# 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