




from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_lowerCamelCase      :  List[Any]      						=						2_0_0
# 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.
_lowerCamelCase      :  List[str]      						=						5_0
# 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.
_lowerCamelCase      :  str      						=						0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))




def 							_UpperCAmelCase  (UpperCamelCase_ : str      ,    UpperCamelCase_ : str       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Union[str, Any]										=      len([g for position, g in enumerate(UpperCamelCase_       ) if g == main_target[position]]       )
				return (item, float(UpperCamelCase_       ))




def 							_UpperCAmelCase  (UpperCamelCase_ : str      ,    UpperCamelCase_ : str       ):
				'''simple docstring'''
				_lowerCAmelCase    :    List[Any]										=      random.randint(0      ,    len(UpperCamelCase_       ) - 1       )
				_lowerCAmelCase    :    List[Any]										=      parent_a[:random_slice] + parent_a[random_slice:]
				_lowerCAmelCase    :    str										=      parent_a[:random_slice] + parent_a[random_slice:]
				return (child_a, child_a)




def 							_UpperCAmelCase  (UpperCamelCase_ : str      ,    UpperCamelCase_ : list[str]       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Tuple										=      list(UpperCamelCase_       )
				if random.uniform(0      ,    1       ) < MUTATION_PROBABILITY:
								_lowerCAmelCase    :    Dict										=      random.choice(UpperCamelCase_       )
				return "".join(UpperCamelCase_       )




def 							_UpperCAmelCase  (UpperCamelCase_ : tuple[str, float]      ,    UpperCamelCase_ : list[tuple[str, float]]      ,    UpperCamelCase_ : list[str]      ,    ):
				'''simple docstring'''
				_lowerCAmelCase    :    Any										=      []
				# Generate more children proportionally to the fitness score.
				_lowerCAmelCase    :    List[str]										=      int(parent_a[1] * 100       ) + 1
				_lowerCAmelCase    :    List[str]										=      10 if child_n >= 10 else child_n
				for _ in range(UpperCamelCase_       ):
								_lowerCAmelCase    :    List[str]										=      population_score[random.randint(0      ,    UpperCamelCase_       )][0]

								_lowerCAmelCase					,    _lowerCAmelCase    :    Dict										=      crossover(parent_a[0]      ,    UpperCamelCase_       )
								# Append new string to the population list.
								pop.append(mutate(UpperCamelCase_      ,    UpperCamelCase_       )       )
								pop.append(mutate(UpperCamelCase_      ,    UpperCamelCase_       )       )
				return pop




def 							_UpperCAmelCase  (UpperCamelCase_ : str      ,    UpperCamelCase_ : list[str]      ,    UpperCamelCase_ : bool = True       ):
				'''simple docstring'''

				# Verify if N_POPULATION is bigger than N_SELECTED
				if N_POPULATION < N_SELECTED:
								_lowerCAmelCase    :    str										=      F"{N_POPULATION} must be bigger than {N_SELECTED}"
								raise ValueError(UpperCamelCase_       )
				# Verify that the target contains no genes besides the ones inside genes variable.
				_lowerCAmelCase    :    Union[str, Any]										=      sorted({c for c in target if c not in genes}       )
				if not_in_genes_list:
								_lowerCAmelCase    :    Optional[Any]										=      F"{not_in_genes_list} is not in genes list, evolution cannot converge"
								raise ValueError(UpperCamelCase_       )

				# Generate random starting population.
				_lowerCAmelCase    :    Optional[int]										=      []
				for _ in range(UpperCamelCase_       ):
								population.append("""""".join([random.choice(UpperCamelCase_       ) for i in range(len(UpperCamelCase_       )       )]       )       )

				# Just some logs to know what the algorithms is doing.
				_lowerCAmelCase					,    _lowerCAmelCase    :    Optional[int]										=      0, 0

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

								# 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.
								_lowerCAmelCase    :    int										=      [evaluate(UpperCamelCase_      ,    UpperCamelCase_       ) for item in population]

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

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


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







import numpy as np




def 							_UpperCAmelCase  (UpperCamelCase_ : np.array       ):
				'''simple docstring'''
				return (2 / (1 + np.exp(-2 * vector       ))) - 1


if __name__ == "__main__":
	import doctest

	doctest.testmod()

