armadillo-6.400.3.tar.gz_armadillo

Armadillo C++ Linear Algebra Library http://arma.sourceforge.net Contents ======== 1: Introduction 2: Citation Details 3: Requirements 4: Linux and Mac OS X: Installation 5: Linux and Mac OS X: Compiling & Linking 6: Windows: Installation 7: Windows: Compiling & Linking 8: Support for OpenBLAS, Intel MKL and AMD ACML 9: Support for ATLAS 10: Documentation / API Reference Manual 11: MEX Interface to Octave 12: Bug Reports and Frequently Asked Questions 13: License 14: Developers and Contributors 15: Related Software 1: Introduction =============== Armadillo is a high quality C++ linear algebra library, aiming towards a good balance between speed and ease of use. It's useful for algorithm development directly in C++, and/or quick conversion of research code into production environments. The syntax (API) is deliberately similar to Matlab. The library provides efficient classes for vectors, matrices and cubes, as well as 150+ associated functions (eg. contiguous and non-contiguous submatrix views). Various matrix decompositions are provided through integration with LAPACK, or one of its high performance drop-in replacements (eg. OpenBLAS, Intel MKL, AMD ACML, Apple Accelerate framework, etc). An automatic expression evaluator (via C++ template meta-programming) combines several operations (at compile time) to increase efficiency. The library can be used for machine learning, pattern recognition, signal processing, bioinformatics, statistics, econometrics, etc. The library is open-source software, and is distributed under a license that is useful in both open-source and commercial/proprietary contexts. Armadillo is primarily developed at Data61 / NICTA (Australia), with contributions from around the world. More information about Data61 can be obtained from http://data61.csiro.au Main developers: Conrad Sanderson - http://conradsanderson.id.au Ryan Curtin - http://ratml.org 2: Citation Details =================== Please cite the following tech report if you use Armadillo in your research and/or software. Citations are useful for the continued development and maintenance of the library. Conrad Sanderson. Armadillo: An Open Source C++ Linear Algebra Library for Fast Prototyping and Computationally Intensive Experiments. Technical Report, NICTA, 2010. 3: Requirements =============== Armadillo makes extensive use of template meta-programming, recursive templates and template based function overloading. As such, C++ compilers which do not fully implement the C++ standard may not work correctly. The functionality of Armadillo is partly dependent on other libraries: LAPACK, BLAS, ARPACK and SuperLU. The LAPACK and BLAS libraries are used for dense matrices, while the ARPACK and SuperLU libraries are used for sparse matrices. Armadillo can work without these libraries, but its functionality will be reduced. In particular, basic functionality will be available (eg. matrix addition and multiplication), but things like eigen decomposition or matrix inversion will not be. Matrix multiplication (mainly for big matrices) may not be as fast. As Armadillo is a template library, we recommended that optimisation is enabled during compilation of programs that use Armadillo. For example, for GCC and Clang compilers use -O2 or -O3 4: Linux and Mac OS X: Installation =================================== You can install Armadillo on your system using the procedure detailed below, or use Armadillo without installation (detailed in section 5). Installation procedure: * Step 1: Ensure a C++ compiler is installed on your system. Caveat: on Mac OS X you will need to install Xcode and then type the following command in a terminal window: xcode-select --install * Step 2: Ensure the CMake tool is installed on your system. You can download it from http://www.cmake.org or (preferably) install it using your package manager. On Linux-based systems, you can get CMake through PackageKit, yum, dnf, apt, aptitude, ... On Mac OS X systems, you can get CMake through MacPorts or Homebrew. * Step 3: Ensure LAPACK and BLAS (or their equivalents) are installed on your system. On Mac OS X this is not necessary. If you are using sparse matrices, also install ARPACK and SuperLU. Caveat: only SuperLU version 4.3 can be used! On Linux-based systems, the following libraries are recommended to be present: LAPACK, BLAS, ARPACK, SuperLU and ATLAS. LAPACK and BLAS are the most important. It is also necessary to install the corresponding development files for each library. For example, when installing the "lapack" package, also install the "lapack-devel" or "lapack-dev" package. Caveat: for better performance, we recommend using the multi-threaded OpenBLAS library instead of standard BLAS. See http://xianyi.github.com/OpenBLAS/ * Step 4: Open a terminal window, change into the directory that was created by unpacking the armadillo archive, and type the following commands: cmake . make The full stop separated from "cmake" by a space is important. CMake will figure out what other libraries are currently installed and will modify Armadillo's configuration correspondingly. CMake will also generate a run-time armadillo library, which is a wrapper for all the relevant libraries present on your system (eg. LAPACK, BLAS, ARPACK, SuperLU, ATLAS). If you need to re-run cmake, it's a good idea to first delete the "CMakeCache.txt" file (not "CMakeLists.txt"). Caveat: out-of-tree builds are currently not fully supported; eg, creating a sub-directory called "build" and running cmake .. from within "build" is currently not supported. * Step 5: If you have access to root/administrator/superuser privileges (ie. able to use "sudo"), type the following command: sudo make install If you don't have root/administrator/superuser privileges, type the following command: make install DESTDIR=my_usr_dir where "my_usr_dir" is for storing C++ headers and library files. Caveat: make sure your C++ compiler is configured to use the "lib" and "include" sub-directories present within this directory. 5: Linux and Mac OS X: Compiling & Linking ========================================== The "examples" directory contains several quick example programs that use the Armadillo library. In general, programs which use Armadillo are compiled along these lines: g++ example1.cpp -o example1 -O2 -larmadillo If you want to use Armadillo without installation, or you're getting linking errors, compile along these lines: g++ example1.cpp -o example1 -O2 -I /home/blah/armadillo-6.400.3/include -DARMA_DONT_USE_WRAPPER -lblas -llapack The above command line assumes that you have unpacked the armadillo archive into /home/blah/ You will need to adjust this for later versions of Armadillo, and/or if you have unpacked into a different directory. Notes: * To use the high speed OpenBLAS library instead of BLAS, replace -lblas -llapack with -lopenblas -llapack To get OpenBLAS, see http://xianyi.github.com/OpenBLAS/ * On most Linux-based systems, using -lblas -llapack should be enough; however, on Ubuntu and Debian you may need to add -lgfortran * On Mac OS X, replace -lblas -llapack with -framework Accelerate * If you have ARPACK present, also link with it by adding -larpack to the command line * If you have SuperLU present, also link with it by adding -lsuperlu to the command line Caveat: only SuperLU version 4.3 can be used! 6: Windows: Installation ======================== The installation is comprised of 3 steps: * Step 1: Copy the entire "include" folder to a convenient locatio