enumerate() 方法使用范例

当然，我可以回答你的问题！

enumerate() 方法用于将一个可遍历的数据对象(如列表、元组或字符串)组合为一个带有索引的序列，常用于 for 循环中。下面是一个使用 enumerate() 的例子：

fruits = ['apple', 'banana', 'cherry']
for index, fruit in enumerate(fruits):
    print(index, fruit)

输出结果为：

0 apple
1 banana
2 cherry

在这个例子中，我们将 fruits 列表中的元素与对应的索引组合在了一起，并通过 for 循环遍历了这个序列。enumerate() 方法的返回值是一个 enumerate 对象，它包含了每个元素的索引和值。

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相关问题

请给出一个参考代码范例

以下是一个使用PyTorch实现的一维信号GAN生成的参考代码范例：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

# 定义生成器网络
class Generator(nn.Module):
    def __init__(self, input_size, output_size, hidden_size):
        super(Generator, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.relu(self.fc2(x))
        x = nn.functional.tanh(self.fc3(x))
        return x

# 定义判别器网络
class Discriminator(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(Discriminator, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, 1)

    def forward(self, x):
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.relu(self.fc2(x))
        x = nn.functional.sigmoid(self.fc3(x))
        return x

# 定义训练过程
def train(generator, discriminator, train_loader, num_epochs, learning_rate):
    # 定义损失函数和优化器
    criterion = nn.BCELoss()
    g_optimizer = optim.Adam(generator.parameters(), lr=learning_rate)
    d_optimizer = optim.Adam(discriminator.parameters(), lr=learning_rate)

    # 开始训练
    for epoch in range(num_epochs):
        for i, real_data in enumerate(train_loader):
            # 训练判别器
            discriminator.zero_grad()
            real_data = real_data.view(-1, 1)
            real_labels = torch.ones(real_data.size(0), 1)
            fake_labels = torch.zeros(real_data.size(0), 1)
            real_pred = discriminator(real_data)
            d_real_loss = criterion(real_pred, real_labels)
            d_real_loss.backward()

            noise = torch.randn(real_data.size(0), 100)
            fake_data = generator(noise)
            fake_pred = discriminator(fake_data.detach())
            d_fake_loss = criterion(fake_pred, fake_labels)
            d_fake_loss.backward()

            d_optimizer.step()

            # 训练生成器
            generator.zero_grad()
            noise = torch.randn(real_data.size(0), 100)
            fake_data = generator(noise)
            fake_pred = discriminator(fake_data)
            g_loss = criterion(fake_pred, real_labels)
            g_loss.backward()

            g_optimizer.step()

            # 输出训练状态
            if i % 100 == 0:
                print("Epoch [{}/{}], Step [{}/{}], d_real_loss: {:.4f}, d_fake_loss: {:.4f}, g_loss: {:.4f}"
                      .format(epoch, num_epochs, i, len(train_loader), d_real_loss.item(), d_fake_loss.item(), g_loss.item()))

# 训练数据集
train_data = torch.randn(1000, 1)

# 创建数据加载器
train_loader = DataLoader(train_data, batch_size=50, shuffle=True)

# 创建生成器和判别器
generator = Generator(100, 1, 128)
discriminator = Discriminator(1, 128)

# 训练GAN模型
train(generator, discriminator, train_loader, 20, 0.001)

# 生成新的一维信号
noise = torch.randn(1000, 100)
generated_data = generator(noise)

这个代码范例使用一个简单的全连接网络作为生成器和判别器，并使用BCE损失函数和Adam优化器进行训练。在训练完成后，使用生成器网络生成了1000个新的一维信号数据。

写一个俄罗斯方块游戏代码范例

以下是一个简单的俄罗斯方块游戏的代码范例：

import pygame
import random

pygame.init()

# 定义游戏窗口大小
WINDOW_WIDTH, WINDOW_HEIGHT = 500, 600
WINDOW = pygame.display.set_mode((WINDOW_WIDTH, WINDOW_HEIGHT))
pygame.display.set_caption("俄罗斯方块")

# 定义方块大小
BLOCK_SIZE = 25

# 定义颜色
WHITE = (255, 255, 255)
BLACK = (0, 0, 0)
RED = (255, 0, 0)
BLUE = (0, 0, 255)
GREEN = (0, 255, 0)
YELLOW = (255, 255, 0)
PURPLE = (255, 0, 255)
ORANGE = (255, 165, 0)

