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116
doc/algebra.md
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@ -1,101 +1,115 @@
# Algebra and algebra elements # Algebraic Structures and Algebraic Elements
The mathematical operations in `kmath` are generally separated from mathematical objects. The mathematical operations in KMath are generally separated from mathematical objects. This means that to perform an
This means that in order to perform an operation, say `+`, one needs two objects of a type `T` and operation, say `+`, one needs two objects of a type `T` and an algebra context, which draws appropriate operation up,
and algebra context which defines appropriate operation, say `Space<T>`. Next one needs to run actual operation say `Space<T>`. Next one needs to run the actual operation in the context:
in the context:
```kotlin ```kotlin
val a: T import scientifik.kmath.operations.*
val b: T
val space: Space<T>
val c = space.run{a + b} val a: T = ...
val b: T = ...
val space: Space<T> = ...
val c = space { a + b }
``` ```
From the first glance, this distinction seems to be a needless complication, but in fact one needs At first glance, this distinction seems to be a needless complication, but in fact one needs to remember that in
to remember that in mathematics, one could define different operations on the same objects. For example, mathematics, one could draw up different operations on same objects. For example, one could use different types of
one could use different types of geometry for vectors. geometry for vectors.
## Algebra hierarchy ## Algebraic Structures
Mathematical contexts have the following hierarchy: Mathematical contexts have the following hierarchy:
**Space** <- **Ring** <- **Field** **Algebra** ← **Space** ← **Ring** ← **Field**
All classes follow abstract mathematical constructs. These interfaces follow real algebraic structures:
[Space](http://mathworld.wolfram.com/Space.html) defines `zero` element, addition operation and multiplication by constant,
[Ring](http://mathworld.wolfram.com/Ring.html) adds multiplication and unit `one` element,
[Field](http://mathworld.wolfram.com/Field.html) adds division operation.
Typical case of `Field` is the `RealField` which works on doubles. And typical case of `Space` is a `VectorSpace`. - [Space](https://mathworld.wolfram.com/VectorSpace.html) defines addition, its neutral element (i.e. 0) and scalar
multiplication;
- [Ring](http://mathworld.wolfram.com/Ring.html) adds multiplication and its neutral element (i.e. 1);
- [Field](http://mathworld.wolfram.com/Field.html) adds division operation.
In some cases algebra context could hold additional operation like `exp` or `sin`, in this case it inherits appropriate A typical implementation of `Field<T>` is the `RealField` which works on doubles, and `VectorSpace` for `Space<T>`.
interface. Also a context could have an operation which produces an element outside of its context. For example
`Matrix` `dot` operation produces a matrix with new dimensions which can be incompatible with initial matrix in
terms of linear operations.
## Algebra element In some cases algebra context can hold additional operations like `exp` or `sin`, and then it inherits appropriate
interface. Also, contexts may have operations, which produce elements outside of the context. For example, `Matrix.dot`
operation produces a matrix with new dimensions, which can be incompatible with initial matrix in terms of linear
operations.
In order to achieve more familiar behavior (where you apply operations directly to mathematical objects), without involving contexts ## Algebraic Element
`kmath` introduces special type objects called `MathElement`. A `MathElement` is basically some object coupled to
To achieve more familiar behavior (where you apply operations directly to mathematical objects), without involving
contexts KMath submits special type objects called `MathElement`. A `MathElement` is basically some object coupled to
a mathematical context. For example `Complex` is the pair of real numbers representing real and imaginary parts, a mathematical context. For example `Complex` is the pair of real numbers representing real and imaginary parts,
but it also holds reference to the `ComplexField` singleton which allows to perform direct operations on `Complex` but it also holds reference to the `ComplexField` singleton, which allows performing direct operations on `Complex`
numbers without explicit involving the context like: numbers without explicit involving the context like:
```kotlin ```kotlin
import scientifik.kmath.operations.*
// Using elements
val c1 = Complex(1.0, 1.0) val c1 = Complex(1.0, 1.0)
val c2 = Complex(1.0, -1.0) val c2 = Complex(1.0, -1.0)
val c3 = c1 + c2 + 3.0.toComplex() val c3 = c1 + c2 + 3.0.toComplex()
//or with field notation:
val c4 = ComplexField.run{c1 + i - 2.0} // Using context
val c4 = ComplexField { c1 + i - 2.0 }
``` ```
Both notations have their pros and cons. Both notations have their pros and cons.
