How do you optimize this code?
At the moment it is running to slow for the amount of data that goes through this loop. This code runs 1-nearest neighbor. It will predict the label of the training_element based off the p_data_set
# [x] , [[x1],[x2],[x3]], [l1, l2, l3]
def prediction(training_element, p_data_set, p_label_set):
temp = np.array([], dtype=float)
for p in p_data_set:
temp = np.append(temp, distance.euclidean(training_element, p))
minIndex = np.argmin(temp)
return p_label_set[minIndex]
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