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# 2642. Design Graph With Shortest Path Calculator
"""
there is a directed weighted graph that consists of 'n' nodes numbered from 0
to 'n - 1'. the edges of the graph are initially represented by the given
array 'edges' where 'edges[i] = [from[i], to[i], edge_cost[i]]' meaning that
there is an edge from 'from[i]' to 'to[i]' with the cost 'edge_cost[i]'.
"""
class Graph(object):
def __init__(self, n, edges):
"""
:type n: int
:type edges: List[List[int]]
"""
self.N = n
self.INF = 10**19
adj_list = collections.defaultdict(list)
for u, v, wt in edges:
adj_list[u].append((v, wt))
self.adj_list = adj_list
def addEdge(self, edge):
"""
:type edge: List[int]
:rtype: None
"""
self.adj_list[edge[0]].append((edge[1], edge[2]))
def shortestPath(self, node1, node2):
"""
:type node1: int
:type node2: int
:rtype: int
"""
h = []
heapq.heappush(h, (0, node1))
dist = [self.INF] * self.N
dist[node1] = 0
while len(h):
d, u = heapq.heappop(h)
if u == node2:
return d
if dist[u] < d:
continue
for v, wt in self.adj_list[u]:
if dist[v] > dist[u] + wt:
dist[v] = dist[u] + wt
heapq.heappush(h, (dist[v], v))
return -1
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