def create_representation(args):
rep_type = args['<representation>']
path = args['<representation_path>']
w_c = args['--w+c']
eig = float(args['--eig'])
if rep_type == 'PPMI':
if w_c:
raise Exception('w+c is not implemented for PPMI.')
else:
return Explicit.load(path, True)
elif rep_type == 'SVD':
if w_c:
return EnsembleEmbedding(SVDEmbedding(path, False, eig, False),
SVDEmbedding(path, False, eig, True),
True)
else:
return SVDEmbedding(path, True, eig)
else:
if w_c:
return EnsembleEmbedding(Embedding.load(path + '.words', False),
Embedding.load(path + '.contexts', False),
True)
else:
return Embedding.load(path, True)
def create_representation(args):
rep_type = args['<representation>']
path = args['<representation_path>']
neg = int(args['--neg'])
w_c = args['--w+c']
eig = float(args['--eig'])
normalize = args['--normalize']
if rep_type == 'PPMI':
if w_c:
raise Exception('w+c is not implemented for PPMI.')
else:
return PositiveExplicit(path, normalize, neg)
elif rep_type == 'SVD':
if w_c:
return EnsembleEmbedding(SVDEmbedding(path, normalize, eig, False),
SVDEmbedding(path, normalize, eig, True),
normalize)
else:
return SVDEmbedding(path, normalize, eig)
else:
if w_c:
return EnsembleEmbedding(Embedding(path + '.words', normalize),
Embedding(path + '.contexts', normalize),
normalize)
else:
return Embedding(path + '.words', normalize)
def create_representation(args):
rep_type = args['<representation>']
path = args['<representation_path>']
neg = int(args['--neg'])
w_c = args['--w+c']
eig = float(args['--eig'])
if rep_type == 'PPMI':
if w_c:
raise Exception('w+c is not implemented for PPMI.')
else:
return PositiveExplicit(path, True, neg)
elif rep_type == 'SVD':
if w_c:
return EnsembleEmbedding(SVDEmbedding(path, False, eig, False),
SVDEmbedding(path, False, eig, True),
True)
else:
return SVDEmbedding(path, True, eig)
elif rep_type == 'SGNS':
if w_c:
return EnsembleEmbedding(Embedding(path + '.words', False),
Embedding(path + '.contexts', False),
True)
else:
return Embedding(path + '.words', True)
elif rep_type == 'discriminative':
return discriminative_embedding(path, True, eig)
elif rep_type == 'discriminative_SGNS':
return discriminative_SGNS(path, True)
elif rep_type == 'projective':
return Projective_embedding(path)
def create_representation(rep_type, path, *args, **kwargs):
if rep_type == 'Explicit' or rep_type == 'PPMI':
return Explicit.load(path, *args, **kwargs)
elif rep_type == 'SVD':
return SVDEmbedding(path, *args, **kwargs)
elif rep_type == 'GIGA':
return GigaEmbedding(path, *args, **kwargs)
elif rep_type == 'Embedding':
return Embedding.load(path, *args, **kwargs)