31 lines
1.3 KiB
Python
31 lines
1.3 KiB
Python
'''
|
|
Result of kruskals algorithm with first 8 cars in the system.
|
|
The array shows the raw output, where the index and first item in the tuple
|
|
is the vehicles position in the sorted `closest_vehicles` array.
|
|
The dictionary is the same data, just the keys and first item in of each tuple
|
|
replaced with the vehicle_id.
|
|
|
|
test_auto_example.png shows the position of these vehicles as well as the starting point.
|
|
'''
|
|
|
|
[
|
|
[(1, 0.0018223418998603907), (2, 0.005142119115696336)],
|
|
[(0, 0.0018223418998603907), (4, 0.0038434786326951562)],
|
|
[(0, 0.005142119115696336)],
|
|
[(6, 0.003218020664940648), (4, 0.004116640377786014), (5, 0.005380458066000523)],
|
|
[(1, 0.0038434786326951562), (3, 0.004116640377786014)],
|
|
[(3, 0.005380458066000523)],
|
|
[(3, 0.003218020664940648), (7, 0.00569171626137669)],
|
|
[(6, 0.00569171626137669)]
|
|
]
|
|
|
|
vehicle_ids = {
|
|
1: [(8, 0.0018223418998603907), (2, 0.005142119115696336)],
|
|
8: [(1, 0.0018223418998603907), (6, 0.0038434786326951562)],
|
|
2: [(1, 0.005142119115696336)],
|
|
7: [(5, 0.003218020664940648), (6, 0.004116640377786014), (3, 0.005380458066000523)],
|
|
6: [(8, 0.0038434786326951562), (7, 0.004116640377786014)],
|
|
3: [(7, 0.005380458066000523)],
|
|
5: [(7, 0.003218020664940648), (4, 0.00569171626137669)],
|
|
4: [(5, 0.00569171626137669)]
|
|
} |