The hierarchy for algebra elements follows the hierarchy for the corresponding algebra. The hierarchy for algebraic elements follows the hierarchy for the corresponding algebraic structures.
**MathElement** <- **SpaceElement** <- **RingElement** <- **FieldElement** **MathElement** ← **SpaceElement** ← **RingElement** ← **FieldElement**
**MathElement** is the generic common ancestor of the class with context. `MathElement<C>` is the generic common ancestor of the class with context.
One important distinction between algebra elements and algebra contexts is that algebra element has three type parameters: One major distinction between algebraic elements and algebraic contexts is that elements have three type
parameters:
1. The type of elements, field operates on. 1. The type of elements, the field operates on.
2. The self-type of the element returned from operation (must be algebra element). 2. The self-type of the element returned from operation (which has to be an algebraic element).
3. The type of the algebra over first type-parameter. 3. The type of the algebra over first type-parameter.
The middle type is needed in case algebra members do not store context. For example, it is not possible to add The middle type is needed for of algebra members do not store context. For example, it is impossible to add a context
a context to regular `Double`. The element performs automatic conversions from context types and back. to regular `Double`. The element performs automatic conversions from context types and back. One should use context
One should used context operations in all important places. The performance of element operations is not guaranteed. operations in all performance-critical places. The performance of element operations is not guaranteed.
## Spaces and fields ## Spaces and Fields
An obvious first choice of mathematical objects to implement in a context-oriented style are algebraic elements like spaces, KMath submits both contexts and elements for builtin algebraic structures:
rings and fields. Those are located in the `scientifik.kmath.operations.Algebra.kt` file. Alongside common contexts, the file includes definitions for algebra elements like `FieldElement`. A `FieldElement` object
stores a reference to the `Field` which contains additive and multiplicative operations, meaning
it has one fixed context attached and does not require explicit external context. So those `MathElements` can be operated without context:
```kotlin ```kotlin
import scientifik.kmath.operations.*
val c1 = Complex(1.0, 2.0) val c1 = Complex(1.0, 2.0)
val c2 = ComplexField.i val c2 = ComplexField.i
val c3 = c1 + c2 val c3 = c1 + c2
// or
val c3 = ComplexField { c1 + c2 }
``` ```
`ComplexField` also features special operations to mix complex and real numbers, for example: Also, `ComplexField` features special operations to mix complex and real numbers, for example:
```kotlin ```kotlin
import scientifik.kmath.operations.*
val c1 = Complex(1.0, 2.0) val c1 = Complex(1.0, 2.0)
val c2 = ComplexField.run{ c1 - 1.0} // Returns: [re:0.0, im: 2.0] val c2 = ComplexField { c1 - 1.0 } // Returns: Complex(re=0.0, im=2.0)
val c3 = ComplexField.run{ c1 - i*2.0} val c3 = ComplexField { c1 - i * 2.0 }
``` ```
**Note**: In theory it is possible to add behaviors directly to the context, but currently kotlin syntax does not support **Note**: In theory it is possible to add behaviors directly to the context, but as for now Kotlin does not support
that. Watch [KT-10468](https://youtrack.jetbrains.com/issue/KT-10468) and [KEEP-176](https://github.com/Kotlin/KEEP/pull/176) for updates. that. Watch [KT-10468](https://youtrack.jetbrains.com/issue/KT-10468) and
[KEEP-176](https://github.com/Kotlin/KEEP/pull/176) for updates.
## Nested fields ## Nested fields
Contexts allow one to build more complex structures. For example, it is possible to create a `Matrix` from complex elements like so: Contexts allow one to build more complex structures. For example, it is possible to create a `Matrix` from complex
elements like so:
```kotlin ```kotlin
val element = NDElement.complex(shape = intArrayOf(2, 2)) { index: IntArray -> val element = NDElement.complex(shape = intArrayOf(2, 2)) { index: IntArray ->
@ -103,8 +117,8 @@ val element = NDElement.complex(shape = intArrayOf(2,2)){ index: IntArray ->
} }
``` ```
The `element` in this example is a member of the `Field` of 2-d structures, each element of which is a member of its own The `element` in this example is a member of the `Field` of 2D structures, each element of which is a member of its own
`ComplexField`. The important thing is one does not need to create a special n-d class to hold complex `ComplexField`. It is important one does not need to create a special n-d class to hold complex
numbers and implement operations on it, one just needs to provide a field for its elements. numbers and implement operations on it, one just needs to provide a field for its elements.
