// C++ program for Dijkstra's single source shortest path // algorithm. The program is for adjacency matrix // representation of the graph #include  using namespace std; #include  // Number of vertices in the graph #define V 9 // A utility function to find the vertex with minimum // distance value, from the set of vertices not yet included // in shortest path tree int minDistance(int dist[], bool sptSet[]) {  // Initialize min value  int min = INT_MAX, min_index;  for (int v = 0; v < V; v++)  if (sptSet[v] == false && dist[v] <= min)  min = dist[v], min_index = v;  return min_index; } // A utility function to print the constructed distance // array void printSolution(int dist[]) {  cout << 'Vertex 	 Distance from Source' << endl;  for (int i = 0; i < V; i++)  cout << i << ' 				' << dist[i] << endl; } // Function that implements Dijkstra's single source // shortest path algorithm for a graph represented using // adjacency matrix representation void dijkstra(int graph[V][V], int src) {  int dist[V]; // The output array. dist[i] will hold the  // shortest  // distance from src to i  bool sptSet[V]; // sptSet[i] will be true if vertex i is  // included in shortest  // path tree or shortest distance from src to i is  // finalized  // Initialize all distances as INFINITE and stpSet[] as  // false  for (int i = 0; i < V; i++)  dist[i] = INT_MAX, sptSet[i] = false;  // Distance of source vertex from itself is always 0  dist[src] = 0;  // Find shortest path for all vertices  for (int count = 0; count < V - 1; count++) {  // Pick the minimum distance vertex from the set of  // vertices not yet processed. u is always equal to  // src in the first iteration.  int u = minDistance(dist, sptSet);  // Mark the picked vertex as processed  sptSet[u] = true;  // Update dist value of the adjacent vertices of the  // picked vertex.  for (int v = 0; v < V; v++)  // Update dist[v] only if is not in sptSet,  // there is an edge from u to v, and total  // weight of path from src to v through u is  // smaller than current value of dist[v]  if (!sptSet[v] && graph[u][v]  && dist[u] != INT_MAX  && dist[u] + graph[u][v] < dist[v])  dist[v] = dist[u] + graph[u][v];  }  // print the constructed distance array  printSolution(dist); } // driver's code int main() {  /* Let us create the example graph discussed above */  int graph[V][V] = { { 0, 4, 0, 0, 0, 0, 0, 8, 0 },  { 4, 0, 8, 0, 0, 0, 0, 11, 0 },  { 0, 8, 0, 7, 0, 4, 0, 0, 2 },  { 0, 0, 7, 0, 9, 14, 0, 0, 0 },  { 0, 0, 0, 9, 0, 10, 0, 0, 0 },  { 0, 0, 4, 14, 10, 0, 2, 0, 0 },  { 0, 0, 0, 0, 0, 2, 0, 1, 6 },  { 8, 11, 0, 0, 0, 0, 1, 0, 7 },  { 0, 0, 2, 0, 0, 0, 6, 7, 0 } };  // Function call  dijkstra(graph, 0);  return 0; } // This code is contributed by shivanisinghss2110>

// C program for Dijkstra's single source shortest path // algorithm. The program is for adjacency matrix // representation of the graph #include  #include  #include  // Number of vertices in the graph #define V 9 // A utility function to find the vertex with minimum // distance value, from the set of vertices not yet included // in shortest path tree int minDistance(int dist[], bool sptSet[]) {  // Initialize min value  int min = INT_MAX, min_index;  for (int v = 0; v < V; v++)  if (sptSet[v] == false && dist[v] <= min)  min = dist[v], min_index = v;  return min_index; } // A utility function to print the constructed distance // array void printSolution(int dist[]) {  printf('Vertex 		 Distance from Source
