output_root = options['output_root']
training_data_path = options['training_data_path']
vocab = (options['wordlist'], options['relalist'])
# --- init RNG --- #
try:
seed = options['seed']
except KeyError:
seed = 1337
bf2f.np.random.seed(seed)
# --- save the options --- #
options.save(output_root+'_options.txt')
# --- training data --- #
dstream = bf2f.data_stream(training_data_path)
if options['online']:
train_data = dstream
else:
train_data = dstream.acquire_all()
# --- initialise parameters --- #
try:
# load parameters from file
initial_params_path = options['initial_params_path']
initial_params = initial_params_path
except KeyError:
# create random
W, R = dstream.get_vocab_sizes()
d = options['dimension']
dpath = ''
wordlist = ''
elif not len(sys.argv) > 1:
sys.exit('ERROR: no data!')
else:
print 'Expecting dpath in config file.'
# overwrite from the options file
# WARNING: DANGEROUS
if 'dpath' in options:
dpath = options['dpath']
if 'wordlist' in options:
wordlist = options['wordlist']
# --- initialise things --- #
dstream = bf2f.data_stream(dpath)
W, R = dstream.get_vocab_sizes()
if W == 5 and d == 3:
C = bf2f.np.array([[0.01481961, -0.01517603, 0.00596634, 1.],
[-0.0080693, 0.00852271, -0.00106983, 1.],
[-0.0012176, 0.02482517, 0.01040345, 1.],
[0.00962732, 0.0100687, 0.00756443, 1.],
[0.00841503, 0.00188252, 0.02689446, 1.]])
V = bf2f.np.array([[-0.00878185, -0.01871243, -0.01610301, 1.],
[-0.02036443, -0.02137387, 0.00874672, 1.],
[0.00898955, 0.00722872, -0.00504091, 1.],
[0.00324052, 0.02674052, 0.00166536, 1.],
[0.01199952, 0.00430334, 0.0040228, 1.]])
#elif W == 5 and d == 2:
# C = bf2f.np.array([[ 1.0, 0.0, 1.0 ],
# [ 0.0, 1.0, 1.0 ],
dpath = ''
wordlist = ''
elif not len(sys.argv) > 1:
sys.exit('ERROR: no data!')
else:
print 'Expecting dpath in config file.'
# overwrite from the options file
# WARNING: DANGEROUS
if 'dpath' in options:
dpath = options['dpath']
if 'wordlist' in options:
wordlist = options['wordlist']
# --- initialise things --- #
dstream = bf2f.data_stream(dpath)
W, R = dstream.get_vocab_sizes()
if W == 5 and d == 3:
C = bf2f.np.array([[ 0.01481961, -0.01517603, 0.00596634, 1. ],
[-0.0080693 , 0.00852271, -0.00106983, 1. ],
[-0.0012176 , 0.02482517, 0.01040345, 1. ],
[ 0.00962732, 0.0100687 , 0.00756443, 1. ],
[ 0.00841503, 0.00188252, 0.02689446, 1. ]])
V = bf2f.np.array([[-0.00878185, -0.01871243, -0.01610301, 1. ],
[-0.02036443, -0.02137387, 0.00874672, 1. ],
[ 0.00898955, 0.00722872, -0.00504091, 1. ],
[ 0.00324052, 0.02674052, 0.00166536, 1. ],
[ 0.01199952, 0.00430334, 0.0040228 , 1. ]])
#elif W == 5 and d == 2:
# C = bf2f.np.array([[ 1.0, 0.0, 1.0 ],
# [ 0.0, 1.0, 1.0 ],
