Showing 4 open source projects for "binary"

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  • 1
    Linguist

    Linguist

    Detect blob languages, suppress generated files and generate graphs

    This library is used on GitHub.com to detect blob languages, ignore binary or vendored files, suppress generated files in diffs, and generate language breakdown graphs. Linguist is a Ruby library so you will need a recent version of Ruby installed. There are known problems with the macOS/XCode supplied version of Ruby that causes problems installing some of the dependencies. Accordingly, we highly recommend you install a version of Ruby using Homebrew, rbenv, rvm, ruby-build, asdf or other packaging system, before attempting to install Linguist and the dependencies. ...
    Downloads: 2 This Week
    Last Update:
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  • 2
    MobileHackersWeapons

    MobileHackersWeapons

    Collection of cool tools used by Mobile hackers

    ...Listed projects span many languages and include both desktop utilities and tools that run directly on mobile devices. Android resources cover APK analysis, decompilation, network monitoring, instrumentation, and device interaction. iOS resources include runtime exploration, binary analysis, device automation, and research tooling. The repository is intended as a discovery reference for authorized mobile-security testing and research.
    Downloads: 20 This Week
    Last Update:
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  • 3
    HexaPDF

    HexaPDF

    Versatile PDF creation and manipulation for Ruby

    HexaPDF is a pure Ruby library with an accompanying application for working with PDF files. It was designed with ease of use and performance in mind. It uses lazy loading and lazy computing when possible and tries to produce small PDF files by default.
    Downloads: 4 This Week
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  • 4
    Decision Tree

    Decision Tree

    ID3-based implementation of the ML Decision Tree algorithm

    DecisionTree is a Ruby library that implements decision tree learning with the ID3 information-gain algorithm. It can train models from discrete, continuous, or mixed attribute data. Continuous features are evaluated across possible split points to build threshold-based binary branches. Discrete models classify unique labels and can be rendered for visual inspection. The library supports inconsistent datasets, multiple or symbolic outputs, and fallback values when no branch matches an input. Its Ruleset trainer converts a tree into pruned rules using a held-out portion of the training data. A bagging trainer builds ten rulesets and combines their predictions through voting.
    Downloads: 0 This Week
    Last Update:
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