input: Write a function to generate a fuzzy search algorithm in Python that can match strings with near similarities based on a given threshold. The function should take a query string, a list of choices, and a threshold value as inputs. It should return a list of choices that are similar to the query string according to the threshold.
  assert find_matches("apple", ["apple", "appel", "banana", "aple"], 2)==["apple", "appel", "aple"]
  assert find_matches("orange", ["orenge", "orage", "grape", "orange"], 1)==["orenge", "orage", "orange"]
  assert find_matches("grape", ["grpae", "grape", "grap", "guava"], 2)==["grpae", "grape", "grap"]
  assert find_matches("mango", ["mango", "mangoa", "manago", "melon"], 1)==["mango", "manago"]
  assert find_matches("cherry", ["chrey", "cher", "cheryy", "cheery"], 2)==["chrey", "cher", "cheryy"]
output: def find_matches(query, choices, threshold):
      from difflib import SequenceMatcher
      return [choice for choice in choices if SequenceMatcher(None, query, choice).quick_ratio() >= threshold / 10]