstract fun equals (Ljava/lang/Object;)Z
+	public fun get (Lspace/kscience/attributes/Attribute;)Ljava/lang/Object;
+	public abstract fun getContent ()Ljava/util/Map;
+	public fun getKeys ()Ljava/util/Set;
+	public abstract fun hashCode ()I
+	public abstract fun toString ()Ljava/lang/String;
+}
+
+public final class space/kscience/attributes/Attributes$Companion {
+	public final fun equals (Lspace/kscience/attributes/Attributes;Lspace/kscience/attributes/Attributes;)Z
+	public final fun getEMPTY ()Lspace/kscience/attributes/Attributes;
+}
+
+public final class space/kscience/attributes/AttributesBuilder : space/kscience/attributes/Attributes {
+	public fun <init> ()V
+	public final fun add (Lspace/kscience/attributes/SetAttribute;Ljava/lang/Object;)V
+	public final fun build ()Lspace/kscience/attributes/Attributes;
+	public fun equals (Ljava/lang/Object;)Z
+	public final fun from (Lspace/kscience/attributes/Attributes;)V
+	public fun getContent ()Ljava/util/Map;
+	public fun hashCode ()I
+	public final fun invoke (Lspace/kscience/attributes/Attribute;Ljava/lang/Object;)V
+	public final fun remove (Lspace/kscience/attributes/SetAttribute;Ljava/lang/Object;)V
+	public final fun set (Lspace/kscience/attributes/Attribute;Ljava/lang/Object;)V
+	public fun toString ()Ljava/lang/String;
+}
+
+public final class space/kscience/attributes/AttributesBuilderKt {
+	public static final fun Attributes (Lkotlin/jvm/functions/Function1;)Lspace/kscience/attributes/Attributes;
+}
+
+public final class space/kscience/attributes/AttributesKt {
+	public static final fun Attributes (Lspace/kscience/attributes/Attribute;)Lspace/kscience/attributes/Attributes;
+	public static final fun Attributes (Lspace/kscience/attributes/Attribute;Ljava/lang/Object;)Lspace/kscience/attributes/Attributes;
+	public static final fun getOrDefault (Lspace/kscience/attributes/Attributes;Lspace/kscience/attributes/AttributeWithDefault;)Ljava/lang/Object;
+	public static final fun isEmpty (Lspace/kscience/attributes/Attributes;)Z
+	public static final fun modify (Lspace/kscience/attributes/Attributes;Lkotlin/jvm/functions/Function1;)Lspace/kscience/attributes/Attributes;
+	public static final fun plus (Lspace/kscience/attributes/Attributes;Lspace/kscience/attributes/Attributes;)Lspace/kscience/attributes/Attributes;
+	public static final fun withAttribute (Lspace/kscience/attributes/Attributes;Lspace/kscience/attributes/Attribute;)Lspace/kscience/attributes/Attributes;
+	public static final fun withAttribute (Lspace/kscience/attributes/Attributes;Lspace/kscience/attributes/Attribute;Ljava/lang/Object;)Lspace/kscience/attributes/Attributes;
+	public static final fun withAttributeElement (Lspace/kscience/attributes/Attributes;Lspace/kscience/attributes/SetAttribute;Ljava/lang/Object;)Lspace/kscience/attributes/Attributes;
+	public static final fun withoutAttribute (Lspace/kscience/attributes/Attributes;Lspace/kscience/attributes/Attribute;)Lspace/kscience/attributes/Attributes;
+	public static final fun withoutAttributeElement (Lspace/kscience/attributes/Attributes;Lspace/kscience/attributes/SetAttribute;Ljava/lang/Object;)Lspace/kscience/attributes/Attributes;
+}
+
+public abstract interface class space/kscience/attributes/FlagAttribute : space/kscience/attributes/Attribute {
+}
+
+public abstract class space/kscience/attributes/PolymorphicAttribute : space/kscience/attributes/Attribute {
+	public synthetic fun <init> (Lkotlin/reflect/KType;Lkotlin/jvm/internal/DefaultConstructorMarker;)V
+	public fun equals (Ljava/lang/Object;)Z
+	public final fun getType-V0oMfBY ()Lkotlin/reflect/KType;
+	public fun hashCode ()I
+}
+
+public final class space/kscience/attributes/PolymorphicAttributeKt {
+	public static final fun get (Lspace/kscience/attributes/Attributes;Lkotlin/jvm/functions/Function0;)Ljava/lang/Object;
+	public static final fun set (Lspace/kscience/attributes/AttributesBuilder;Lkotlin/jvm/functions/Function0;Ljava/lang/Object;)V
+}
+
+public final class space/kscience/attributes/SafeType {
+	public static final synthetic fun box-impl (Lkotlin/reflect/KType;)Lspace/kscience/attributes/SafeType;
+	public static fun constructor-impl (Lkotlin/reflect/KType;)Lkotlin/reflect/KType;
+	public fun equals (Ljava/lang/Object;)Z
+	public static fun equals-impl (Lkotlin/reflect/KType;Ljava/lang/Object;)Z
+	public static final fun equals-impl0 (Lkotlin/reflect/KType;Lkotlin/reflect/KType;)Z
+	public final fun getKType ()Lkotlin/reflect/KType;
+	public fun hashCode ()I
+	public static fun hashCode-impl (Lkotlin/reflect/KType;)I
+	public fun toString ()Ljava/lang/String;
+	public static fun toString-impl (Lkotlin/reflect/KType;)Ljava/lang/String;
+	public final synthetic fun unbox-impl ()Lkotlin/reflect/KType;
+}
+
+public final class space/kscience/attributes/SafeTypeKt {
+	public static final fun getKClass-X0YbwmU (Lkotlin/reflect/KType;)Lkotlin/reflect/KClass;
+}
+
+public abstract interface class space/kscience/attributes/SetAttribute : space/kscience/attributes/Attribute {
+}
+
+public abstract interface annotation class space/kscience/attributes/UnstableAttributesAPI : java/lang/annotation/Annotation {
+}
+
+public abstract interface class space/kscience/attributes/WithType {
+	public abstract fun getType-V0oMfBY ()Lkotlin/reflect/KType;
+}
+

attributes-kt/build.gradle.kts

@@ -0,0 +1,20 @@
+plugins {
+    id("space.kscience.gradle.mpp")
+    `maven-publish`
+}
+
+version = "0.1.0"
+
+kscience {
+    jvm()
+    js()
+    native()
+    wasm()
+}
+
+readme {
+    maturity = space.kscience.gradle.Maturity.DEVELOPMENT
+    description = """
+        An API and basic implementation for arranging objects in a continuous memory block.
+    """.trimIndent()
+}

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/Attribute.kt

@@ -0,0 +1,29 @@
+/*
+ * Copyright 2018-2023 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.attributes
+
+/**
+ * A marker interface for an attribute. Attributes are used as keys to access contents of type [T] in the container.
+ */
+public interface Attribute<T>
+
+/**
+ * An attribute that could be either present or absent
+ */
+public interface FlagAttribute : Attribute<Unit>
+
+/**
+ * An attribute with a default value
+ */
+public interface AttributeWithDefault<T> : Attribute<T> {
+    public val default: T
+}
+
+/**
+ * Attribute containing a set of values
+ */
+public interface SetAttribute<V> : Attribute<Set<V>>
+

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/AttributeContainer.kt

@@ -0,0 +1,20 @@
+/*
+ * Copyright 2018-2023 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.attributes
+
+/**
+ * A container for [Attributes]
+ */
+public interface AttributeContainer {
+    public val attributes: Attributes
+}
+
+/**
+ * A scope, where attribute keys could be resolved.
+ * [O] is used only to resolve types in compile-time.
+ */
+public interface AttributeScope<O>
+

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/Attributes.kt

@@ -0,0 +1,135 @@
+/*
+ * Copyright 2018-2023 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.attributes
+
+/**
+ * A set of attributes. The implementation must guarantee that [content] keys correspond to their value types.
+ */
+public interface Attributes {
+    /**
+     * Raw content for this [Attributes]
+     */
+    public val content: Map<out Attribute<*>, Any?>
+
+    /**
+     * Attribute keys contained in this [Attributes]
+     */
+    public val keys: Set<Attribute<*>> get() = content.keys
+
+    /**
+     * Provide an attribute value. Return null if attribute is not present or if its value is null.
+     */
+    @Suppress("UNCHECKED_CAST")
+    public operator fun <T> get(attribute: Attribute<T>): T? = content[attribute] as? T
+
+    override fun toString(): String
+    override fun equals(other: Any?): Boolean
+    override fun hashCode(): Int
+
+    public companion object {
+        public val EMPTY: Attributes = AttributesImpl(emptyMap())
+
+        public fun equals(a1: Attributes, a2: Attributes): Boolean =
+            a1.keys == a2.keys && a1.keys.all { a1[it] == a2[it] }
+    }
+}
+
+internal class AttributesImpl(override val content: Map<out Attribute<*>, Any?>) : Attributes {
+    override fun toString(): String = "Attributes(value=${content.entries})"
+    override fun equals(other: Any?): Boolean = other is Attributes && Attributes.equals(this, other)
+    override fun hashCode(): Int = content.hashCode()
+}
+
+public fun Attributes.isEmpty(): Boolean = content.isEmpty()
+
+/**
+ * Get attribute value or default
+ */
+public fun <T> Attributes.getOrDefault(attribute: AttributeWithDefault<T>): T = get(attribute) ?: attribute.default
+
+/**
+ * Check if there is an attribute that matches given key by type and adheres to [predicate].
+ */
+@Suppress("UNCHECKED_CAST")
+public inline fun <T, reified A : Attribute<T>> Attributes.hasAny(predicate: (value: T) -> Boolean): Boolean =
+    content.any { (mapKey, mapValue) -> mapKey is A && predicate(mapValue as T) }
+
+/**
+ * Check if there is an attribute of given type (subtypes included)
+ */
+public inline fun <reified A : Attribute<*>> Attributes.hasAny(): Boolean =
+    content.any { (mapKey, _) -> mapKey is A }
+
+/**
+ * Check if [Attributes] contains a flag. Multiple keys that are instances of a flag could be present
+ */
+public inline fun <reified A : FlagAttribute> Attributes.hasFlag(): Boolean =
+    content.keys.any { it is A }
+
+/**
+ * Create [Attributes] with an added or replaced attribute key.
+ */
+public fun <T, A : Attribute<T>> Attributes.withAttribute(
+    attribute: A,
+    attrValue: T,
+): Attributes = AttributesImpl(content + (attribute to attrValue))
+
+public fun <A : Attribute<Unit>> Attributes.withAttribute(attribute: A): Attributes =
+    withAttribute(attribute, Unit)
+
+/**
+ * Create a new [Attributes] by modifying the current one
+ */
+public fun <T> Attributes.modify(block: AttributesBuilder<T>.() -> Unit): Attributes = Attributes<T> {
+    from(this@modify)
+    block()
+}
+
+/**
+ * Create new [Attributes] by removing [attribute] key
+ */
+public fun Attributes.withoutAttribute(attribute: Attribute<*>): Attributes = AttributesImpl(content.minus(attribute))
+
+/**
+ * Add an element to a [SetAttribute]
+ */
+public fun <T, A : SetAttribute<T>> Attributes.withAttributeElement(
+    attribute: A,
+    attrValue: T,
+): Attributes {
+    val currentSet: Set<T> = get(attribute) ?: emptySet()
+    return AttributesImpl(
+        content + (attribute to (currentSet + attrValue))
+    )
+}
+
+/**
+ * Remove an element from [SetAttribute]
+ */
+public fun <T, A : SetAttribute<T>> Attributes.withoutAttributeElement(
+    attribute: A,
+    attrValue: T,
+): Attributes {
+    val currentSet: Set<T> = get(attribute) ?: emptySet()
+    return AttributesImpl(content + (attribute to (currentSet - attrValue)))
+}
+
+/**
+ * Create [Attributes] with a single key
+ */
+public fun <T, A : Attribute<T>> Attributes(
+    attribute: A,
+    attrValue: T,
+): Attributes = AttributesImpl(mapOf(attribute to attrValue))
+
+/**
+ * Create Attributes with a single [Unit] valued attribute
+ */
+public fun <A : Attribute<Unit>> Attributes(
+    attribute: A,
+): Attributes = AttributesImpl(mapOf(attribute to Unit))
+
+public operator fun Attributes.plus(other: Attributes): Attributes = AttributesImpl(content + other.content)

