

"""simple docstring"""




from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
A_				:      int           =			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.
A_				:      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.
A_				:      str           =			0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))

def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   len([g for position, g in enumerate(snake_case__     ) if g == main_target[position]]     )
			return (item, float(snake_case__     ))

def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   random.randint(0		,					len(snake_case__     ) - 1     )
			SCREAMING_SNAKE_CASE__										=   parent_a[:random_slice] + parent_a[random_slice:]
			SCREAMING_SNAKE_CASE__										=   parent_a[:random_slice] + parent_a[random_slice:]
			return (child_a, child_a)

def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   list(snake_case__     )
			if random.uniform(0		,					1     ) < MUTATION_PROBABILITY:
						SCREAMING_SNAKE_CASE__										=   random.choice(snake_case__     )
			return "".join(snake_case__     )

def 						A							(  snake_case__		,					snake_case__		,					snake_case__		,					):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   []
			# Generate more children proportionally to the fitness score.
			SCREAMING_SNAKE_CASE__										=   int(parent_a[1] * 1_00     ) + 1
			SCREAMING_SNAKE_CASE__										=   10 if child_n >= 10 else child_n
			for _ in range(snake_case__     ):
						SCREAMING_SNAKE_CASE__										=   population_score[random.randint(0		,					snake_case__     )][0]

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

def 						A							(  snake_case__		,					snake_case__		,					snake_case__ = True     ):




			'''simple docstring'''


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

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

			# Just some logs to know what the algorithms is doing.
			SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   0, 0

			# This loop will end when we find a perfect match for our target.
			while True:
						generation += 1
						total_population += len(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.
						SCREAMING_SNAKE_CASE__										=   [evaluate(snake_case__		,					snake_case__     ) for item in population]

						# Check if there is a matching evolution.
						SCREAMING_SNAKE_CASE__										=   sorted(snake_case__		,					key=lambda snake_case__     : x[1]		,					reverse=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.
						SCREAMING_SNAKE_CASE__										=   population[: int(N_POPULATION / 3     )]
						population.clear()
						population.extend(snake_case__     )
						# Normalize population score to be between 0 and 1.
						SCREAMING_SNAKE_CASE__										=   [
						    (item, score / len(snake_case__     )) for item, score in population_score
						]

						# This is selection
						for i in range(snake_case__     ):
									population.extend(select(population_score[int(snake_case__     )]		,					snake_case__		,					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(snake_case__     ) > N_POPULATION:
												break


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





"""simple docstring"""




import numpy as np

def 						A							(  snake_case__     ):




			'''simple docstring'''


			return (2 / (1 + np.exp(-2 * vector     ))) - 1


if __name__ == "__main__":
						import doctest

						doctest.testmod()


