Open Source Python Algorithms - Page 4

Narcissistic number library for Python

Automated computer program grading solutions designed for trainings on computer languages, data structures and algorithms. See http://openjudge.net for details.

The optex module for Python 2.4 helps user scripts to parse command line arguments found in sys.argv. Options are parsed in a different manner than the Unix getopt() and Python getopt module.

pgapack, the parallel genetic algorithm library is a powerfull genetic algorithm library by D. Levine, Mathematics and Computer Science Division Argonne National Laboratory. The library is written in C. PGAPy wraps this library for use with Python.

Image processing tool that encodes pixel data as indices within the first 16.7 million digits of PI (π). Features high-performance Numba-accelerated search and a signature 'film-grain' aesthetic upon reconstruction. ZIP also include 16 MB file with 16,7 mil numbers of PI Benchmark(Single-Thread): Hardware & Environment Apple Silicon: Apple M2 (Mac mini/MacBook) x86_64 Platform: Intel Core Ultra 5 225F (Arrow Lake, 10 Cores) OS 1: Fedora 43 (GNOME) OS 2: Windows 11 Pro (23H2/24H2) Software: Python 3.14.3 + Numba JIT (latest) Results (Lower is better) Platform / OS CPU Time (Seconds) macOS (Native) Apple M2 52.151311 s (in default setup) Fedora Linux Intel Core Ultra 5 225F 58.536457 s (in default Power Management: Balanced) Windows 11 Intel Core Ultra 5 225F 59.681427 s (important! Power Management set as High Performance .. in Balanced is slow)

PRML repository is a respected and well-maintained project that implements the foundational algorithms from the famous textbook Pattern Recognition and Machine Learning by Christopher M. Bishop, providing a practical and accessible Python reference for both students and professionals. Rather than just summarizing concepts, the repository includes working code that demonstrates linear regression and classification, kernel methods, neural networks, graphical models, mixture models with EM algorithms, approximate inference, and sequential data methods — all following the book’s structure and notation. Many of these algorithms are paired with Jupyter notebooks that let users interact with the code, visualize results, and experiment with parameters in a way that deeply strengthens theoretical understanding.

Generic engine to filter information. We wish to show that the power of expression of a filter makes it possible to appreciably reduce the size of the code necessary to extract information and that it is possible in Python.

Functions for creating random (pronouncable) "names", such as are used in a rogue-like. Some of the better names: Kehizarich, Awzecharz, Vizoueurv, Rieri

A collection of phonetic algorithms such as metaphone or soundex for use in Python.

PixLab is a peculiar raster-based graphic editor giving one more additional drawing aspect to an artist. Not only traditional elements of painting, such as color and shape, but also artist's brush dynamics is fixed and displayed.

Es un editor de código ligero y fácil de usar, diseñado específicamente para la programación en Python. Su interfaz intuitiva permite a los usuarios, tanto principiantes como experimentados, escribir, editar y ejecutar código de manera eficiente.

Program by: Ravi Sharma (rvisharma) Prime-Number-Finder-using-Sieve-of-Eratosthenes =============================================== Finds Prime Numbers using Sieve of Eratosthenes Algorithm This Program gives you the prime number upto N Integers, Sieve of Eratosthenes Algorithm is used to determine the Prime Number To find the Prime Number, in the console Shell call the prime_finder(N) function, prime number will be given upto N integer. At first you will be prompted with an input, after that you can call it using function prime_finder(n) example call: >>>prime_finder(10) #output [2,3,5,7] (Int) -> (list)

A proximity forest is a data structure that allows for efficient computation of approximate nearest neighbors of arbitrary data elements in a metric space. See: O'Hara and Draper, "Are You Using the Right Approximate Nearest Neighbor Algorithm?", WACV 2013 (best student paper award). One application of a ProximityForest is given in the following CVPR publication: Stephen O'Hara and Bruce A. Draper, "Scalable Action Recognition with a Subspace Forest," IEEE Conference on Computer Vision and Pattern Recognition, 2012. This source code is provided without warranty and is available under the GPL license. More commercially-friendly licenses may be available. Please contact Stephen O'Hara for license options. Please view the wiki on this site for installation instructions and examples on reproducing the results of the papers.

PyLife is an implementation of the game of life algorithm featuring parallel programming. It uses MPI and python to achieve a consistent software architecture and reliably performance.

PyPlayground is an environment for developing algorithms involving movement in a space of up to three dimensions using Python.

A Python module to learn all the major algorithms on the go! Purely for educational purposes. If you are using Python 2.7 use pip instead. Depending on your permissions, you might need to use pip install, user pygorithm to install. To see all the available functions in a module, you can just type help() with the module name as an argument.

Pytholog Tool (Command line & API) An executable tool, built in python, that enables logic programming and prolog syntax through interactive shell that mimics prolog language and / or RESTful API that can be called from other applications. The tool is based on the python library pytholog which can be found here: https://github.com/mnoorfawi/pytholog The tool starts normally from the command line. Let's look at the arguments that can be specified while initiating the tool: $ ./Pytholog -h usage: Pytholog [-h] [-c CONSULT] -n NAME [-i] [-a] pytholog executable tool: prolog experience at command line and a logic knowledge base with no dependencies optional arguments: -h, --help show this help message and exit -c CONSULT, --consult CONSULT read an existing prolog file/knowledge base -n NAME, --name NAME knowledge base name -i, --interactive start an interactive prolog-like session -a, --api start a flask api

A Python library for easy creation and manipulation of Google Earth KML and KMZ placemark files. Please get your copy from http://pykml.cvs.sourceforge.net/viewvc/pykml/pykml/?view=tar

A neural net module written in python. The aim of the project is to provide a large set of neural network types accessed by an API that is easy to use and powerful.

This project provides a set of Python tools for creating various kinds of neural networks, which can also be powered by genetic algorithms using grammatical evolution. MLP, backpropagation, recurrent, sparse, and skip-layer networks are supported.

A threaded Web graph (Power law random graph) generator written in Python. It can generate a synthetic Web graph of about one million nodes in a few minutes on a desktop machine. It implements a threaded variant of the RMAT algorithm.

Bit operations on integers for Python - fast C implementation of bit extraction, counting, reversal etc.

This is a projct simulation code for my term report. It will be written in Python.

Abandoned version of qbc. DOES NOT WORK properly

Reinforcement-learning is a widely used educational repository that provides implementations, exercises, and solutions for a broad range of reinforcement learning algorithms, designed to complement foundational texts and courses in the field. The project collects popular approaches such as dynamic programming, Monte Carlo methods, temporal difference learning, Q-learning, SARSA, deep Q-networks, and policy gradient techniques, often demonstrated with Python and OpenAI Gym environments so users can experiment with agents learning in simulated tasks. For each algorithm category, the repository pairs conceptual descriptions with runnable code and often illustrated exercises that help solidify understanding by bridging theory with practice. It’s structured to serve learners progressing from basic tabular methods to function approximation and deep learning extensions, making it suitable for students, researchers, or practitioners exploring reinforcement learning fundamentals.