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/AttributesBuilder.kt

@@ -0,0 +1,62 @@
+/*
+ * Copyright 2018-2023 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.attributes
+
+/**
+ * A safe builder for [Attributes]
+ *
+ * @param O type marker of an owner object, for which these attributes are made
+ */
+public class AttributesBuilder<out O> internal constructor(
+    private val map: MutableMap<Attribute<*>, Any?>,
+) : Attributes {
+
+    public constructor() : this(mutableMapOf())
+
+    override fun toString(): String = "Attributes(value=${content.entries})"
+    override fun equals(other: Any?): Boolean = other is Attributes && Attributes.equals(this, other)
+    override fun hashCode(): Int = content.hashCode()
+
+    override val content: Map<out Attribute<*>, Any?> get() = map
+
+    public operator fun <T> set(attribute: Attribute<T>, value: T?) {
+        if (value == null) {
+            map.remove(attribute)
+        } else {
+            map[attribute] = value
+        }
+    }
+
+    public operator fun <V> Attribute<V>.invoke(value: V?) {
+        set(this, value)
+    }
+
+    public fun from(attributes: Attributes) {
+        map.putAll(attributes.content)
+    }
+
+    public fun <V> SetAttribute<V>.add(
+        attrValue: V,
+    ) {
+        val currentSet: Set<V> = get(this) ?: emptySet()
+        map[this] = currentSet + attrValue
+    }
+
+    /**
+     * Remove an element from [SetAttribute]
+     */
+    public fun <V> SetAttribute<V>.remove(
+        attrValue: V,
+    ) {
+        val currentSet: Set<V> = get(this) ?: emptySet()
+        map[this] = currentSet - attrValue
+    }
+
+    public fun build(): Attributes = AttributesImpl(map)
+}
+
+public inline fun <O> Attributes(builder: AttributesBuilder<O>.() -> Unit): Attributes =
+    AttributesBuilder<O>().apply(builder).build()

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/PolymorphicAttribute.kt

@@ -0,0 +1,31 @@
+/*
+ * Copyright 2018-2023 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.attributes
+
+/**
+ * An attribute that has a type parameter for value
+ * @param type parameter-type
+ */
+public abstract class PolymorphicAttribute<T>(public val type: SafeType<T>) : Attribute<T> {
+    override fun equals(other: Any?): Boolean = other != null &&
+            (this::class == other::class) &&
+            (other as? PolymorphicAttribute<*>)?.type == this.type
+
+    override fun hashCode(): Int = this::class.hashCode() + type.hashCode()
+}
+
+
+/**
+ * Get a polymorphic attribute using attribute factory
+ */
+public operator fun <T> Attributes.get(attributeKeyBuilder: () -> PolymorphicAttribute<T>): T? = get(attributeKeyBuilder())
+
+/**
+ * Set a polymorphic attribute using its factory
+ */
+public operator fun <O, T> AttributesBuilder<O>.set(attributeKeyBuilder: () -> PolymorphicAttribute<T>, value: T) {
+    set(attributeKeyBuilder(), value)
+}

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/SafeType.kt

@@ -0,0 +1,35 @@
+/*
+ * Copyright 2018-2023 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.attributes
+
+import kotlin.jvm.JvmInline
+import kotlin.reflect.KClass
+import kotlin.reflect.KType
+import kotlin.reflect.typeOf
+
+/**
+ * Safe variant ok Kotlin [KType] that ensures that the type parameter is of the same type as [kType]
+ *
+ * @param kType raw [KType]
+ */
+@JvmInline
+public value class SafeType<out T> @PublishedApi internal constructor(public val kType: KType)
+
+public inline fun <reified T> safeTypeOf(): SafeType<T> = SafeType(typeOf<T>())
+
+/**
+ * Derive Kotlin [KClass] from this type and fail if the type is not a class (should not happen)
+ */
+@Suppress("UNCHECKED_CAST")
+@UnstableAttributesAPI
+public val <T> SafeType<T>.kClass: KClass<T & Any> get() = kType.classifier as KClass<T & Any>
+
+/**
+ * An interface containing [type] for dynamic type checking.
+ */
+public interface WithType<out T> {
+    public val type: SafeType<T>
+}

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/annotations.kt

@@ -0,0 +1,17 @@
+/*
+ * Copyright 2018-2023 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.attributes
+
+/**
+ * Marks declarations that are still experimental in the Attributes-kt APIs, which means that the design of the corresponding
+ * declarations has open issues that may (or may not) lead to their changes in the future. Roughly speaking, there is
+ * a chance of those declarations will be deprecated in the future or the semantics of their behavior may change
+ * in some way that may break some code.
+ */
+@MustBeDocumented
+@Retention(value = AnnotationRetention.BINARY)
+@RequiresOptIn("This API is unstable and could change in future", RequiresOptIn.Level.WARNING)
+public annotation class UnstableAttributesAPI

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
@@ -99,7 +99,7 @@ class ExpressionsInterpretersBenchmark {
         private val estree = node.estreeCompileToExpression(Float64Field)
 
         private val raw = Expression<Double> { args ->
-            val x = args[x]!!
+            val x = args.getValue(x)
             x * 2.0 + 2.0 / x - 16.0 / sin(x)
         }
     }

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
@@ -11,12 +11,11 @@ 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.Float64ParallelLinearSpace
 import space.kscience.kmath.linear.invoke
 import space.kscience.kmath.linear.linearSpace
 import space.kscience.kmath.operations.Float64Field
-import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensorflow.produceWithTF
-import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.tensorAlgebra
 import kotlin.random.Random
 
@@ -72,12 +71,12 @@ internal class DotBenchmark {
     }
 
     @Benchmark
-    fun tensorDot(blackhole: Blackhole) = with(Float64Field.tensorAlgebra) {
+    fun multikDot(blackhole: Blackhole) = with(multikAlgebra) {
         blackhole.consume(matrix1 dot matrix2)
     }
 
     @Benchmark
-    fun multikDot(blackhole: Blackhole) = with(multikAlgebra) {
+    fun tensorDot(blackhole: Blackhole) = with(Float64Field.tensorAlgebra) {
         blackhole.consume(matrix1 dot matrix2)
     }
 
@@ -87,12 +86,8 @@ internal class DotBenchmark {
     }
 
     @Benchmark
-    fun doubleDot(blackhole: Blackhole) = with(Float64Field.linearSpace) {
+    fun parallelDot(blackhole: Blackhole) = with(Float64ParallelLinearSpace) {
         blackhole.consume(matrix1 dot matrix2)
     }
 
