print "=" * 10, "EPOCH", i + 1, "=" * 10 # get the sets for training and testing training_set_names = set_names[:] test_set_names = [training_set_names.pop(i)] training_set = fileutils.get_data_set(features_save_path, training_set_names, dictionary_size) test_set = fileutils.get_data_set(features_save_path, test_set_names, dictionary_size) print "Training sets:", training_set_names print "Test set:", test_set_names # train and test p.train(training_set) p.test(test_set) (precision, recall, f1) = p.get_results() # prints the results and calc the avg results print_results(precision, recall, f1) avg_precision += precision / len(set_names) avg_recall += recall / len(set_names) avg_f1 += f1 / len(set_names) print "" print "=" * 10, "Average results", "=" * 10 print_results(avg_precision, avg_recall, avg_f1)
def train_and_perception(): p = Perception(2, f) input_vecs, labels = get_training_dataset() p.train(input_vecs, labels, 10, 0.1) return p