PyArmadillo: an alternative approach to linear algebra in Python

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Deep Learning data-science machine-learning linear-algebra calculations armadillo-library
Overview

PyArmadillo

PyArmadillo: an alternative approach to linear algebra in Python

PyArmadillo is a linear algebra library for the Python language, with an emphasis on ease of use. It aims to provide a high-level syntax and functionality deliberately similar to Matlab/Octave, allowing mathematical operations to be expressed in a familiar and natural manner. PyArmadillo provides objects for matrices and cubes, as well as over 200 associated functions for manipulating data stored in the objects. All functions are accessible in one flat structure. Integer, floating point and complex numbers are supported. Various matrix factorisations are provided through integration with LAPACK, or one of its high performance drop-in replacements such as Intel MKL or OpenBLAS.

While frameworks such as NumPy and SciPy are available for Python, they tend to be unnecessarily verbose and cumbersome to use from a linear algebra point of view. These frameworks require users to handle data types that are not immediately intuitive, have a structure that complicates the use of common functions, and use syntax that considerably differs from Matlab.

This library is co-led by Jason Rumengan, me and Conrad Sanderson.

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Comments
  • may you do me a favor. i wanna transfer matlab function to python in pyarma

    may you do me a favor. i wanna transfer matlab function to python in pyarma

    matlab function ` function output =NN_F(input)

    layer_nodes_num=[200,200,150,150]; bias=0.00001; %input=traindata(:,1); pre_layer_nodes_num=length(input); pre_layer_nodes_value=input'; % input for l=1:length(layer_nodes_num) > curr_layer_nodes_num=layer_nodes_num(l);

    clear curr_layer_nodes_value;
for it=1:curr_layer_nodes_num
    curr_node_input_weight=randn(1,pre_layer_nodes_num);
    xx=sum(pre_layer_nodes_value.*curr_node_input_weight);
    curr_layer_nodes_value(it)=tanh(xx/2.5);
end
pre_layer_nodes_value=curr_layer_nodes_value;
pre_layer_nodes_num=curr_layer_nodes_num;

    end output=curr_layer_nodes_value; end `

    opened by luyifanlu 6
  • Need thoughts on v0.500.0!

    Need thoughts on v0.500.0!

    Hi community,

    Thanks for the support! ~~v0.500.0 is in preparation.~~

    ~~Feel free to give out some thoughts on PyArmadillo v0.400.0 after having a play!~~

    We are happy to announce v0.500.0 is released! 🥳 🥳 🥳

    You are more than welcome to use our library in your own projects and other work :D

    opened by terryyz 6
Releases(v0.500.0)

  • v0.500.0(Feb 10, 2021)

    v0.500.0 Updates:

    • instances of mat and cube are initialised to contain zero-valued elements by default
    • added standalone zeros(), ones(), randu(), randn(), eye()
    • added pyarma_rng.set_seed(value) and pyarma_rng.set_seed_random()
    • added extra forms for lu(), qr(), qr_econ(), qz(), svd_econ()
    • added subscripting for size objects
    • range() renamed to spread() to prevent conflicts with built-in range() in Python
    • for solve(), solve_opts_* flags renamed to solve_opts.* (eg. solve_opts_fast is now solve_opts.fast)
    • for mat and cube constructors, fill_* flags renamed to fill.* (eg. fill_zeros is now fill.zeros)

    Download

    For downloading the packages, please visit here

    Source code(tar.gz)
    Source code(zip)

  • v0.490.0(Feb 8, 2021)

  • v0.400.0(Feb 3, 2021)

    Installation Notes

    • See the README file in the .tar.xz package for full installation instructions

    • Installation requirements:

      • at least Python 3.6; the minimum recommended version is Python 3.8
      • a C++ compiler that supports at least the C++11 standard
      • at least 8 GB of RAM
      • 64-bit CPU, preferably with 4+ cores
      • OpenBLAS and LAPACK

    • If you encounter any bugs or regressions, please report them

    • If you use PyArmadillo in your research and/or software, please cite the associated papers; citations are useful for the continued development and maintenance of the library

    • Linux based operating systems (eg. Fedora, Ubuntu, CentOS, Red Hat, Debian, etc)

      • Before installing PyArmadillo, first install OpenBLAS, LAPACK, Python 3, and pip3, along with the corresponding development/header files

      • On CentOS 8 / RHEL 8, the CentOS PowerTools repository may first need to be enabled: dnf config-manager --set-enabled powertools

      • Recommended packages to install before installing PyArmadillo: Fedora, CentOS, RHEL: gcc-c++, libstdc++-devel, openblas-devel, lapack-devel, python3-devel, python3-pip Ubuntu and Debian: g++, libopenblas-dev, liblapack-dev, python3-dev, python3-pip

    • macOS

      • Before installing PyArmadillo, install Xcode (version 8 or later) and then type the following command in a terminal window: xcode-select --install

      • Xcode command-line tools include the Python 3 development files, but pip3 needs to be updated: pip3 install --user --upgrade pip

      • The "Accelerate" framework is used for accessing BLAS and LAPACK functions; see the README file in the package for more information

    • Windows (x64)

      • Before installing Pyarmadillo, fist install Microsoft Visual Studio (2019 or later) and use the x64 Native Tools Command Prompt

      • The PyArmadillo package contains pre-compiled OpenBLAS 0.3.10, which is used for accessing BLAS and LAPACK functions

      • Alternative implementations and/or distributions of BLAS and LAPACK are available at:

        • http://software.intel.com/en-us/intel-mkl/
        • http://icl.cs.utk.edu/lapack-for-windows/lapack/
        • http://ylzhao.blogspot.com.au/2013/10/blas-lapack-precompiled-binaries-for.html

      • Caveat: 32-bit Windows (x86) is currently not supported

      • Caveat: for any high performance scientific/engineering workloads, we strongly recommend using a Linux based operating system.

    Source code(tar.gz)
    Source code(zip)
    pyarmadillo-0.400.0.tar.xz(6.56 MB)
Owner
Terry Zhuo
Undergraduate @UNSWComputing; RA @MonashNLP
Terry Zhuo
GitHub https://pyarma.sourceforge.io
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