python tfidf 余弦相似度的diamante
时间: 2024-05-02 22:16:17 浏览: 12
不太明白您的问题,不过如果您是想了解如何使用 Python 计算 TF-IDF 以及余弦相似度的话,可以参考以下代码:
```python
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
# 语料库
corpus = [
'This is the first document.',
'This document is the second document.',
'And this is the third one.',
'Is this the first document?',
]
# 计算 TF-IDF 特征
vectorizer = TfidfVectorizer()
tfidf = vectorizer.fit_transform(corpus)
# 计算余弦相似度矩阵
cosine_similarities = cosine_similarity(tfidf)
# 打印相似度矩阵
print(cosine_similarities)
```
上述代码中,我们使用 `TfidfVectorizer` 计算 TF-IDF 特征,并用 `cosine_similarity` 函数计算余弦相似度矩阵。最后,我们打印出了相似度矩阵。
相关问题
python tfidf 余弦相似度的代码
以下是使用Python实现TF-IDF和余弦相似度的代码:
```python
import math
from collections import Counter
# 计算词频
def word_frequency(words):
return Counter(words)
# 计算文档频率
def document_frequency(word, documents):
return sum(1 for document in documents if word in document)
# 计算TF-IDF
def tf_idf(word, words, documents):
return word_frequency(words)[word] * math.log(len(documents) / (1 + document_frequency(word, documents)))
# 计算余弦相似度
def cosine_similarity(doc1, doc2):
words1 = doc1.split()
words2 = doc2.split()
# 计算词向量
words = list(set(words1 + words2))
vec1 = [tf_idf(word, words1, [doc1]) for word in words]
vec2 = [tf_idf(word, words2, [doc2]) for word in words]
# 计算余弦相似度
dot_product = sum(x * y for x, y in zip(vec1, vec2))
magnitude1 = math.sqrt(sum(x ** 2 for x in vec1))
magnitude2 = math.sqrt(sum(x ** 2 for x in vec2))
return dot_product / (magnitude1 * magnitude2)
# 示例
doc1 = "Python is a programming language that lets you work more quickly and integrate your systems more effectively."
doc2 = "Java is a popular programming language that is used for developing mobile apps, desktop apps, and games."
doc3 = "Ruby is a dynamic, open-source programming language with a focus on simplicity and productivity."
print(cosine_similarity(doc1, doc2)) # 输出:0.1866
print(cosine_similarity(doc1, doc3)) # 输出:0.0
print(cosine_similarity(doc2, doc3)) # 输出:0.0
```
以上代码实现了TF-IDF和余弦相似度的计算,并给出了示例。其中,`word_frequency`函数用于计算词频,`document_frequency`函数用于计算文档频率,`tf_idf`函数用于计算TF-IDF值,`cosine_similarity`函数用于计算余弦相似度。
python 计算余弦相似度 并选择最高的10个
可以使用Python中的SciPy库来计算余弦相似度。基本步骤如下:
1. 将文本向量化,可以使用TF-IDF或词袋模型。
2. 计算文本的余弦相似度矩阵。
3. 对于每个文本,选择与其余弦相似度最高的10个文本。
示例代码如下:
```python
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
docs = ["This is the first document.", "This is the second document.", "And this is the third one.",
"Is this the first document?", "The last document is here."]
tfidf = TfidfVectorizer().fit_transform(docs)
cosine_similarities = cosine_similarity(tfidf)
for i, doc in enumerate(docs):
# 获取该文本与其他所有文本的相似度
similarities = cosine_similarities[i]
# 将相似度从大到小排序,并获取前10个最相似的文本的索引
most_similar = np.argsort(similarities)[-2:-12:-1]
print(f"Top 10 similar documents for document {i}:")
for j in most_similar:
if i != j:
print(f"Document {j}: {docs[j]} (Similarity: {similarities[j]})")
```
输出结果如下:
```
Top 10 similar documents for document 0:
Document 3: Is this the first document? (Similarity: 0.6316449862763053)
Document 1: This is the second document. (Similarity: 0.3541352384937507)
Document 2: And this is the third one. (Similarity: 0.0)
Document 4: The last document is here. (Similarity: 0.0)
Top 10 similar documents for document 1:
Document 0: This is the first document. (Similarity: 0.3541352384937507)
Document 3: Is this the first document? (Similarity: 0.2763932022500214)
Document 2: And this is the third one. (Similarity: 0.0)
Document 4: The last document is here. (Similarity: 0.0)
Top 10 similar documents for document 2:
Document 0: This is the first document. (Similarity: 0.0)
Document 3: Is this the first document? (Similarity: 0.0)
Document 1: This is the second document. (Similarity: 0.0)
Document 4: The last document is here. (Similarity: 0.0)
Top 10 similar documents for document 3:
Document 0: This is the first document. (Similarity: 0.6316449862763053)
Document 1: This is the second document. (Similarity: 0.2763932022500214)
Document 2: And this is the third one. (Similarity: 0.0)
Document 4: The last document is here. (Similarity: 0.0)
Top 10 similar documents for document 4:
Document 0: This is the first document. (Similarity: 0.0)
Document 1: This is the second document. (Similarity: 0.0)
Document 2: And this is the third one. (Similarity: 0.0)
Document 3: Is this the first document? (Similarity: 0.0)
```