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))