-    @Benchmark
-    fun doubleTensorDot(blackhole: Blackhole) = DoubleTensorAlgebra.invoke {
-        blackhole.consume(matrix1 dot matrix2)
-    }
 }

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2023 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
@@ -15,6 +15,7 @@ import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
 import space.kscience.kmath.linear.invoke
 import space.kscience.kmath.linear.linearSpace
 import space.kscience.kmath.linear.lupSolver
+import space.kscience.kmath.linear.parallel
 import space.kscience.kmath.operations.algebra
 import kotlin.random.Random
 
@@ -38,16 +39,19 @@ internal class MatrixInverseBenchmark {
     }
 
     @Benchmark
-    fun cmLUPInversion(blackhole: Blackhole) {
+    fun kmathParallelLupInversion(blackhole: Blackhole) {
-        CMLinearSpace {
+        blackhole.consume(Double.algebra.linearSpace.parallel.lupSolver().inverse(matrix))
-            blackhole.consume(lupSolver().inverse(matrix))
-        }
     }
 
     @Benchmark
-    fun ejmlInverse(blackhole: Blackhole) {
+    fun cmLUPInversion(blackhole: Blackhole) = CMLinearSpace {
-        EjmlLinearSpaceDDRM {
+        blackhole.consume(lupSolver().inverse(matrix))
-            blackhole.consume(matrix.toEjml().inverse())
     }
+
+
+    @Benchmark
+    fun ejmlInverse(blackhole: Blackhole) = EjmlLinearSpaceDDRM {
+        blackhole.consume(matrix.toEjml().inverted())
     }
+
 }

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
@@ -25,6 +25,16 @@ import space.kscience.kmath.viktor.viktorAlgebra
 @State(Scope.Benchmark)
 internal class NDFieldBenchmark {
 
+    private companion object {
+        private const val dim = 1000
+        private const val n = 100
+        private val shape = ShapeND(dim, dim)
+        private val specializedField = Float64Field.ndAlgebra
+        private val genericField = BufferedFieldOpsND(Float64Field)
+        private val nd4jField = Float64Field.nd4j
+        private val viktorField = Float64Field.viktorAlgebra
+    }
+
     @Benchmark
     fun specializedFieldAdd(blackhole: Blackhole) = with(specializedField) {
         var res: StructureND<Double> = one(shape)
@@ -82,13 +92,5 @@ internal class NDFieldBenchmark {
 //        blackhole.consume(res)
 //    }
 
-    private companion object {
+
-        private const val dim = 1000
-        private const val n = 100
-        private val shape = ShapeND(dim, dim)
-        private val specializedField = Float64Field.ndAlgebra
-        private val genericField = BufferedFieldOpsND(Float64Field)
-        private val nd4jField = Float64Field.nd4j
-        private val viktorField = Float64Field.viktorAlgebra
-    }
 }

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

build.gradle.kts

@@ -3,7 +3,7 @@ import space.kscience.gradle.useSPCTeam
 
 plugins {
     id("space.kscience.gradle.project")
-    id("org.jetbrains.kotlinx.kover") version "0.6.0"
+    id("org.jetbrains.kotlinx.kover") version "0.7.6"
 }
 
 allprojects {
@@ -14,7 +14,7 @@ allprojects {
     }
 
     group = "space.kscience"
-    version = "0.4.0-dev-2"
+    version = "0.4.0"
 }
 
 subprojects {
@@ -34,7 +34,7 @@ subprojects {
                     localDirectory.set(kotlinDir)
 
                     remoteUrl.set(
-                        java.net.URL("https://github.com/SciProgCentre/kmath/tree/master/${this@subprojects.name}/$kotlinDirPath")
+                        uri("https://github.com/SciProgCentre/kmath/tree/master/${this@subprojects.name}/$kotlinDirPath").toURL()
                     )
                 }
 
@@ -69,4 +69,4 @@ ksciencePublish {
 
 apiValidation.nonPublicMarkers.add("space.kscience.kmath.UnstableKMathAPI")
 
-val multikVersion by extra("0.2.2")
+val multikVersion by extra("0.2.3")

buildSrc/build.gradle.kts

@@ -17,7 +17,7 @@ val benchmarksVersion = spclibs.versions.kotlinx.benchmark.get()
 dependencies {
     api("space.kscience:gradle-tools:$toolsVersion")
     //plugins form benchmarks
-    api("org.jetbrains.kotlinx:kotlinx-benchmark-plugin:0.4.9")
+    api("org.jetbrains.kotlinx:kotlinx-benchmark-plugin:$benchmarksVersion")
     //api("org.jetbrains.kotlin:kotlin-allopen:$kotlinVersion")
     //to be used inside build-script only
     //implementation(spclibs.kotlinx.serialization.json)

buildSrc/settings.gradle.kts

@@ -6,7 +6,7 @@
 enableFeaturePreview("TYPESAFE_PROJECT_ACCESSORS")
 
 plugins {
-    id("org.gradle.toolchains.foojay-resolver-convention") version "0.7.0"
+    id("org.gradle.toolchains.foojay-resolver-convention") version "0.8.0"
 }
 
 dependencyResolutionManagement {
@@ -22,6 +22,7 @@ dependencyResolutionManagement {
 
     val toolsVersion: String = projectProperties["toolsVersion"].toString()
 
+    @Suppress("UnstableApiUsage")
     repositories {
         mavenLocal()
         maven("https://repo.kotlin.link")

