Added more comments

Author: Wwarrior1

.gitignore

@@ -87,3 +87,23 @@ ENV/
 
 # Rope project settings
 .ropeproject
+
+### JetBrains template
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and Webstorm
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+.idea/
+
+## File-based project format:
+*.iws
+
+## Plugin-specific files:
+
+# IntelliJ
+/out/
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+

algorithm/algorithm.py

@@ -11,28 +11,36 @@ def execute(data):
  :return: score of the best solution found
  """
 
- n = 5
+ n = 4 # nr of bees == solutions
  m = 3
  e = 1
- nep = 4
- nsp = 2
- ngh = 5
- max_iter = 2
-
+ nep = 4 # 1 elite bee goes to 4 different places
+ # from these 4 places chooses exactly 1
+ nsp = 2 # 2 good bees goes to 2 different places
+ # from these 4 places chooses exactly 1
+ ngh = 4 # max distance from center (e.g. elite solution)
+ max_iter = 8
+
+ # n losowych rozwiazan (pszczol)
  pool = [AlgorithmUtils.random_solution(data) for _ in range(n)]
 
  for i in range(max_iter):
  print("Iter ", i)
 
+ # calcuate all solutions and sort them all
  solutions_ranking = sorted(pool, key=lambda s: calculate_score(data, s), reverse=True)
+ # choose 1 elite
  elite_solutions = solutions_ranking[0:e]
  good_solutions = solutions_ranking[e:m]
 
  pool = []
+ # choose 1 solution from each elite solutions
  for es in elite_solutions:
  pool.append(AlgorithmUtils.best_in_neighbourhood(data, es, ngh, nep))
+ # choose 1 solution from each good solutions
  for gs in good_solutions:
  pool.append(AlgorithmUtils.best_in_neighbourhood(data, gs, ngh, nsp))
+ # dla pszczol ktore byly slabe wybieramy dla nich nowe rozwiazania (leca gdzie indziej)
  for _ in range(n-m):
  pool.append(AlgorithmUtils.random_solution(data))
 

algorithm/utils.py

@@ -19,7 +19,7 @@ def random_solution(data):
 
  res = dict()
  for ids in range(data.amount_of_cache_servers):
- random_video = random.randint(0, len(data.videos_sizes)-1)
+ random_video = random.randint(0, len(data.videos_sizes)-1) # film to jest indeks na liscie video_sizes
  res[ids] = [random_video] if data.videos_sizes[random_video] <= data.cache_size else []
  return res
 
@@ -40,6 +40,11 @@ def random_neighbour(data, solution, radius):
  if radius == 0:
  return res
 
+ # TODO - optimize free_space here
+ # I guess free_space is invoked many times in list comprehension
+ # we could previously preapre a list of free_spaces for each server
+
+ # lista wszystkich operacji
  acceptable_ops = [(s, v)
  for s in range(data.amount_of_cache_servers)
  for v in range(len(data.videos_sizes))

main.py

@@ -1,21 +1,27 @@
 from algorithm.algorithm import Algorithm
 from parsers.input_parser import parse_input
 from utils.solution_checker import check_solution
-
+import time
 
 def main():
  # input_file = "files/videos_worth_spreading.in"
  # input_file = "files/kittens.in"
  # input_file = "files/example1.in"
- input_file = "files/me_at_the_zoo.in"
- # input_file = "files/trending_today.in"
+ # input_file = "files/me_at_the_zoo.in"
+ input_file = "files/trending_today.in"
 
  # solution_file = "files/example1.out"
  # solution = check_solution(input_file, solution_file)
  # print(solution)
 
+ before = time.time()
  score = Algorithm.execute(parse_input(input_file))
- print(score)
+ after = time.time()
+
+ print("-----------------------------")
+ print("Algorithm took time: " + str(round(after-before, 2)) + " s")
+ print("-----------------------------")
+ print("Saved time: " + str(round(score / 1000, 2)) + " s")
 
 if __name__ == '__main__':
  main()