def MST_prima(self, start):
start_found = False
cur_list = self.adjacency_lists.head
# Check if start vertex in the graph
while cur_list != None and start_found == False:
if cur_list.value == start:
start_found = True
cur_list = cur_list.next_list
if start_found == True:
tree = UndirectedGraph(
) # Create new graph that will be returned as a result
max_value = self.max()
distances = [sys.maxsize] * (
max_value + 1) # Distances between adjacent vertices in graph
parent = [-1] * (max_value + 1) # Parents of each vertex
minHeap = MinHeap.Minheap(
) # Min heap for vertixes that are not processed yet
minHeap.size = max_value + 1 # Min heap size
# Add all vertices to the min heap
for i in range(max_value + 1):
minHeap.heap.append(minHeap.new_node(i, distances[i]))
minHeap.pos.append(i)
minHeap.pos[start] = start
distances[start] = 0 # Distance from start vertex to itself is 0
minHeap.decrease_key(
start, distances[start]
) # Change vertex position in heap according to it's new distance
while minHeap.size > 1: # Until all vertices are processed
cur_min = minHeap.extract_min(
) # Get minimal vertex with minimal distance from set of processed vertices
if cur_min.vertex != start:
tree.insert(cur_min.vertex, parent[cur_min.vertex],
cur_min.distance)
cur_list = self.adjacency_lists.head
while cur_list != None and cur_list.value != cur_min.vertex:
cur_list = cur_list.next_list
if cur_list != None:
# Loop through adjacent vertices of current minimal vertex and update their distances if it's needed
cur = cur_list.next
while cur != None:
if minHeap.isInMinHeap(cur.value) and distances[
cur_min.
vertex] != sys.maxsize and cur.edge_value < distances[
cur.value]:
distances[
cur.value] = cur.edge_value # Change distance
minHeap.decrease_key(cur.value, distances[
cur.value]) # Change position in min heap
parent[
cur.
value] = cur_min.vertex # Add parent of minimal vertex
cur = cur.next
return tree
else:
raise Exception("Vertex does not exist")
def dijkstra(self, start, end):
cur_list = self.adjacency_lists.head
start_found = False
end_found = False
# Check if start and end vertices are in the graph
while cur_list != None and (start_found == False
or end_found == False):
if cur_list.value == start:
start_found = True
if cur_list.value == end:
end_found = True
cur_list = cur_list.next_list
if start_found == True and end_found == True:
max_value = self.max() # Max value in the graph
distances = [sys.maxsize] * (max_value + 1
) # Distances from start to vertex[i]
minHeap = MinHeap.Minheap(
) # Min heap for vertixes that are not processed yet
minHeap.size = max_value + 1 # Min heap size
# Add all vertices to the min heap
for i in range(max_value + 1):
minHeap.heap.append(minHeap.new_node(i, distances[i]))
minHeap.pos.append(i)
minHeap.pos[start] = start
distances[start] = 0 # Distance from start vertex to itself is 0
minHeap.decrease_key(
start, distances[start]
) # Change vertex position in heap according to it's new distance
while minHeap.min_el(
) != end: # While distance from start to end is not found
cur_min = minHeap.extract_min(
) # Get minimal vertex with minimal distance from set of processed vertices
cur_list = self.adjacency_lists.head
while cur_list != None and cur_list.value != cur_min.vertex:
cur_list = cur_list.next_list
# Loop through adjacent vertices of current minimal vertex and update their distances if it's needed
if cur_list != None:
cur = cur_list.next
while cur != None:
if minHeap.isInMinHeap(cur.value) and distances[
cur_min.
vertex] != sys.maxsize and cur.edge_value + distances[
cur_min.vertex] < distances[cur.value]:
distances[cur.value] = cur.edge_value + distances[
cur_min.vertex] # Change distance
minHeap.decrease_key(cur.value, distances[
cur.value]) # Change position in min heap
cur = cur.next
return distances[end]
else:
raise Exception("Path does not exist")