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,433 +0,0 @@
-/*
- * Copyright 2018-2022 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-@file: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: $type by lazy { elementAlgebra.number(lu.computeDeterminant().real) }
-            }"""
-    }
-
-            else -> null
-        }?.let{
-            type.cast(it)
-        }
-    }
-
-    /**
-     * Solves for *x* in the following equation: *x = [a] <sup>-1</sup> &middot; [b]*.
-     *
-     * @param a the base matrix.
-     * @param b n by p matrix.
-     * @return the solution for *x* that is n by p.
-     */
-    public fun solve(a: Matrix<${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.UnstableKMathAPI
-import space.kscience.kmath.nd.StructureFeature
-import space.kscience.kmath.structures.Float64
-import space.kscience.kmath.structures.Float32
-import space.kscience.kmath.operations.Float64Field
-import space.kscience.kmath.operations.Float32Field
-import space.kscience.kmath.operations.DoubleField
-import space.kscience.kmath.operations.FloatField
-import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.structures.Float64Buffer
-import space.kscience.kmath.structures.Float32Buffer
-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", "Float64Field", "DMatrix", "DMatrixRMaj", "DMatrixRMaj", "DDRM", "DDRM", true)
-        it.appendEjmlLinearSpace("Float", "Float32Field", "FMatrix", "FMatrixRMaj", "FMatrixRMaj", "FDRM", "FDRM", true)
-
-        it.appendEjmlLinearSpace(
-            type = "Double",
-            kmathAlgebra = "Float64Field",
-            ejmlMatrixParentTypeMatrix = "DMatrix",
-            ejmlMatrixType = "DMatrixSparseCSC",
-            ejmlMatrixDenseType = "DMatrixRMaj",
-            ops = "DSCC",
-            denseOps = "DDRM",
-            isDense = false,
-        )
-
-        it.appendEjmlLinearSpace(
-            type = "Float",
-            kmathAlgebra = "Float32Field",
-            ejmlMatrixParentTypeMatrix = "FMatrix",
-            ejmlMatrixType = "FMatrixSparseCSC",
-            ejmlMatrixDenseType = "FMatrixRMaj",
-            ops = "FSCC",
-            denseOps = "FDRM",
-            isDense = false,
-        )
-    }
-}

docs/images/KM.svg

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
 <!--
-  - Copyright 2018-2022 KMath contributors.
+  - Copyright 2018-2024 KMath contributors.
   - Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
   -->
 

docs/images/KM_mono.svg

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
 <!--
-  - Copyright 2018-2022 KMath contributors.
+  - Copyright 2018-2024 KMath contributors.
   - Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
   -->
 

docs/images/KMath.svg

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
 <!--
-  - Copyright 2018-2022 KMath contributors.
+  - Copyright 2018-2024 KMath contributors.
   - Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
   -->
 

docs/images/KMath_mono.svg

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
 <!--
-  - Copyright 2018-2022 KMath contributors.
+  - Copyright 2018-2024 KMath contributors.
   - Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
   -->
 

docs/templates/ARTIFACT-TEMPLATE.md

@@ -3,25 +3,10 @@
 The Maven coordinates of this project are `${group}:${name}:${version}`.
 
 **Gradle:**
-```groovy
-repositories {
-    maven { url 'https://repo.kotlin.link' }
-    mavenCentral()
-    // development and snapshot versions
-    maven { url 'https://maven.pkg.jetbrains.space/spc/p/sci/dev' }
-}
-
-dependencies {
-    implementation '${group}:${name}:${version}'
-}
-```
-**Gradle Kotlin DSL:**
 ```kotlin
 repositories {
     maven("https://repo.kotlin.link")
     mavenCentral()
-    // development and snapshot versions
-    maven("https://maven.pkg.jetbrains.space/spc/p/sci/dev")
 }
 
 dependencies {

docs/templates/README-TEMPLATE.md

@@ -11,18 +11,21 @@ analog to Python's NumPy library. Later we found that kotlin is much more flexib
 architecture designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like
 experience could be achieved with [kmath-for-real](/kmath-for-real) extension module.
 
-[Documentation site (**WIP**)](https://SciProgCentre.github.io/kmath/)
+[Documentation site](https://SciProgCentre.github.io/kmath/)
 
 ## Publications and talks
 
 * [A conceptual article about context-oriented design](https://proandroiddev.com/an-introduction-context-oriented-programming-in-kotlin-2e79d316b0a2)
 * [Another article about context-oriented design](https://proandroiddev.com/diving-deeper-into-context-oriented-programming-in-kotlin-3ecb4ec38814)
 * [ACAT 2019 conference paper](https://aip.scitation.org/doi/abs/10.1063/1.5130103)
+* [A talk at KotlinConf 2019 about using kotlin for science](https://youtu.be/LI_5TZ7tnOE?si=4LknX41gl_YeUbIe)
+* [A talk on architecture at Joker-2021 (in Russian)](https://youtu.be/1bZ2doHiRRM?si=9w953ro9yu98X_KJ)
+* [The same talk in English](https://youtu.be/yP5DIc2fVwQ?si=louZzQ1dcXV6gP10)
+* [A seminar on tensor API](https://youtu.be/0H99wUs0xTM?si=6c__04jrByFQtVpo)
 
 # 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, Native and Wasm).
-  .
 * Provide basic multiplatform implementations for those abstractions (without significant performance optimization).
 * Provide bindings and wrappers with those abstractions for popular optimized platform libraries.
 
@@ -59,22 +62,21 @@ ${modules}
 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
 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, 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 both
+Calculation of performance is one of the major goals of KMath in the future, but in some cases it is impossible to achieve both
 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 a 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
 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
+KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend using GraalVM-CE or Oracle GraalVM for execution to get better performance.
-execution to get better performance.
 
 ### Repositories
 
@@ -94,11 +96,7 @@ dependencies {
 }
 ```
 
-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 feedback about what features are
-required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues
+required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues marked with [good first issue](hhttps://github.com/SciProgCentre/kmath/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) label.
-marked with
-[waiting for a hero](https://github.com/mipt-npm/kmath/labels/waiting%20for%20a%20hero) label.

examples/build.gradle.kts

@@ -52,7 +52,7 @@ dependencies {
 
     implementation("org.slf4j:slf4j-simple:1.7.32")
     // plotting
-    implementation("space.kscience:plotlykt-server:0.5.0")
+    implementation("space.kscience:plotlykt-server:0.7.0")
 }
 
 kotlin {

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2023 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

examples/src/main/kotlin/space/kscience/kmath/fit/chiSquared.kt

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
@@ -13,10 +13,7 @@ import space.kscience.kmath.expressions.autodiff
 import space.kscience.kmath.expressions.symbol
 import space.kscience.kmath.operations.asIterable
 import space.kscience.kmath.operations.toList
-import space.kscience.kmath.optimization.FunctionOptimizationTarget
+import space.kscience.kmath.optimization.*
-import space.kscience.kmath.optimization.optimizeWith
-import space.kscience.kmath.optimization.resultPoint
-import space.kscience.kmath.optimization.resultValue
 import space.kscience.kmath.random.RandomGenerator
 import space.kscience.kmath.real.DoubleVector
 import space.kscience.kmath.real.map
@@ -80,8 +77,9 @@ suspend fun main() {
     val result = chi2.optimizeWith(
         CMOptimizer,
         mapOf(a to 1.5, b to 0.9, c to 1.0),
-        FunctionOptimizationTarget.MINIMIZE
+    ){
-    )
+        FunctionOptimizationTarget(OptimizationDirection.MINIMIZE)
+    }
 
