添加注释 if self._content: self._current = self._current - 1 return self._content.pop(0)

# 如果_content不为空，执行以下操作：
if self._content:
# 修改_current的值，使得下一次调用next()方法时可以返回正确的值
self._current = self._current - 1
# 删除_content列表的第一个元素，并返回该元素的值
return self._content.pop(0)

相关问题

Python freegames.maze 加详细注释

好的，下面是 `freegames.maze` 模块的详细注释：

"""
freegames.maze

This module provides functions to create and manipulate mazes.

Functions:

    maze(width, height, complexity=0.75, density=0.75):
        Create a maze with the given dimensions and complexity/density.

Classes:

    Maze:
        A maze object that can be displayed and solved using Turtle graphics.

"""

import random

def maze(width, height, complexity=0.75, density=0.75):
    """
    Create a maze with the given dimensions and complexity/density.

    Parameters:
        - width (int): the width of the maze in cells.
        - height (int): the height of the maze in cells.
        - complexity (float): a value between 0 and 1 that determines
            the amount of complexity/number of twists and turns in the maze.
        - density (float): a value between 0 and 1 that determines the
            amount of density/number of cells that are filled in the maze.

    Returns:
        - A 2D list of booleans representing the maze, where True indicates
          a wall and False indicates a path.
    """
    # Determine the dimensions of the maze in cells.
    rows = height // 2 + 1
    cols = width // 2 + 1

    # Create the maze as a 2D list of booleans.
    maze = [[False] * cols for i in range(rows)]

    # Fill in the border cells as walls.
    for i in range(rows):
        maze[i][0] = maze[i][-1] = True
    for j in range(cols):
        maze[0][j] = maze[-1][j] = True

    # Fill in the maze with walls and paths.
    for i in range(1, rows - 1):
        for j in range(1, cols - 1):
            if random.random() < density:
                maze[i][j] = True
            elif i % 2 == 0 or j % 2 == 0:
                maze[i][j] = True

    # Carve out the maze starting from the center.
    start_x, start_y = rows // 2, cols // 2
    maze[start_x][start_y] = False
    cells = [(start_x, start_y)]
    while cells:
        current = cells.pop(random.randint(0, len(cells) - 1))
        x, y = current
        neighbors = []
        if x > 1 and not maze[x - 2][y]:
            neighbors.append((x - 2, y))
        if x < rows - 2 and not maze[x + 2][y]:
            neighbors.append((x + 2, y))
        if y > 1 and not maze[x][y - 2]:
            neighbors.append((x, y - 2))
        if y < cols - 2 and not maze[x][y + 2]:
            neighbors.append((x, y + 2))
        if neighbors:
            cells.append(current)
            neighbor = neighbors[random.randint(0, len(neighbors) - 1)]
            nx, ny = neighbor
            if nx == x - 2:
                maze[x - 1][y] = False
            elif nx == x + 2:
                maze[x + 1][y] = False
            elif ny == y - 2:
                maze[x][y - 1] = False
            elif ny == y + 2:
                maze[x][y + 1] = False
            maze[nx][ny] = False

    return maze


class Maze:
    """
    A maze object that can be displayed and solved using Turtle graphics.

    Attributes:
        - maze (list): a 2D list of booleans representing the maze.
        - width (int): the width of the maze in cells.
        - height (int): the height of the maze in cells.
        - cell_size (int): the size of each cell in pixels.
        - turtle (turtle.Turtle): the turtle used to draw the maze.
        - screen (turtle.Screen): the screen used to display the maze.

    Methods:
        - __init__(self, maze, cell_size=10): create a new Maze object.
        - _draw_wall(self, row, col): draw a wall at the given cell.
        - _draw_path(self, row, col): draw a path at the given cell.
        - display(self): display the maze using Turtle graphics.
        - solve(self, start=(0, 0), end=None): solve the maze using the given
          start and end positions, and return a list of (row, col) tuples
          representing the solution path.
    """

    def __init__(self, maze, cell_size=10):
        """
        Create a new Maze object.

        Parameters:
            - maze (list): a 2D list of booleans representing the maze.
            - cell_size (int): the size of each cell in pixels.
        """
        self.maze = maze
        self.width = len(maze[0])
        self.height = len(maze)
        self.cell_size = cell_size
        self.turtle = None
        self.screen = None

    def _draw_wall(self, row, col):
        """
        Draw a wall at the given cell.

        Parameters:
            - row (int): the row number of the cell.
            - col (int): the column number of the cell.
        """
        x = col * self.cell_size
        y = row * self.cell_size
        self.turtle.goto(x, y)
        self.turtle.setheading(0)
        self.turtle.pendown()
        for i in range(4):
            self.turtle.forward(self.cell_size)
            self.turtle.left(90)
        self.turtle.penup()

    def _draw_path(self, row, col):
        """
        Draw a path at the given cell.

        Parameters:
            - row (int): the row number of the cell.
            - col (int): the column number of the cell.
        """
        x = col * self.cell_size + self.cell_size // 2
        y = row * self.cell_size + self.cell_size // 2
        self.turtle.goto(x, y)
        self.turtle.dot(self.cell_size // 2)

    def display(self):
        """
        Display the maze using Turtle graphics.
        """
        if not self.turtle:
            import turtle
            self.turtle = turtle.Turtle()
            self.turtle.hideturtle()
            self.turtle.speed(0)
            self.turtle.penup()
            self.turtle.setheading(0)
            self.turtle.goto(0, 0)
            self.turtle.pendown()
            self.turtle.color('black')
            self.screen = self.turtle.getscreen()
            self.screen.setworldcoordinates(0, 0,
                                             self.width * self.cell_size,
                                             self.height * self.cell_size)

        for row in range(self.height):
            for col in range(self.width):
                if self.maze[row][col]:
                    self._draw_wall(row, col)
                else:
                    self._draw_path(row, col)

        self.screen.exitonclick()

    def solve(self, start=(0, 0), end=None):
        """
        Solve the maze using the given start and end positions, and return a
        list of (row, col) tuples representing the solution path.

