Open Source Python Algorithms - Page 2
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View 5728 business solutionsBrowse free open source Python Algorithms and projects below. Use the toggles on the left to filter open source Python Algorithms by OS, license, language, programming language, and project status.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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No-Nonsense Code-to-Cloud Security for Devs | AikidoAikido provides a unified security platform for developers, combining 12 powerful scans like SAST, DAST, and CSPM. AI-driven AutoFix and AutoTriage streamline vulnerability management, while runtime protection blocks attacks.
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ngram is a module to compute the similarity between two strings. It is different to python's "difflib.SequenceMatcher" in that it cares more about the size of both strings. ngram is an port and extension of the perl module called "String::Trigram
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3D Box rotation
Simple example of draw and rotate 3D box
Simple source .java file; .bat for fast re-compile and run; and pre-compiled .jar Java program with example from scratch writed in Notepad++ without Eclipse, etc., How to draw and rotate 3D box most simple way. Rotation speed regulated in simple Loop with 10 ms sleep. Use Java version 8 (OpenJDK 8, OracleJDK 8, OracleJRE 8, ..). Higher versions have an anti-aliasing error in the BufferedImage ( Windows 10 ). Python version with tkinter and math imports. Including calculated faces, moving lights and shadows only with CPU. -
Secure remote access solution to your private network, in the cloud or on-prem.OpenVPN is here to bring simple, flexible, and cost-effective secure remote access to companies of all sizes, regardless of where their resources are located.
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Based on the introduction of Genetic Algorithms in the excellent book "Collective Intelligence" I have put together some python classes to extend the original concepts.
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The Automatic Model Optimization Reference Implementation, AMORI, is a framework that integrates the modelling and the optimization processes by providing a plug-in interface for both. A genetic algorithm and Markov simulations are currently implemented.
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Active Learning is a Python-based research framework developed by Google for experimenting with and benchmarking various active learning algorithms. It provides modular tools for running reproducible experiments across different datasets, sampling strategies, and machine learning models. The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. The main experiment runner (run_experiment.py) supports a wide range of configurations, including batch sizes, dataset subsets, model selection, and data preprocessing options. It includes several established active learning strategies such as uncertainty sampling, k-center greedy selection, and bandit-based methods, while also allowing for custom algorithm implementations. The framework integrates with both classical machine learning models (SVM, logistic regression) and neural networks.
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AsyncSocket is a python module used for asynchronous socket connections that supports connection and read/write timeouts.
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Belkerda
a customizable number-guessing system
Belkerda is a simple Python AI program that takes a user's input, builds a log of random numbers, picks a random entry, and displays it. If it is correct, then it reenters that number back into the log several times, overwriting the original, random numbers. If it is not, however, it overwrites a lower amount of entries. -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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Binarytree
Python library for studying Binary Trees
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. -
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This is a Python script for Blender which uses short (quaternion-based, floretion-based) algorithms to draw curves in space. The user can create new shapes and curves by setting a variety of parameters.
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Blender Multibool, Searching for Fractal
Multibool function is slow ;)
Booleans all meshes on the screen. Plus: A fractal generator and attractor generator with a delicious SEARCH function ;) <meta name="google-site-verification" content="cULQewNGfx-aXCQR9fTJZ1Z7DXbLA5GjqjvAfF_oH5I" /> -
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Modules for developing, configuring and running a computation based on function blocks entirely in Python. Function block based computation is a data, event and state driven approach to data processing.
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"Blue Planet" is a research project simulating the behaviour and darwinian evolution of unicellular lifeforms, each controlled by its own genetic program. Moreover, "Blue Planet Inhabitants" are suited for swarm intelligence and swarm research.
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CSST
Cascade and Sharing Survival Trees, an ensemble for survival analysis
Cascading and Sharing Survival Trees (CSST) is a tree-based enseble that allows to efficiently analize survival data. It is a strightforward extension of the CS4 method for lifetime collections of data. The CSST software comes along with its companion the CSST Prediction tool, to use the ensemble prediction in everyday life. Please, refer to the user's manual for further information. -
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An experimental language designed to be very simple, but expressive enough to represent mathematical constructions and have strong introspective capabilities.
