}
# using Euclidean
print('using Euclidean...')
correctness = 0
for i, each in enumerate(testing):
print(i, "of", len(testing))
label = each[0]
data = each[1:]
min_dist = float('Inf')
min_pos = -1
for j, candidate in enumerate(training_with_label):
dist = DTW.euclid_dist(data, candidate['data'])
if dist < min_dist:
min_dist = dist
min_pos = j
predicted = training_with_label[min_pos]['label']
if predicted == label:
correctness += 1
print('min_dist:', min_dist)
print('min_pos:', min_pos)
print('predicted label:', predicted)
print('actual label:', label)
