Multidimensional array library for Kotlin.
multik-core— contains ndarrays, methods called on them and [math], [stat] and [linalg] interfaces.multik-default— implementation includingmultik-kotlinandmultik-openblasfor performance.multik-kotlin— implementation of [math], [stat] and [linalg] interfaces on JVM.multik-openblas— implementation of [math], [stat] and [linalg] interfaces in native code using OpenBLAS.
In your Gradle build script:
- Add the Maven Central Repository.
- Add the
org.jetbrains.kotlinx:multik-core:$multik_versionapi dependency. - Add an implementation dependency:
org.jetbrains.kotlinx:multik-default:$multik_version,org.jetbrains.kotlinx:multik-kotlin:$multik_versionororg.jetbrains.kotlinx:multik-openblas:$multik_version.
build.gradle:
repositories { mavenCentral() } dependencies { implementation "org.jetbrains.kotlinx:multik-core:0.2.0" implementation "org.jetbrains.kotlinx:multik-default:0.2.0" }build.gradle.kts:
repositories { mavenCentral() } dependencies { implementation("org.jetbrains.kotlinx:multik-core:0.2.0") implementation("org.jetbrains.kotlinx:multik-default:0.2.0") }For a multiplatform project, set the dependency in a common block:
kotlin { sourceSets { val commonMain by getting { dependencies { implementation("org.jetbrains.kotlinx:multik-core:0.2.0") } } } }or in a platform-specific block:
kotlin { sourceSets { val jvmName by getting { dependencies { implementation("org.jetbrains.kotlinx:multik-core-jvm:0.2.0") } } } }Install Kotlin kernel for Jupyter or just visit to Datalore.
Import stable multik version into notebook:
%use multik | Platforms | multik-core | multik-kotlin | multik-openblas | multik-default |
|---|---|---|---|---|
| JS | ✅ | ✅ | ❌ | ✅ |
| linuxX64 | âś… | âś… | âś… | âś… |
| mingwX64 | âś… | âś… | âś… | âś… |
| macosX64 | âś… | âś… | âś… | âś… |
| macosArm64 | âś… | âś… | âś… | âś… |
| iosArm64 | ✅ | ✅ | ❌ | ✅ |
| iosX64 | ✅ | ✅ | ❌ | ✅ |
| iosSimulatorArm64 | ✅ | ✅ | ❌ | ✅ |
| JVM | ✅ | ✅ | linuxX64 - ✅ mingwX64 - ✅ macosX64 - ✅ macosArm64 - ✅ androidArm64 - ✅ | androidArm32 - ❌ androidX86 - ❌ androidX64 - ❌ |
For Kotlin/JS, we use the new IR. We also use the new memory model in Kotlin/Native. Keep this in mind when using Multik in your multiplatform projects.
Note:
- on ubuntu 18.04 and older
multik-openblasdoesn't work due to older versions of glibc. multik-openblasfor desktop targets (linuxX64, mingwX64, macosX64, macosArm64) is experimental and unstable. We will improve stability and perfomance as Kotlin/Native evolves.- JVM target
multik-openblasfor Android only supports arm64-v8a processors.
Visit Multik documentation for a detailed feature overview.
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) // creare 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]] */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 elementsval 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]] */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) // meadianval 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 datac.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 elementsa[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..2, 1] // select elements at rows 0 and 1 in column 1 b[0..1..1] // select all elements at row 0 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, }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]To build the entire project, you need to set up an environment for building multik-openblas:
- JDK 1.8 or higher
- JAVA_HOME environment - to search for jni files
- Compilers gcc, g++, gfortran version 8 or higher. It is important that they are of the same version.
Run ./gradlew assemble to build all modules. If you don't need to build multik-openblas, just disable the cmake_build task and build the module you need.
There is an opportunity to contribute to the project:
- Implement math, linalg, stat interfaces.
- Create your own engine successor from Engine, for example - JvmEngine.
- Use mk.addEngine and mk.setEngine to use your implementation.