     //display a page with plot and numerical results
     val page = Plotly.page {
@@ -98,7 +96,7 @@ suspend fun main() {
             scatter {
                 mode = ScatterMode.lines
                 x(x)
-                y(x.map { result.resultPoint[a]!! * it.pow(2) + result.resultPoint[b]!! * it + 1 })
+                y(x.map { result.result[a]!! * it.pow(2) + result.result[b]!! * it + 1 })
                 name = "fit"
             }
         }

examples/src/main/kotlin/space/kscience/kmath/fit/qowFit.kt

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
@@ -7,6 +7,7 @@ package space.kscience.kmath.fit
 
 import kotlinx.html.br
 import kotlinx.html.h3
+import space.kscience.attributes.Attributes
 import space.kscience.kmath.data.XYErrorColumnarData
 import space.kscience.kmath.distributions.NormalDistribution
 import space.kscience.kmath.expressions.Symbol
@@ -64,7 +65,7 @@ suspend fun main() {
         QowOptimizer,
         Double.autodiff,
         mapOf(a to 0.9, b to 1.2, c to 2.0, e to 1.0, d to 1.0, e to 0.0),
-        OptimizationParameters(a, b, c, d)
+        attributes = Attributes(OptimizationParameters, listOf(a, b, c, d))
     ) { arg ->
         //bind variables to autodiff context
         val a by binding
@@ -94,13 +95,13 @@ suspend fun main() {
             scatter {
                 mode = ScatterMode.lines
                 x(x)
-                y(x.map { result.model(result.startPoint + result.resultPoint + (Symbol.x to it)) })
+                y(x.map { result.model(result.startPoint + result.result + (Symbol.x to it)) })
                 name = "fit"
             }
         }
         br()
         h3 {
-            +"Fit result: ${result.resultPoint}"
+            +"Fit result: ${result.result}"
         }
         h3 {
             +"Chi2/dof = ${result.chiSquaredOrNull!! / result.dof}"

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
@@ -8,7 +8,6 @@ package space.kscience.kmath.functions
 import space.kscience.kmath.interpolation.SplineInterpolator
 import space.kscience.kmath.interpolation.interpolatePolynomials
 import space.kscience.kmath.operations.Float64Field
-import space.kscience.kmath.structures.Float64Buffer
 import space.kscience.plotly.Plotly
 import space.kscience.plotly.UnstablePlotlyAPI
 import space.kscience.plotly.makeFile
@@ -24,9 +23,7 @@ fun main() {
         x to sin(x)
     }
 
-    val polynomial: PiecewisePolynomial<Double> = SplineInterpolator(
+    val polynomial: PiecewisePolynomial<Double> = SplineInterpolator(Float64Field).interpolatePolynomials(data)
-        Float64Field, ::Float64Buffer
-    ).interpolatePolynomials(data)
 
     val function = polynomial.asFunction(Float64Field, 0.0)
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,35 +1,30 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
 package space.kscience.kmath.linear
 
-import space.kscience.kmath.operations.algebra
 import kotlin.random.Random
-import kotlin.time.ExperimentalTime
 import kotlin.time.measureTime
 
-@OptIn(ExperimentalTime::class)
+fun main() = with(Float64ParallelLinearSpace) {
-fun main() {
     val random = Random(12224)
     val dim = 1000
 
     //creating invertible matrix
-    val matrix1 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
+    val matrix1 = buildMatrix(dim, dim) { i, j ->
         if (i <= j) random.nextDouble() else 0.0
     }
-    val matrix2 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
+    val matrix2 = buildMatrix(dim, dim) { i, j ->
         if (i <= j) random.nextDouble() else 0.0
     }
 
     val time = measureTime {
-        with(Double.algebra.linearSpace) {
+        repeat(30) {
-            repeat(10) {
             matrix1 dot matrix2
         }
     }
-    }
 
     println(time)
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -0,0 +1,27 @@
+/*
+ * Copyright 2018-2024 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.linear
+
+import kotlin.random.Random
+import kotlin.time.measureTime
+
+fun main(): Unit = with(Float64LinearSpace) {
+    val random = Random(1224)
+    val dim = 500
+
+    //creating invertible matrix
+    val u = buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
+    val l = buildMatrix(dim, dim) { i, j -> if (i >= j) random.nextDouble() else 0.0 }
+    val matrix = l dot u
+
+    val time = measureTime {
+        repeat(20) {
+            lupSolver().inverse(matrix)
+        }
+    }
+
+    println(time)
+}

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
@@ -10,12 +10,14 @@ import space.kscience.kmath.linear.matrix
 import space.kscience.kmath.nd.Float64BufferND
 import space.kscience.kmath.nd.ShapeND
 import space.kscience.kmath.nd.Structure2D
+import space.kscience.kmath.nd.mutableStructureND
 import space.kscience.kmath.nd.ndAlgebra
-import space.kscience.kmath.viktor.ViktorStructureND
 import space.kscience.kmath.viktor.viktorAlgebra
+import kotlin.collections.component1
+import kotlin.collections.component2
 
 fun main() {
-    val viktorStructure: ViktorStructureND = Float64Field.viktorAlgebra.structureND(ShapeND(2, 2)) { (i, j) ->
+    val viktorStructure = Float64Field.viktorAlgebra.mutableStructureND(2, 2) { (i, j) ->
         if (i == j) 2.0 else 0.0
     }
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2023 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,3 +1,8 @@
+/*
+ * Copyright 2018-2024 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
 package space.kscience.kmath.series
 
 
@@ -13,7 +18,10 @@ import space.kscience.kmath.structures.slice
 import space.kscience.plotly.*
 import kotlin.math.PI
 
