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The Algorithms-Python project is a comprehensive collection of Python implementations for a wide range of algorithms and data structures. It serves primarily as an educational resource for learners and developers who want to understand how algorithms work under the hood. Each implementation is designed with clarity in mind, favoring readability and comprehension over performance optimization. The project covers various domains including mathematics, cryptography, machine learning, sorting, graph theory, and more. With contributions from a large global community, it continually grows and improves through collaboration and peer review. ...

...It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. The gym library is a collection of test problems — environments — that you can use to work out your reinforcement learning algorithms. These environments have a shared interface, allowing you to write general algorithms.

Binarytree is Python library that lets you generate, visualize, inspect and manipulate binary trees. Skip the tedious work of setting up test data, and dive straight into practicing algorithms. Heaps and BSTs (binary search trees) are also supported. Binarytree supports another representation which is more compact but without the indexing properties. Traverse trees using different algorithms.

LZHW Command Line Lossless Compression Tool is a Windows command line tool used to compress and decompress files from and to any form, csv, excel etc without any dependencies or installations. Using an optimized algorithm (LZHW) developed from Lempel-Ziv, Huffman and LZ-Welch algorithms. The tool can work in parallel and most of its code is written in Cython, so it is pretty fast. It is based on python lzhw library. Full tool documentation can be found at: https://mnoorfawi.github.io/lzhw/6%20Using%20the%20lzhw%20command%20line%20tool/ While the documentation for the python library is at: https://mnoorfawi.github.io/lzhw/

...Historically, “baselines” repositories are often used for baseline implementations of reinforcement learning algorithms or reference models (e.g. in the RL domain). If there was an OpenAI “baselines” repo, it might have contained reference implementations for reinforcement learning or model policy baselines to compare new work against. However, I couldn’t locate an active “openai/baselines” in the latest OpenAI repos, so it may have been archived, removed, or merged into other projects. If you meant a different “baselines” (e.g. OpenAI Baselines for reinforcement learning), I can look up that specific one.

This work is some notes of learning and practicing data structures and algorithms. Part I is a brief introduction of basic data structures and algorithms, such as, linked lists, stack, queues, trees, sorting and etc. This book notes about learning data structure and algorithms. It was written in Simplified Chinese but other languages such as English and Traditional Chinese are also working in progress.

Abandoned version of qbc. DOES NOT WORK properly

This is a python implementation that handles floating points correctly,there are still some bugs but I'm working on it . The point was to work around some stuff that made no sense for floats,like 0.1+0.2 == 0.3 is false.

Institute of Technology, Blanchardstown Computer Science code by the class of 2007-2011 on course BN104. In this project we are open sourcing all of our project work to the public in the hopes it can be reused, built-upon, and used in education.

Cosmos (by OpenGenus Foundation) is your personal offline collection of every algorithm and data structure one will ever encounter and use in a lifetime. This provides solutions in various languages spanning C, C++, Java, JavaScript, Swift, Python, Go and others. This work is maintained by a community of hundreds of people and is a massive collaborative effort to bring the readily available coding knowledge offline.