请用python完成对两句中文短语的编辑距离,tfidf计算和textrank计算
时间: 2024-02-09 11:11:32 浏览: 62
好的,我可以帮你回答这个问题。以下是Python代码:
编辑距离:
```python
import numpy as np
def edit_distance(str1, str2):
m, n = len(str1), len(str2)
distance = np.zeros((m+1, n+1))
for i in range(m+1):
distance[i][0] = i
for j in range(n+1):
distance[0][j] = j
for i in range(1, m+1):
for j in range(1, n+1):
if str1[i-1] == str2[j-1]:
cost = 0
else:
cost = 1
distance[i][j] = min(distance[i-1][j]+1, distance[i][j-1]+1, distance[i-1][j-1]+cost)
return distance[m][n]
```
TF-IDF计算:
```python
from sklearn.feature_extraction.text import TfidfVectorizer
corpus = ['这是第一句话', '这是第二句话', '这是第三句话']
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
print(X.toarray())
```
TextRank计算:
```python
import jieba.analyse
import networkx as nx
text = '这是一段需要进行TextRank计算的文本。'
keywords = jieba.analyse.textrank(text, topK=10, withWeight=True, allowPOS=('ns', 'n', 'vn', 'v'))
graph = nx.Graph()
for word, weight in keywords:
graph.add_node(word, weight=weight)
for i, (word_i, weight_i) in enumerate(keywords):
for j, (word_j, weight_j) in enumerate(keywords):
if i == j:
continue
else:
similarity = jieba.analyse.textrank(text, topK=1, withWeight=True, allowPOS=('ns', 'n', 'vn', 'v'),
keyword_freq={word_i: weight_i, word_j: weight_j})
if similarity:
graph.add_edge(word_i, word_j, weight=similarity[0][1])
scores = nx.pagerank(graph)
print(scores)
```
希望这些代码能够帮助你完成这道问题,如果你还有其他问题,可以随时问我。