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
A project that uses optical flow and machine learning to detect aimhacking in video clips.

waldo-anticheat A project that aims to use optical flow and machine learning to visually detect cheating or hacking in video clips from fps games. Che

waldo.vision 542 Dec 03, 2022
The code from the paper Character Transformations for Non-Autoregressive GEC Tagging

Character Transformations for Non-Autoregressive GEC Tagging Milan Straka, Jakub Náplava, Jana Straková Charles University Faculty of Mathematics and

ÚFAL 5 Dec 10, 2022
Official code for Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset

Official code for our Interspeech 2021 - Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset [1]*. Visually-grounded spoken language datasets c

Ian Palmer 3 Jan 26, 2022
A toy project using OpenCV and PyMunk

A toy project using OpenCV, PyMunk and Mediapipe the source code for my LindkedIn post It's just a toy project and I didn't write a documentation yet,

Amirabbas Asadi 82 Oct 28, 2022
Forecasting directional movements of stock prices for intraday trading using LSTM and random forest

Forecasting directional movements of stock-prices for intraday trading using LSTM and random-forest https://arxiv.org/abs/2004.10178 Pushpendu Ghosh,

Pushpendu Ghosh 270 Dec 24, 2022
sktime companion package for deep learning based on TensorFlow

NOTE: sktime-dl is currently being updated to work correctly with sktime 0.6, and wwill be fully relaunched over the summer. The plan is Refactor and

sktime 573 Jan 05, 2023
Simple (but Strong) Baselines for POMDPs

Recurrent Model-Free RL is a Strong Baseline for Many POMDPs Welcome to the POMDP world! This repo provides some simple baselines for POMDPs, specific

Tianwei V. Ni 172 Dec 29, 2022
Kroomsa: A search engine for the curious

Kroomsa A search engine for the curious. It is a search algorithm designed to en

Wingify 7 Jun 20, 2022
PPLNN is a Primitive Library for Neural Network is a high-performance deep-learning inference engine for efficient AI inferencing

PPLNN is a Primitive Library for Neural Network is a high-performance deep-learning inference engine for efficient AI inferencing

943 Jan 07, 2023
A Deep Learning based project for creating line art portraits.

ArtLine The main aim of the project is to create amazing line art portraits. Sounds Intresting,let's get to the pictures!! Model-(Smooth) Model-(Quali

Vijish Madhavan 3.3k Jan 07, 2023
An open source library for face detection in images. The face detection speed can reach 1000FPS.

libfacedetection This is an open source library for CNN-based face detection in images. The CNN model has been converted to static variables in C sour

Shiqi Yu 11.4k Dec 27, 2022
Example Of Fine-Tuning BERT For Named-Entity Recognition Task And Preparing For Cloud Deployment Using Flask, React, And Docker

Example Of Fine-Tuning BERT For Named-Entity Recognition Task And Preparing For Cloud Deployment Using Flask, React, And Docker This repository contai

Nikita 12 Dec 14, 2022
Implementation of Geometric Vector Perceptron, a simple circuit for 3d rotation equivariance for learning over large biomolecules, in Pytorch. Idea proposed and accepted at ICLR 2021

Geometric Vector Perceptron Implementation of Geometric Vector Perceptron, a simple circuit with 3d rotation equivariance for learning over large biom

Phil Wang 59 Nov 24, 2022
A working implementation of the Categorical DQN (Distributional RL).

Categorical DQN. Implementation of the Categorical DQN as described in A distributional Perspective on Reinforcement Learning. Thanks to @tudor-berari

Florin Gogianu 98 Sep 20, 2022
Machine learning, in numpy

numpy-ml Ever wish you had an inefficient but somewhat legible collection of machine learning algorithms implemented exclusively in NumPy? No? Install

David Bourgin 11.6k Dec 30, 2022
Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking

Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking (CVPR 2021) Pytorch implementation of the ArTIST motion model. In this repo

Fatemeh 38 Dec 12, 2022
RGBD-Net - This repository contains a pytorch lightning implementation for the 3DV 2021 RGBD-Net paper.

[3DV 2021] We propose a new cascaded architecture for novel view synthesis, called RGBD-Net, which consists of two core components: a hierarchical depth regression network and a depth-aware generator

Phong Nguyen Ha 4 May 26, 2022
MoCoGAN: Decomposing Motion and Content for Video Generation

MoCoGAN: Decomposing Motion and Content for Video Generation This repository contains an implementation and further details of MoCoGAN: Decomposing Mo

Sergey Tulyakov 514 Dec 18, 2022
Unity Propagation in Bayesian Networks Handling Inconsistency via Unity Smoothing

This repository contains the scripts needed to generate the results from the paper Unity Propagation in Bayesian Networks Handling Inconsistency via U

0 Jan 19, 2022
Styleformer - Official Pytorch Implementation

Styleformer -- Official PyTorch implementation Styleformer: Transformer based Generative Adversarial Networks with Style Vector(https://arxiv.org/abs/

Jeeseung Park 159 Dec 12, 2022