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As there is no test file in this pull request nor any test function or class in the file machine_learning/ao_star.py, please provide doctest for the function _update_node
| def _update_node( | ||
| graph: dict[Any, list[list[Any]]], | ||
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| h: dict[Any, float], |
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Please provide descriptive name for the parameter: h
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| def _ao_star( |
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As there is no test file in this pull request nor any test function or class in the file machine_learning/ao_star.py, please provide doctest for the function _ao_star
| def _ao_star( | ||
| graph: dict[Any, list[list[Any]]], | ||
| node: Any, | ||
| h: dict[Any, float], |
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Please provide descriptive name for the parameter: h
| def ao_star( | ||
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| start: Any, | ||
| h: dict[Any, float], |
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Please provide descriptive name for the parameter: h
1 participant
Describe your change:
This pull request introduces a new implementation of the AO* (And-Or Star) search algorithm for solving AND-OR graphs in the
machine_learning/ao_star.pyfile. The AO* algorithm is useful for problems where nodes can represent both OR decisions and AND decompositions, generalizing standard search trees. The implementation includes documentation, type hints, and several usage examples, making it easy to understand and use.New AO* Algorithm Implementation
machine_learning/ao_star.py, including helper functions for node updates and recursive solving.ao_starfunction, demonstrating how to construct AND-OR graphs and apply the algorithm.__main__block, allowing users to run and observe the algorithm's output directly.Checklist: