
"""simple docstring"""




from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowercase  : Dict						   =							200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
lowercase  : Optional[int]						   =							50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
lowercase  : List[str]						   =							0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:
       lowercase							:					List[Any]	     =				len([g for position, g in enumerate(SCREAMING_SNAKE_CASE__    ) if g == main_target[position]]    )
       return (item, float(SCREAMING_SNAKE_CASE__    ))







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   str:
       lowercase							:					List[Any]	     =				random.randint(0 ,     len(SCREAMING_SNAKE_CASE__    ) - 1    )
       lowercase							:					List[Any]	     =				parent_a[:random_slice] + parent_a[random_slice:]
       lowercase							:					Tuple	     =				parent_a[:random_slice] + parent_a[random_slice:]
       return (child_a, child_a)







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Union[str, Any]:
       lowercase							:					Union[str, Any]	     =				list(SCREAMING_SNAKE_CASE__    )
       if random.uniform(0 ,     1    ) < MUTATION_PROBABILITY:
              lowercase							:					int	     =				random.choice(SCREAMING_SNAKE_CASE__    )
       return "".join(SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     )			->   Dict:
       lowercase							:					List[Any]	     =				[]
       # Generate more children proportionally to the fitness score.
       lowercase							:					Dict	     =				int(parent_a[1] * 100    ) + 1
       lowercase							:					Any	     =				10 if child_n >= 10 else child_n
       for _ in range(SCREAMING_SNAKE_CASE__    ):
              lowercase							:					List[Any]	     =				population_score[random.randint(0 ,     SCREAMING_SNAKE_CASE__    )][0]

              lowercase     ,      lowercase							:					Optional[Any]	     =				crossover(parent_a[0] ,     SCREAMING_SNAKE_CASE__    )
              # Append new string to the population list.
              pop.append(mutate(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )    )
              pop.append(mutate(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )    )
       return pop







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ = True    )			->   int:





       if N_POPULATION < N_SELECTED:
              lowercase							:					Dict	     =				f"{N_POPULATION} must be bigger than {N_SELECTED}"
              raise ValueError(SCREAMING_SNAKE_CASE__    )
       # Verify that the target contains no genes besides the ones inside genes variable.
       lowercase							:					Tuple	     =				sorted({c for c in target if c not in genes}    )
       if not_in_genes_list:
              lowercase							:					Any	     =				f"{not_in_genes_list} is not in genes list, evolution cannot converge"
              raise ValueError(SCREAMING_SNAKE_CASE__    )

       # Generate random starting population.
       lowercase							:					str	     =				[]
       for _ in range(SCREAMING_SNAKE_CASE__    ):
              population.append("""""".join([random.choice(SCREAMING_SNAKE_CASE__    ) for i in range(len(SCREAMING_SNAKE_CASE__    )    )]    )    )

       # Just some logs to know what the algorithms is doing.
       lowercase     ,      lowercase							:					str	     =				0, 0

       # This loop will end when we find a perfect match for our target.
       while True:
              generation += 1
              total_population += len(SCREAMING_SNAKE_CASE__    )

              # Random population created. Now it's time to evaluate.

              # Adding a bit of concurrency can make everything faster,
              #
              # import concurrent.futures
              # population_score: list[tuple[str, float]] = []
              # with concurrent.futures.ThreadPoolExecutor(
              #                                   max_workers=NUM_WORKERS) as executor:
              #     futures = {executor.submit(evaluate, item) for item in population}
              #     concurrent.futures.wait(futures)
              #     population_score = [item.result() for item in futures]
              #
              # but with a simple algorithm like this, it will probably be slower.
              # We just need to call evaluate for every item inside the population.
              lowercase							:					str	     =				[evaluate(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ) for item in population]

              # Check if there is a matching evolution.
              lowercase							:					Any	     =				sorted(SCREAMING_SNAKE_CASE__ ,     key=lambda SCREAMING_SNAKE_CASE__    : x[1] ,     reverse=SCREAMING_SNAKE_CASE__    )
              if population_score[0][0] == target:
                     return (generation, total_population, population_score[0][0])

              # Print the best result every 10 generation.
              # Just to know that the algorithm is working.
              if debug and generation % 10 == 0:
                     print(
                         f"\nGeneration: {generation}"
                         f"\nTotal Population:{total_population}"
                         f"\nBest score: {population_score[0][1]}"
                         f"\nBest string: {population_score[0][0]}"    )

              # Flush the old population, keeping some of the best evolutions.
              # Keeping this avoid regression of evolution.
              lowercase							:					Any	     =				population[: int(N_POPULATION / 3    )]
              population.clear()
              population.extend(SCREAMING_SNAKE_CASE__    )
              # Normalize population score to be between 0 and 1.
              lowercase							:					Optional[int]	     =				[
                  (item, score / len(SCREAMING_SNAKE_CASE__    )) for item, score in population_score
              ]

              # This is selection
              for i in range(SCREAMING_SNAKE_CASE__    ):
                     population.extend(select(population_score[int(SCREAMING_SNAKE_CASE__    )] ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )    )
                     # Check if the population has already reached the maximum value and if so,
                     # break the cycle.  If this check is disabled, the algorithm will take
                     # forever to compute large strings, but will also calculate small strings in
                     # a far fewer generations.
                     if len(SCREAMING_SNAKE_CASE__    ) > N_POPULATION:
                            break


if __name__ == "__main__":
  lowercase  : Optional[int]						   =							(
      """This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
  )
  lowercase  : int						   =							list(
      """ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
      """nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\"""
  )
  lowercase   ,  lowercase   ,  lowercase  : Any						   =							basic(target_str, genes_list)
  print(
      F'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
  )


from __future__ import annotations

import numpy as np







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   tuple[np.ndarray, np.ndarray]:
       lowercase     ,      lowercase							:					Dict	     =				np.shape(SCREAMING_SNAKE_CASE__    )
       if rows != columns:
              lowercase							:					str	     =				(
                  """'table' has to be of square shaped array but got a """
                  f"{rows}x{columns} array:\n{table}"
              )
              raise ValueError(SCREAMING_SNAKE_CASE__    )

       lowercase							:					Any	     =				np.zeros((rows, columns)    )
       lowercase							:					int	     =				np.zeros((rows, columns)    )
       for i in range(SCREAMING_SNAKE_CASE__    ):
              for j in range(SCREAMING_SNAKE_CASE__    ):
                     lowercase							:					Optional[int]	     =				sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE__    )    )
                     if upper[j][j] == 0:
                            raise ArithmeticError("""No LU decomposition exists"""    )
                     lowercase							:					str	     =				(table[i][j] - total) / upper[j][j]
              lowercase							:					Optional[Any]	     =				1
              for j in range(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ):
                     lowercase							:					Any	     =				sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE__    )    )
                     lowercase							:					Tuple	     =				table[i][j] - total
       return lower, upper


if __name__ == "__main__":
  import doctest

  doctest.testmod()
