Search Results for "regression" - Page 3
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Showing 124 open source projects for "regression"
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Awesome Graph Classification
Graph embedding, classification and representation learning papers
A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations. Relevant graph classification benchmark datasets are available. Similar collections about community detection, classification/regression tree, fraud detection, Monte Carlo tree search, and gradient boosting papers with implementations. -
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Frontend Regression Validator (FRED)
Visual regression tool used to compare baseline and updated instances
Visual regression tool used to compare baseline and updated instances of a website in a deployment pipeline. FRED is an opensource visual regression tool used to compare two instances of a website. FRED is responsible for automatic visual regression testing, with the purpose of ensuring that functionality is not broken by comparing a current(baseline) and an updated version of a website. -
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Zipline
Zipline, a Pythonic algorithmic trading library
Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies. Quantopian also offers a fully managed service for professionals that includes Zipline, Alphalens, Pyfolio, FactSet data, and more. Installing Zipline is slightly more involved than the average Python... -
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AyeSpy
A performant visual regression testing tool
Aye Spy is a high-performance visual regression tool to catch UI regressions. Aye Spy takes inspiration from existing projects such as Wraith and BackstopJs. We have found visual regression testing to be one of the most effective ways to catch regressions. It's a great tool to have in your pipeline, but the current solutions on the market were missing one key component we felt was essential for a great developer experience performance. -
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CloudTest-Cloud java unit test framework
A redefined framework with new approach and methodology for unit test
CloudTest is a redefined unit testing approach and methodology, which can make your testing jobs become much more easy and efficient. It is a pure java lightweight framework integrated test cases management, test data management, assert management, automation regression, performance monitor and test report in one. -
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AdaNet
Fast and flexible AutoML with learning guarantees
...At each iteration, it measures the ensemble loss for each candidate, and selects the best one to move onto the next iteration. Adaptive neural architecture search and ensemble learning in a single train call. Regression, binary and multi-class classification, and multi-head task support. A tf.estimator.Estimator API for training, evaluation, prediction, and serving models. -
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os-autoinst
OS-level test automation
os-autoinst is a testing framework that enables automated testing of operating systems and applications. It is particularly useful for testing installation processes and system configurations, allowing for comprehensive validation of software behavior in various scenarios. -
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Differencify
Differencify is a library for visual regression testing
Differencify is a library for visual regression testing via comparing your local changes with reference screenshots of your website. It is built on top of chrome headless using Puppeteer. -
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Vim colorschemes
One colorscheme pack to rule them all!
This repository is a large collection of classic and modern Vim color schemes aggregated in one place. Instead of hunting down dozens of separate repos, you can install this bundle and instantly try many popular themes. It is useful for quickly auditioning palettes, testing readability, and finding a scheme that matches your terminal and font. The project organizes color files in a predictable structure so :colorscheme just works. Because it is a collection, it includes both minimalist... -
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The Neural Process Family
This repository contains notebook implementations
...They can learn distributions over functions from data and efficiently make predictions at new inputs with calibrated uncertainty — making them useful for few-shot learning, Bayesian regression, and meta-learning. Each notebook includes theoretical explanations, key building blocks, and executable code that runs directly in Google Colab, requiring no local setup. Implementations rely only on standard dependencies such as NumPy, TensorFlow, and Matplotlib, and provide visualizations of model performance. -
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Page Monitor
capture webpage and diff the dom change with phantomjs
...It exposes a Monitor API with methods for capturing pages and manually comparing two saved states. Overall, it is a practical utility for developers who need automated webpage change detection, regression checks, or historical DOM comparison. -
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benchm-ml
A benchmark of commonly used open source implementations
...It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. The benchmarks cover algorithms like logistic regression, random forest, gradient boosting, and deep neural networks, and they compare across toolkits such as scikit-learn, R packages, xgboost, H2O, Spark MLlib, etc. The repository is structured in logical folders, each corresponding to algorithm categories. -
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Wraith
A responsive screenshot comparison tool
