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praca_magisterska/algorithms/a_star.py

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Changed binary heap to Fibonaccie's heap. Got rid of placeholder data.
2022-09-23 16:06:04 +02:00
import fibheap
Rewrote all 3 algorithms. Generated 10 random graphs and grids for tests. Created testing structure which uses generated graphs and grids as input data to algorithms, measures execution time and memory blocks used by them. Takes these measurements and found weights and builds tables in latex format which compare these data among those three algorithms.
2022-09-25 16:41:41 +02:00
import math
Changed binary heap to Fibonaccie's heap. Got rid of placeholder data.
2022-09-23 16:06:04 +02:00
Rewrote all 3 algorithms. Generated 10 random graphs and grids for tests. Created testing structure which uses generated graphs and grids as input data to algorithms, measures execution time and memory blocks used by them. Takes these measurements and found weights and builds tables in latex format which compare these data among those three algorithms.
2022-09-25 16:41:41 +02:00
def heuristic_cost(node1, node2):
Implemented A* algorithm (needs better heuristic function).
2021-10-31 13:01:58 +01:00
return 0
Rewrote all 3 algorithms. Generated 10 random graphs and grids for tests. Created testing structure which uses generated graphs and grids as input data to algorithms, measures execution time and memory blocks used by them. Takes these measurements and found weights and builds tables in latex format which compare these data among those three algorithms.
2022-09-25 16:41:41 +02:00
def heuristic_cost_manhattan(node1, node2):
part1 = (node1[0]-node2[0])**2
part1 = part1
part2 = (node1[1]-node2[1])**2
return math.sqrt(part1 + part2)
def a_star_algorithm(s, t, N, A, w, h):
if s == t:
return [s], 0
H = fibheap.makefheap()
d = {}
heap_node_connection = {}
pred = {}
f = {}
for i in N:
d[i] = float('inf')
d[s] = 0
f[s] = d[s] + h(s, t)
pred[s] = None
heap_node_connection[s] = fibheap.fheappush(H, 0, s)
success = False
while H.num_nodes > 0:
i = fibheap.fheappop(H)[1]
if i == t:
success = True
break
for j in A[i]:
path_sum = d[i] + w[(i, j)]
if d[j] > path_sum:
pred[j] = i
f[j] = path_sum + h(j, t)
if d[j] == float('inf'):
d[j] = path_sum
heap_node_connection[j] = fibheap.fheappush(H, f[j], j)
Added handing of directed graphs to a_star and dijkstra as well as to reading graphs from file.
2022-01-15 14:29:52 +01:00
else:
Rewrote all 3 algorithms. Generated 10 random graphs and grids for tests. Created testing structure which uses generated graphs and grids as input data to algorithms, measures execution time and memory blocks used by them. Takes these measurements and found weights and builds tables in latex format which compare these data among those three algorithms.
2022-09-25 16:41:41 +02:00
d[j] = path_sum
H.decrease_key(heap_node_connection[j], f[j])
if success:
path = [t]
current_node = t
while pred[current_node] is not None:
path.append(pred[current_node])
current_node = pred[current_node]
path.reverse()
return path, d[t]
return None, None
Implemented A* algorithm (needs better heuristic function).
2021-10-31 13:01:58 +01:00
if __name__ == "__main__":
Rewrote all 3 algorithms. Generated 10 random graphs and grids for tests. Created testing structure which uses generated graphs and grids as input data to algorithms, measures execution time and memory blocks used by them. Takes these measurements and found weights and builds tables in latex format which compare these data among those three algorithms.
