






import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase = True ,		__lowerCAmelCase = math.inf ,		__lowerCAmelCase = -math.inf ,		__lowerCAmelCase = math.inf ,		__lowerCAmelCase = -math.inf ,		__lowerCAmelCase = False ,		__lowerCAmelCase = 100 ,		__lowerCAmelCase = 0.01 ,		__lowerCAmelCase = 1 ,		)->  Tuple:





						"""simple docstring"""



						_UpperCAmelCase									=			False
						_UpperCAmelCase									=			search_prob
						_UpperCAmelCase									=			start_temperate
						_UpperCAmelCase									=			[]
						_UpperCAmelCase									=			0
						_UpperCAmelCase									=			None

						while not search_end:
												_UpperCAmelCase									=			current_state.score()
												if best_state is None or current_score > best_state.score():
																		_UpperCAmelCase									=			current_state
												scores.append(_UpperCAmelCase	)
												iterations += 1
												_UpperCAmelCase									=			None
												_UpperCAmelCase									=			current_state.get_neighbors()
												while (
												    next_state is None and neighbors
												):  # till we do not find a neighbor that we can move to
																		_UpperCAmelCase									=			random.randint(0 ,		len(_UpperCAmelCase	) - 1	)  # picking a random neighbor
																		_UpperCAmelCase									=			neighbors.pop(_UpperCAmelCase	)
																		_UpperCAmelCase									=			picked_neighbor.score() - current_score

																		if (
																		    picked_neighbor.x > max_x
																		    or picked_neighbor.x < min_x
																		    or picked_neighbor.y > max_y
																		    or picked_neighbor.y < min_y
																		):
																								continue  # neighbor outside our bounds

																		if not find_max:
																								_UpperCAmelCase									=			change * -1  # in case we are finding minimum
																		if change > 0:  # improves the solution
																								_UpperCAmelCase									=			picked_neighbor
																		else:
																								_UpperCAmelCase									=			(math.e) ** (
																								    change / current_temp
																								)  # probability generation function
																								if random.random() < probability:  # random number within probability
																														_UpperCAmelCase									=			picked_neighbor
												_UpperCAmelCase									=			current_temp - (current_temp * rate_of_decrease)

												if current_temp < threshold_temp or next_state is None:
																		# temperature below threshold, or could not find a suitable neighbor
																		_UpperCAmelCase									=			True
												else:
																		_UpperCAmelCase									=			next_state

						if visualization:
												from matplotlib import pyplot as plt

												plt.plot(range(_UpperCAmelCase	) ,		_UpperCAmelCase	)
												plt.xlabel('Iterations'	)
												plt.ylabel('Function values'	)
												plt.show()
						return best_state


if __name__ == "__main__":


		def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  Optional[Any]:





								"""simple docstring"""



								return (x**2) + (y**2)

		# starting the problem with initial coordinates (12, 47)
		_a      	=  SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
		_a      	=  simulated_annealing(
		    prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
		)
		print(
		    '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
		    F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
		)

		# starting the problem with initial coordinates (12, 47)
		_a      	=  SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
		_a      	=  simulated_annealing(
		    prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
		)
		print(
		    '''The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
		    F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
		)


		def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  str:





								"""simple docstring"""



								return (3 * x**2) - (6 * y)

		_a      	=  SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		_a      	=  simulated_annealing(prob, find_max=False, visualization=True)
		print(
		    '''The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
		    F'''{local_min.score()}'''
		)

		_a      	=  SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		_a      	=  simulated_annealing(prob, find_max=True, visualization=True)

		print(
		    '''The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
		    F'''{local_min.score()}'''
		)





from __future__ import annotations

import random

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





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				len([g for position, g in enumerate(_UpperCAmelCase				) if g == main_target[position]]				)
    return (item, float(_UpperCAmelCase				))





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				random.randint(0   , len(_UpperCAmelCase				) - 1				)
    __a						       =				parent_a[:random_slice] + parent_a[random_slice:]
    __a						       =				parent_a[:random_slice] + parent_a[random_slice:]
    return (child_a, child_a)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				list(_UpperCAmelCase				)
    if random.uniform(0   , 1				) < MUTATION_PROBABILITY:
        __a						       =				random.choice(_UpperCAmelCase				)
    return "".join(_UpperCAmelCase				)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , ):
    __a						       =				[]
    # Generate more children proportionally to the fitness score.
    __a						       =				int(parent_a[1] * 100				) + 1
    __a						       =				10 if child_n >= 10 else child_n
    for _ in range(_UpperCAmelCase				):
        __a						       =				population_score[random.randint(0   , _UpperCAmelCase				)][0]

        __a       ,   __a						       =				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      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase = True				):

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

    # Generate random starting population.
    __a						       =				[]
    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.
    __a       ,   __a						       =				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.
        __a						       =				[evaluate(_UpperCAmelCase   , _UpperCAmelCase				) for item in population]

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





