## apply constraints (this is the costliest step...)
geneval.apply_mark_list(mt, constraints)
if args.timed:
mark = timer(" Added violations in")
# If Gaussian priors file is provided, read in to a list
if args.gaussian_priors_file:
mt.read_priors_file(args.gaussian_priors_file)
# Optimize mt.weights
optimizer.learn_weights(mt, args.L1, args.L2, args.precision)
if args.timed:
mark = timer(" Optimized weights in")
# Write the final MegaTableau to file
if args.outfile:
mt.write_output(args.outfile)
# Read in testing items, generate predictions for them
if args.testing_infile:
testing_mt = megatableau.MegaTableau()
testing_mt.weights = mt.weights
geneval.read_data_only(testing_mt, args.testing_infile)
geneval.apply_mark_list(testing_mt, mt.constraints)
optimizer.update_maxent_values(testing_mt.weights, testing_mt.tableau)
# Write tableau to file
if args.testing_outfile:
testing_mt.write_output(args.testing_outfile)
help='Gaussian priors file name. If specified, maxent.py \
uses the mu and sigma values in the file instead of \
standard L1 and L2 priors to learn weights.')
args = parser.parse_args()
#####################################################################
## Main code
#####################################################################
# Construct MegaTableau
mt = megatableau.MegaTableau(args.input_file_name)
# If weights are provided, return the probability of the tableau
if args.weights_file:
mt.read_weights_file(args.weights_file)
optimizer.update_maxent_values(mt.weights, mt.tableau) # Needed if -outfile
print('\nLog probability of data: {}'.format(- optimizer.neg_log_probability
(mt.weights, mt.tableau, args.L1, args.L2)))
# If Gaussian priors file is provided, read in to a list
if args.gaussian_priors_file:
mt.read_priors_file(args.gaussian_priors_file)
# Optimize mt.weights (unless weights were specified)
if not args.weights_file:
optimizer.learn_weights(mt, args.L1, args.L2, args.precision)
# Output file
if args.outfile:
mt.write_output(args.outfile)
