armadillo-code

source code repo for Armadillo C++ linear algebra (matrix) library - http://arma.sourceforge.net

Armadillo C++ Linear Algebra Library http://arma.sourceforge.net Copyright 2008-2016 Conrad Sanderson (http://conradsanderson.id.au) Copyright 2008-2016 National ICT Australia (NICTA) Contents ======== 1: Introduction 2: Citation Details 3: Distribution License 4: Compilers and External Dependencies 5: Linux and Mac OS X: Installation 6: Linux and Mac OS X: Compiling & Linking 7: Windows: Installation 8: Windows: Compiling & Linking 9: Support for OpenBLAS and Intel MKL 10: Support for ATLAS 11: Support for C++11/C++14 Features 12: API Documentation 13: API Stability and Versioning 14: Bug Reports and Frequently Asked Questions 15: MEX Interface to Octave/Matlab 16: 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 200+ 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, Apple Accelerate framework, etc). A sophisticated expression evaluator (via C++ template meta-programming) automatically combines several operations (at compile time) to increase speed and efficiency. The library can be used for machine learning, pattern recognition, computer vision, signal processing, bioinformatics, statistics, finance, etc. Authors: Conrad Sanderson - http://conradsanderson.id.au Ryan Curtin - http://ratml.org 2: Citation Details =================== Please cite the following article if you use Armadillo in your research and/or software. Citations are useful for the continued development and maintenance of the library. Conrad Sanderson and Ryan Curtin. Armadillo: a template-based C++ library for linear algebra. Journal of Open Source Software, Vol. 1, pp. 26, 2016. 3: Distribution License ======================= Armadillo can be used in both open-source and proprietary (closed-source) software. Armadillo is licensed under the Apache License, Version 2.0 (the “License”). A copy of the License is included in the “LICENSE.txt” file. Any software that incorporates or distributes Armadillo in source or binary form must include, in the documentation and/or other materials provided with the software, a readable copy of the attribution notices present in the “NOTICE.txt” file. See the License for details. The contents of the “NOTICE.txt” file are for informational purposes only and do not modify the License. 4: Compilers and External Dependencies ====================================== 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 5: Linux and Mac OS X: Installation =================================== * 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 using yum, dnf, apt, aptitude, … On Mac OS X systems, you can get CMake through MacPorts or Homebrew. * Step 3: Ensure LAPACK and BLAS are installed on your system. On Mac OS X this is not necessary. For better performance, we recommend installing the OpenBLAS library. See http://xianyi.github.com/OpenBLAS/ If you are using sparse matrices, also install ARPACK and SuperLU. Caveat: only SuperLU version 5.2 can be used! On Linux-based systems, the following libraries are recommended to be present: OpenBLAS, LAPACK, SuperLU and ARPACK. 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. * 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 detect which relevant libraries are installed on your system (eg. OpenBLAS, LAPACK, SuperLU, ARPACK, etc) and will modify Armadillo’s configuration correspondingly. CMake will also generate a run-time armadillo library, which is a wrapper for all the detected libraries. 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. 6: 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 (not recommended), compile along these lines: g++ example1.cpp -o example1 -O2 -I /home/blah/armadillo-7.200.3/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 (ie. change the 7.200.3 part) and/or if you have unpacked the armadillo archive into a different directory. Replace -lblas with -lopenblas if you have OpenBLAS. On Mac OS X, replace -lblas -llapack with -framework Accelerate 7: Windows: Installation ======================== The installation is comprised of 3 steps: * Step 1: Copy the entire “include” folder to a convenient location and tell your compiler to use that location for header files (in addition to the locations it uses already). Alternatively, you can use the “include” folder directly. * Step 2: Modify “include/armadillo_bits/config.hpp” to indicate which libraries are currently available on your system. For example, if you have LAPACK, BLAS (or OpenBLAS), ARPACK and SuperLU present, uncomment the following lines: #define ARMA_USE_LAPACK #define ARMA_USE_BLAS #define ARMA_USE_ARPACK #define ARMA_USE_SUPERLU If you don’t need sparse matrices, don’t worry about ARPACK or SuperLU. * Step 3: Configure your compiler to link with LAPACK and BLAS (and optionally ARPACK and SuperLU). 8: Windows: Compiling & Linking =============================== Within the “examples” folder, we have included an MSVC project named “example1_win64” which can be used to compile “example1.cpp”. The project needs to be compiled as a 64 bit program: the active solution platform must be set to x64, instead of win32. The MSVC project was tested on 64 bit Windows 7 with Visual C++ 2012. You may need to make adaptations for 32 bit systems, later versions of Windows and/or the compiler. For example, you may have to enable or disable ARMA_BLAS_LONG and ARMA_BLAS_UNDERSCORE defines in “armadillo_bits/config.hpp”. The folder “examples/lib_win64” contains standard LAPACK and BLAS libraries compiled for 64 bit Windows. The compilation was done by a third party. USE AT YOUR OWN RISK. The compiled versions of LAPACK and BLAS were obtained from: http://ylzhao.blogspot.com.au/2013/10/blas-lapack-precompiled-binaries-for.html You can find the original sources for standard BLAS and LAPACK at: http://www.netlib.org/blas/ http://www.netlib.org/lapack/ Faster and/or alternative implementations of BLAS and LAPACK are available: http://xianyi.github.com/OpenBLAS/ http://software.intel.com/en-us/intel-mkl/ http://icl.cs.utk.edu/lapack-for-windows/lapack/ The OpenBLAS and Intel MKL libraries are generally the fastest. For better performance, we recommend the following high-quality C++ compilers: GCC from MinGW: http://www.mingw.org/ GCC from CygWin: http://www.cygwin.com/ Intel C++ compiler: http://software.intel.com/en-us/intel-compilers/ For the GCC compiler, use version 4.6 or later. For the Intel compiler, use version 13.0 or later. For best results we also recommend using an operating system that’s more reliable and more suitable for heavy duty work, such as Mac OS X, or various Linux-based systems: Ubuntu http://www.ubuntu.com/ Debian http://www.debian.org/ OpenSUSE http://www.opensuse.org/ Fedora http://fedoraproject.org/ Scientific Linux http://www.scientificlinux.org/ CentOS http://centos.org/ Red Hat Enterprise Linux http://www.redhat.com/ 9: Support for OpenBLAS and Intel MKL ===================================== Armadillo can use OpenBLAS or Intel Math Kernel Library (MKL) as high-speed replacements for BLAS and LAPACK. In essence this involves linking with the replacement libraries instead of BLAS and LAPACK. You may need to make minor modifications to include/armadillo_bits/config.hpp to make sure Armadillo uses the same integer sizes and style of function names as used by the replacement libraries. Specifically, you may need comment or uncomment the following defines: ARMA_USE_WRAPPER ARMA_BLAS_CAPITALS ARMA_BLAS_UNDERSCORE ARMA_BLAS_LONG ARMA_BLAS_LONG_LONG See the documentation for more information on the above defines. On Linux systems, MKL might be installed in a non-standard location such as /opt which can cause problems during linking. Before installing Armadillo, the system should know where the MKL libraries are located. For example, “/opt/intel/mkl/lib/intel64/”. This can be achieved by setting the LD_LIBRARY_PATH environment variable, or for a more permanent solution, adding the directory locations to “/etc/ld.so.conf”. It may also be possible to store a text file with the locations in the “/etc/ld.so.conf.d” directory. For example, “/etc/ld.so.conf.d/mkl.conf”. If you modify “/etc/ld.so.conf” or create “/etc/ld.so.conf.d/mkl.conf”, you will need to run “/sbin/ldconfig” afterwards. Example of the contents of “/etc/ld.so.conf.d/mkl.conf” on a RHEL 6 system, where Intel MKL version 11.0.3 is installed in “/opt/intel”: /opt/intel/lib/intel64 /opt/intel/mkl/lib/intel64 The default installations of MKL 10.2.2.025 are known to have issues with SELinux, which is turned on by default in Fedora and RHEL. The problem may manifest itself during run-time, where the run-time linker reports permission problems. It is possible to work around the problem by applying an appropriate SELinux type to all MKL libraries. If you have MKL installed and its persistently giving you problems during linking, you can disable the support for MKL by editing the “CMakeLists.txt” file, deleting “CMakeCache.txt” and re-running the CMake based installation. Comment out the lines containing: INCLUDE(ARMA_FindMKL) INCLUDE(ARMA_FindACMLMP) INCLUDE(ARMA_FindACML) 10: Support for ATLAS ===================== Armadillo can use the ATLAS library for faster versions of certain LAPACK and BLAS functions. Not all ATLAS functions are currently used, and as such LAPACK should still be installed. The minimum recommended version of ATLAS is 3.8. Old versions (eg. 3.6) can produce incorrect results as well as corrupting memory, leading to random crashes. Users of older Ubuntu and Debian based systems should explicitly check that ATLAS 3.6 is not installed. It’s better to remove the old version and use the standard LAPACK library. 