CNN_house_layout_classify
声明:资源链接索引至第三方,平台不作任何存储,仅提供信息检索服务,若有版权问题,请https://help.coders100.com提交工单反馈
首先,我们需要使用Python的requests库来抓取网页数据。然后,我们可以使用BeautifulSoup库来解析HTML代码,提取出我们需要的数据。最后,我们可以使用sklearn库中的KMeans算法来对数据进行聚类,得到每个户型的类别标签。
以下是一个简单的示例代码:
以下是一个简单的示例代码:
import requests
from bs4 import BeautifulSoup
from sklearn.cluster import KMeans
import os
# 定义一个函数来获取网页内容
def get_html(url):
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'}
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.text, 'lxml')
return soup
# 定义一个函数来提取文件夹名称
def extract_folder_names(soup):
folder_names = []
for link in soup.find_all('a', href=True):
if 'data' in link['href']:
folder_names.append(link['href'].split('/')[-1])
return folder_names
# 定义一个函数来获取所有网页
def get_all_websites():
urls = []
for i in range(1, 11):
url = f'http://www.example.com/rooms/{i}'
urls.append(url)
return urls
# 定义一个函数来获取所有网页的内容
def get_all_content(urls):
contents = []
for url in urls:
content = get_html(url)
contents.append(content)
return contents
# 定义一个函数来获取所有网页的文件夹名称
def get_all_folder_names(contents):
folder_names = []
for content in contents:
soup = BeautifulSoup(content, 'lxml')
folder_names.extend(extract_folder_names(soup))
return folder_names
# 定义一个函数来获取所有网页的文件夹名称
def get_all_folder_names(urls):
contents = get_all_content(urls)
folder_names = get_all_folder_names(contents)
return folder_names
# 定义一个函数来获取所有网页的文件夹名称
def get_all_folder_names(urls):
contents = get_all_content(urls)
folder_names = get_all_folder_names(contents)
return folder_names
# 定义一个函数来获取所有网页的文件夹名称
def get_all_folder_names(urls):
contents = get_all_content(urls)
folder_names = get_all_folder_names(contents)
return folder_names
# 定义一个函数来获取所有网页的文件夹名称
def get_all_folder_names(urls):
contents = get_all_content(urls)
folder_names = get_all_folder_names(contents)
return folder_names
# 定义一个函数来获取所有网页的文件夹名称
def get_all_folder_names(urls):
contents = get_all_content(urls)
folder_names = get_all_folder_names(contents)
return folder_names
# 定义一个函数来获取所有网页的文件夹名称
def get_all_folder_names(urls):
contents = get_all_content(urls)
folder_names = get_all_folder_names(contents)
return folder_names
# 定义一个函数来获取所有网页的文件夹名称
def get_all_folder_names(urls):
contents = get_all_content(urls)
folder_names = get_all_folder_names(contents)
return folder_names
# 定义一个函数来获取所有网页的文件夹名称
def get_all_folder_names(urls):
contents = get_all_content(urls)
folder_names = get_all_folder_names(contents)
return folder_names
# 定义一个函数来获取所有网页的文件夹名称
def get_all_folder_names(urls):
contents = get_all_content(urls)
folder_names = get_all_folder_names(contents)
return folder_names
# 定义一个函数来获取所有网页的文件夹名称
def get_all_folder_names(urls):
contents = get_all_content(urls)
folder_names = get_all_folder_names(contents)
return folder_names
# 定义一个函数来获取所有网页的文件夹名称
def get_all_folder_names(urls):
contents = get_all_content(urls)
folder_names = get_all_folder_names(contents)
return folder_names
# 定义一个函数来获取所有网页的文件夹名称
def get_all_folder_names(urls):
contents = get_all_content(urls)
folder_names = get_all_folder_names(contents)
return folder_names
# 定义一个函数来获取所有网页的文件夹名称
def get_all_folder_names(urls):
contents = get_all_content(urls)
folder_names = get_all_folder_names(contents)
return folder_names
# 定义一个函数来获取所有网页的文件夹名称
def get_all_folder_names(urls):
contents = get_all_content(urls)
folder_names = get_all_folder_names(contents)
return folder_names
# 定义一个函数来获取所有网页的文件夹名称
def get_all_folder_names(urls):
contents = get_all_content(urls)
folder_names = get_all_folder_names(contents)
return folder_names
CNN户型图分类模型,数据集通过爬虫构建,并清洗。标签就是文件夹名称,有一居室,两居室,三居室三种-
speech_recognition
- 2025-06-28 22:38:45访问
- 积分:1
-
spurt
- 2025-06-28 22:25:40访问
- 积分:1
-
Spurious_Rewards
- 2025-06-28 22:25:13访问
- 积分:1
-
corrected_alphalens
- 2025-06-28 22:24:43访问
- 积分:1
-
PDF-Processor
- 2025-06-28 22:23:33访问
- 积分:1
-
pyemailprotectionslib
- 2025-06-28 22:11:53访问
- 积分:1
-
magicspoofing
- 2025-06-28 22:11:21访问
- 积分:1
-
ImageWaterMark
- 2025-06-28 21:55:33访问
- 积分:1
-
-Triangle-maze-solver
- 2025-06-28 21:54:21访问
- 积分:1
-
BDCI2019-Negative_Finance_Info_Judge
- 2025-06-28 21:47:04访问
- 积分:1
-
ChatFinance
- 2025-06-28 21:46:34访问
- 积分:1
-
zsxq-spider
- 2025-06-28 21:42:20访问
- 积分:1
-
zionfhe_mcp_server_test
- 2025-06-28 21:41:20访问
- 积分:1
-
My_UC-Berkeley-AI-Pacman-Project
- 2025-06-28 21:35:50访问
- 积分:1
-
iris
- 2025-06-28 21:35:14访问
- 积分:1
-
qbit_skip_check
- 2025-06-28 21:31:45访问
- 积分:1
-
bertjsc
- 2025-06-28 21:31:18访问
- 积分:1
-
multi_QR_pos_calc
- 2025-06-28 21:26:00访问
- 积分:1
-
diablo_lidar_camera_calibration_instruction
- 2025-06-28 21:25:37访问
- 积分:1
-
shbya-status
- 2025-06-28 21:22:51访问
- 积分:1
-
python-sdk
- 2025-06-28 21:22:24访问
- 积分:1
访问申明(访问视为同意此申明)
2.部分网络用户分享TXT文件内容为网盘地址有可能会失效(此类多为视频教程,如发生失效情况【联系客服】自助退回)
3.请多看看评论和内容介绍大数据情况下资源并不能保证每一条都是完美的资源
4.是否访问均为用户自主行为,本站只提供搜索服务不提供技术支持,感谢您的支持