有没有办法向量化并将新值添加到预先存储的 tfidfvectorized 向量
[英]Is there a way to vectorize and add new values to a pre-stored tfidfvectorized vector
问题描述
这是我的代码
strings=["india","good morning","universe","galaxy","good evening","germany","country"]
vectorizer= TfidfVectorizer()
vectors=vectorizer.fit_transform(strings)
string2=["good afternoon world"]
现在我想 fit_transform string2 并将其添加到向量中，以便我可以使用向量进行推荐
0 个解决方案
有没有办法将假设存储为向量的字符串转换为向量？
[英]Is there a way to convert a string, that was suppose to be stored as a vector, into a vector?
Python，在随机索引处向向量添加值的最有效方法是什么？
[英]Python, what is the most efficient way to add values to a vector at a random indexes?
如何使用新向量在预先计算的矩阵中找到余弦相似度？
[英]how to find cosine similarity in a pre-computed matrix with a new vector?
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