94 lines
5.3 KiB
Markdown
94 lines
5.3 KiB
Markdown
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# ND4S: Scala bindings for ND4J
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[](https://gitter.im/deeplearning4j/deeplearning4j?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
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ND4S is open-source Scala bindings for [ND4J](https://github.com/deeplearning4j/nd4j). Released under an Apache 2.0 license.
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# Main Features
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* NDArray manipulation syntax sugar with safer type.
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* NDArray slicing syntax, similar with NumPy.
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# Installation
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## Install via Maven
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ND4S is already included in official Maven repositories.
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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.
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As an alternative, one may simply add the line below to `build.sbt` and re-build project.
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```scala
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val nd4jVersion = "1.0.0-alpha"
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libraryDependencies += "org.nd4j" % "nd4j-native-platform" % nd4jVersion
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libraryDependencies += "org.nd4j" %% "nd4s" % nd4jVersion
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```
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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.
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No need for git-cloning & compiling!
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## Clone from the GitHub Repo
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ND4S is actively developed. You can clone the repository, compile it, and reference it in your project.
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Clone the repository:
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```
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$ git clone https://github.com/deeplearning4j/nd4s.git
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```
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Compile the project:
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```
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$ cd nd4s
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$ sbt +publish-local
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```
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## Try ND4S in REPL
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The easiest way to play ND4S around is cloning this repository and run the following command.
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```
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$ cd nd4s
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$ sbt test:console
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```
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It starts REPL with importing `org.nd4s.Implicits._` and `org.nd4j.linalg.factory.Nd4j` automatically. It uses jblas backend at default.
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```scala
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scala> val arr = (1 to 9).asNDArray(3,3)
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arr: org.nd4j.linalg.api.ndarray.INDArray =
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[[1.00,2.00,3.00]
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[4.00,5.00,6.00]
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[7.00,8.00,9.00]]
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scala> val sub = arr(0->2,1->3)
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sub: org.nd4j.linalg.api.ndarray.INDArray =
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[[2.00,3.00]
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[5.00,6.00]]
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```
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# CheatSheet(WIP)
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| ND4S syntax | Equivalent NumPy syntax | Result |
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|--------------------------------------------|---------------------------------------------|----------------------------------------------------------------|
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| 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]] |
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| 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]] |
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| arr(0,0) | arr[0,0] | 1.0 |
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| arr(0,->) | arr[0,:] | [1.0, 2.0, 3.0] |
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| arr(--->) | arr[...] | [[1.0, 2.0, 3.0] [4.0, 5.0, 6.0] ,[7.0, 8.0, 9.0]] |
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| arr(0 -> 3 by 2, ->) | arr[0:3:2,:] | [[1.0, 2.0, 3.0] [7.0, 8.0, 9.0]] |
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| arr(0 to 2 by 2, ->) | arr[0:3:2,:] | [[1.0, 2.0, 3.0] [7.0, 8.0, 9.0]] |
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| 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]] |
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| arr.map(_ % 3) | | [[1.0, 2.0, 0.0] [1.0, 2.0, 0.0] ,[1.0, 2.0, 0.0]] |
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| arr.filterBit(_ < 4) | | [[1.0, 1.0, 1.0] [0.0, 0.0, 0.0] ,[0.0, 0.0, 0.0]] |
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| arr + arr | arr + arr | [[2.0, 4.0, 6.0] [8.0, 10.0, 12.0] ,[14.0, 16.0, 18.0]] |
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| arr * arr | arr * arr | [[1.0, 4.0, 9.0] [16.0, 25.0, 36.0] ,[49.0, 64.0, 81.0]] |
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| 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]] |
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| arr.sumT | np.sum(arr) | 45.0 //returns Double value |
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| val comp = Array(1 + i, 1 + 2 * i).toNDArray | comp = np.array([1 + 1j, 1 + 2j]) | [1.0 + 1.0i ,1.0 + 2.0i] |
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| comp.sumT | np.sum(comp) | 2.0 + 3.0i //returns IComplexNumber value |
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| for(row <- arr.rowP if row.get(0) > 1) yield row*2 | | [[8.00,10.00,12.00] [14.00,16.00,18.00]] |
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| 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]]] |
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| for(slice <- tensor.sliceP if slice.get(0) > 1) yield slice*2 | |[[[10.00,12.00][14.00,16.00]]] |
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|arr(0 -> 3 by 2, ->) = 0 | | [[0.00,0.00,0.00] [4.00,5.00,6.00] [0.00,0.00,0.00]] |
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