Search Results for "regression" - Page 9
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Showing 358 open source projects for "regression"
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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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Retop Math Library
A Java Math Library for creating advanced programs
Regression Polynomial Regression Noise generator Advanced Matrix and Vector classes Physics Collisions Window to visualize functions -
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rem
REM - Regression models based on expectation maximization algorithm
This project implements regression models based on expectation maximization (EM) algorithms in case of missing data -
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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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MOSGWA
genome wide association studies / Bayesian-type information criteria
MOSGWA is a tool for genome wide association studies (GWAS). It currently implements a search strategy using logistic regression and linear regression. -
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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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TestCaseSummaryReport
Agile Project Test Cases and Summary Report Generation
Aim is to Maintain the test cases properly to get the Regression and smoke suite with all recent changes 1. Place all your test cases in a folder. 2. Use the test case template attached 3. Update your test case with Status column to generate Test case summary report 4. Update the 'Suite' column to generate Regression or Smoke suites 4. Click on the QA Doc button (File:TestcaseReport Sheet-TestCaseSummaryReport) -
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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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Win-Brash
A Windows Native Bourne Shell Interpreter
Win-Brash is a recoding, from scratch, of the Bourne shell as a native Microsoft Windows multi-threaded command line application. It uses Bourne shell syntax but recognizes Windows file names. The TAB key helps convert Unix style pathnames to fully valid windows pathnames. It also adds missing backslashes -- to reduce the need to always type two. For Documentation see: http://www.bordoon.com/brash To give brash.exe a try, use the download link to get winbrash-1.2.16.zip. Unzip... -
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fscaret_shiny
UI for fscaret
User Interface (ui) application which implements the automated feature selection provided by the 'fscaret' package of R-environment. -
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This application allow user to predict dissolution profile of solid dispersion systems based on algorithms like symbolic regression, deep neural networks, random forests or generalized boosted models. Those techniques can be combined to create expert system. Application was created as a part of project K/DSC/004290 subsidy for young researchers from Polish Ministry of Higher Education.
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BioPPSy
A biochemical property prediction system
...The program models a given property's dependence on a collection of molecular and structural descriptors using a training set of molecules. Neural networks and support vector regression are available, as well as linear models. The models generated by this analysis can then be used to predict the properties of compounds during the development of new and novel drugs. The program and its databases are all open-source. -
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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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Clus is an open source machine learning system that implements predictive clustering trees (PCTs) and predictive clustering rules (PCRs). PCTs generalize traditional classification and regression trees by altering the heuristic that is used to constr
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This project aims to develop and share fast frequent subgraph mining and graph learning algorithms. Currently we release the frequent subgraph mining package FFSM and later we will include new functions for graph regression and classification package
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L1-norm-robust-regression
linear regression on the basis of minimal absolute deviations error
This software provides a Fortran95 implemented routine to call and obtain the L1-norm regression coefficients. The "standard" linear regression minimizes the squared error. (L2-norm-regression). It allows for a simple solution process, hence its popularity. But it is not robust to outlier points. L1-norm regression is robust with respect to outliers but the solution algorithm is more difficult. It minimizes the sum of absolute deviations. -
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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. -
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sentinela
Visual regression test with Sentinela and webdriver
What is sentinela? With the sentinela it´s possible can perform visual regression test quickly and efficiently. Sentinela make prints from window browser, manager baseline a makes comparisons of test results. -
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...The former Matlab toolbox Gait-CAD was designed for the visualization and analysis of time series and features with a special focus to data mining problems including classification, regression, and clustering.
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Random Bits Regression
Random Bits Regression is a strong general predictor.
We proposed an accurate, robust and fast general predictor (RBR) for regression and classification in big data era. The application of this method is very broad, from science to industry, finance and health. The accuracy and robustness improvement of our method over existing method could bring huge benefits in some critical applications. For example, natural disaster prediction, stock price prediction, personal/population disease prediction. -
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Random Bits Forest
RBF: a Strong Classifier/Regressor for Big Data
We present a classification and regression algorithm called Random Bits Forest (RBF). RBF integrates neural network (for depth), boosting (for wideness) and random forest (for accuracy). It first generates and selects ~10,000 small three-layer threshold random neural networks as basis by gradient boosting scheme. These binary basis are then feed into a modified random forest algorithm to obtain predictions. -
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JStats
JStats is a Java application/applet for statistical testing.
...The following tests are supported: * Parametric tests: T-test, ANOVA, Repeated Measures ANOVA * Non-parametric tests: Wilcoxon Rank-Sum, Wilcoxon Signed-Ranks, Kruskal-Wallis, Friedman * Check if datasets are normally distributed: Jarque-Bera, Shapiro-Wilk * Check if datasets have equal variances: F-test, Bartlett's test, John, Nagao and Sugiura's test * Correlation: Correlation coefficient, Spearman Rank correlation, linear regression * Confidence intervals test * Outliers: Generalized Extreme Studentized (ESD) test, outliers in ANOVA The latest version is available as applet on http://aiguy.org/Statistics.html -
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nccmp
Compare two NetCDF files in-place.
...(gitlab.com/remikz/nccmp) Compares two NetCDF files in-place to find specific variables, dimensions and/or attributes that differ. Highly recommended for any test-driven environment and regression testing large datasets.
