Java is everywhere in backend systems, enterprise applications, and production environments. But when it comes to numerical computing, data manipulation, and scientific-style operations, Python's NumPy ecosystem has become the standard.

I wanted a similar experience in Java: a lightweight, dependency-free library for working with multidimensional arrays and linear algebra.

That idea became NumPy4J.

What is NumPy4J?

NumPy4J is an open-source numerical computing library for Java inspired by NumPy.

It provides:

Multidimensional arrays (NDArray)

NumPy-style broadcasting

Array creation utilities

Reshaping and slicing

Element-wise operations

Linear algebra operations

Example:

NDArray A = NDArray.of(new double[]{

    1, 2,

    3, 4

}, 2, 2);



NDArray B = NDArray.ones(2, 2);



NDArray C = A.add(B);



Matrix operations:



NDArray result = LinearAlgebra.matmul(A, B);

Solving equations:

NDArray x = LinearAlgebra.solve(A, b);

Why build another numerical library?

There are already excellent Java math libraries available.

The goal of NumPy4J is different:

Provide a NumPy-like API experience

Make multidimensional arrays a first-class concept in Java

Keep the API simple and approachable

Create a foundation for future scientific computing features

Testing approach

To make sure behavior stays consistent, NumPy4J uses Python NumPy as a reference implementation.

Test cases are generated with NumPy and validated against the Java implementation, covering:

Broadcasting

Matrix operations

Reshaping

Transpose

Linear solving

Element-wise calculations

What's next?

The roadmap includes:

More NumPy-compatible operations

Matrix decompositions (QR, LU, Cholesky)

Eigenvalue computation

More statistics functions

Performance improvements

Try it out

If you work with Java and need NumPy-style numerical operations, I would love for you to try NumPy4J, provide feedback, and contribute ideas.

GitHub:

Wiki:

Java #OpenSource #NumPy #MachineLearning #LinearAlgebra #NumericalComputing