ND4S: Scala bindings for ND4J

ND4S is open-source Scala bindings for ND4J. Released under an Apache 2.0 license.

Main Features

NDArray manipulation syntax sugar with safer type.
NDArray slicing syntax, similar with NumPy.

Installation

Install via Maven

ND4S is already included in official Maven repositories.

With IntelliJ, incorporation of ND4S is easy: just create a new Scala project, go to "Project Settings"/Libraries, add "From Maven...", and search for nd4s.

As an alternative, one may simply add the line below to build.sbt and re-build project.

val nd4jVersion = "0.7.2"

libraryDependencies += "org.nd4j" % "nd4j-native-platform" % nd4jVersion
libraryDependencies += "org.nd4j" %% "nd4s" % nd4jVersion

One may want to check our maven repository page and replace 0.7.2 with the latest version.

No need for git-cloning & compiling!

Clone from the GitHub Repo

ND4S is actively developed. You can clone the repository, compile it, and reference it in your project.

Clone the repository:

$ git clone https://github.com/deeplearning4j/nd4s.git

Compile the project:

$ cd nd4s
$ sbt +publish-local

Try ND4S in REPL

The easiest way to play ND4S around is cloning this repository and run the following command.

$ cd nd4s
$ sbt test:console

It starts REPL with importing org.nd4s.Implicits._ and org.nd4j.linalg.factory.Nd4j automatically. It uses jblas backend at default.

scala> val arr = (1 to 9).asNDArray(3,3) 
arr: org.nd4j.linalg.api.ndarray.INDArray =
[[1.00,2.00,3.00]
 [4.00,5.00,6.00]
 [7.00,8.00,9.00]]

scala> val sub = arr(0->2,1->3)
sub: org.nd4j.linalg.api.ndarray.INDArray =
[[2.00,3.00]
 [5.00,6.00]]

CheatSheet(WIP)

ND4S syntax	Equivalent NumPy syntax	Result
Array(Array(1,2,3),Array(4,5,6)).toNDArray	np.array([[1, 2 , 3], [4, 5, 6]])	[[1.0, 2.0, 3.0] [4.0, 5.0, 6.0]]
val arr = (1 to 9).asNDArray(3,3)	arr = np.arange(1,10).reshape(3,3)	[[1.0, 2.0, 3.0] [4.0, 5.0, 6.0] ,[7.0, 8.0, 9.0]]
arr(0,0)	arr[0,0]	1.0
arr(0,->)	arr[0,:]	[1.0, 2.0, 3.0]
arr(--->)	arr[...]	[[1.0, 2.0, 3.0] [4.0, 5.0, 6.0] ,[7.0, 8.0, 9.0]]
arr(0 -> 3 by 2, ->)	arr[0:3:2,:]	[[1.0, 2.0, 3.0] [7.0, 8.0, 9.0]]
arr(0 to 2 by 2, ->)	arr[0:3:2,:]	[[1.0, 2.0, 3.0] [7.0, 8.0, 9.0]]
arr.filter(_ > 3)	np.where(arr > 3, arr, 0)	[[0.0, 0.0, 0.0] [4.0, 5.0, 6.0] ,[7.0, 8.0, 9.0]]
arr.map(_ % 3)		[[1.0, 2.0, 0.0] [1.0, 2.0, 0.0] ,[1.0, 2.0, 0.0]]
arr.filterBit(_ < 4)		[[1.0, 1.0, 1.0] [0.0, 0.0, 0.0] ,[0.0, 0.0, 0.0]]
arr + arr	arr + arr	[[2.0, 4.0, 6.0] [8.0, 10.0, 12.0] ,[14.0, 16.0, 18.0]]
arr * arr	arr * arr	[[1.0, 4.0, 9.0] [16.0, 25.0, 36.0] ,[49.0, 64.0, 81.0]]
arr dot arr	np.dot(arr, arr)	[[30.0, 36.0, 42.0] [66.0, 81.0, 96.0] ,[102.0, 126.0, 150.0]]
arr.sumT	np.sum(arr)	45.0 //returns Double value
val comp = Array(1 + i, 1 + 2 * i).toNDArray	comp = np.array([1 + 1j, 1 + 2j])	[1.0 + 1.0i ,1.0 + 2.0i]
comp.sumT	np.sum(comp)	2.0 + 3.0i //returns IComplexNumber value
for(row <- arr.rowP if row.get(0) > 1) yield row*2		[[8.00,10.00,12.00] [14.00,16.00,18.00]]
val tensor = (1 to 8).asNDArray(2,2,2)	tensor = np.arange(1,9).reshape(2,2,2)	[[[1.00,2.00] [3.00,4.00]] [[5.00,6.00] [7.00,8.00]]]
for(slice <- tensor.sliceP if slice.get(0) > 1) yield slice*2		[[[10.00,12.00][14.00,16.00]]]
arr(0 -> 3 by 2, ->) = 0		[[0.00,0.00,0.00] [4.00,5.00,6.00] [0.00,0.00,0.00]]