ND4J: N-Dimensional Arrays for Java

ND4J is a scientific computing library for the JVM. It is meant to be used in production environments rather than as a research tool, which means routines are designed to run fast with minimum RAM requirements.

Main features

• Versatile n-dimensional array object
• Multiplatform functionality including GPUs
• Linear algebra and signal processing functions

A usability gap has separated Java, Scala and Clojure programmers from the most powerful tools in data analysis, like NumPy or Matlab. With ND4J, intuitive scientific computing tools once limited to the Python community are now open source, distributed and integrated with GPUs on the JVM.

Think SLF4J. Now imagine Numpy with Theano built in. That’s ND4J. It gives engineers in production environments an easy way to port their algorithms and interface with other libraries in the Java and Scala ecosystems.

Click here to get started, or read on.

ND4J Specifics

• Supports GPUs via CUDA and Native via Jblas and Netlib Blas.
• All of this is wrapped in a unifying interface.
• The API mimics the semantics of Numpy, Matlab and scikit-learn.

Code Example

Create a 2 x 2 NDarray:

INDArray arr1 = Nd4j.create(new float[]{1,2,3,4},new int[]{2,2});
System.out.println(arr1);

Output:

[[1.0 ,3.0]
[2.0 ,4.0]
]

Adding a scalar with in-place operations:

arr1.addi(1);
System.out.println(arr1);

Each element is incremented by one:

[[2.0 ,4.0]
[3.0 ,5.0]
]

Create a second array (arr2) and adding it to the first (arr1):

INDArray arr2 = ND4j.create(new float[]{5,6,7,8},new int[]{2,2});
arr1.addi(arr2);
System.out.println(arr1);

Output:

[[7.0 ,11.0]
[9.0 ,13.0]
]

You get the idea. Now get started.