Showing 4 open source projects for "test"

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    Context for your AI agents

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  • 1
    Draper

    Draper

    Decorators/view-models for Rails applications

    ...Without Draper, this functionality might have been tangled up in procedural helpers or adding bulk to your models. With Draper decorators, you can wrap your models with presentation-related logic to organize and test this layer of your app much more effectively. Imagine your application has an Article model. With Draper, you'd create a corresponding ArticleDecorator. The decorator wraps the model, and deals only with presentational concerns. In the controller, you decorate the article before handing it off to the view. In the view, you can use the decorator in exactly the same way as you would have used the model. ...
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  • 2
    DeepMind Lab

    DeepMind Lab

    A customizable 3D platform for agent-based AI research

    ...If you use DeepMind Lab in your research and would like to cite the DeepMind Lab environment, we suggest you cite the DeepMind Lab paper. To enable compiler optimizations, pass the flag --compilation_mode=opt, or -c opt for short, to each bazel build, bazel test and bazel run command. The flag is omitted from the examples here for brevity, but it should be used for real training and evaluation where performance matters. DeepMind Lab ships with an example random agent in python/random_agent.py which can be used as a starting point for implementing a learning agent.
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  • 3

    cvsba

    OpenCV wrapper for sba library

    ...The main features are: - Based on sba-1.6, one of the most popular and robust bundle adjustment implementation, which is extensively used and tested by the community - sba installation is not needed since it is included in cvsba - New CMake structure which makes the library compilation, installation and linkage easier - Similar interface than Bundle Adjustment implementation on cv::LevMarqSparse::bundleAdjust() - Include examples to test the library on synthetically generated data - GPL licence
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  • 4
    This project aims to create a complete mesh sequence segmentation environment: a framework to create, test and compare mesh sequence segmentation algorithms. It will also provide building blocks to create these algorithms and a graphical environment.
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