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Awesome GitHub RepositoriesAlgorithm Customization Frameworks

Mechanisms that allow swapping internal algorithmic logic and selection rules to modify data processing.

Distinct from Custom Analytical Algorithms: None of the candidates describe a general architectural framework for swapping algorithm components in an optimization library.

Explore 2 awesome GitHub repositories matching software engineering & architecture · Algorithm Customization Frameworks. Refine with filters or upvote what's useful.

Awesome Algorithm Customization Frameworks GitHub Repositories

AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
  • guofei9987/scikit-optguofei9987 का अवतार

    guofei9987/scikit-opt

    6,583GitHub पर देखें↗

    scikit-opt is a Python optimization library and numerical framework designed to solve complex global optimization problems. It provides a suite of metaheuristic algorithms and tools for finding global minima or maxima of objective functions. The library implements a variety of nature-inspired and swarm intelligence algorithms, including Genetic Algorithms, Particle Swarm Optimization, Differential Evolution, Simulated Annealing, and Ant Colony Optimization. It includes specialized solvers for discrete combinatorial challenges, such as the Traveling Salesman Problem. The framework supports th

    Provides a component-based architecture for swapping internal logic and selection rules within optimization algorithms.

    Python
    GitHub पर देखें↗6,583
  • pytorch/captumpytorch का अवतार

    pytorch/captum

    5,652GitHub पर देखें↗

    Captum is an open-source library for explaining model predictions by attributing them to input features, neurons, and layers using gradient-based and perturbation-based methods. It provides a modular framework for implementing, evaluating, and combining a range of explanation techniques, including gradient-based attribution, perturbation-based analysis, game-theoretic Shapley value approximation, and surrogate model explanations, with support for parallelization and noise stabilization. The library distinguishes itself through its breadth of attribution methods and its support for advanced in

    Provides generic base classes and hooks to implement, benchmark, and share new attribution methods.

    Python
    GitHub पर देखें↗5,652
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