def __init__(self, path, normalize=True, eig=0.0, transpose=False): if transpose: ut = np.load(path + '.vt.npy') self.wi, self.iw = load_vocabulary(path + '.contexts.vocab') else: ut = np.load(path + '.ut.npy') self.wi, self.iw = load_vocabulary(path + '.words.vocab') s = np.load(path + '.s.npy') if eig == 0.0: self.m = ut.T elif eig == 1.0: self.m = s * ut.T else: self.m = np.power(s, eig) * ut.T self.dim = self.m.shape[1] if normalize: self.normalize()
def load(cls, path, normalize=True, restricted_context=None, thresh=None): mat = load_matrix(path, thresh) word_vocab, context_vocab = load_vocabulary(mat, path) return cls(mat, word_vocab, context_vocab, normalize, restricted_context)