Description:

Lush is an object-oriented Lisp dialect with a super-simple way of integrating C/C++ code and libraries. It includes extensive libraries for numerical computing, machine learning, and computer vision.

If you do research and development in signal processing, image processing, machine learning, computer vision, bio-informatics, data mining, statistics, simulation, optimization, or artificial intelligence, and feel limited by Matlab and other existing tools, Lush is for you. If you want a simple environment to experiment with graphics, video, and sounds, Lush is for you.

Lush is designed to be used in situations where one would want to combine the flexibility of a high-level, weakly-typed interpreted language (a dialect of Lisp), with the efficiency of a strongly-typed, natively-compiled language, and with the easy integration of code written in C, C++, or other languages.

Lush's main features includes:

• A very clean, simple, and easy to learn Lisp-like syntax.
• A compiler that produces very efficient C code and relies on the C compiler to produce efficient native code (no inefficient bytecode or virtual machine).
• An easy way to interface C functions and libraries, and a powerful dynamic linker/loader for object files or libraries (.o, .a and .so files) written in other compiled languages.
• The ability to freely mix Lisp and C in a single function.
• A powerful set of vector/matrix/tensor operations.
• A huge library of over 10,000 numerical routines, including full interfaces to GSL, LAPACK, and BLAS.
• A library of image and signal processing routines.
• An extensive set of graphic routines, including an object-oriented GUI toolkit, an interface to OpenGL/GLU/GLUT, and the OpenInventor scene rendering engine.
• An interface to the Simple Directmedia Layer (SDL) multimedia library, including a sprite class with pixel-accurate collision detection (perfect for 2D games).
• Sound and video grabbing (using ALSA and Video4Linux).
• Several libraries for machine learning, neural net (including convolutional nets), statistical estimation, Hidden Markov Models, kernel methods (gblearn2, Torch, HTK, SVM).
• libraries for computer vision (OpenCV)

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Supported Operating Systems: Cygwin, Linux, Macosx

Data Formats: None

Tags: Structure Learning, Graph, Sequence Analysis, Structured Outputs, Svm, Classification, Preprocessing

Archive: download here

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