ND4S alternatives and similar packages
Based on the "Science and Data Analysis" category.
Alternatively, view ND4S alternatives based on common mentions on social networks and blogs.
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MLLib
Apache Spark - A unified analytics engine for large-scale data processing -
PredictionIO
PredictionIO, a machine learning server for developers and ML engineers. -
Zeppelin
Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more. -
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Spark Notebook
Interactive and Reactive Data Science using Scala and Spark. -
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Squants
The Scala API for Quantities, Units of Measure and Dimensional Analysis -
FACTORIE
FACTORIE is a toolkit for deployable probabilistic modeling, implemented as a software library in Scala. It provides its users with a succinct language for creating relational factor graphs, estimating parameters and performing inference. -
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OpenMOLE
Workflow engine for exploration of simulation models using high throughput computing -
Clustering4Ever
C4E, a JVM friendly library written in Scala for both local and distributed (Spark) Clustering. -
rscala
The Scala interpreter is embedded in R and callbacks to R from the embedded interpreter are supported. Conversely, the R interpreter is embedded in Scala. -
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Synapses
A group of neural-network libraries for functional and mainstream languages -
Axle
Axle Domain Specific Language for Scientific Cloud Computing and Visualization
Developer Ecosystem Survey 2022
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
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README
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]] |
*Note that all licence references and agreements mentioned in the ND4S README section above
are relevant to that project's source code only.