data = ["This two-wheeler is really good on slippery roads"] sentce=["This is really good"] from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer from sklearn.metrics.pairwise import cosine_similarity vectorizer = CountVectorizer() X_train_termcounts = vectorizer.fit_transform(data) tfidf_transformer = TfidfTransformer() X_train_tfidf = tfidf_transformer.fit_transform(X_train_termcounts) print ("\nTfidf of training data:", X_train_tfidf.toarray()) X_input_termcounts = vectorizer.transform(sentce) X_input_tfidf = tfidf_transformer.transform(X_input_termcounts) print ("\nTfidf of training data:", X_input_tfidf.toarray()) print("\nCosine of data:",cosine_similarity(X_train_tfidf,X_input_tfidf))代码注释
时间: 2024-02-09 19:08:53 浏览: 154
下面是对这段代码的注释:
```
data = ["This two-wheeler is really good on slippery roads"] # 定义训练数据
sentce=["This is really good"] # 定义输入句子
from sklearn.feature_extraction.text import CountVectorizer # 导入文本特征提取库
from sklearn.feature_extraction.text import TfidfTransformer # 导入TF-IDF转换库
from sklearn.metrics.pairwise import cosine_similarity # 导入余弦相似度计算库
vectorizer = CountVectorizer() # 创建词袋模型
X_train_termcounts = vectorizer.fit_transform(data) # 对训练数据进行特征提取和向量化
tfidf_transformer = TfidfTransformer() # 创建TF-IDF转换器
X_train_tfidf = tfidf_transformer.fit_transform(X_train_termcounts) # 对训练数据进行TF-IDF转换
print ("\nTfidf of training data:", X_train_tfidf.toarray()) # 打印训练数据的TF-IDF矩阵
X_input_termcounts = vectorizer.transform(sentce) # 对输入句子进行特征提取和向量化
X_input_tfidf = tfidf_transformer.transform(X_input_termcounts) # 对输入句子进行TF-IDF转换
print ("\nTfidf of input data:", X_input_tfidf.toarray()) # 打印输入句子的TF-IDF矩阵
print("\nCosine of data:",cosine_similarity(X_train_tfidf,X_input_tfidf)) # 计算训练数据和输入句子的余弦相似度
```
这段代码通过使用CountVectorizer和TfidfTransformer库对文本数据进行特征提取和向量化,并使用cosine_similarity计算训练数据和输入句子的余弦相似度。输出结果是训练数据和输入句子的TF-IDF矩阵以及它们之间的余弦相似度。
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