def word_vec(self, word, use_norm=False):
"""Get the word's representations in vector space, as a 1D numpy array.
Parameters
----------
word : str
A single word whose vector needs to be returned.
use_norm : bool
If True, returns normalized vector.
Returns
-------
:class:`numpy.ndarray`
The word's representations in vector space, as a 1D numpy array.
Raises
------
KeyError
For words with all ngrams absent, a KeyError is raised.
Example
-------
>>> from gensim.models import FastText
>>> sentences = [["cat", "say", "meow"], ["dog", "say", "woof"]]
>>>
>>> model = FastText(sentences, min_count=1)
>>> meow_vector = model.word_vec('meow') # get vector for word
"""
return FastTextKeyedVectors.word_vec(self.wv, word, use_norm=use_norm)
def word_vec(self, word, use_norm=False):
return FastTextKeyedVectors.word_vec(self.wv, word, use_norm=use_norm)
def word_vec(self, word, use_norm=False):
return FastTextKeyedVectors.word_vec(self.wv, word, use_norm=use_norm)