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Awesome GitHub RepositoriesArray Shape Validation

Defines strict structures for arrays by specifying required keys, optional keys, and value types.

Distinguishing note: Focuses on static analysis of array structures rather than runtime array processing.

Explore 4 awesome GitHub repositories matching testing & quality assurance · Array Shape Validation. Refine with filters or upvote what's useful.

Awesome Array Shape Validation GitHub Repositories

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  • phpstan/phpstanphpstan 的头像

    phpstan/phpstan

    13,999在 GitHub 上查看↗

    This project is a static analysis engine and type checker designed for PHP codebases. It evaluates source code structure and type annotations to identify potential bugs, type mismatches, and logic errors without executing the application. By parsing code into an abstract syntax tree and applying a rule-based validation framework, it enforces code quality and safety standards across a project. What distinguishes this tool is its sophisticated type inference engine, which models dynamic language features, magic methods, and conditional types to maintain accuracy even in unconventional code. It

    Defines strict structures for arrays by specifying required keys, optional keys, and value types for each element.

    PHPphpphp7phpstan
    在 GitHub 上查看↗13,999
  • dask/daskdask 的头像

    dask/dask

    13,746在 GitHub 上查看↗

    Dask 是一个并行计算框架和分布式任务调度器,旨在将 Python 数据科学工作流从单机扩展到大型集群。它作为一个集群资源管理器,通过将任务及其依赖项表示为有向无环图来编排计算逻辑。这种架构允许系统在管理复杂执行要求的同时,自动将工作负载分配到可用硬件上。 该项目通过一个延迟评估引擎脱颖而出,该引擎将数据操作推迟到明确请求时才执行,从而实现全局图优化和高效的资源分配。它结合了内存感知数据溢出功能,以防止在处理超过可用内存的数据集时系统崩溃,并利用任务图融合将操作序列组合成单个执行步骤,从而最大限度地减少调度开销和节点间通信。 该平台为大规模数据分析提供了全面的功能面,包括对分布式机器学习、高性能计算集成和并行数据处理的支持。它提供了用于集群生命周期管理、性能分析和任务执行实时监控的广泛工具。用户可以在各种基础设施上部署这些环境,包括本地硬件、云提供商、容器化系统和高性能计算集群。

    Registers custom functions to execute automatically whenever an array is created, enabling automated logging, debugging, or validation.

    Pythondasknumpypandas
    在 GitHub 上查看↗13,746
  • srush/tensor-puzzlessrush 的头像

    srush/Tensor-Puzzles

    4,162在 GitHub 上查看↗

    Tensor-Puzzles 是一套教育练习和数值计算教程,旨在掌握 PyTorch 中的张量运算和广播规则。它作为一个实现训练器,用户通过重新实现深度学习数学原语来练习将数学公式转换为代码。 该项目利用一套基于约束的练习,限制可用的库调用以强制使用特定的张量原语。这些挑战被结构化为顺序谜题,要求用户使用模块化实现模式解决任务,其中复杂函数被分解为更简单的依赖运算。 通过集成的 PyTorch 执行环境确保正确性,该环境使用参考实现验证和数值容差检查。系统验证用户定义的输出是否与参考结果匹配,并遵守标准的多元数组广播规则。

    Provides runtime verification to ensure tensor dimensions adhere to broadcasting alignment rules.

    Jupyter Notebookmachine-learningpuzzletorch
    在 GitHub 上查看↗4,162
  • xtensor-stack/xtensorxtensor-stack 的头像

    xtensor-stack/xtensor

    3,748在 GitHub 上查看↗

    xtensor is a C++ multidimensional array library for numerical computing that provides N-dimensional containers with an interface mirroring the NumPy API. It utilizes a lazy evaluation expression engine to defer numerical computations until assignment, which minimizes memory allocations and intermediate copies. The library features a foreign memory array adaptor that allows it to wrap external buffers, such as NumPy arrays, to perform numerical operations in-place without duplicating data. It further optimizes performance through lazy broadcasting and a system that manages the lifetime of temp

    Checks if two objects possess the same shape or match a target shape at runtime.

    C++c-plus-plus-14multidimensional-arraysnumpy
    在 GitHub 上查看↗3,748
  1. Home
  2. Testing & Quality Assurance
  3. Array Shape Validation

探索子标签

  • Construction HooksMechanisms to execute custom logic automatically upon the creation of array structures. **Distinct from Array Shape Validation:** Distinct from Array Shape Validation: focuses on lifecycle hooks during construction rather than static shape verification.
  • Runtime Shape VerificationValidates that array dimensions match expected shapes during execution. **Distinct from Array Shape Validation:** Distinct from Array Shape Validation: focuses on runtime verification of object shapes rather than static schema analysis.