doc2vec.py 文件源码

def __init__(self,
                 analyzer=None, matching=None,
                 name=None,
                 verbose=0,
                 n_epochs=10,
                 alpha=0.25,
                 min_alpha=0.05,
                 n_jobs=4,
                 **kwargs):
        # self.model = model
        self.alpha = alpha
        self.min_alpha = min_alpha
        self.verbose = verbose
        self.name = "paragraph-vectors" if name is None else name

        if matching is True:
            self._matching = Matching()
        elif matching is False or matching is None:
            self._matching = None
        else:
            self._matching = Matching(**dict(matching))

        self.analyzer = analyzer
        self.model = Doc2Vec(alpha=alpha,
                             min_alpha=alpha,
                             size=500,
                             window=8,
                             min_count=1,
                             sample=1e-5,
                             workers=n_jobs,
                             negative=20,
                             dm=0, dbow_words=1,  # words only with dm!=0?
                             dm_mean=0,  # unused when in concat mode
                             dm_concat=1,
                             dm_tag_count=1
                             )
        self.n_epochs = n_epochs
        self._neighbors = NearestNeighbors(**kwargs)