cavis/nd4s

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 = "1.0.0-alpha"

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

One may want to check our maven repository page and replace 1.0.0-alpha 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/eclipse/deeplearning4j.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