def main(args):
if not args.extra:
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
args.dataset_file)
pred_p = c.classifyPerceptron(train_set, train_labels, dev_set,
args.lrate, args.max_iter)
print("Perceptron")
accuracy, f1, precision, recall = compute_accuracies(
pred_p, dev_set, dev_labels)
pred_lr = c.classifyLR(train_set, train_labels, dev_set, args.lrate,
args.max_iter)
print("\nLogistic Regression")
accuracy, f1, precision, recall = compute_accuracies(
pred_lr, dev_set, dev_labels)
else:
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
args.dataset_file, extra=True)
predicted_labels = c.classifyEC(train_set, train_labels, dev_set,
args.k)
print("kNN, k = {}".format(args.k))
accuracy, f1, precision, recall = compute_accuracies(
predicted_labels, dev_set, dev_labels)
def main(args):
if args.method == 'perceptron':
train_set, train_labels, dev_set,dev_labels = reader.load_dataset(args.dataset_file)
pred_p = c.classifyPerceptron(train_set, train_labels, dev_set, args.lrate, args.max_iter)
print("Perceptron")
accuracy,f1,precision,recall = compute_accuracies(pred_p, dev_labels)
elif args.method == 'knn':
train_set, train_labels, dev_set,dev_labels = reader.load_dataset(args.dataset_file, extra=True)
predicted_labels = c.classifyKNN(train_set, train_labels, dev_set, args.k)
print("kNN, k = {}".format(args.k))
accuracy,f1,precision,recall = compute_accuracies(predicted_labels, dev_labels)
else:
print("Method must be either perceptron or knn!")