



'''simple docstring'''
from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
snake_case_			:     Any  	=  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.
snake_case_			:     Any  	=  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.
snake_case_			:     Optional[int]  	=  0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase :		Tuple			    =       len([g for position, g in enumerate(UpperCAmelCase_							) if g == main_target[position]]							)
						return (item, float(UpperCAmelCase_							))






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase :		Optional[int]			    =       random.randint(0  ,  len(UpperCAmelCase_							) - 1							)
						_UpperCamelCase :		str			    =       parent_a[:random_slice] + parent_a[random_slice:]
						_UpperCamelCase :		Any			    =       parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase :		List[Any]			    =       list(UpperCAmelCase_							)
						if random.uniform(0  ,  1							) < MUTATION_PROBABILITY:
												_UpperCamelCase :		Union[str, Any]			    =       random.choice(UpperCAmelCase_							)
						return "".join(UpperCAmelCase_							)






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  ):
						_UpperCamelCase :		str			    =       []
						# Generate more children proportionally to the fitness score.
						_UpperCamelCase :		Dict			    =       int(parent_a[1] * 1_0_0							) + 1
						_UpperCamelCase :		Any			    =       1_0 if child_n >= 1_0 else child_n
						for _ in range(UpperCAmelCase_							):
												_UpperCamelCase :		List[Any]			    =       population_score[random.randint(0  ,  UpperCAmelCase_							)][0]

												_UpperCamelCase    ,      _UpperCamelCase :		Optional[Any]			    =       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 A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_ = True							):

						# Verify if N_POPULATION is bigger than N_SELECTED
						if N_POPULATION < N_SELECTED:
												_UpperCamelCase :		List[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.
						_UpperCamelCase :		Any			    =       sorted({c for c in target if c not in genes}							)
						if not_in_genes_list:
												_UpperCamelCase :		Dict			    =       f'{not_in_genes_list} is not in genes list, evolution cannot converge'
												raise ValueError(UpperCAmelCase_							)

						# Generate random starting population.
						_UpperCamelCase :		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.
						_UpperCamelCase    ,      _UpperCamelCase :		Any			    =       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.
												_UpperCamelCase :		Tuple			    =       [evaluate(UpperCAmelCase_  ,  UpperCAmelCase_							) for item in population]

												# Check if there is a matching evolution.
												_UpperCamelCase :		Optional[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 % 1_0 == 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.
												_UpperCamelCase :		Dict			    =       population[: int(N_POPULATION / 3							)]
												population.clear()
												population.extend(UpperCAmelCase_							)
												# Normalize population score to be between 0 and 1.
												_UpperCamelCase :		Any			    =       [
												    (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__":
					snake_case_			:     List[Any]  	=  (
					    'This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'
					)
					snake_case_			:     Tuple  	=  list(
					    ' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'
					    'nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'
					)
					snake_case_	,							snake_case_	,							snake_case_			:     List[str]  	=  basic(target_str, genes_list)
					print(
					    F"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
					)







'''simple docstring'''
from __future__ import annotations

import numpy as np






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase    ,      _UpperCamelCase :		Optional[int]			    =       np.shape(UpperCAmelCase_							)
						if rows != columns:
												_UpperCamelCase :		Union[str, Any]			    =       (
												    '\'table\' has to be of square shaped array but got a '
												    f'{rows}x{columns} array:\n{table}'
												)
												raise ValueError(UpperCAmelCase_							)

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


if __name__ == "__main__":
					import doctest

					doctest.testmod()


