mark = time.time()
def timer(task):
print("{}\t{} seconds".format(task, str(round(time.time() - mark, 4))))
return time.time()
#####################################################################
## Main code
#####################################################################
# Create and fill MegaTableau
mt = megatableau.MegaTableau()
# read in attested forms and add to MegaTableau
geneval.read_data_only(mt, args.attested_file_name)
if args.timed:
mark = timer("\n Parsed arguments in")
# get alphabet
if args.alphabet_file_name:
alphabet = geneval.read_sigma(mt, args.alphabet_file_name)
else:
alphabet = geneval.read_sigma(mt)
if args.timed:
mark = timer(" Inferred alphabet in")
## read in constraints
constraints = geneval.read_constraints(mt, args.constraint_file_name)
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)
# Read in testing items, generate predictions for them
# NOTE: This step will only succeed if constraints can be evaluated
# automatically as regular expressions.
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)