请写出你优化后的代码
时间: 2023-02-20 10:44:46 浏览: 109
我优化后的代码如下:
#include <iostream>
using namespace std;
int main()
{
int a, b;
cout << "请输入两个整数:" << endl;
cin >> a >> b;
int max = a > b ? a : b;
cout << "最大值为:" << max << endl;
return 0;
}
相关问题
写出刚刚代码优化后的代码
很抱歉,我作为一个自然语言处理模型,并没有编写代码的能力,但是我可以给你提供一些可能有用的修改建议。
假设您要爬取一个网站的文章列表,以下是一个简单的使用 Python requests 库的示例代码:
```
import requests
url = 'https://www.example.com/articles'
response = requests.get(url)
if response.status_code == 200:
articles = response.json()
for article in articles:
title = article['title']
content_url = article['content_url']
# do something with the title and content_url
```
这段代码可以正常工作,但是存在一些可以优化的地方:
1. 频率控制:在对同一个网站进行频繁的请求操作时,建议添加一些延迟,以避免被封IP或者被认为是恶意爬虫。例如,可以使用 time 库来添加一些随机的延迟,例如:
```
import time
import random
import requests
url = 'https://www.example.com/articles'
response = requests.get(url)
if response.status_code == 200:
articles = response.json()
for article in articles:
title = article['title']
content_url = article['content_url']
# do something with the title and content_url
time.sleep(random.uniform(1, 3)) # add a random delay between 1 to 3 seconds
```
2. 请求头设置:为了模拟真实的浏览器请求,可以设置合理的请求头,例如:
```
import time
import random
import requests
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36',
'Referer': 'https://www.example.com',
'Accept-Language': 'en-US,en;q=0.8',
}
url = 'https://www.example.com/articles'
response = requests.get(url, headers=headers)
if response.status_code == 200:
articles = response.json()
for article in articles:
title = article['title']
content_url = article['content_url']
# do something with the title and content_url
time.sleep(random.uniform(1, 3))
```
3. 代理IP设置:使用代理IP可以隐藏真实IP地址,防止被网站封禁。例如,可以使用 requests 库提供的 proxies 参数来设置代理IP:
```
import time
import random
import requests
proxies = {
'http': 'http://10.10.1.10:3128',
'https': 'https://10.10.1.10:1080',
}
url = 'https://www.example.com/articles'
response = requests.get(url, proxies=proxies)
if response.status_code == 200:
articles = response.json()
for article in articles:
title = article['title']
content_url = article['content_url']
# do something with the title and content_url
time.sleep(random.uniform(1, 3))
```
以上是一些可能有用的代码优化建议,您可以根据自己的具体需求进行修改和调整。
请用python写出灰狼优化算法的代码
以下是灰狼优化算法的Python代码:
```python
import numpy as np
# 灰狼优化算法
def grey_wolf_optimizer(cost_function, lb, ub, dim, SearchAgents_no, Max_iter):
# 初始化灰狼群
Alpha_pos = np.zeros(dim)
Beta_pos = np.zeros(dim)
Delta_pos = np.zeros(dim)
Alpha_score = float("inf")
Beta_score = float("inf")
Delta_score = float("inf")
Positions = np.zeros((SearchAgents_no, dim))
for i in range(dim):
Positions[:, i] = np.random.uniform(0, 1, SearchAgents_no) * (ub[i] - lb[i]) + lb[i]
# 开始迭代
for l in range(0, Max_iter):
for i in range(0, SearchAgents_no):
# 计算适应度值
fitness = cost_function(Positions[i, :])
# 更新Alpha, Beta, Delta位置和适应度值
if fitness < Alpha_score:
Alpha_score = fitness
Alpha_pos = Positions[i, :]
if (fitness > Alpha_score) and (fitness < Beta_score):
Beta_score = fitness
Beta_pos = Positions[i, :]
if (fitness > Alpha_score) and (fitness > Beta_score) and (fitness < Delta_score):
Delta_score = fitness
Delta_pos = Positions[i, :]
# 更新狼群位置
a = 2 - 2 * l / Max_iter
for i in range(0, SearchAgents_no):
r1 = np.random.rand(dim)
r2 = np.random.rand(dim)
A = 2 * a * r1 - a
C = 2 * r2
D_alpha = abs(C * Alpha_pos - Positions[i, :])
X1 = Alpha_pos - A * D_alpha
r1 = np.random.rand(dim)
r2 = np.random.rand(dim)
A = 2 * a * r1 - a
C = 2 * r2
D_beta = abs(C * Beta_pos - Positions[i, :])
X2 = Beta_pos - A * D_beta
r1 = np.random.rand(dim)
r2 = np.random.rand(dim)
A = 2 * a * r1 - a
C = 2 * r2
D_delta = abs(C * Delta_pos - Positions[i, :])
X3 = Delta_pos - A * D_delta
Positions[i, :] = (X1 + X2 + X3) / 3
# 返回最优解和适应度值
return Alpha_pos, Alpha_score
```
注意:此代码仅供参考,具体实现可能会因为不同的问题而有所不同。