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Clone Digger is a duplicate code detection tool, which supports Python and Java languages. Discovered clones can differ in small subexpressions; comments and whitespaces are ignored. Clone digger is platform-independent and is written in Python language
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Coach
Enables easy experimentation with state of the art algorithms
Coach is a python framework that models the interaction between an agent and an environment in a modular way. With Coach, it is possible to model an agent by combining various building blocks, and training the agent on multiple environments. The available environments allow testing the agent in different fields such as robotics, autonomous driving, games and more. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms and allows simple integration of new environments to solve. Coach collects statistics from the training process and supports advanced visualization techniques for debugging the agent being trained. Coach supports many state-of-the-art reinforcement learning algorithms, which are separated into three main classes - value optimization, policy optimization, and imitation learning. Coach supports a large number of environments which can be solved using reinforcement learning. -
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Cryptography Tools
Classic & Modern Cryptography tools
Cryptography Tools is a project to develop demonstration tools on classic (currently Caesar and Playfair) & modern crypto-systems, including private & public key encryptions, digital signatures, cryptographic hashes and authenticated encryption. -
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DEBay
Deconvolutes qPCR data to estimate cell-type-specific gene expression
DEBay: Deconvolution of Ensemble through Bayes-approach DEBay estimates cell type-specific gene expression by deconvolution of quantitative PCR data of a mixed population. It will be useful in experiments where the segregation of different cell types in a sample is arduous, but the proportion of different cell types in the sample can be measured. DEBay uses the population distribution data and the qPCR data to calculate the relative expression of the target gene in different cell types in the sample. The user manual of DEBay: https://sourceforge.net/projects/debay/files/UserManual.pdf Sample data: https://sourceforge.net/projects/debay/files/Test_data/ Citation Information: Vimalathithan Devaraj, Biplab Bose. DEBay: A computational tool for deconvolution of quantitative PCR data for estimation of cell type-specific gene expression in a mixed population. Heliyon, 2020, 6(7), e04489. https://doi.org/10.1016/j.heliyon.2020.e04489 -
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Data Algorithm/leetcode/lintcode
Data Structure and Algorithm notes
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. -
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Detectron2
Next-generation platform for object detection and segmentation
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll open source more research projects in this way. It trains much faster. Models can be exported to TorchScript format or Caffe2 format for deployment. With a new, more modular design, Detectron2 is flexible and extensible, and able to provide fast training on single or multiple GPU servers. Detectron2 includes high-quality implementations of state-of-the-art object detection. -
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Elementary Algorithms
Book of elementary algorithms and data structures
This book introduces elementary algorithms and data structure. It includes side-by-side comparison of purely functional realization and their imperative counterpart. From 2020/12, I started re-writing this book. The PDF can be downloaded for preview (EN, 中文). The 1st edition in Chinese (中文) was published in 2017. I recently switched my focus to the Mathematics of programming, the new book is also available in (github). To build the book in PDF format from the sources, you need the following software pre-installed, TeXLive, The book is built with XeLaTeX, a Unicode friendly version of TeX. You need the GNU make tool, in Debian/Ubuntu like Linux, it can be installed through the apt-get command. -
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FATE
An industrial grade federated learning framework
FATE (Federated AI Technology Enabler) is the world's first industrial grade federated learning open source framework to enable enterprises and institutions to collaborate on data while protecting data security and privacy. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). Supporting various federated learning scenarios, FATE now provides a host of federated learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning. FATE became open-source in February 2019. FATE TSC was established to lead FATE open-source community, with members from major domestic cloud computing and financial service enterprises. FedAI is a community that helps businesses and organizations build AI models effectively and collaboratively, by using data in accordance with user privacy protection, data security, data confidentiality and government regulations. -
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A cross-platform library that computes fast and accurate SIFT image features. libsiftfast provides Octave/Matlab scripts, a command line interface, and a python interface (siftfastpy). Optimized with SIMD instructions and OpenMP .