');  for (int i = 0; i < V; i++)  printf('%d 				 %d
', i, dist[i]); } // Function that implements Dijkstra's single source // shortest path algorithm for a graph represented using // adjacency matrix representation void dijkstra(int graph[V][V], int src) {  int dist[V]; // The output array. dist[i] will hold the  // shortest  // distance from src to i  bool sptSet[V]; // sptSet[i] will be true if vertex i is  // included in shortest  // path tree or shortest distance from src to i is  // finalized  // Initialize all distances as INFINITE and stpSet[] as  // false  for (int i = 0; i < V; i++)  dist[i] = INT_MAX, sptSet[i] = false;  // Distance of source vertex from itself is always 0  dist[src] = 0;  // Find shortest path for all vertices  for (int count = 0; count < V - 1; count++) {  // Pick the minimum distance vertex from the set of  // vertices not yet processed. u is always equal to  // src in the first iteration.  int u = minDistance(dist, sptSet);  // Mark the picked vertex as processed  sptSet[u] = true;  // Update dist value of the adjacent vertices of the  // picked vertex.  for (int v = 0; v < V; v++)  // Update dist[v] only if is not in sptSet,  // there is an edge from u to v, and total  // weight of path from src to v through u is  // smaller than current value of dist[v]  if (!sptSet[v] && graph[u][v]  && dist[u] != INT_MAX  && dist[u] + graph[u][v] < dist[v])  dist[v] = dist[u] + graph[u][v];  }  // print the constructed distance array  printSolution(dist); } // driver's code int main() {  /* Let us create the example graph discussed above */  int graph[V][V] = { { 0, 4, 0, 0, 0, 0, 0, 8, 0 },  { 4, 0, 8, 0, 0, 0, 0, 11, 0 },  { 0, 8, 0, 7, 0, 4, 0, 0, 2 },  { 0, 0, 7, 0, 9, 14, 0, 0, 0 },  { 0, 0, 0, 9, 0, 10, 0, 0, 0 },  { 0, 0, 4, 14, 10, 0, 2, 0, 0 },  { 0, 0, 0, 0, 0, 2, 0, 1, 6 },  { 8, 11, 0, 0, 0, 0, 1, 0, 7 },  { 0, 0, 2, 0, 0, 0, 6, 7, 0 } };  // Function call  dijkstra(graph, 0);  return 0; }>

// A Java program for Dijkstra's single source shortest path // algorithm. The program is for adjacency matrix // representation of the graph import java.io.*; import java.lang.*; import java.util.*; class ShortestPath {  // A utility function to find the vertex with minimum  // distance value, from the set of vertices not yet  // included in shortest path tree  static final int V = 9;  int minDistance(int dist[], Boolean sptSet[])  {  // Initialize min value  int min = Integer.MAX_VALUE, min_index = -1;  for (int v = 0; v < V; v++)  if (sptSet[v] == false && dist[v] <= min) {  min = dist[v];  min_index = v;  }  return min_index;  }  // A utility function to print the constructed distance  // array  void printSolution(int dist[])  {  System.out.println(  'Vertex 		 Distance from Source');  for (int i = 0; i < V; i++)  System.out.println(i + ' 		 ' + dist[i]);  }  // Function that implements Dijkstra's single source  // shortest path algorithm for a graph represented using  // adjacency matrix representation  void dijkstra(int graph[][], int src)  {  int dist[] = new int[V]; // The output array.  // dist[i] will hold  // the shortest distance from src to i  // sptSet[i] will true if vertex i is included in  // shortest path tree or shortest distance from src  // to i is finalized  Boolean sptSet[] = new Boolean[V];  // Initialize all distances as INFINITE and stpSet[]  // as false  for (int i = 0; i < V; i++) {  dist[i] = Integer.MAX_VALUE;  sptSet[i] = false;  }  // Distance of source vertex from itself is always 0  dist[src] = 0;  // Find shortest path for all vertices  for (int count = 0; count < V - 1; count++) {  // Pick the minimum distance vertex from the set  // of vertices not yet processed. u is always  // equal to src in first iteration.  