Add Python doctest to interval-scheduling.py (#162)

* Add Python doctest to interval-scheduling.py

* URL to a description of the algorithm used

* Remove a stale comment

scheduling/python/interval-scheduling.py

@@ -5,29 +5,37 @@ The Strategy: At each step choose the job with earliest finish time
 Algorithm Type: Greedy
 Time Complexity: O(n*log(n))
 """
+jobs = [
+  (0, 2, 8),  # (job_id, start_time, finish_time)
+  (1, 6, 10),
+  (2, 1, 3),
+  (3, 4, 7),
+  (4, 3, 6),
+  (5, 1, 2),
+  (6, 8, 10),
+  (7, 10, 15),
+  (8, 12, 16),
+  (9, 14, 16)
+]
 
-def get_opt_schedule(jobs): # Returns the job_id's in the optimal schedule
+
+def get_opt_schedule(jobs):
+  """
+  Return the job_id's in the optimal jobs
+  https://en.wikipedia.org/wiki/Interval_scheduling#Greedy_polynomial_solution
+
+  >>> get_opt_schedule(jobs)
+  [5, 4, 1, 7]
+  """  
   opt_schedule = []
   sorted_jobs = sorted(jobs, key=lambda j: j[2])
-  n = len(sorted_jobs)
+  number_of_jobs = len(sorted_jobs)
   opt_schedule.append(sorted_jobs[0][0])
-  for i in range(1, n):
+  for i in range(1, number_of_jobs):
     if sorted_jobs[i][1] >= jobs[opt_schedule[-1]][2]:
       opt_schedule.append(sorted_jobs[i][0])
   return opt_schedule
 
-jobs = [
-  [0, 2, 8], # [job_id, start_time, finish_time]
-  [1, 6, 10],
-  [2, 1, 3],
-  [3, 4, 7],
-  [4, 3, 6],
-  [5, 1, 2],
-  [6, 8, 10],
-  [7, 10, 15],
-  [8, 12, 16],
-  [9, 14, 16]
-]
 
-opt_schedule = get_opt_schedule(jobs)
-print(opt_schedule)
+if __name__ == "__main__":
+  print(get_opt_schedule(jobs))