Search Results for "binary compare"

The smallest, simplest and fastest JavaScript pixel-level image comparison library, originally created to compare screenshots in tests. Features accurate anti-aliased pixels detection and perceptual color difference metrics. Inspired by Resemble.js and Blink-diff. Unlike these libraries, pixelmatch is around 150 lines of code, has no dependencies, and works on raw typed arrays of image data, so it's blazing fast and can be used in any environment (Node or browsers). Compares two images,...

Adrix.NT Java Works :: Sources & Build Projects includes - Paged Lists of Object & Iterators Library for Java - Action / Entity Model for Java - VR Ax AWT Clock Java Component - VR Multi Dimensions Array Library - VR MDArray List Manager Library - VR Adjacency (List | Matrix) Direct Graph Lib - VRJMosaic cells sliding game with automatic resolver - VR RectsWorld Sample Applic - VR Free Chess 2D Applic also contains - Other Utils Libraries - Demo and Test Applications -...

Vincent Radio {Adrix.NT} Sources Library & Applications : Delphi C++ Java VRCalc++ C# VRCalc++ Object Oriented Scripting Language - Engine Source Pascal Code - Delphi Packages Build Prjs - VRCalc++ Scripted System Std RT Library - Guides & Docs (CHM, PDF, DOCX) - VCL & FMX (FireMonkey) Support - Script Test Code (Lang RTL VCL FMX) - Visual Stage Project : VCL & FMX Paged Lists & Iterators : Delphi C++ Java C# Multi-Dim Arrays & Direct Graph Classes : Delphi C++ Java VRCalc++...

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.

This repository is designed to provide a minimal benchmark framework comparing commonly used machine learning libraries in terms of scalability, speed, and classification accuracy. The focus is on binary classification tasks without missing data, where inputs can be numeric or categorical (after one-hot encoding). 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. ...