


class   a__	:

       def __init__(   self      :     Optional[Any]      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     Union[str, Any]    ):
            a_    :     int										=    name
            a_    :     str										=    value
            a_    :     str										=    weight

       def __repr__(   self      :     Optional[Any]    ):
            return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''

       def 						UpperCAmelCase(   self      :     int    ):
            return self.value

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            return self.name

       def 						UpperCAmelCase(   self      :     Any    ):
            return self.weight

       def 						UpperCAmelCase(   self      :     Any    ):
            return self.value / self.weight



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     Optional[Any]										=    []
     for i in range(len(__UpperCamelCase ) ):
          menu.append(Things(name[i]  ,  value[i]  ,  weight[i] ) )
     return menu



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     str										=    sorted(__UpperCamelCase  ,  key=__UpperCamelCase  ,  reverse=__UpperCamelCase )
     a_    :     Dict										=    []
     a_			,			a_    :     Any										=    0.0, 0.0
     for i in range(len(__UpperCamelCase ) ):
          if (total_cost + items_copy[i].get_weight()) <= max_cost:
               result.append(items_copy[i] )
               total_cost += items_copy[i].get_weight()
               total_value += items_copy[i].get_value()
     return (result, total_value)



def  _a			(						):
     pass


if __name__ == "__main__":
       import doctest

       doctest.testmod()







from __future__ import annotations

import random

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



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     List[Any]										=    len([g for position, g in enumerate(__UpperCamelCase ) if g == main_target[position]] )
     return (item, float(__UpperCamelCase ))



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     List[Any]										=    random.randint(0  ,  len(__UpperCamelCase ) - 1 )
     a_    :     Tuple										=    parent_a[:random_slice] + parent_a[random_slice:]
     a_    :     Optional[Any]										=    parent_a[:random_slice] + parent_a[random_slice:]
     return (child_a, child_a)



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     Optional[int]										=    list(__UpperCamelCase )
     if random.uniform(0  ,  1 ) < MUTATION_PROBABILITY:
          a_    :     int										=    random.choice(__UpperCamelCase )
     return "".join(__UpperCamelCase )



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  ):
     a_    :     Tuple										=    []
     # Generate more children proportionally to the fitness score.
     a_    :     Any										=    int(parent_a[1] * 1_0_0 ) + 1
     a_    :     int										=    1_0 if child_n >= 1_0 else child_n
     for _ in range(__UpperCamelCase ):
          a_    :     Tuple										=    population_score[random.randint(0  ,  __UpperCamelCase )][0]

          a_			,			a_    :     List[str]										=    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:
          a_    :     Union[str, Any]										=    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_    :     List[str]										=    sorted({c for c in target if c not in genes} )
     if not_in_genes_list:
          a_    :     Optional[Any]										=    F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
          raise ValueError(__UpperCamelCase )

     # Generate random starting population.
     a_    :     str										=    []
     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_    :     List[str]										=    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_    :     Tuple										=    [evaluate(__UpperCamelCase  ,  __UpperCamelCase ) for item in population]

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



