h2o-py/h2o/model/word_embedding.py
def find_synonyms(self, word, count=20):
        """
        Find synonyms using a word2vec model.

        :param str word: A single word to find synonyms for.
        :param int count: The first "count" synonyms will be returned.

        :returns: the approximate reconstruction of the training data.
        """
        j = h2o.api("GET /3/Word2VecSynonyms", data={'model': self.model_id, 'word': word, 'count': count})
        return OrderedDict(sorted(zip(j['synonyms'], j['scores']), key=lambda t: t[1], reverse=True))
def find_synonyms(self, word, count=20):
        j = h2o.api("GET /3/Word2VecSynonyms", data={'model': self.model_id, 'word': word, 'count': count})
        return OrderedDict(sorted(zip(j['synonyms'], j['scores']), key=lambda t: t[1], reverse=True))
def find_synonyms ( self , word , count = 20 ) : j = h2o . api ( "GET /3/Word2VecSynonyms" , data = { 'model' : self . model_id , 'word' : word , 'count' : count } ) return OrderedDict ( sorted ( zip ( j [ 'synonyms' ] , j [ 'scores' ] ) , key = lambda t : t [ 1 ] , reverse = True ) )
Find synonyms using a word2vec model.

        :param str word: A single word to find synonyms for.
        :param int count: The first "count" synonyms will be returned.

        :returns: the approximate reconstruction of the training data.
Find synonyms using a word2vec model .