def calc_text_token_distance(cls, attr_name, item_list, selected_item):
if selected_item and getattr(selected_item, attr_name):
item_tokens = vector_utils.to_vector(attr_name, item_list)
tokens = vector_utils.to_value(getattr(selected_item, attr_name))
clusters, vectors = vector_utils.make_text_clusters(item_tokens)
target_vector = vector_utils.classify_text_tokens(tokens, clusters)
distances = [vector_utils.calc_vector_distance(target_vector, v) for v in vectors]
inv_distance = [4 if d == 0 else 1 - math.log(d) for d in distances]
# 4 is large enough in f(x) = 1-log(x)
return cls.normalize(inv_distance)
else:
raise NotCalculatable("selected item's " + attr_name + " is None")
def test_classify_text_token(self):
classified = vector_utils.make_text_clusters([["AAA", "BBB", "CCC"], ["AAA", "ZZZ"], ["aaa", "BBB", "CCC"]])
print(classified)