**Note**: Fields themselves do not solve the problem of JVM boxing, but it is possible to solve with special contexts like **Note**: Fields themselves do not solve the problem of JVM boxing, but it is possible to solve with special contexts like

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doc/buffers.md
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# Buffers # Buffers
Buffer is one of main building blocks of kmath. It is a basic interface allowing random-access read and write (with `MutableBuffer`). Buffer is one of main building blocks of kmath. It is a basic interface allowing random-access read and write (with `MutableBuffer`).
There are different types of buffers: There are different types of buffers:
@ -12,4 +13,5 @@ Some kmath features require a `BufferFactory` class to operate properly. A gener
buffer for given reified type (for types with custom memory buffer it still better to use their own `MemoryBuffer.create()` factory). buffer for given reified type (for types with custom memory buffer it still better to use their own `MemoryBuffer.create()` factory).
## Buffer performance ## Buffer performance
One should avoid using default boxing buffer wherever it is possible. Try to use primitive buffers or memory buffers instead One should avoid using default boxing buffer wherever it is possible. Try to use primitive buffers or memory buffers instead

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

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doc/linear.md
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## Basic linear algebra layout ## Basic linear algebra layout
Kmath support for linear algebra organized in a context-oriented way. Meaning that operations are in most cases declared KMath support for linear algebra organized in a context-oriented way. Meaning that operations are in most cases declared
in context classes, and are not the members of classes that store data. This allows more flexible approach to maintain multiple in context classes, and are not the members of classes that store data. This allows more flexible approach to maintain multiple
back-ends. The new operations added as extensions to contexts instead of being member functions of data structures. back-ends. The new operations added as extensions to contexts instead of being member functions of data structures.

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doc/nd-structure.md
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# Nd-structure generation and operations # ND-structure generation and operations
**TODO** **TODO**

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kmath-ast/README.md
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# Abstract syntax tree expression representation and operations (`kmath-ast`) # Abstract Syntax Tree Expression Representation and Operations (`kmath-ast`)
This subproject implements the following features: This subproject implements the following features:
@ -38,7 +38,7 @@ This subproject implements the following features:
> ``` > ```
> >
## Dynamic expression code generation with ObjectWeb ASM ## Dynamic Expression Code Generation with ObjectWeb ASM
`kmath-ast` JVM module supports runtime code generation to eliminate overhead of tree traversal. Code generator builds `kmath-ast` JVM module supports runtime code generation to eliminate overhead of tree traversal. Code generator builds
a special implementation of `Expression<T>` with implemented `invoke` function. a special implementation of `Expression<T>` with implemented `invoke` function.
@ -75,7 +75,7 @@ public final class AsmCompiledExpression_1073786867_0 implements Expression<Doub
### Example Usage ### Example Usage
This API is an extension to MST and MstExpression, so you may optimize as both of them: This API extends MST and MstExpression, so you may optimize as both of them:
```kotlin ```kotlin
RealField.mstInField { symbol("x") + 2 }.compile() RealField.mstInField { symbol("x") + 2 }.compile()

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kmath-core/README.md Normal file
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@ -0,0 +1,40 @@
# The Core Module (`kmath-ast`)
The core features of KMath:
- Algebraic structures: contexts and elements.
- ND structures.
- Buffers.
- Functional Expressions.
- Domains.
- Automatic differentiation.
> #### Artifact:
> This module is distributed in the artifact `scientifik:kmath-core:0.1.4-dev-8`.
>
> **Gradle:**
>
> ```gradle
> repositories {
> maven { url 'https://dl.bintray.com/mipt-npm/scientifik' }
> maven { url 'https://dl.bintray.com/mipt-npm/dev' }
> maven { url https://dl.bintray.com/hotkeytlt/maven' }
> }
>
> dependencies {
> implementation 'scientifik:kmath-core:0.1.4-dev-8'
> }
> ```
> **Gradle Kotlin DSL:**
>
> ```kotlin
> repositories {
> maven("https://dl.bintray.com/mipt-npm/scientifik")
> maven("https://dl.bintray.com/mipt-npm/dev")
> maven("https://dl.bintray.com/hotkeytlt/maven")
> }
>
> dependencies {``
> implementation("scientifik:kmath-core:0.1.4-dev-8")
> }
> ```

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kmath-core/build.gradle.kts
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@ -1,11 +1,7 @@
plugins { plugins { id("scientifik.mpp") }
id("scientifik.mpp")
}
kotlin.sourceSets { kotlin.sourceSets {
commonMain { commonMain {
dependencies { dependencies { api(project(":kmath-memory")) }
api(project(":kmath-memory"))
}
} }
} }

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kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/Algebra.kt
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@ -58,7 +58,8 @@ interface NumericAlgebra<T> : Algebra<T> {
inline operator fun <A : Algebra<*>, R> A.invoke(block: A.() -> R): R = run(block) inline operator fun <A : Algebra<*>, R> A.invoke(block: A.() -> R): R = run(block)
/** /**
* Represents semigroup, i.e. algebraic structure with associative binary operation called "addition". * Represents semispace, i.e. algebraic structure with associative binary operation called "addition" as well as
* multiplication by scalars.
* *
* In KMath groups are called spaces, and also define multiplication of element by [Number]. * In KMath groups are called spaces, and also define multiplication of element by [Number].
* *
@ -174,10 +175,8 @@ interface SpaceOperations<T> : Algebra<T> {
} }
/** /**
* Represents group, i.e. algebraic structure with associative binary operation called "addition" and its neutral * Represents linear space, i.e. algebraic structure with associative binary operation called "addition" and its neutral
* element. * element as well as multiplication by scalars.
*
* In KMath groups are called spaces, and also define multiplication of element by [Number].
* *
* @param T the type of element of this group. * @param T the type of element of this group.
*/ */

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kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/chains/Chain.kt
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@ -22,7 +22,6 @@ import kotlinx.coroutines.flow.FlowCollector
import kotlinx.coroutines.sync.Mutex import kotlinx.coroutines.sync.Mutex
import kotlinx.coroutines.sync.withLock import kotlinx.coroutines.sync.withLock
/** /**
* A not-necessary-Markov chain of some type * A not-necessary-Markov chain of some type
* @param R - the chain element type * @param R - the chain element type
@ -71,7 +70,7 @@ class MarkovChain<out R : Any>(private val seed: suspend () -> R, private val ge
private var value: R? = null private var value: R? = null
fun value() = value fun value(): R? = value
override suspend fun next(): R { override suspend fun next(): R {
mutex.withLock { mutex.withLock {
@ -97,12 +96,11 @@ class StatefulChain<S, out R>(
private val forkState: ((S) -> S), private val forkState: ((S) -> S),
private val gen: suspend S.(R) -> R private val gen: suspend S.(R) -> R
) : Chain<R> { ) : Chain<R> {
private val mutex: Mutex = Mutex()
private val mutex = Mutex()
private var value: R? = null private var value: R? = null
fun value() = value fun value(): R? = value
override suspend fun next(): R { override suspend fun next(): R {
mutex.withLock { mutex.withLock {
@ -112,9 +110,7 @@ class StatefulChain<S, out R>(
} }
} }
override fun fork(): Chain<R> { override fun fork(): Chain<R> = StatefulChain(forkState(state), seed, forkState, gen)
return StatefulChain(forkState(state), seed, forkState, gen)
}
} }
/** /**
@ -163,7 +159,8 @@ fun <T, R> Chain<T>.collect(mapper: suspend (Chain<T>) -> R): Chain<R> = object
