def setUp(self):
self.fh = FibonacciHeap()
#
# 17 24 23 7 21 3
# | /\ / | \
# 30 26 46 18 52 41
# | | |
# 35 39 44
#
self.elems = {
v: HeapNode(v)
for v in (30, 7, 35, 26, 46, 24, 23, 17, 21, 39, 18, 52, 44, 41, 3)
}
self.snd_min_elem = sorted(self.elems.values())[1]
self.elems[41].link(self.elems[44])
self.elems[18].link(self.elems[39])
self.elems[3].link(self.elems[41])
self.elems[3].link(self.elems[52])
self.elems[3].link(self.elems[18])
self.fh.add_tree(self.elems[3])
self.fh.add_tree(self.elems[21])
self.fh.add_tree(self.elems[7])
self.fh.add_tree(self.elems[23])
self.elems[26].link(self.elems[35])
self.elems[24].link(self.elems[46])
self.elems[24].link(self.elems[26])
self.fh.add_tree(self.elems[24])
self.elems[17].link(self.elems[30])
self.fh.add_tree(self.elems[17])
# Remove `3`
self.fh.extract_min()
self.exp_num_trees = 3
def setUp(self):
self.fh = FibonacciHeap()
self.num_elems = 30
self.elems = [
HeapNode(random.randint(1, 100)) for i in range(self.num_elems)
]
self.min_elem = min(self.elems)
for elem in self.elems:
self.fh.add_tree(elem)
def dijkstra(self, source):
"""
Computes Dijkstra's algorithm (shortest path from
a source vertex to all other verticies).
Assumption(s):
- "source" is in the graph.
- The graph has no negative edge-weights.
- The graph has no negative cycles.
:param source: The vertex to perform Dijkstra's on.
:return Two dictionaries:
-A dictionary containing the cost to traverse from "source" to each
reachable vertex in the graph.
-A dictionary encoding the shortest path from "source" to each
reachable vertex in the graph.
"""
V = self.get_vertices()
dist, prev = {}, {}
assert source in V
pq = FibonacciHeap()
for v in V:
dist[v] = INFINITY if v != source else 0
prev[v] = None
pq.push(v, dist[v])
while not pq.empty():
u = pq.pop()
neighbors = self.get_neighbors(u)
for neighbor in neighbors:
alt = dist[u] + self.get_edge_value(u, neighbor)
if alt >= dist[neighbor]:
continue
dist[neighbor] = alt
prev[neighbor] = u
if neighbor not in pq:
pq.push(neighbor, alt)
else:
pq.decrease_key(neighbor, alt)
return dist, prev
def setUp(self):
self.fh = FibonacciHeap()
#
# 7 18 38
# / | \ /\ |
# 24 17 23 21 39 41
# / \ | |
# 26 46 30 52
# / \ |
# 35 88 72
#
self.elems = {
v: HeapNode(v)
for v in (41, 38, 39, 52, 21, 18, 23, 30, 17, 72, 46, 88, 35, 26,
24, 7)
}
self.elems[38].link(self.elems[41])
self.fh.add_tree(self.elems[38])
self.elems[21].link(self.elems[52])
self.elems[18].link(self.elems[39])
self.elems[18].link(self.elems[21])
self.fh.add_tree(self.elems[18])
self.elems[17].link(self.elems[30])
self.elems[46].link(self.elems[72])
self.elems[26].link(self.elems[88])
self.elems[26].link(self.elems[35])
self.elems[24].link(self.elems[46])
self.elems[24].link(self.elems[26])
self.elems[7].link(self.elems[23])
self.elems[7].link(self.elems[17])
self.elems[7].link(self.elems[24])
self.fh.add_tree(self.elems[7])
self.elems[29] = self.fh.dec_key(self.elems[46], 29)
del self.elems[46]
self.elems[15] = self.fh.dec_key(self.elems[29], 15)
del self.elems[29]
self.elems[35].parent.mark()
self.elems[35].parent.parent.mark()
self.elems[5] = self.fh.dec_key(self.elems[35], 5)
self.exp_num_trees = 7
self.min_elem = min(self.elems.values())
def dijkstra(vertice_list, src, num_ver):
parent = []
#marca todos os vertices como nao visitados
for i in range(num_ver):
parent.append(-1)
#vertice inicial tem distancia 0. Na 1 iteracao todos os demais estao setados com 'infinidade'
vertice_list[src].dist = 0
from fibheap import FibonacciHeap
f = FibonacciHeap()
f.insert(vertice_list[src].dist, src)
#print("Nodes:",f.total_nodes)
#pelo numero de vertices, escolha o vertice de menor distancia atualmente no grafo.
#Na primeira iteracao sera o source.
i = 0
while (f.total_nodes != 0):
#while(i<10):
i += 1
#print(i)
v_min_node = f.extract_min() # 1.1
#print('key:',v_min_node.key)
#print("Nodes:",f.total_nodes)
v_min = vertice_list[v_min_node.value]
if (v_min == None):
continue
v_min.visited = 1 #marque vertice minimo como ja visitado. Desse modo o 1.2 sera feito, pois nao sera mais contado
#para cada vertice adjacente ao vertice minimo
for key in v_min.adjs: # 1.3
adj_ver = vertice_list[
v_min.adjs[key].id] #pega o vertice adjacente
#Se a distancia atual do vertice minimo, mais o seu peso, for menor que a distancia do vert adjacente
if (adj_ver.visited == 0
and v_min.dist + v_min.weights[key] < adj_ver.dist):
adj_ver.dist = v_min.dist + v_min.weights[
key] #a distancia do vertice adjacente tera esse novo valor
parent[
adj_ver.
id] = v_min.id # a pos que era do vertice adjacente, sera do menor v agora
f.insert(adj_ver.dist, adj_ver.id)
def mst(self):
"""
Computes the minimum spanning tree of the graph using
Prim's algorithm.
:return: The minimum spanning tree (in a list of connecting edges).
"""
V = self.get_vertices()
heap = FibonacciHeap()
cost, prev = {}, {}
for u in V:
cost[u] = INFINITY
prev[u] = None
v = random.choice(V)
cost[v] = 0
for u in V:
heap.push(u, cost[u])
while not heap.empty():
u = heap.pop()
neighbors = self.get_neighbors(u)
for v in neighbors:
edge_weight = self.get_edge_value(u, v)
if v in heap and cost[v] > edge_weight:
cost[v] = edge_weight
prev[v] = u
heap.decrease_key(v, cost[v])
mst = set()
for v in V:
if prev[v] is None:
continue
edge = (prev[v], v) if prev[v] < v else (v, prev[v])
mst.add(edge)
return mst
def setUp(self):
self.fh = FibonacciHeap()
self.elem = HeapNode(random.randint(1, 100))
self.fh.add_tree(self.elem)
def setUp(self):
self.fh = FibonacciHeap()