def vector_space_search(self, words=[], **kwargs):
        top = kwargs.pop("top", 10)
        if not isinstance(words, Document):
            kwargs.setdefault("filter", lambda w: w) # pass-through.
            kwargs.setdefault("stopwords", True)
            words = Document(words)
        if len([w for w in words if w in self.vector]) == 0:
            return []
        m, words._model = words._model, self # So we can calculate tf-idf.
        n, words._model = self.nearest_neighbors(words, top), m
        words._model = m
        return n
Returns related documents from the model as a list of (similarity, document)-tuples.
dataset/ETHPy150Open clips/pattern/pattern/vector/__init__.py/Model.vector_space_search