# 定义方块类型和颜色
BLOCKS = {
    "I": (BLUE, [(0, 0), (1, 0), (2, 0), (3, 0)]),
    "J": (ORANGE, [(0, 0), (0, 1), (1, 1), (2, 1)]),
    "L": (YELLOW, [(2, 0), (0, 1), (1, 1), (2, 1)]),
    "O": (GREEN, [(0, 0), (1, 0), (0, 1), (1, 1)]),
    "S": (PURPLE, [(1, 0), (2, 0), (0, 1), (1, 1)]),
    "T": (RED, [(1, 0), (0, 1), (1, 1), (2, 1)]),
    "Z": (WHITE, [(0, 0), (1, 0), (1, 1), (2, 1)])
}

# 定义游戏区域
GAME_AREA_WIDTH, GAME_AREA_HEIGHT = 10, 20
GAME_AREA = [[BLACK for _ in range(GAME_AREA_WIDTH)] for _ in range(GAME_AREA_HEIGHT)]

# 定义游戏区域起始位置
GAME_AREA_X, GAME_AREA_Y = 50, 50

# 定义当前方块和下一个方块
current_block = None
next_block = None

# 定义游戏速度和计时器
game_speed = 100
game_timer = 0

# 定义得分
score = 0

# 定义字体
FONT = pygame.font.SysFont("Arial", 30)

def draw_game_area():
    """
    绘制游戏区域
    """
    for y, row in enumerate(GAME_AREA):
        for x, color in enumerate(row):
            pygame.draw.rect(WINDOW, color, (GAME_AREA_X + x * BLOCK_SIZE, GAME_AREA_Y + y * BLOCK_SIZE, BLOCK_SIZE, BLOCK_SIZE))


def draw_block(block, x, y):
    """
    绘制方块
    """
    for bx, by in block[1]:
        pygame.draw.rect(WINDOW, block[0], (GAME_AREA_X + (bx + x) * BLOCK_SIZE, GAME_AREA_Y + (by + y) * BLOCK_SIZE, BLOCK_SIZE, BLOCK_SIZE))


def generate_block():
    """
    生成方块
    """
    global current_block, next_block
    if next_block is None:
        current_block = random.choice(list(BLOCKS.items()))
    else:
        current_block = next_block
    next_block = random.choice(list(BLOCKS.items()))
    return current_block


def is_collision(block, x, y):
    """
    检查方块是否与游戏区域或已放置的方块发生碰撞
    """
    for bx, by in block[1]:
        if x + bx < 0 or x + bx >= GAME_AREA_WIDTH or y + by >= GAME_AREA_HEIGHT:
            return True
        if y + by >= 0 and GAME_AREA[y + by][x + bx] != BLACK:
            return True
    return False


def update_game_area():
    """
    更新游戏区域状态
    """
    global current_block, score
    for bx, by in current_block[1]:
        GAME_AREA[by + current_y][bx + current_x] = current_block[0]
    for y, row in enumerate(GAME_AREA):
        if BLACK not in row:
            GAME_AREA.pop(y)
            GAME_AREA.insert(0, [BLACK for _ in range(GAME_AREA_WIDTH)])
            score += 10


def draw_score():
    """
    绘制得分
    """
    score_text = FONT.render("Score: {}".format(score), True, WHITE)
    WINDOW.blit(score_text, (50, 10))


clock = pygame.time.Clock()

while True:
    for event in pygame.event.get():
        if event.type == pygame.QUIT:
            pygame.quit()
            quit()

    # 更新计时器
    game_timer += clock.tick()

    # 根据游戏速度更新游戏状态
    if game_timer >= game_speed:
        game_timer = 0

        # 如果当前方块为空，则生成新的方块
        if current_block is None:
            current_block = generate_block()
            current_x, current_y = GAME_AREA_WIDTH // 2 - 2, 0
            if is_collision(current_block, current_x, current_y):
                pygame.quit()
                quit()

        # 如果当前方块与游戏区域或已放置的方块发生碰撞，则更新游戏区域状态并清空当前方块
        if is_collision(current_block, current_x, current_y + 1):
            update_game_area()
            current_block = None

        # 否则将当前方块下落一格
        else:
            current_y += 1

        # 绘制游戏界面
        WINDOW.fill(BLACK)
        draw_game_area()
        draw_block(current_block, current_x, current_y)
        draw_score()
        pygame.display.update()