2022-09-25 16:41:41 +02:00
N = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
A = {1: [2, 8, 6], 2: [1, 3, 5], 8: [1, 4], 3: [2], 5: [2, 7, 9, 10], 4: [6, 7, 8], 6: [4, 1], 7: [4, 5], 9: [5], 10: [5]}
A_b = {2: [1, 3, 5], 1: [2, 8, 6], 8: [1, 4], 3: [2], 5: [2, 7, 9, 10], 6: [4, 1], 4: [6, 7, 8], 7: [4, 5], 9: [5], 10: [5]}
w = {(1, 2): 84, (2, 1): 84, (1, 8): 52, (8, 1): 52, (2, 3): 4, (3, 2): 4, (2, 5): 39, (5, 2): 39, (4, 6): 74, (6, 4): 74, (4, 7): 81, (7, 4): 81, (5, 7): 45, (7, 5): 45, (5, 9): 66, (9, 5): 66, (5, 10): 19, (10, 5): 19, (4, 8): 87, (8, 4): 87, (1, 6): 4, (6, 1): 4}
print(a_star_algorithm(1, 10, N, A, w, heuristic_cost))
N = {(3, 4), (5, 7), (7, 7), (6, 5), (4, 5), (3, 3), (4, 8), (3, 6), (8, 5), (6, 4), (6, 7), (3, 5), (5, 5), (5, 8), (8, 7), (2, 6), (6, 6), (7, 5), (7, 8)}
A = {(2, 6): [(3, 6)], (3, 3): [(3, 4)], (3, 4): [(3, 3), (3, 5)], (3, 5): [(4, 5), (3, 4), (3, 6)], (3, 6): [(2, 6), (3, 5)], (4, 5): [(3, 5), (5, 5)], (4, 8): [(5, 8)], (5, 5): [(4, 5), (6, 5)], (5, 7): [(6, 7), (5, 8)], (5, 8): [(4, 8), (5, 7)], (6, 4): [(6, 5)], (6, 5): [(5, 5), (7, 5), (6, 4), (6, 6)], (6, 6): [(6, 5), (6, 7)], (6, 7): [(5, 7), (7, 7), (6, 6)], (7, 5): [(6, 5), (8, 5)], (7, 7): [(6, 7), (8, 7), (7, 8)], (7, 8): [(7, 7)], (8, 5): [(7, 5)], (8, 7): [(7, 7)]}
A_b = {(3, 6): [(2, 6), (3, 5)], (3, 4): [(3, 3), (3, 5)], (3, 3): [(3, 4)], (3, 5): [(3, 4), (3, 6), (4, 5)], (4, 5): [(3, 5), (5, 5)], (2, 6): [(3, 6)], (5, 5): [(4, 5), (6, 5)], (5, 8): [(4, 8), (5, 7)], (6, 5): [(5, 5), (6, 4), (6, 6), (7, 5)], (6, 7): [(5, 7), (6, 6), (7, 7)], (4, 8): [(5, 8)], (5, 7): [(5, 8), (6, 7)], (7, 5): [(6, 5), (8, 5)], (6, 4): [(6, 5)], (6, 6): [(6, 5), (6, 7)], (7, 7): [(6, 7), (7, 8), (8, 7)], (8, 5): [(7, 5)], (8, 7): [(7, 7)], (7, 8): [(7, 7)]}
w = {((2, 6), (3, 6)): 1, ((3, 3), (3, 4)): 1, ((3, 4), (3, 3)): 1, ((3, 4), (3, 5)): 1, ((3, 5), (4, 5)): 1, ((3, 5), (3, 4)): 1, ((3, 5), (3, 6)): 1, ((3, 6), (2, 6)): 1, ((3, 6), (3, 5)): 1, ((4, 5), (3, 5)): 1, ((4, 5), (5, 5)): 1, ((4, 8), (5, 8)): 1, ((5, 5), (4, 5)): 1, ((5, 5), (6, 5)): 1, ((5, 7), (6, 7)): 1, ((5, 7), (5, 8)): 1, ((5, 8), (4, 8)): 1, ((5, 8), (5, 7)): 1, ((6, 4), (6, 5)): 1, ((6, 5), (5, 5)): 1, ((6, 5), (7, 5)): 1, ((6, 5), (6, 4)): 1, ((6, 5), (6, 6)): 1, ((6, 6), (6, 5)): 1, ((6, 6), (6, 7)): 1, ((6, 7), (5, 7)): 1, ((6, 7), (7, 7)): 1, ((6, 7), (6, 6)): 1, ((7, 5), (6, 5)): 1, ((7, 5), (8, 5)): 1, ((7, 7), (6, 7)): 1, ((7, 7), (8, 7)): 1, ((7, 7), (7, 8)): 1, ((7, 8), (7, 7)): 1, ((8, 5), (7, 5)): 1, ((8, 7), (7, 7)): 1}
print(a_star_algorithm((3, 3), (4, 8), N, A, w, heuristic_cost_manhattan))
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