Democritus functions[1] for working with algorithms.
[1] Democritus functions are simple, effective, modular, well-tested, and well-documented Python functions.
We use d8s (pronounced "dee-eights") as an abbreviation for democritus (you can read more about this here).
pip install d8s-algorithms
You import the library like:
from d8s_algorithms import *Once imported, you can use any of the functions listed below.
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def amb(validation_function: Callable[..., bool], *args: Any) -> Iterable[Any]: """."""
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def depth_first_traverse( data: Any, get_children_function: Callable[[Any], Optional[Iterable]], *, collect_items_function: Optional[Callable[[Any], Any]] = None ) -> Iterable[Any]: """Traverse the data in a depth-first manner. The get_children_function specifies how children will be identified from each node of the data. The collect_items_function, if provided, allows you to collect items from the data by... returning them from the collect_items_function."""
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def breadth_first_traverse( data: Any, get_children_function: Callable[[Any], Optional[Iterable]], *, collect_items_function: Optional[Callable[[Any], Any]] = None ) -> Iterable[Any]: """Traverse the data in a breadth-first manner. The get_children_function specifies how children will be identified from each node of the data. The collect_items_function, if provided, allows you to collect items from the data by... returning them from the collect_items_function."""
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def genetic_algorithm_run( data: Iterable[Any], scoring_function: Callable[[Any], Union[int, float]], selection_function: Callable[[Dict[Any, Union[int, float]]], Iterable[Any]], mutation_function: Callable[[Iterable[Any]], Iterable[Any]], max_epochs: int, ) -> Dict[Any, Union[int, float]]: """."""
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def genetic_algorithm_best_mutation_function( starting_values: Iterable[Any], generations: int, scoring_function: Callable[[Any], Union[int, float]], mutation_functions: List[Callable[[Any], Any]], ): """Find the best mutation function. The best function is the one which produces values from the starting values... that score the highest (as measured by the scoring_function) after generations."""
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