fun <T, S, R> Chain<T>.collectWithState(state: S, stateFork: (S) -> S, mapper: suspend S.(Chain<T>) -> R): Chain<R> = fun <T, S, R> Chain<T>.collectWithState(state: S, stateFork: (S) -> S, mapper: suspend S.(Chain<T>) -> R): Chain<R> =
object : Chain<R> { object : Chain<R> {
override suspend fun next(): R = state.mapper(this@collectWithState) override suspend fun next(): R = state.mapper(this@collectWithState)
override fun fork(): Chain<R> = this@collectWithState.fork().collectWithState(stateFork(state), stateFork, mapper) override fun fork(): Chain<R> =
this@collectWithState.fork().collectWithState(stateFork(state), stateFork, mapper)
} }
/** /**

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kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/coroutines/coroutinesExtra.kt
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@ -4,7 +4,8 @@ import kotlinx.coroutines.*
import kotlinx.coroutines.channels.produce import kotlinx.coroutines.channels.produce
import kotlinx.coroutines.flow.* import kotlinx.coroutines.flow.*
val Dispatchers.Math: CoroutineDispatcher get() = Dispatchers.Default val Dispatchers.Math: CoroutineDispatcher
get() = Default
/** /**
* An imitator of [Deferred] which holds a suspended function block and dispatcher * An imitator of [Deferred] which holds a suspended function block and dispatcher
@ -42,7 +43,7 @@ fun <T, R> Flow<T>.async(
} }
@FlowPreview @FlowPreview
fun <T, R> AsyncFlow<T>.map(action: (T) -> R) = fun <T, R> AsyncFlow<T>.map(action: (T) -> R): AsyncFlow<R> =
AsyncFlow(deferredFlow.map { input -> AsyncFlow(deferredFlow.map { input ->
//TODO add function composition //TODO add function composition
LazyDeferred(input.dispatcher) { LazyDeferred(input.dispatcher) {
@ -82,9 +83,9 @@ suspend fun <T> AsyncFlow<T>.collect(concurrency: Int, collector: FlowCollector<
@ExperimentalCoroutinesApi @ExperimentalCoroutinesApi
@FlowPreview @FlowPreview
suspend fun <T> AsyncFlow<T>.collect(concurrency: Int, action: suspend (value: T) -> Unit): Unit { suspend fun <T> AsyncFlow<T>.collect(concurrency: Int, action: suspend (value: T) -> Unit) {
collect(concurrency, object : FlowCollector<T> { collect(concurrency, object : FlowCollector<T> {
override suspend fun emit(value: T) = action(value) override suspend fun emit(value: T): Unit = action(value)
}) })
} }
@ -98,5 +99,3 @@ fun <T, R> Flow<T>.mapParallel(
flow { emit(transform(value)) } flow { emit(transform(value)) }
}.flowOn(dispatcher) }.flowOn(dispatcher)
} }

2
kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/streaming/BufferFlow.kt
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@ -11,7 +11,7 @@ import scientifik.kmath.structures.asBuffer
/** /**
* Create a [Flow] from buffer * Create a [Flow] from buffer
*/ */
fun <T> Buffer<T>.asFlow() = iterator().asFlow() fun <T> Buffer<T>.asFlow(): Flow<T> = iterator().asFlow()
/** /**
* Flat map a [Flow] of [Buffer] into continuous [Flow] of elements * Flat map a [Flow] of [Buffer] into continuous [Flow] of elements

6
kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/streaming/RingBuffer.kt
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@ -5,7 +5,6 @@ import kotlinx.coroutines.sync.withLock
import scientifik.kmath.structures.Buffer import scientifik.kmath.structures.Buffer
import scientifik.kmath.structures.MutableBuffer import scientifik.kmath.structures.MutableBuffer
import scientifik.kmath.structures.VirtualBuffer import scientifik.kmath.structures.VirtualBuffer
import kotlin.reflect.KClass
/** /**
* Thread-safe ring buffer * Thread-safe ring buffer
@ -16,8 +15,7 @@ class RingBuffer<T>(
private var startIndex: Int = 0, private var startIndex: Int = 0,
size: Int = 0 size: Int = 0
) : Buffer<T> { ) : Buffer<T> {
private val mutex: Mutex = Mutex()
private val mutex = Mutex()
override var size: Int = size override var size: Int = size
private set private set
@ -28,7 +26,7 @@ class RingBuffer<T>(
return buffer[startIndex.forward(index)] as T return buffer[startIndex.forward(index)] as T
} }
fun isFull() = size == buffer.size fun isFull(): Boolean = size == buffer.size
/** /**
* Iterator could provide wrong results if buffer is changed in initialization (iteration is safe) * Iterator could provide wrong results if buffer is changed in initialization (iteration is safe)

