d8s-algorithms 0.7.0

Democritus functions for working with algorithms.

Democritus Algorithms (a.k.a. d8s-algorithms)

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).

Installation

pip install d8s-algorithms

Usage

You import the library like:

from d8s_algorithms import *

Once imported, you can use any of the functions listed below.

Functions

• def amb(validation_function: Callable[..., bool], *args: Any) -> Iterable[Any]:
      """."""
  

• 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."""
  

• 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."""
  

• 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]]:
      """."""
  

• 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."""
  

Development

👋  If you want to get involved in this project, we have some short, helpful guides below:

• contribute to this project 🥇
• test it 🧪
• lint it 🧹
• explore it 🔭

If you have any questions or there is anything we did not cover, please raise an issue and we'll be happy to help.

Credits

This package was created with Cookiecutter and Floyd Hightower's Python project template.