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

Installation

Latest version:

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:

repositories {
    mavenCentral()
}

dependencies {
    implementation("org.jetbrains.kotlinx:multik-default:$multikVersion")
}

repositories {
    mavenCentral()
}

dependencies {
    implementation "org.jetbrains.kotlinx:multik-default:$multikVersion"
}

Kotlin Multiplatform

kotlin {
    sourceSets {
        commonMain {
            dependencies {
                implementation("org.jetbrains.kotlinx:multik-default:$multikVersion")
            }
        }
    }
}

Jupyter Notebook

Install Kotlin kernel for Jupyter or just visit to Datalore.

Import stable multik version into notebook:

%use multik

Supported Platforms

[!IMPORTANT]

• On Linux distributions with glibc older than 2.31, multik-openblas doesn't work.
• multik-openblas for desktop native targets (linuxX64, mingwX64, macosX64, macosArm64) is experimental and unstable.
• JVM target multik-openblas for Android only supports arm64-v8a processors.

Quickstart

Visit Multik documentation for a detailed feature overview.

Creating arrays

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]]
 */

Array properties

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

Arithmetic operations

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]]
*/

Math, Linear Algebra, and Statistics

See documentation for other methods of mathematics, linear algebra, statistics.

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

Copying arrays

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

Collection Operations

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

Indexing/Slicing/Iterating

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,
}

Inplace

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]

Building

Full build (with OpenBLAS)

Requires:

• JAVA_HOME environment variable set
• gcc, g++, gfortran version 8 or higher (must be the same version)

./gradlew assemble

Without OpenBLAS

./gradlew assemble -x build_cmake

Individual modules

./gradlew :multik-core:build

Running tests

./gradlew :multik-core:jvmTest

Contributing

See CONTRIBUTING.md for guidelines on submitting issues, pull requests, and building the project.

License

Multik is licensed under the Apache License 2.0.