with open('sentences.txt','r',encoding='utf-8') as f: sentences = f.read().splitlines()
时间: 2024-01-24 12:01:37 浏览: 23
This code opens a file named "sentences.txt" in read mode with UTF-8 encoding and assigns it to the variable "f". It then reads the contents of the file using the read() method and splits the text into a list of sentences using the splitlines() method. The resulting list of sentences is assigned to the variable "sentences".
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import logging import jieba import gensim from gensim.models import Word2Vec def get_Segment(): texts = [] jieba.load_userdict("data\\name_dict.txt") with open('data\\in_the_name_of_people.txt','r',encoding='utf-8') as f: for line in f.readlines(): texts.append(list(jieba.cut(line.strip()))) with open('data\\in_the_name_of_people_segment.txt','w',encoding='utf-8')as f: for line in texts: f.write(" ".join(w for w in line)) f.write("\r\n") def getmodel(): logging.basicConfig(format='%(asctime)s : %(LeveLname)s : %(message)s',level=logging.INFO) sentences = word2vec.LineSentence('data\\in_the_name_of_people_segment.txt') model = word2vec.Word2Vec(sentences,min_count=1) return model if __name__=='__main__': get_Segment() model = getmodel() print('相似度: ',model.wv.similarity('人民','名义')) print(model.wv.similarity('候亮平','钟小艾')) print(model.mv.most_similar('候亮平',topn=10))
这段代码存在一些问题,我来帮你解释一下:
1. 代码中缺少了部分 import 语句,需要加上:
```
from gensim.models import word2vec
```
2. 在 `logging.basicConfig()` 函数的第一个参数中,`LeveLname` 应该是 `levelname`,需要修正。
3. 在 `getmodel()` 函数中,`word2vec.LineSentence()` 应该是 `word2vec.Text8Corpus()`,需要修正。
4. 在 `getmodel()` 函数中,`model.mv.most_similar()` 应该是 `model.wv.most_similar()`,需要修正。
修正后的代码如下:
```
import logging
import jieba
import gensim
from gensim.models import word2vec
def get_Segment():
texts = []
jieba.load_userdict("data\\name_dict.txt")
with open('data\\in_the_name_of_people.txt','r',encoding='utf-8') as f:
for line in f.readlines():
texts.append(list(jieba.cut(line.strip())))
with open('data\\in_the_name_of_people_segment.txt','w',encoding='utf-8')as f:
for line in texts:
f.write(" ".join(w for w in line))
f.write("\r\n")
def getmodel():
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
sentences = word2vec.Text8Corpus('data\\in_the_name_of_people_segment.txt')
model = word2vec.Word2Vec(sentences, min_count=1)
return model
if __name__=='__main__':
get_Segment()
model = getmodel()
print('相似度: ',model.wv.similarity('人民','名义'))
print(model.wv.similarity('候亮平','钟小艾'))
print(model.wv.most_similar('候亮平',topn=10))
```
希望能帮到你!
pattern = r'<img src="(.+?)"' for item in findall(pattern, content): item = urljoin(url, item) with urlopen(item) as fp_web: with open(rf'{child}\{basename(item)}', 'wb') as fp_local: fp_local.write(fp_web.read()) else: print(title, '已存在,跳过...') fn = rf'{child}\{title}.txt' with open(fn, encoding='utf8') as fp: for line in fp: sentences.append(line)解释
这段代码主要是用于从HTML文本中提取图片并下载到本地,如果图片已经下载过,则跳过下载并读取已经下载好的文本文件。
具体解释如下:
1. `pattern = r'<img src="(.+?)"'`:定义正则表达式模式,用于匹配HTML文本中的图片链接。
2. `for item in findall(pattern, content):`:使用re模块的findall()函数,对HTML文本进行匹配,找出所有符合模式的图片链接。
3. `item = urljoin(url, item)`:使用urljoin()函数将相对路径的图片链接转换为绝对路径。
4. `with urlopen(item) as fp_web:`:使用urllib.request模块的urlopen()函数打开图片链接。
5. `with open(rf'{child}\{basename(item)}', 'wb') as fp_local:`:打开一个二进制文件,用于写入下载下来的图片。
6. `fp_local.write(fp_web.read())`:将从图片链接中读取的二进制数据写入到本地文件中。
7. `else:`:如果图片已经下载过,则跳过下载图片,并读取已经下载好的文本文件。
8. `print(title, '已存在,跳过...')`:向控制台输出提示信息。
9. `fn = rf'{child}\{title}.txt'`:构造文本文件的文件名。
10. `with open(fn, encoding='utf8') as fp:`:打开文本文件。
11. `for line in fp:`:遍历文本文件中的每一行。
12. `sentences.append(line)`:将文本文件中的每一行加入到列表sentences中。
整段代码的作用是:从HTML文本中提取出所有的图片链接,并将图片下载到本地。如果图片已经下载过,则跳过下载并读取已经下载好的文本文件。最后将文本文件中的每一行加入到一个列表sentences中,用于后续的处理和分析。