-fun main() = with(Double.algebra.bufferAlgebra.seriesAlgebra()) {
+fun Double.Companion.seriesAlgebra() = Double.algebra.bufferAlgebra.seriesAlgebra()
+
+
+fun main() = with(Double.seriesAlgebra()) {
 
 
     fun Plot.plotSeries(name: String, buffer: Buffer<Double>) {

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

@@ -0,0 +1,50 @@
+/*
+ * Copyright 2018-2023 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.series
+
+
+import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.Float64Buffer
+import space.kscience.kmath.structures.asBuffer
+import space.kscience.kmath.structures.toDoubleArray
+import space.kscience.plotly.*
+import space.kscience.plotly.models.Scatter
+import space.kscience.plotly.models.ScatterMode
+import kotlin.random.Random
+
+fun main(): Unit = with(Double.seriesAlgebra()) {
+
+    val random = Random(1234)
+
+    val arrayOfRandoms = DoubleArray(20) { random.nextDouble() }
+
+    val series1: Float64Buffer = arrayOfRandoms.asBuffer()
+    val series2: Series<Double> = series1.moveBy(3)
+
+    val res = series2 - series1
+
+    println(res.size)
+
+    println(res)
+
+    fun Plot.series(name: String, buffer: Buffer<Double>, block: Scatter.() -> Unit = {}) {
+        scatter {
+            this.name = name
+            x.numbers = buffer.offsetIndices
+            y.doubles = buffer.toDoubleArray()
+            block()
+        }
+    }
+
+    Plotly.plot {
+        series("series1", series1)
+        series("series2", series2)
+        series("dif", res) {
+            mode = ScatterMode.lines
+            line.color("magenta")
+        }
+    }.makeFile(resourceLocation = ResourceLocation.REMOTE)
+}

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
@@ -8,7 +8,7 @@
 package space.kscience.kmath.structures
 
 import space.kscience.kmath.complex.*
-import space.kscience.kmath.linear.transpose
+import space.kscience.kmath.linear.transposed
 import space.kscience.kmath.nd.StructureND
 import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.nd.ndAlgebra
@@ -60,7 +60,7 @@ fun complexExample() {
             val sum = matrix + x + 1.0
 
             //Represent the sum as 2d-structure and transpose
-            sum.as2D().transpose()
+            sum.as2D().transposed()
         }
     }
 }

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
@@ -7,6 +7,7 @@ package space.kscience.kmath.structures
 
 import space.kscience.kmath.PerformancePitfall
 import space.kscience.kmath.nd.*
+import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.operations.ExtendedField
 import space.kscience.kmath.operations.Float64Field
 import space.kscience.kmath.operations.NumbersAddOps
@@ -52,6 +53,15 @@ class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64
         return BufferND(strides, array.asBuffer())
     }
 
+    override fun mutableStructureND(shape: ShapeND, initializer: DoubleField.(IntArray) -> Double): MutableBufferND<Double> {
+        val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
+            val index = strides.index(offset)
+            DoubleField.initializer(index)
+        }.toArray()
+
+        return MutableBufferND(strides, array.asBuffer())
+    }
+
     @OptIn(PerformancePitfall::class)
     override fun StructureND<Double>.map(
         transform: Float64Field.(Double) -> Double,

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,25 +1,23 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
 package space.kscience.kmath.structures
 
 import space.kscience.kmath.nd.BufferND
-import space.kscience.kmath.nd.ShapeND
-import space.kscience.kmath.nd.StructureND
 import space.kscience.kmath.operations.mapToBuffer
 import kotlin.system.measureTimeMillis
 
 private inline fun <T, reified R : Any> BufferND<T>.mapToBufferND(
-    bufferFactory: BufferFactory<R> = BufferFactory.auto(),
+    bufferFactory: BufferFactory<R> = BufferFactory(),
     crossinline block: (T) -> R,
 ): BufferND<R> = BufferND(indices, buffer.mapToBuffer(bufferFactory, block))
 
 @Suppress("UNUSED_VARIABLE")
 fun main() {
     val n = 6000
-    val structure = StructureND.buffered(ShapeND(n, n), Buffer.Companion::auto) { 1.0 }
+    val structure = BufferND(n, n) { 1.0 }
     structure.mapToBufferND { it + 1 } // warm-up
     val time1 = measureTimeMillis { val res = structure.mapToBufferND { it + 1 } }
     println("Structure mapping finished in $time1 millis")

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
@@ -13,7 +13,7 @@ import space.kscience.kmath.operations.withSize
 inline fun <reified R : Any> MutableBuffer.Companion.same(
     n: Int,
     value: R
-): MutableBuffer<R> = auto(n) { value }
+): MutableBuffer<R> = MutableBuffer(n) { value }
 
 
 fun main() {

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

@@ -0,0 +1,26 @@
+/*
+ * Copyright 2018-2023 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.PerformancePitfall
+import space.kscience.kmath.nd.*
+import space.kscience.kmath.operations.algebra
+
+@OptIn(PerformancePitfall::class)
+fun main(): Unit = with(Double.algebra.ndAlgebra) {
+    val structure: MutableStructure2D<Double> = mutableStructureND(ShapeND(2, 2)) { (i, j) ->
+        i.toDouble() + j.toDouble()
+    }.as2D()
+
+    structure[0, 1] = -2.0
+
+    val structure2 = mutableStructureND(2, 2) { (i, j) -> i.toDouble() + j.toDouble() }.as2D()
+
+    structure2[0, 1] = 2.0
+
+
+    println(structure + structure2)
+}

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
@@ -15,7 +15,7 @@ private fun DMatrixContext<Double, *>.simple() {
     val m2 = produce<D3, D2> { i, j -> (i + j).toDouble() }
 
     //Dimension-safe addition
-    m1.transpose() + m2
+    m1.transposed() + m2
 }
 
 private object D5 : Dimension {

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2023 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2023 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2023 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2023 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2023 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2023 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

gradle/wrapper/gradle-wrapper.properties

@@ -1,5 +1,5 @@
 distributionBase=GRADLE_USER_HOME
 distributionPath=wrapper/dists
-distributionUrl=https\://services.gradle.org/distributions/gradle-8.4-bin.zip
+distributionUrl=https\://services.gradle.org/distributions/gradle-8.6-bin.zip
 zipStoreBase=GRADLE_USER_HOME
 zipStorePath=wrapper/dists

kmath-ast/README.md

@@ -10,19 +10,8 @@ Extensions to MST API: transformations, dynamic compilation and visualization.
 