        Parameters:
            - start (tuple): a (row, col) tuple representing the starting
              position. Defaults to (0, 0).
            - end (tuple): a (row, col) tuple representing the ending
              position. Defaults to the bottom-right corner of the maze.

        Returns:
            - A list of (row, col) tuples representing the solution path.
        """
        if not end:
            end = (self.height - 1, self.width - 1)

        queue = [(start, [start])]
        visited = set()

        while queue:
            (row, col), path = queue.pop(0)

            if (row, col) == end:
                return path

            if (row, col) in visited:
                continue

            visited.add((row, col))

            if row > 0 and not self.maze[row - 1][col]:
                queue.append(((row - 1, col), path + [(row - 1, col)]))
            if row < self.height - 1 and not self.maze[row + 1][col]:
                queue.append(((row + 1, col), path + [(row + 1, col)]))
            if col > 0 and not self.maze[row][col - 1]:
                queue.append(((row, col - 1), path + [(row, col - 1)]))
            if col < self.width - 1 and not self.maze[row][col + 1]:
                queue.append(((row, col + 1), path + [(row, col + 1)]))

        return None

以上就是 `freegames.maze` 模块的详细注释，希望能帮助你更好地理解它的实现和使用。

NSGA-II的python带注释的代码

以下是NSGA-II算法的Python代码（附有注释）：

import random
import numpy as np

# 定义一个类来表示每个个体
class Individual:
    def __init__(self, x, y):
        self.x = x
        self.y = y
        self.rank = None
        self.crowding_distance = None

    # 计算个体的适应度函数值
    def evaluate(self):
        self.fitness = [self.x**2, self.y**2]

# 初始化种群
def initialize_population(pop_size):
    population = []
    for i in range(pop_size):
        x = random.uniform(-5, 5)
        y = random.uniform(-5, 5)
        individual = Individual(x, y)
        individual.evaluate()
        population.append(individual)
    return population

# 计算支配关系
def dominates(x, y):
    return all(x_i >= y_i for x_i, y_i in zip(x.fitness, y.fitness)) and any(x_i > y_i for x_i, y_i in zip(x.fitness, y.fitness))

# 计算非支配排序
def non_dominated_sort(population):
    # 存储支配个体的数量
    domination_count = [0] * len(population)
    # 存储每个个体被哪些个体支配
    dominated_individuals = [[] for _ in range(len(population))]
    # 存储每个个体支配的个体
    dominating_individuals = [[] for _ in range(len(population))]
    # 存储每个个体的等级
    ranks = [None] * len(population)

    # 第一层中的个体没有被任何个体支配
    front_1 = []
    for i, individual in enumerate(population):
        for j, other_individual in enumerate(population):
            if i == j:
                continue
            if dominates(individual, other_individual):
                # i 支配 j
                dominating_individuals[i].append(j)
                dominated_individuals[j].append(i)
            elif dominates(other_individual, individual):
                # i 被 j 支配
                domination_count[i] += 1

        if domination_count[i] == 0:
            ranks[i] = 1
            front_1.append(i)

    fronts = [front_1]
    current_front = 0
    while True:
        next_front = []
        for i in fronts[current_front]:
            for j in dominating_individuals[i]:
                domination_count[j] -= 1
                if domination_count[j] == 0:
                    ranks[j] = current_front + 2
                    next_front.append(j)

        if not next_front:
            break

        fronts.append(next_front)
        current_front += 1

    for i, individual in enumerate(population):
        individual.rank = ranks[i]

    return fronts

# 计算拥挤度
def crowding_distance(front):
    distances = [0] * len(front)

    # 对每个目标函数都进行排序
    for i in range(2):
        front.sort(key=lambda individual: individual.fitness[i])
        distances[0] = distances[-1] = float('inf')
        fitness_range = front[-1].fitness[i] - front[0].fitness[i]

        # 计算每个个体的拥挤度
        for j in range(1, len(front) - 1):
            distances[j] += (front[j+1].fitness[i] - front[j-1].fitness[i]) / fitness_range

    for i, individual in enumerate(front):
        individual.crowding_distance = distances[i]

# 计算新的种群
def reproduce(fronts, pop_size):
    new_population = []
    current_size = 0
    front_index = 0

    # 按照等级从前往后遍历每一层
    while current_size < pop_size and front_index < len(fronts):
        # 如果加入这一层的所有个体总数小于等于种群大小，则全部加入新种群
        if current_size + len(fronts[front_index]) <= pop_size:
            for individual_index in fronts[front_index]:
                new_population.append(population[individual_index])
                current_size += 1
            front_index += 1
        else:
            # 否则，按照拥挤度选择部分个体加入新种群
            crowding_distances = [individual.crowding_distance for individual in fronts[front_index]]
            sort_order = np.argsort(crowding_distances)[::-1]
            for i in sort_order:
                new_population.append(population[fronts[front_index][i]])
                current_size += 1

                if current_size == pop_size:
                    break

            front_index += 1

    return new_population

# 初始化种群
population = initialize_population(100)

# 迭代次数
num_generations = 50

# 进化
for generation in range(num_generations):
    fronts = non_dominated_sort(population)
    for front in fronts:
        crowding_distance(front)

    population = reproduce(fronts, len(population))

# 输出 Pareto 最优解
pareto_front = non_dominated_sort(population)[0]
for index in pareto_front:
    print(population[index].fitness)