以后再也不用担心写爬虫ip被封，不用担心没钱买代理ip的烦恼了 在使用python写爬虫时候，你会遇到所要爬取的网站有反爬取技术比如用同一个IP反复爬取同一个网页，很可能会被封。如何有效的解决这个问题呢？我们可以使用代理ip，来设置代理ip池。 现在教大家一个可获取大量免费有效快速的代理ip方法，我们访问西刺免费代理ip网址 这里面提供了许多代理ip，但是我们尝试过后会发现并不是每一个都是有效的。所以我们现在所要做的就是从里面提供的筛选出有效快速稳定的ip。 以下介绍的免费获取代理ip池的方法： 优点：免费、数量多、有效、速度快 缺点：需要定期筛选 主要思路： 从网址上爬取ip地址并存储 验证ip是否能使用-（随机访问网址判断响应码） 格式化ip地址 代码如下： 1.导入包 import requests from lxml import etree import time 1 2 3 2.获取西刺免费代理ip网址上的代理ip def get_all_proxy(): url = 'http://www.xicidaili.com/nn/1' headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36', } response = requests.get(url, headers=headers) html_ele = etree.HTML(response.text) ip_eles = html_ele.xpath('//table[@id="ip_list"]/tr/td[2]/text()') port_ele = html_ele.xpath('//table[@id="ip_list"]/tr/td[3]/text()') proxy_list = [] for i in range(0,len(ip_eles)): proxy_str = 'http://' + ip_eles[i] + ':' + port_ele[i] proxy_list.append(proxy_str) return proxy_list 1 2 3 4 5 6 7 8 9 10 11 12 13 14 3.验证获取的ip def check_all_proxy(proxy_list): valid_proxy_list = [] for proxy in proxy_list: url = 'http://www.baidu.com/' proxy_dict = { 'http': proxy } try: start_time = time.time() response = requests.get(url, proxies=proxy_dict, timeout=5) if response.status_code == 200: end_time = time.time() print('代理可用：' + proxy) print('耗时:' + str(end_time - start_time)) valid_proxy_list.append(proxy) else: print('代理超时') except: print('代理不可用--------------->'+proxy) return valid_proxy_list 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 4.输出获取ip池 if __name__ == '__main__': proxy_list = get_all_proxy() valid_proxy_list = check_all_proxy(proxy_list) print('--'*30) print(valid_proxy_list) 1 2 3 4 5 技术能力有限欢迎提出意见，保证积极向上不断学习 ———————————————— 版权声明：本文为CSDN博主「彬小二」的原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接及本声明。 原文链接：https://blog.csdn.net/qq_39884947/article/details/86609930
上传时间： 2019-11-15
上传用户：fygwz1982
使用matlab实现gibbs抽样，MCMC: The Gibbs Sampler 多元高斯分布的边缘概率和条件概率 Marginal and conditional distributions of multivariate normal distribution
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上传用户：real_
Artificial Intelligence (AI) has undoubtedly been one of the most important buz- zwords over the past years. The goal in AI is to design algorithms that transform com- puters into “intelligent” agents. By intelligence here we do not necessarily mean an extraordinary level of smartness shown by superhuman; it rather often involves very basic problems that humans solve very frequently in their day-to-day life. This can be as simple as recognizing faces in an image, driving a car, playing a board game, or reading (and understanding) an article in a newspaper. The intelligent behaviour ex- hibited by humans when “reading” is one of the main goals for a subfield of AI called Natural Language Processing (NLP). Natural language 1 is one of the most complex tools used by humans for a wide range of reasons, for instance to communicate with others, to express thoughts, feelings and ideas, to ask questions, or to give instruc- tions. Therefore, it is crucial for computers to possess the ability to use the same tool in order to effectively interact with humans.
标签： Embeddings Processing Language Natural in
上传时间： 2020-06-10
上传用户：shancjb