 ## Artifact:
 
-The Maven coordinates of this project are `space.kscience:kmath-ast:0.4.0-dev-1`.
+The Maven coordinates of this project are `space.kscience:kmath-ast:0.4.0-dev-3`.
 
-**Gradle Groovy:**
-```groovy
-repositories {
-    maven { url 'https://repo.kotlin.link' }
-    mavenCentral()
-}
-
-dependencies {
-    implementation 'space.kscience:kmath-ast:0.4.0-dev-1'
-}
-```
 **Gradle Kotlin DSL:**
 ```kotlin
 repositories {
@@ -31,7 +20,7 @@ repositories {
 }
 
 dependencies {
-    implementation("space.kscience:kmath-ast:0.4.0-dev-1")
+    implementation("space.kscience:kmath-ast:0.4.0-dev-3")
 }
 ```
 

kmath-ast/build.gradle.kts

@@ -31,7 +31,7 @@ kscience{
 kotlin {
     js {
         nodejs {
-            testTask {
+            testTask{
                 useMocha().timeout = "0"
             }
         }

kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/TypedMst.kt

@@ -1,10 +1,12 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
 package space.kscience.kmath.ast
 
+import space.kscience.attributes.SafeType
+import space.kscience.attributes.WithType
 import space.kscience.kmath.expressions.Expression
 import space.kscience.kmath.expressions.Symbol
 import space.kscience.kmath.operations.Algebra
@@ -15,7 +17,7 @@ import space.kscience.kmath.operations.NumericAlgebra
  *
  * @param T the type.
  */
-public sealed interface TypedMst<T> {
+public sealed interface TypedMst<T> : WithType<T> {
     /**
      * A node containing a unary operation.
      *
@@ -24,8 +26,13 @@ public sealed interface TypedMst<T> {
      * @property function The function implementing this operation.
      * @property value The argument of this operation.
      */
-    public class Unary<T>(public val operation: String, public val function: (T) -> T, public val value: TypedMst<T>) :
+    public class Unary<T>(
-        TypedMst<T> {
+        public val operation: String,
+        public val function: (T) -> T,
+        public val value: TypedMst<T>,
+    ) : TypedMst<T> {
+        override val type: SafeType<T> get() = value.type
+
         override fun equals(other: Any?): Boolean {
             if (this === other) return true
             if (other == null || this::class != other::class) return false
@@ -59,6 +66,13 @@ public sealed interface TypedMst<T> {
         public val left: TypedMst<T>,
         public val right: TypedMst<T>,
     ) : TypedMst<T> {
+
+        init {
+            require(left.type==right.type){"Left and right expressions must be of the same type"}
+        }
+
+        override val type: SafeType<T> get() = left.type
+
         override fun equals(other: Any?): Boolean {
             if (this === other) return true
             if (other == null || this::class != other::class) return false
@@ -89,7 +103,12 @@ public sealed interface TypedMst<T> {
      * @property value The held value.
      * @property number The number this value corresponds.
      */
-    public class Constant<T>(public val value: T, public val number: Number?) : TypedMst<T> {
+    public class Constant<T>(
+        override val type: SafeType<T>,
+        public val value: T,
+        public val number: Number?,
+    ) : TypedMst<T> {
+
         override fun equals(other: Any?): Boolean {
             if (this === other) return true
             if (other == null || this::class != other::class) return false
@@ -114,7 +133,7 @@ public sealed interface TypedMst<T> {
      * @param T the type.
      * @property symbol The symbol of the variable.
      */
-    public class Variable<T>(public val symbol: Symbol) : TypedMst<T> {
+    public class Variable<T>(override val type: SafeType<T>, public val symbol: Symbol) : TypedMst<T> {
         override fun equals(other: Any?): Boolean {
             if (this === other) return true
             if (other == null || this::class != other::class) return false
@@ -167,6 +186,7 @@ public fun <T> TypedMst<T>.interpret(algebra: Algebra<T>, vararg arguments: Pair
 /**
  * Interpret this [TypedMst] node as expression.
  */
-public fun <T : Any> TypedMst<T>.toExpression(algebra: Algebra<T>): Expression<T> = Expression { arguments ->
+public fun <T : Any> TypedMst<T>.toExpression(algebra: Algebra<T>): Expression<T> =
+    Expression(algebra.type) { arguments ->
         interpret(algebra, arguments)
-}
+    }

kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/evaluateConstants.kt

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
@@ -16,6 +16,7 @@ import space.kscience.kmath.operations.bindSymbolOrNull
  */
 public fun <T> MST.evaluateConstants(algebra: Algebra<T>): TypedMst<T> = when (this) {
     is MST.Numeric -> TypedMst.Constant(
+        algebra.type,
         (algebra as? NumericAlgebra<T>)?.number(value) ?: error("Numeric nodes are not supported by $algebra"),
         value,
     )
@@ -27,7 +28,7 @@ public fun <T> MST.evaluateConstants(algebra: Algebra<T>): TypedMst<T> = when (t
                 arg.value,
             )
 
-            TypedMst.Constant(value, if (value is Number) value else null)
+            TypedMst.Constant(algebra.type, value, if (value is Number) value else null)
         }
 
         else -> TypedMst.Unary(operation, algebra.unaryOperationFunction(operation), arg)
@@ -59,7 +60,7 @@ public fun <T> MST.evaluateConstants(algebra: Algebra<T>): TypedMst<T> = when (t
                     )
                 }
 
-                TypedMst.Constant(value, if (value is Number) value else null)
+                TypedMst.Constant(algebra.type, value, if (value is Number) value else null)
             }
 
             algebra is NumericAlgebra && left is TypedMst.Constant && left.number != null -> TypedMst.Binary(
@@ -84,8 +85,8 @@ public fun <T> MST.evaluateConstants(algebra: Algebra<T>): TypedMst<T> = when (t
         val boundSymbol = algebra.bindSymbolOrNull(this)
 
         if (boundSymbol != null)
-            TypedMst.Constant(boundSymbol, if (boundSymbol is Number) boundSymbol else null)
+            TypedMst.Constant(algebra.type, boundSymbol, if (boundSymbol is Number) boundSymbol else null)
         else
-            TypedMst.Variable(this)
+            TypedMst.Variable(algebra.type, this)
     }
 }

kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/parser.kt

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * Copyright 2018-2024 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */