LAML is a stand-alone pure Java library for linear algebra and machine learning. The goal is to build efficient and easy-to-use linear algebra and machine learning libraries. The reason why linear algebra and machine learning are built together is that full control of the basic data structures for matrices and vectors is required to have fast implementation for machine learning methods. Additionally, LAML provides a lot of commonly used matrix functions in the same signature to MATLAB, thus can also be used to manually convert MATLAB code to Java code.
- Stand-alone Java library, completely cross-platform
- Built-in Linear Algebra (LA) library
- Full control of matrices and vectors
- Many general-purpose optimization algorithms
- Fast implementation of Machine Learning (ML) methods
- Matrix functions with almost the same signature to MATLAB
- Well documented source code and friendly API, very easy to use
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