Wraith is a screenshot comparison tool, created by developers at BBC News. Wraith uses a headless browser to create screenshots of webpages on different environments (or at different moments in time) and then creates a diff of the two images; the affected areas are highlighted in blue. There are two main modes for using Wraith, 'capture' mode and 'history' mode. Wraith has some built-in JavaScript and configuration file templates for you to get started with right away. If you wish to take... -
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Active Learning
Framework and examples for active learning with machine learning model
...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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Nightwatch VRT
Visual Regression Testing tools for nightwatch.js
Nightwatch Visual Regression Testing tools for nightwatch.js. Nightwatch VRT extends nightwatch.js with an assertion that captures a screenshot of a DOM element identified by a selector and compares the screenshot against a baseline screenshot. If the baseline screenshot does not exist, it will be created the first time you run the test and the assertion will pass. -
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BackstopJS
Catch CSS curve balls
Visual regression testing for web apps. Supports screen rendering with Chrome-headless. Add your own interactions with the Playwright and Puppeteer scripting. BackstopJS automates visual regression testing of your responsive web UI by comparing DOM screenshots over time. -
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Acceptance/regression testing framework based on full screen automation (via terminal emulation) with integrated unit testing engine (standard Ruby test/unit). Moved to: https://github.com/tsc-collection/tsc-act Now part of GitHub's TSC Collection available at https://github.com/tsc-collection
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Gemini Testing
Utility for regression testing the visual appearance of web pages
Gemini is a utility for regression testing the visual appearance of web pages. Each of the blocks that are being tested may be in one of the determined states. States are tested with the help of chains of step-by-step actions declared in a block's test suites. -
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Git Time Machine
Atom package that allows you to travel back in commit history
...Instead of just opening a commit log or diff, git-time-machine gives an interactive, incremental experience where you can slide through versions and see content changes in place. This is especially helpful when diagnosing when bugs were introduced, exploring regression contexts, or reviewing code evolution visually. For projects with many commits, it accelerates historical exploration, and it is commonly integrated into editors so developers can use it in their working environment. -
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jest-puppe-shots
A Jest plugin for creating screenshots of React components
A Jest plugin for creating screenshots of React components with a little help of Puppeteer. A jest-puppy-shots is a Jest plugin that can help you create Visual Regression tests for your React components. If you ever used Enzyme or Snapshots you will love the jest-puppe-shots. The Snapshot testing approach is really awesome but it has one downside: it cannot be used to make visual regression tests of your components. Thanks to the Puppeteer API it's now possible to use the Chromium browser in Jest and create real screenshots of your components. -
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PhantomCSS
Visual/CSS regression testing with PhantomJS
CSS regression testing. A CasperJS module for automating visual regression testing with PhantomJS 2 or SlimerJS and Resemble.js. For testing Web apps, live style guides, and responsive layouts. PhantomCSS takes screenshots captured by CasperJS and compares them to baseline images using Resemble.js to test for rgb pixel differences. PhantomCSS then generates image diffs to help you find the cause. -
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VBot
JSON based visual regression testing library
A visual regression testing library/tool, aims to quickly automate browser-based tests with minimum development overhead. To test flexibly in different testing scenarios, either testing against web UI wired with backends or just static web pages, VBot exposes API to be used with testing frameworks like mocha or ava, etc. Therefore, the test data variables can be updated according to different conditions, in order to decouple from real backends and avoid false negative results. -
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Accord.NET Framework
Machine learning, computer vision, statistics and computing for .NET
The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and extensive documentation and a wiki help fill in the details. The Accord.NET project provides... -
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Accord.NET Framework
Scientific computing, machine learning and computer vision for .NET
The Accord.NET Framework provides machine learning, mathematics, statistics, computer vision, computer audition, and several scientific computing related methods and techniques to .NET. The project is compatible with the .NET Framework. NET Standard, .NET Core, and Mono. -
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Swift
Selenium Hybrid Automation Framework with BDD tool Cucumber
Mastek has successfully developed a highly configurable Hybrid Automation framework: “Swift” using Selenium tool that offers quick turnaround for Automation Suite Implementation with support of Keyword & Data Driven framework approach. The Automation regression suite can be created or modified using tester friendly keywords for modules, screens, and verifications, thereby ensuring a more effective and efficient Test automation process and better ROI through the complete application life cycle. Swift is also integrated with Behaviour driven developement ( BDD ) tool Cucumber.
