以下是Python代码实现： ```python def max_sum(*args): max_num = max(args) sum_num = sum(args) print("最大值为：", max_num) print("所有整数之和为：", sum_num) # 测试 max_sum(1, 2, 3, 4, 5) # 最大值为： 5，所有整数之和为： 15 max_sum(10, 20, 30, 40) # 最大值为： 40，所有整数之和为： 100 ``` 函数`max_sum`接受任意多个整数，使用`max`函数求出其中的最大值，使用`sum`函数求出所有整数之和，最后输出结果。

好的，已经理解了您的问题。这是一个Python函数，名为`max_sum`，接受任意多个整数作为参数，使用`max`函数求出其中的最大值，使用`sum`函数求出所有整数之和，最后输出结果。以下是一个例子：

def max_sum(*args):
    max_num = max(args)
    sum_num = sum(args)
    print("最大值为：", max_num)
    print("所有整数之和为：", sum_num)

max_sum(1, 2, 3, 4, 5)  # 最大值为： 5，所有整数之和为： 15
max_sum(10, 20, 30, 40)  # 最大值为： 40，所有整数之和为： 100

希望这个回答能够解决您的问题。

相关问题

def predict_from_naive_bayes_model(model, matrix): """Use a Naive Bayes model to compute predictions for a target matrix. This function should be able to predict on the models that fit_naive_bayes_model outputs. Args: model: A trained model from fit_naive_bayes_model matrix: A numpy array containing word counts Returns: A numpy array containg the predictions from the model """ # *** START CODE HERE *** log_probabilities = [] for i in range(model.num_classes): log_probabilities.append(np.sum(matrix * model.log_class_priors[i] + model.log_likelihoods[i])) return np.argmax(log_probabilities) # Return the class with the highest probability # *** END CODE HERE ***代码报错AttributeError: 'dict' object has no attribute 'log_class_priors'请帮我修改

根据报错信息，我们可以看出，`model` 参数传入的是一个字典，而字典没有 `log_class_priors` 属性。因此，我们需要修改这个函数，使其能够处理字典作为输入。具体的修改方法如下：

def predict_from_naive_bayes_model(model, matrix):
    """Use a Naive Bayes model to compute predictions for a target matrix. This function should be able to predict on the models that fit_naive_bayes_model outputs.

    Args:
    model: A trained model from fit_naive_bayes_model
    matrix: A numpy array containing word counts

    Returns:
    A numpy array containing the predictions from the model
    """
    
    # *** START CODE HERE ***
    log_probabilities = []
    for i in range(model['num_classes']):
        log_probabilities.append(np.sum(matrix * model['log_class_priors'][i] + model['log_likelihoods'][i]))
    return np.argmax(log_probabilities) # Return the class with the highest probability
    # *** END CODE HERE ***

在这个修改后的函数中，我们使用了 `model['log_class_priors']` 和 `model['log_likelihoods']` 来获取训练得到的朴素贝叶斯模型的参数，这些参数存储在字典 `model` 中。同时，我们也使用了 `model['num_classes']` 来获取模型中的类别数目。

import requests from bs4 import BeautifulSoup from threading import Thread def crawl_books(start, end): session = requests.Session() for i in range(start, end): url = 'http://search.dangdang.com/?key=%BC%C6%CB%E3%BB%FA&act=input&page_index={}'.format(i) try: response = session.get(url, timeout=10) except requests.exceptions.Timeout: print('Timeout occurred when accessing: ' + url) continue page = response.text soup = BeautifulSoup(page, 'lxml') books = soup.find('ul', class_='bigimg') for book in books.find_all('li'): title = book.find('a', class_='pic').get('title') author = book.find('p', class_='search_book_author').text price = book.find('p', class_='price').find('span', class_='search_now_price').text book_link = 'https:' + book.find('a', class_='pic').get('href') try: response = session.get(book_link, timeout=10) except requests.exceptions.Timeout: print('Timeout occurred when accessing: ' + book_link) continue page = response.text soup = BeautifulSoup(page, 'lxml') comment_num_tag = soup.find('a', class_='review_num') if comment_num_tag: comment_num = comment_num_tag.text else: comment_num = '0' print(title, author, price, comment_num) threads = [] for i in range(1, 101, 10): t = Thread(target=crawl_books, args=(i, i+10)) threads.append(t) t.start() for t in threads: t.join()再优化一次，使评论数量能够得到

可以使用线程池来优化代码，使用`concurrent.futures`模块中的`ThreadPoolExecutor`类来实现线程池。在每个任务中获取评论数量后，将其保存到一个列表中，在所有任务执行完后，统计评论数量的总和。代码如下：

import requests
from bs4 import BeautifulSoup
from concurrent.futures import ThreadPoolExecutor

def crawl_books(start, end):
    session = requests.Session()
    comments = []
    for i in range(start, end):
        url = 'http://search.dangdang.com/?key=%BC%C6%CB%E3%BB%FA&act=input&page_index={}'.format(i)
        try:
            response = session.get(url, timeout=10)
        except requests.exceptions.Timeout:
            print('Timeout occurred when accessing: ' + url)
            continue
        page = response.text
        soup = BeautifulSoup(page, 'lxml')
        books = soup.find('ul', class_='bigimg')
        for book in books.find_all('li'):
            title = book.find('a', class_='pic').get('title')
            author = book.find('p', class_='search_book_author').text
            price = book.find('p', class_='price').find('span', class_='search_now_price').text
            book_link = 'https:' + book.find('a', class_='pic').get('href')
            try:
                response = session.get(book_link, timeout=10)
            except requests.exceptions.Timeout:
                print('Timeout occurred when accessing: ' + book_link)
                continue
            page = response.text
            soup = BeautifulSoup(page, 'lxml')
            comment_num_tag = soup.find('a', class_='review_num')
            if comment_num_tag:
                comment_num = int(comment_num_tag.text)
            else:
                comment_num = 0
            comments.append(comment_num)
            print(title, author, price, comment_num)
    return comments

if __name__ == '__main__':
    with ThreadPoolExecutor(max_workers=10) as executor:
        results = executor.map(crawl_books, range(1, 101, 10))
    comments = [num for result in results for num in result]
    total_comments = sum(comments)
    print('总评论数：', total_comments)

这样可以更加高效地爬取数据，并且得到评论数量的总和。