2
kmath-coroutines/src/jvmMain/kotlin/scientifik/kmath/chains/ChainExt.kt
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@ -6,7 +6,7 @@ import kotlin.sequences.Sequence
/** /**
* Represent a chain as regular iterator (uses blocking calls) * Represent a chain as regular iterator (uses blocking calls)
*/ */
operator fun <R> Chain<R>.iterator() = object : Iterator<R> { operator fun <R> Chain<R>.iterator(): Iterator<R> = object : Iterator<R> {
override fun hasNext(): Boolean = true override fun hasNext(): Boolean = true
override fun next(): R = runBlocking { next() } override fun next(): R = runBlocking { next() }

21
kmath-coroutines/src/jvmMain/kotlin/scientifik/kmath/structures/LazyNDStructure.kt
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@ -8,10 +8,9 @@ class LazyNDStructure<T>(
override val shape: IntArray, override val shape: IntArray,
val function: suspend (IntArray) -> T val function: suspend (IntArray) -> T
) : NDStructure<T> { ) : NDStructure<T> {
private val cache: MutableMap<IntArray, Deferred<T>> = hashMapOf()
private val cache = HashMap<IntArray, Deferred<T>>() fun deferred(index: IntArray): Deferred<T> = cache.getOrPut(index) {
fun deferred(index: IntArray) = cache.getOrPut(index) {
scope.async(context = Dispatchers.Math) { scope.async(context = Dispatchers.Math) {
function(index) function(index)
} }
@ -42,21 +41,21 @@ class LazyNDStructure<T>(
result = 31 * result + cache.hashCode() result = 31 * result + cache.hashCode()
return result return result
} }
} }
fun <T> NDStructure<T>.deferred(index: IntArray) = fun <T> NDStructure<T>.deferred(index: IntArray): Deferred<T> =
if (this is LazyNDStructure<T>) this.deferred(index) else CompletableDeferred(get(index)) if (this is LazyNDStructure<T>) this.deferred(index) else CompletableDeferred(get(index))
suspend fun <T> NDStructure<T>.await(index: IntArray) = suspend fun <T> NDStructure<T>.await(index: IntArray): T =
if (this is LazyNDStructure<T>) this.await(index) else get(index) if (this is LazyNDStructure<T>) this.await(index) else get(index)
/** /**
* PENDING would benifit from KEEP-176 * PENDING would benefit from KEEP-176
*/ */
fun <T, R> NDStructure<T>.mapAsyncIndexed(scope: CoroutineScope, function: suspend (T, index: IntArray) -> R) = fun <T, R> NDStructure<T>.mapAsyncIndexed(
LazyNDStructure(scope, shape) { index -> function(get(index), index) } scope: CoroutineScope,
function: suspend (T, index: IntArray) -> R
): LazyNDStructure<R> = LazyNDStructure(scope, shape) { index -> function(get(index), index) }
fun <T, R> NDStructure<T>.mapAsync(scope: CoroutineScope, function: suspend (T) -> R) = fun <T, R> NDStructure<T>.mapAsync(scope: CoroutineScope, function: suspend (T) -> R): LazyNDStructure<R> =
LazyNDStructure(scope, shape) { index -> function(get(index)) } LazyNDStructure(scope, shape) { index -> function(get(index)) }

3
kmath-coroutines/src/jvmTest/kotlin/scientifik/kmath/streaming/BufferFlowTest.kt
View File

@ -15,8 +15,7 @@ import kotlin.test.Test
@InternalCoroutinesApi @InternalCoroutinesApi
@FlowPreview @FlowPreview
class BufferFlowTest { class BufferFlowTest {
val dispatcher: CoroutineDispatcher = Executors.newFixedThreadPool(4).asCoroutineDispatcher()
val dispatcher = Executors.newFixedThreadPool(4).asCoroutineDispatcher()
@Test @Test
@Timeout(2000) @Timeout(2000)

8
kmath-coroutines/src/jvmTest/kotlin/scientifik/kmath/streaming/RingBufferTest.kt
View File

@ -22,14 +22,14 @@ class RingBufferTest {
fun windowed() { fun windowed() {
val flow = flow { val flow = flow {
var i = 0 var i = 0
while(true){ while (true) emit(i++)
emit(i++)
}
} }
val windowed = flow.windowed(10) val windowed = flow.windowed(10)
runBlocking { runBlocking {
val first = windowed.take(1).single() val first = windowed.take(1).single()
val res = windowed.take(15).map { it -> it.asSequence().average() }.toList() val res = windowed.take(15).map { it.asSequence().average() }.toList()
assertEquals(0.0, res[0]) assertEquals(0.0, res[0])
assertEquals(4.5, res[9]) assertEquals(4.5, res[9])
assertEquals(9.5, res[14]) assertEquals(9.5, res[14])