def main(): node_pairs_list = read_file("digit-examples-all.txt") train_split = 1 print("Split: {0}0% train, {1}0% test".format(str(train_split), str(10 - train_split))) train_set_size = (5620 // 10) * train_split training_set = node_pairs_list[0:train_set_size + 1] test_set = node_pairs_list[train_set_size + 1:] #training_set = node_pairs_list[0 : 50] #test_set = node_pairs_list[50 : 100] weights = [] for i in range(64): weights.append([random.uniform(-1, 1) for x in range(10)]) for pair in training_set: neural_net = NeuralNetwork(pair[0], pair[1], weights) neural_net.train_NN() weights = neural_net.weights_list euclidean_distance = 0 for pair in test_set: neural_net = NeuralNetwork(pair[0], pair[1], weights) euclidean_distance += neural_net.test_NN() weights = neural_net.weights_list if (euclidean_distance == 0): avg_euclidean_distance = 0 else: avg_euclidean_distance = euclidean_distance / len(test_set) print("Average Euclidean Distance: {}".format(avg_euclidean_distance))