return exemplars
####################
# Main program
#
if len(sys.argv) != 3:
print "Usage: python label.py training_file test_file"
sys.exit()
(train_file, test_file) = sys.argv[1:3]
print "Learning model..."
solver = Solver()
train_data = read_data(train_file)
solver.train(train_data)
print "Loading test data..."
test_data = read_data(test_file)
print "Testing classifiers..."
scorer = Score()
Algorithms = ("Naive", "Sampler", "Max marginal", "MAP", "Best")
for (s, gt) in test_data:
outputs = {"0. Ground truth" : [[gt,], []]}
# run all algorithms on the sentence
for i in range(0, len(Algorithms)):
outputs[ str(i+1) + ". " + Algorithms[i] ] = solver.solve(Algorithms[i], s)
return exemplars
####################
# Main program
#
if len(sys.argv) != 3:
print "Usage: python label.py training_file test_file"
sys.exit()
(train_file, test_file) = sys.argv[1:3]
print "Learning model..."
solver = Solver()
train_data = read_data(train_file)
solver.train(train_data)
print "Loading test data..."
test_data = read_data(test_file)
print "Testing classifiers..."
scorer = Score()
Algorithms = ("Simplified", "HMM", "Complex")
for (s, gt) in test_data:
outputs = {
"0. Ground truth": [[
gt,
], []]
}
#print exemplars[0]
return exemplars
####################
# Main program
#
if len(sys.argv) != 3:
print "Usage: python label.py training_file test_file"
sys.exit()
(train_file, test_file) = sys.argv[1:3]
print "Learning model..."
solver = Solver()
train_data = read_data(train_file)
solver.train(train_data)
print "Loading test data..."
test_data = read_data(test_file)
print "Testing classifiers..."
scorer = Score()
Algorithms = ("Simplified", "HMM", "Complex")
for (s, gt) in test_data:
outputs = {
"0. Ground truth": [[
gt,
], []]
}
