




from __future__ import annotations

import random

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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: str  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: str  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: list[str]  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: tuple[str, float]			,							lowercase			: list[tuple[str, float]]			,							lowercase			: list[str]			,							):


      '''simple docstring'''



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

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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: list[str]			,							lowercase			: bool = True  ):


      '''simple docstring'''



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

      # Generate random starting population.
      lowerCamelCase_	  =					[]
      for _ in range(lowercase  ):
            population.append(''.join([random.choice(lowercase  ) for i in range(len(lowercase  )  )]  )  )

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

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

            # 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_	  =					[evaluate(lowercase			,							lowercase  ) for item in population]

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

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


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









lowerCamelCase       :	Dict									=     "Alexander Joslin"

import operator as op

from .stack import Stack

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      lowerCamelCase_	  =					{'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

      lowerCamelCase_	  =					Stack()
      lowerCamelCase_	  =					Stack()

      for i in equation:
            if i.isdigit():
                  # RULE 1
                  operand_stack.push(int(lowercase  )  )
            elif i in operators:
                  # RULE 2
                  operator_stack.push(lowercase  )
            elif i == ")":
                  # RULE 4
                  lowerCamelCase_	  =					operator_stack.peek()
                  operator_stack.pop()
                  lowerCamelCase_	  =					operand_stack.peek()
                  operand_stack.pop()
                  lowerCamelCase_	  =					operand_stack.peek()
                  operand_stack.pop()

                  lowerCamelCase_	  =					operators[opr](lowercase			,							lowercase  )
                  operand_stack.push(lowercase  )

    # RULE 5
      return operand_stack.peek()


if __name__ == "__main__":
 lowerCamelCase       :	Any									=     "(5 + ((4 * 2) * (2 + 3)))"
 # answer = 45
 print(F"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



