We present Glyph - a Python package for genetic programming based symbolic regression. Glyph is designed for usage let by numerical simulations let by real world experiments. For experimentalists, glyph-remote provides a separation of tasks: a ZeroMQ interface splits the genetic programming optimization task from the evaluation of an experimental (or numerical) run. Glyph can be accessed at http://github.com/ambrosys/glyph . Domain experts are be able to employ symbolic regression in their experiments with ease, even if they are not expert programmers. The reuse potential is kept high by a generic interface design. Glyph is available on PyPI and Github.
翻译:我们介绍Glyph- 基于基于象征性回归的基因编程的Python软件包。 Glyph 的设计是用数字模拟来进行真实世界实验的数值模拟。 对于实验家来说, glyph-remote 提供了一种分离的任务: 零MQ 接口将基因编程优化任务与实验运行( 或数字) 的评估分开。 Glyph 可以在 http:// github.com/ ambrosyss/ glyph 上查阅。 域专家可以轻松地在实验中使用象征性回归, 即使他们不是专家程序设计师。 通用界面设计可以保持较高的再利用潜力。 Glyph 可以在 Pypi 和 Github 上查阅 。