Open Source Python Algorithms - Page 4

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.

A univariate and multivariate analysis UI. This project is no longer under development. Please use as you wish.

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.

PyVision is a object-oriented Computer Vision Toolkit for researchers that contains vision and machine learning algorithms and algorithm analysis and easily interfaces with scipy/numpy, PIL, opencv and other computer and machine learning libraries.

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.

PythonRobotics is a Python code collection and textbook for learning robotics algorithms through readable examples. It covers practical topics such as localization, mapping, path planning, path tracking, control, SLAM, and autonomous navigation. The project is written to make each algorithm’s core idea easy to understand, rather than hiding the logic behind large frameworks. It keeps dependencies minimal so learners can focus on the math, implementation, and behavior of each robotics method. Visual examples and simulations help users see how algorithms move, estimate, plan, and react. PythonRobotics is especially useful for students, researchers, and engineers who want a hands-on reference for robotics fundamentals in Python.

The Pythonic Data Structures and Algorithms repository by keon is a hands-on collection of implementations of classical data structures and algorithms written in Python. It offers working, often well-commented code for many standard algorithmic problems — from sorting/searching to graph algorithms, dynamic programming, data structures, and more — making it a valuable resource for learning and reference. For students preparing for technical interviews, self-learners brushing up on fundamentals, or developers wanting to understand algorithm internals, this repository provides ready-to-run examples, and can serve as a sandbox to experiment, benchmark, or adapt code. Because it’s in pure Python, it’s easy to read and modify, making it accessible even to those with modest programming experience. The repo helps bridge the gap between theoretical algorithm descriptions and real-world code, giving concrete, working implementations that one can study, debug, or extend.

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

Abandoned version of qbc. DOES NOT WORK properly

A collection of algorithms for querying a set of documents and returning the ones most relevant to the query. The most common use case for these algorithms is, as you might have guessed, to create search engines.

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.

Schborg is off.