11: Support for C++11 / C++14 Features ====================================== Armadillo works with compilers supporting the older C++98 and C++03 standards, as well as the newer C++11 and C++14 standards. Armadillo will enable extra features (such as move constructors) when a C++11/C++14 compiler is detected. You can also force Armadillo to make use C++11 features by defining ARMA_USE_CXX11 before #include in your code. You may need to explicitly enable C++11 mode in your compiler. For example, use the -std=c++11 option in gcc & clang. Caveat: use of the C++11 “auto” keyword is not recommended with Armadillo objects and expressions. Armadillo has a template meta-programming framework which creates lots of short lived temporaries that are not handled by auto. 12: API Documentation ===================== Documentation of functions, classes and options is available at: http://arma.sourceforge.net/docs.html The documentation is also in the “docs.html” file in this folder, which can be viewed with a web browser. 13: API Stability and Versioning ================================ Each release of Armadillo has its public API (functions, classes, constants) described in the accompanying API documentation (docs.html) specific to that release. Each release of Armadillo has its full version specified as A.B.C, where A is a major version number, B is a minor version number, and C is a patch level (indicating bug fixes). Within a major version (eg. 7), each minor version has a public API that strongly strives to be backwards compatible (at the source level) with the public API of preceding minor versions. For example, user code written for version 7.100 should work with version 7.200, 7.300, 7.400, etc. However, as later minor versions may have more features (API extensions) than preceding minor versions, user code specifically written for version 7.400 may not work with 7.300. An increase in the patch level, while the major and minor versions are retained, indicates modifications to the code and/or documentation which aim to fix bugs without altering the public API. We don’t like changes to existing public API and strongly prefer not to break any user software. However, to allow evolution, we reserve the right to alter the public API in future major versions of Armadillo while remaining backwards compatible in as many cases as possible (eg. major version 8 may have slightly different public API than major version 7). CAVEAT: any function, class, constant or other code not explicitly described in the public API documentation is considered as part of the underlying internal implementation details, and may change or be removed without notice. (In other words, don’t use internal functionality). 14: Bug Reports and Frequently Asked Questions ============================================== Armadillo has gone through extensive testing and has been successfully used in production environments. However, as with almost all software, it’s impossible to guarantee 100% correct functionality. If you find a bug in the library or the documentation, we are interested in hearing about it. Please make a small and self-contained program which exposes the bug, and then send the program source and the bug description to the developers. The contact details are at: http://arma.sourceforge.net/contact.html Further information about Armadillo as well as answers to frequently asked questions are at: http://arma.sourceforge.net/faq.html 15: MEX Interface to Octave/Matlab ================================== The “mex_interface” folder contains examples of how to interface Octave/Matlab with C++ code that uses Armadillo matrices. 16: Related Software ==================== * MLPACK: C++ library for machine learning and pattern recognition, built on top of Armadillo. http://mlpack.org * libpca: C++ library for principal component analysis http://sourceforge.net/projects/libpca/ * ArmaNpy: interfaces Armadillo matrices with Python http://sourceforge.net/projects/armanpy/ * matlab2cpp: conversion of Matlab code to Armadillo based C++ code https://github.com/jonathf/matlab2cpp

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