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, ], []] }