Jblas: Linear Algebra for Java
 | This article has multiple issues. Please help improve it or discuss these issues on the talk page. (Learn how and when to remove these messages)
|
 | The MadmanBot has performed a web search with the contents of this page, and it appears to include material copied directly from:
It will soon be reviewed to determine if there are any copyright issues. The content should not be mirrored or otherwise reused until the issue has been resolved. If substantial content is duplicated, unless evidence is provided to the contrary (e.g. evidence of permission to use this content under terms consistent with the Wikimedia Terms of Use or public domain status; see Wikipedia:Donating copyrighted materials), editors will assume that this text is a copyright violation, and will soon delete the copy.
Before removing this notice, you should:
|
| Original author(s) | Mikio L. Braun |
|---|---|
| Stable release | 1.2.3
/ February 13, 2013 |
| Operating system | Cross-platform |
| Type | Library |
| License | BSD Revised |
| Website | jblas |
jblas is a linear algebra library for the Java programming language. jblas is a fast library due to its use of native implementations of BLAS and LAPACK, which are the de-facto industry standard for matrix computations. jblas improved upon the archaic API found in BLAS and LAPACK (an artifact of them originating from the Fortran community) by providing an easy to use high level interface for Java users. By design, jblas removes much of the tediousness of using JNI interfaces and hide the Fortran calling conventions.
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 recent performance study of Java matrix libraries,[6] jblas is 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);
References
- ^ C. Dharmadhikar, Shweta (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).
{{cite journal}}: Unknown parameter|coauthors=ignored (|author=suggested) (help) - ^ Davis, Nicholas (2011). "Real-world comparison of CPU and GPU implementations of SNPrank: a network analysis tool for GWAS". Bioinformatics. 27 (2): 284–285.
{{cite journal}}: Unknown parameter|coauthors=ignored (|author=suggested) (help) - ^ Muller, Jan Saputra (2011). SSA Toolbox 1.3 Manual (PDF). Retrieved September 25, 2013.
{{cite book}}: Unknown parameter|coauthors=ignored (|author=suggested) (help) - ^ Papadimitriou, Stergios. "JLabGroovy". Retrieved September 23, 2013.
- ^ Arndt, Holger. "Universal Java Matrix Package". Retrieved September 25, 2013.
- ^ Abeles, Peter. "Java Matrix Benchmark". Retrieved September 23, 2013.