[](https://kotlinlang.org/docs/components-stability.html)
[](https://confluence.jetbrains.com/display/ALL/JetBrains+on+GitHub)
[](https://central.sonatype.com/artifact/org.jetbrains.kotlinx/multik-core)
[](https://www.apache.org/licenses/LICENSE-2.0)
<!---IMPORT samples.ReadmeSnippets-->
# Multik
Multidimensional array library for Kotlin.
Multik provides N-dimensional arrays with type-safe dimensions, math operations, linear algebra, and statistics.
It works across JVM, JS, WasmJS, iOS, and desktop native targets via Kotlin Multiplatform,
with optional OpenBLAS acceleration for high performance.
## Modules
| Module | Description |
|---------------------|-------------------------------------------------------------------------------------------------------|
| **multik-core** | Core ndarray types, the `mk` entry point, and Math/LinAlg/Statistics API interfaces. All platforms. |
| **multik-default** | Combines `multik-kotlin` and `multik-openblas` for optimal performance on every platform. |
| **multik-kotlin** | Pure Kotlin implementation. JVM, JS, WasmJS, iOS, and desktop native. |
| **multik-openblas** | Native implementation backed by OpenBLAS via C++/JNI. JVM and desktop native (macOS, Linux, Windows). |
## Installation
Latest
version: [](https://central.sonatype.com/artifact/org.jetbrains.kotlinx/multik-core)
### Gradle Kotlin DSL
`multik-core` provides ndarray types, creation functions, and basic operations.
For linear algebra, statistics, and math engines, add an engine dependency —
`multik-default`, `multik-kotlin`, or `multik-openblas`.
Engine dependencies transitively include `multik-core`.
`build.gradle.kts`:
```kotlin
repositories {
mavenCentral()
}
dependencies {
implementation("org.jetbrains.kotlinx:multik-default:$multikVersion")
}
```
<details>
<summary>Gradle Groovy DSL</summary>
`build.gradle`:
```groovy
repositories {
mavenCentral()
}
dependencies {
implementation "org.jetbrains.kotlinx:multik-default:$multikVersion"
}
```
</details>
### Kotlin Multiplatform
```kotlin
kotlin {
sourceSets {
commonMain {
dependencies {
implementation("org.jetbrains.kotlinx:multik-default:$multikVersion")
}
}
}
}
```
### Jupyter Notebook
Install [Kotlin kernel](https://github.com/Kotlin/kotlin-jupyter) for
[Jupyter](https://jupyter.org/)
or just visit to [Datalore](https://datalore.jetbrains.com/).
Import stable `multik` version into notebook:
```
%use multik
```
## Supported Platforms
| Platform | `multik-core` | `multik-kotlin` | `multik-openblas` | `multik-default` |
|:---------------------:|:-------------:|:---------------:|:-----------------:|:----------------:|
| **JVM** | ✅ | ✅ | ✅ | ✅ |
| **JS** | ✅ | ✅ | — | ✅ |
| **WasmJS** | ✅ | ✅ | — | ✅ |
| **linuxX64** | ✅ | ✅ | ✅ | ✅ |
| **mingwX64** | ✅ | ✅ | ✅ | ✅ |
| **macosArm64** | ✅ | ✅ | ✅ | ✅ |
| **iosArm64** | ✅ | ✅ | — | ✅ |
| **iosX64** | ✅ | ✅ | — | ✅ |
| **iosSimulatorArm64** | ✅ | ✅ | — | ✅ |
> [!IMPORTANT]
> - On Linux distributions with **glibc** older than 2.31, `multik-openblas` doesn't work.
> - `multik-openblas` for desktop native targets (_linuxX64_, _mingwX64_, _macosArm64_) is experimental and
> unstable.
> - JVM target `multik-openblas` for Android only supports **arm64-v8a** processors.
## Quickstart
Visit [Multik documentation](https://kotlin.github.io/multik) for a detailed feature overview.
### Creating arrays
<!---FUN creatingArrays-->
```kotlin
val a = mk.ndarray(mk[1, 2, 3])
/* [1, 2, 3] */
val b = mk.ndarray(mk[mk[1.5, 2.1, 3.0], mk[4.0, 5.0, 6.0]])
/*
[[1.5, 2.1, 3.0],
[4.0, 5.0, 6.0]]
*/
val c = mk.ndarray(mk[mk[mk[1.5f, 2f, 3f], mk[4f, 5f, 6f]], mk[mk[3f, 2f, 1f], mk[4f, 5f, 6f]]])
/*
[[[1.5, 2.0, 3.0],
[4.0, 5.0, 6.0]],
[[3.0, 2.0, 1.0],
[4.0, 5.0, 6.0]]]
*/
mk.zeros<Double>(3, 4) // create an array of zeros
/*
[[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]
*/
mk.ndarray<Float, D2>(setOf(30f, 2f, 13f, 12f), intArrayOf(2, 2)) // create an array from a collection
/*
[[30.0, 2.0],
[13.0, 12.0]]
*/
val d = mk.ndarray(
doubleArrayOf(1.0, 1.3, 3.0, 4.0, 9.5, 5.0),
2, 3
) // create an array of shape(2, 3) from a primitive array
/*
[[1.0, 1.3, 3.0],
[4.0, 9.5, 5.0]]
*/
mk.d3array(2, 2, 3) { it * it } // create an array of 3 dimension
/*
[[[0, 1, 4],
[9, 16, 25]],
[[36, 49, 64],
[81, 100, 121]]]
*/
mk.d2arrayIndices(3, 3) { i, j -> ComplexFloat(i, j) }
/*
[[0.0+(0.0)i, 0.0+(1.0)i, 0.0+(2.0)i],
[1.0+(0.0)i, 1.0+(1.0)i, 1.0+(2.0)i],
[2.0+(0.0)i, 2.0+(1.0)i, 2.0+(2.0)i]]
*/
mk.arange<Long>(10, 25, 5) // create an array with elements in the interval [10, 25) with step 5
/* [10, 15, 20] */
mk.linspace<Double>(0, 2, 9) // create an array of 9 elements in the interval [0, 2]
/* [0.0, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0] */
val e = mk.identity<Double>(3) // create an identity array of shape (3, 3)
/*
[[1.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0]]
*/
val diag = mk.diagonal(mk[2, 4, 8]) // create a diagonal array
/*
[[2, 0, 0],
[0, 4, 0],
[0, 0, 8]]
*/
```
<!---END-->
### Array properties
<!---FUN arrayProperties-->
```kotlin
a.shape // Array dimensions
a.size // Size of array
a.dim // object Dimension
a.dim.d // number of array dimensions
a.dtype // Data type of array elements
```
<!---END-->
### Arithmetic operations
<!---FUN arithmeticOperations-->
```kotlin
val f = b - d // subtraction
/*
[[0.5, 0.8, 0.0],
[0.0, -4.5, 1.0]]
*/
d + f // addition
/*
[[1.5, 2.1, 3.0],
[4.0, 5.0, 6.0]]
*/
b / d // division
/*
[[1.5, 1.6153846153846154, 1.0],
[1.0, 0.5263157894736842, 1.2]]
*/
f * d // multiplication
/*
[[0.5, 1.04, 0.0],
[0.0, -42.75, 5.0]]
*/
```
<!---END-->
### Math, Linear Algebra, and Statistics
See documentation for other methods of
[mathematics](https://kotlin.github.io/multik/multik-core/org.jetbrains.kotlinx.multik.api.math/index.html),
[linear algebra](https://kotlin.github.io/multik/multik-core/org.jetbrains.kotlinx.multik.api.linalg/index.html),
[statistics](https://kotlin.github.io/multik/multik-core/org.jetbrains.kotlinx.multik.api.stat/index.html).
<!---FUN mathFunctions-->
```kotlin
a.sin() // element-wise sin, equivalent to mk.math.sin(a)
a.cos() // element-wise cos, equivalent to mk.math.cos(a)
b.log() // element-wise natural logarithm, equivalent to mk.math.log(b)
b.exp() // element-wise exp, equivalent to mk.math.exp(b)
d dot e // dot product, equivalent to mk.linalg.dot(d, e)
mk.math.sum(c) // array-wise sum
mk.math.min(c) // array-wise minimum elements
mk.math.maxD3(c, axis = 0) // maximum value of an array along axis 0
mk.math.cumSum(b, axis = 1) // cumulative sum of the elements
mk.stat.mean(a) // mean
mk.stat.median(b) // median
```
<!---END-->
### Copying arrays
<!---FUN copyingArrays-->
```kotlin
val f = a.copy() // create a copy of the array and its data
val h = b.deepCopy() // create a copy of the array and copy the meaningful data
```
<!---END-->
### Collection Operations
<!---FUN collectionOperations-->
```kotlin
c.filter { it < 3 } // select all elements less than 3
b.map { (it * it).toInt() } // return squares
c.groupNDArrayBy { it % 2 } // group elements by condition
c.sorted() // sort elements
```
<!---END-->
### Indexing/Slicing/Iterating
<!---FUN indexingSlicingIterating-->
```kotlin
a[2] // select the element at the 2 index
b[1, 2] // select the element at row 1 column 2
b[1] // select row 1
b[0..1, 1] // select elements at rows 0 to 1 in column 1
b[0, 0..2..1] // select elements at row 0 in columns 0 to 2 with step 1
for (el in b) {
print("$el, ") // 1.5, 2.1, 3.0, 4.0, 5.0, 6.0,
}
// for n-dimensional
val q = b.asDNArray()
for (index in q.multiIndices) {
print("${q[index]}, ") // 1.5, 2.1, 3.0, 4.0, 5.0, 6.0,
}
```
<!---END-->
### Inplace
<!---FUN inplaceOperations-->
```kotlin
val a = mk.linspace<Float>(0, 1, 10)
/*
a = [0.0, 0.1111111111111111, 0.2222222222222222, 0.3333333333333333, 0.4444444444444444, 0.5555555555555556,
0.6666666666666666, 0.7777777777777777, 0.8888888888888888, 1.0]
*/
val b = mk.linspace<Float>(8, 9, 10)
/*
b = [8.0, 8.11111111111111, 8.222222222222221, 8.333333333333334, 8.444444444444445, 8.555555555555555,
8.666666666666666, 8.777777777777779, 8.88888888888889, 9.0]
*/
a.inplace {
math {
(this - b) * b
abs()
}
}
// a = [64.0, 64.88888, 65.77778, 66.66666, 67.55556, 68.44444, 69.333336, 70.22222, 71.111115, 72.0]
```
<!---END-->
## Building
### Full build (with OpenBLAS)
Requires:
* `JAVA_HOME` environment variable set
* `gcc`, `g++`, `gfortran` version 8 or higher (must be the same version)
```bash
./gradlew assemble
```
### Without OpenBLAS
```bash
./gradlew assemble -x build_cmake
```
### Individual modules
```bash
./gradlew :multik-core:build
```
### Running tests
```bash
./gradlew :multik-core:jvmTest
```
## Contributing
See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on submitting issues, pull requests, and building the project.
## License
Multik is licensed under the [Apache License 2.0](LICENSE).