PyArmadillo: an alternative approach to linear algebra in Python

Related tags

Deep Learningdata-sciencemachine-learninglinear-algebracalculationsarmadillo-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.

You might also like...
QR2Pass-project - A proof of concept for an alternative (passwordless) authentication system to a web server

QR2Pass This is a proof of concept for an alternative (passwordless) authenticat

null 4 Dec 9, 2022
Plover-tapey-tape: an alternative to Plover’s built-in paper tape

plover-tapey-tape plover-tapey-tape is an alternative to Plover’s built-in paper

null 7 May 29, 2022
A commany has recently introduced a new type of bidding, the average bidding, as an alternative to the bid given to the current maximum bidding
A commany has recently introduced a new type of bidding, the average bidding, as an alternative to the bid given to the current maximum bidding

Business Problem A commany has recently introduced a new type of bidding, the average bidding, as an alternative to the bid given to the current maxim

Kübra Bilinmiş 1 Jan 15, 2022
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

Chao Ma 3k Jan 3, 2023
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

Chao Ma 2.8k Feb 12, 2021
A python library to build Model Trees with Linear Models at the leaves.
A python library to build Model Trees with Linear Models at the leaves.

A python library to build Model Trees with Linear Models at the leaves.

Marco Cerliani 212 Dec 30, 2022
Creating a Linear Program Solver by Implementing the Simplex Method in Python with NumPy
Creating a Linear Program Solver by Implementing the Simplex Method in Python with NumPy

Creating a Linear Program Solver by Implementing the Simplex Method in Python with NumPy Simplex Algorithm is a popular algorithm for linear programmi

Reda BELHAJ 2 Oct 12, 2022
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

H2O H2O is an in-memory platform for distributed, scalable machine learning. H2O uses familiar interfaces like R, Python, Scala, Java, JSON and the Fl

H2O.ai 6.1k Jan 5, 2023
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Machine Learning From Scratch About Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The purpose

Erik Linder-Norén 21.8k Jan 9, 2023
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 Repository https://pyarma.sourceforge.io
Employee-Managment - Company employee registration software in the face recognition system

Employee-Managment Company employee registration software in the face recognitio

Alireza Kiaeipour 7 Jul 10, 2022
An Implicit Function Theorem (IFT) optimizer for bi-level optimizations

iftopt An Implicit Function Theorem (IFT) optimizer for bi-level optimizations. Requirements Python 3.7+ PyTorch 1.x Installation $ pip install git+ht

The Money Shredder Lab 2 Dec 02, 2021
A python-image-classification web application project, written in Python and served through the Flask Microframework

A python-image-classification web application project, written in Python and served through the Flask Microframework. This Project implements the VGG16 covolutional neural network, through Keras and

Gerald Maduabuchi 19 Dec 12, 2022
TorchOk - The toolkit for fast Deep Learning experiments in Computer Vision

TorchOk - The toolkit for fast Deep Learning experiments in Computer Vision

52 Dec 23, 2022
Open-Domain Question-Answering for COVID-19 and Other Emergent Domains

Open-Domain Question-Answering for COVID-19 and Other Emergent Domains This repository contains the source code for an end-to-end open-domain question

7 Sep 27, 2022
Implementation of "Selection via Proxy: Efficient Data Selection for Deep Learning" from ICLR 2020.

Selection via Proxy: Efficient Data Selection for Deep Learning This repository contains a refactored implementation of "Selection via Proxy: Efficien

Stanford Future Data Systems 70 Nov 16, 2022
GLIP: Grounded Language-Image Pre-training

GLIP: Grounded Language-Image Pre-training Updates 12/06/2021: GLIP paper on arxiv https://arxiv.org/abs/2112.03857. Code and Model are under internal

Microsoft 862 Jan 01, 2023
Python scripts form performing stereo depth estimation using the HITNET model in Tensorflow Lite.

TFLite-HITNET-Stereo-depth-estimation Python scripts form performing stereo depth estimation using the HITNET model in Tensorflow Lite. Stereo depth e

Ibai Gorordo 22 Oct 20, 2022
PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech

PortaSpeech - PyTorch Implementation PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech. Model Size Module Nor

Keon Lee 279 Jan 04, 2023
Character-Input - Create a program that asks the user to enter their name and their age

Character-Input Create a program that asks the user to enter their name and thei

PyLaboratory 0 Feb 06, 2022
Txt2Xml tool will help you convert from txt COCO format to VOC xml format in Object Detection Problem.

TXT 2 XML All codes assume running from root directory. Please update the sys path at the beginning of the codes before running. Over View Txt2Xml too

Nguyễn Trường Lâu 4 Nov 24, 2022
Doods2 - API for detecting objects in images and video streams using Tensorflow

DOODS2 - Return of DOODS Dedicated Open Object Detection Service - Yes, it's a b

Zach 101 Jan 04, 2023
Implementation of ML models like Decision tree, Naive Bayes, Logistic Regression and many other

ML_Model_implementaion Implementation of ML models like Decision tree, Naive Bayes, Logistic Regression and many other dectree_model: Implementation o

Anshuman Dalai 3 Jan 24, 2022
Improving Convolutional Networks via Attention Transfer (ICLR 2017)

Attention Transfer PyTorch code for "Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Tran

Sergey Zagoruyko 1.4k Dec 23, 2022
RADIal is available now! Check the download section

Latest news: RADIal is available now! Check the download section. However, because we are currently working on the data anonymization, we provide for

valeo.ai 55 Jan 03, 2023
Implementation for paper MLP-Mixer: An all-MLP Architecture for Vision

MLP Mixer Implementation for paper MLP-Mixer: An all-MLP Architecture for Vision. Give us a star if you like this repo. Author: Github: bangoc123 Emai

Ngoc Nguyen Ba 86 Dec 10, 2022
PyTorch code accompanying our paper on Maximum Entropy Generators for Energy-Based Models

Maximum Entropy Generators for Energy-Based Models All experiments have tensorboard visualizations for samples / density / train curves etc. To run th

Rithesh Kumar 135 Oct 27, 2022
Minimisation of a negative log likelihood fit to extract the lifetime of the D^0 meson (MNLL2ELDM)

Minimisation of a negative log likelihood fit to extract the lifetime of the D^0 meson (MNLL2ELDM) Introduction The average lifetime of the $D^{0}$ me

Son Gyo Jung 1 Dec 17, 2021
The official homepage of the COCO-Stuff dataset.

The COCO-Stuff dataset Holger Caesar, Jasper Uijlings, Vittorio Ferrari Welcome to official homepage of the COCO-Stuff [1] dataset. COCO-Stuff augment

Holger Caesar 715 Dec 31, 2022
2021搜狐校园文本匹配算法大赛 分比我们低的都是帅哥队

sohu_text_matching 2021搜狐校园文本匹配算法大赛Top2：分比我们低的都是帅哥队 本repo包含了本次大赛决赛环节提交的代码文件及答辩PPT，提交的模型文件可在百度网盘获取（链接：https://pan.baidu.com/s/1T9FtwiGFZhuC8qqwXKZSNA ，

hflserdaniel 43 Oct 01, 2022