1. # n - liczba grup
2. # k_i - liczba zajęć dla danej grupy i = 1..n
3. # pl - prowadzący
4.  
5. # timeslot = 8-16 * 5
6.  
7. # 1 - k_i typów zajęć - wpierdolić w timeslot
8.  
9. # DNA - wszystkie grupy - wszystkie zajecia
10.  
11. """
12. [ # 1..k_i typów zajęć - RR/MD
13.     [ # timesloty dla MD
14.         [obiekt_zajecie-GRUPA/PROWADZACY], # zajecie
15.         ["D2,Prowadzacy"], # zajecie
16.         ["D1,Prowadzacy"],
17.         [],
18.     ],
19.     [
20.  
21.     ]
22. ]
23. """
24. from pprint import pprint
25. import random
26. from itertools import groupby
27.  
28.  
29. class Lecture:
30.     def __init__(self, group, lecturer):
31.         self.group = group
32.         self.lecturer = lecturer
33.  
34.     def __repr__(self):
35.         return f"({self.group}, {self.lecturer})"
36.  
37.  
38. n = 5
39. k_i = 10
40. pl = 1
41. timeslots = 5*8
42.  
43. def generate_initial_population(count):
44.     out = []
45.     for i in range(count):
46.         state = [[[] for i in range(timeslots)] for l in range(k_i)]
47.         for lecture_type in range(k_i):
48.             for group in range(n):
49.                 chosen_timeslot = random.choice(state[lecture_type])
50.                 chosen_timeslot.append(Lecture(group, lecture_type))
51.         out.append(state)
52.  
53.     return out
54.  
55. def count_in_group_collisions(state):
56.     collisions = 0
57.     for timeslot in range(timeslots):
58.         for group in range(n):
59.             lectures_for_group_in_timeslot_count = 0
60.             for lecture_type in range(k_i):
61.                 timeslot_list = state[lecture_type][timeslot]
62.                 for lecture in timeslot_list:
63.                     if lecture.group == group:
64.                         lectures_for_group_in_timeslot_count += 1
65.                 if lectures_for_group_in_timeslot_count > 1:
66.                     collisions += 1
67.     return collisions
68.  
69.  
70. def count_lecturer_collisions(state):
71.     collisions = 0
72.     for lecture_type in range(k_i):
73.         for timeslot in range(timeslots):
74.             if len(state[lecture_type][timeslot]) > 1:
75.                 if any(filter(lambda x: x > 1,
76.                               [len(list(group))
77.                                for key, group in groupby(sorted(state[lecture_type][timeslot], key=lambda x: x.lecturer),
78.                                                          key=lambda x: x.lecturer)])):
79.                     collisions += 1
80.     return collisions
81.  
82.  
83. def count_okienkas(state):
84.     pass
85.  
86.  
87.  
88.  
89.  
90. population = generate_initial_population(5)
91. print (population[0])
92.  
93. for state in population:
94.     print(count_lecturer_collisions(state))
95.     print(count_in_group_collisions(state))
96.  
97.  
98.  
99.  
100.