# ND4S: Scala bindings for ND4J [](https://gitter.im/deeplearning4j/deeplearning4j?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) ND4S is open-source Scala bindings for [ND4J](https://github.com/eclipse/deeplearning4j/tree/master/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. ```scala 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](https://mvnrepository.com/artifact/org.nd4j/nd4s_2.11) 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 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]] |