Jblas: Linear Algebra for Java

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Jblas: Linear Algebra for Java
Original author(s)Mikio L. Braun
Stable release
1.2.4 / May 12, 2015 (2015-05-12)
Cross-platform
TypeLibrary
LicenseBSD Revised
Websitejblas.org

jblas is a linear algebra library, created by Mikio Braun, for the Java programming language built upon

BLAS and LAPACK
, removing much of the tediousness.

Since its initial release, jblas has been gaining popularity in scientific computing. With applications in a range of applications, such as text classification,[1] network analysis,[2] and stationary subspace analysis.[3] It is part of software packages, such as JLabGroovy,[4] and Universal Java Matrix Library (UJMP).[5] In a performance study of Java matrix libraries,[6] jblas was the highest performing library, when libraries with native code are considered.

Capabilities

The following is an overview of jblas's capabilities, as listed on the project's website:

  • Eigen – eigendecomposition
  • Solve – solving linear equations
  • Singular – singular value decomposition
  • Decompose – LU, Cholesky, ...
  • Geometry – centering, normalizing, ...

Usage example

Example of Eigenvalue Decomposition: 

DoubleMatrix[] evd = Eigen.symmetricEigenvectors(matA);
DoubleMatrix V = evd[0];
DoubleMatrix D = evd[1];

Example of matrix multiplication: 

DoubleMatrix result = matA.mmul(matB);

See also

References

  1. ^ C. Dharmadhikar, Shweta; Maya Ingle; Parag Kulkarn (July 2012). "A Novel Multi label Text Classification Model using Semi supervised learning". International Journal of Data Mining & Knowledge Ma Nagement Process (IJDKP). 2 (4).
  2. PMID 21115438
    .
  3. ^ Muller, Jan Saputra; Paul von Bunau; Frank C. Meinecke; Franz J. Kiraly; Klaus-Robert Muller (2011). SSA Toolbox 1.3 Manual (PDF). Retrieved September 25, 2013.
  4. ^ Papadimitriou, Stergios. "JLabGroovy". Retrieved September 23, 2013.
  5. ^ Arndt, Holger. "Universal Java Matrix Package". Retrieved September 25, 2013.
  6. ^ Abeles, Peter. "Java Matrix Benchmark". Retrieved September 23, 2013.
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