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(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)
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:
return Embedding.load(path, *args, **kwargs)
def simple_create_representation(rep_type,
path,
restricted_context=None,
thresh=None,
normalize=True):
if rep_type == 'PPMI':
return Explicit.load(path,
normalize=normalize,
restricted_context=restricted_context,
thresh=thresh)
else:
return Embedding.load(path, True)
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 simple_create_representation(rep_type, path, restricted_context=None, thresh=None, normalize=True):
if rep_type == 'PPMI':
return Explicit.load(path, normalize=normalize, restricted_context=restricted_context, thresh=thresh)
else:
return Embedding.load(path, True)
