Showing 7 open source projects for "binary compare"

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  • 1
    pixelmatch

    pixelmatch

    The smallest, simplest JavaScript pixel-level image comparison library

    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,...
    Downloads: 15 This Week
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  • 2
    high-performance-go

    high-performance-go

    high performance coding with golang

    High Performance Go is an educational repository focused on writing faster and more memory-efficient Go programs. It teaches developers how to measure performance before attempting optimization. Benchmarking and pprof are used to identify bottlenecks and compare implementation choices. The material examines strings, slices, loops, reflection, empty structs, and memory alignment. It also explores concurrency performance through mutexes, goroutine control, sync.Pool, sync.Once, and sync.Cond. Additional chapters cover compiler optimization, escape analysis, dead-code elimination, binary size, and common Go traps.
    Downloads: 1 This Week
    Last Update:
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  • 3
    VR Ax Java Sources & Build Projects

    VR Ax Java Sources & Build Projects

    VR Adrix Java Works - Paged Lists - Action-Entity Model ...

    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 -...
    Downloads: 0 This Week
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  • 4
    Delphi : VRCalc++ OOSL (Script) and more

    Delphi : VRCalc++ OOSL (Script) and more

    Delphi : VRCalc++ OOSL & + (Paged List, TextEditor, VRAstroVision ...)

    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++...
    Downloads: 1 This Week
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  • 5
    Binarytree

    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.
    Downloads: 0 This Week
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  • 6
    algo

    algo

    50 Code Implementations You Must Know About Data Structures

    ...This multilingual layout helps learners compare syntax and implementation choices while studying the same concepts. The repository is well suited to interview preparation, classroom exercises, and deliberate coding practice.
    Downloads: 2 This Week
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  • 7
    benchm-ml

    benchm-ml

    A benchmark of commonly used open source implementations

    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. ...
    Downloads: 0 This Week
    Last Update:
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