int u = minDistance(dist, sptSet);  // Mark the picked vertex as processed  sptSet[u] = true;  // Update dist value of the adjacent vertices of  // the picked vertex.  for (int v = 0; v < V; v++)  // Update dist[v] only if is not in sptSet,  // there is an edge from u to v, and total  // weight of path from src to v through u is  // smaller than current value of dist[v]  if (!sptSet[v] && graph[u][v] != 0  && dist[u] != Integer.MAX_VALUE  && dist[u] + graph[u][v] < dist[v])  dist[v] = dist[u] + graph[u][v];  }  // print the constructed distance array  printSolution(dist);  }  // Driver's code  public static void main(String[] args)  {  /* Let us create the example graph discussed above  */  int graph[][]  = new int[][] { { 0, 4, 0, 0, 0, 0, 0, 8, 0 },  { 4, 0, 8, 0, 0, 0, 0, 11, 0 },  { 0, 8, 0, 7, 0, 4, 0, 0, 2 },  { 0, 0, 7, 0, 9, 14, 0, 0, 0 },  { 0, 0, 0, 9, 0, 10, 0, 0, 0 },  { 0, 0, 4, 14, 10, 0, 2, 0, 0 },  { 0, 0, 0, 0, 0, 2, 0, 1, 6 },  { 8, 11, 0, 0, 0, 0, 1, 0, 7 },  { 0, 0, 2, 0, 0, 0, 6, 7, 0 } };  ShortestPath t = new ShortestPath();  // Function call  t.dijkstra(graph, 0);  } } // This code is contributed by Aakash Hasija>

# Python program for Dijkstra's single # source shortest path algorithm. The program is # for adjacency matrix representation of the graph # Library for INT_MAX import sys class Graph(): def __init__(self, vertices): self.V = vertices self.graph = [[0 for column in range(vertices)] for row in range(vertices)] def printSolution(self, dist): print('Vertex 	Distance from Source') for node in range(self.V): print(node, '	', dist[node]) # A utility function to find the vertex with # minimum distance value, from the set of vertices # not yet included in shortest path tree def minDistance(self, dist, sptSet): # Initialize minimum distance for next node min = sys.maxsize # Search not nearest vertex not in the # shortest path tree for u in range(self.V): if dist[u] < min and sptSet[u] == False: min = dist[u] min_index = u return min_index # Function that implements Dijkstra's single source # shortest path algorithm for a graph represented # using adjacency matrix representation def dijkstra(self, src): dist = [sys.maxsize] * self.V dist[src] = 0 sptSet = [False] * self.V for cout in range(self.V): # Pick the minimum distance vertex from # the set of vertices not yet processed. # x is always equal to src in first iteration x = self.minDistance(dist, sptSet) # Put the minimum distance vertex in the # shortest path tree sptSet[x] = True # Update dist value of the adjacent vertices # of the picked vertex only if the current # distance is greater than new distance and # the vertex in not in the shortest path tree for y in range(self.V): if self.graph[x][y]>0 un sptSet[y] == False un  dist[y]> dist[x] + self.graph[x][y]: dist[y] = dist[x] + self.graph[x][y] self.printSolution(dist) # Draivera kods, ja __name__ == '__main__': g = Graph(9) g.graph = [[0, 4, 0, 0, 0, 0, 0, 8, 0], [4, 0, 8, 0, 0, 0, 0, 11, 0], [0, 8, 0, 7, 0, 4, 0, 0, 2], [0, 0, 7, 0, 9, 14, 0, 0, 0], [0, 0, 0, 9, 0, 10, 0, 0, 0], [0, 0, 4, 14, 10, 0, 2, 0, 0], [0, 0, 0, 0, 0, 2, 0, 1, 6], [8, 11, 0, 0, 0, 0, 1, 0, 7], [0, 0, 2, 0, 0, 0, 6, 7, 0] ] g.dijkstra(0) # Šo kodu ir sagatavojis Divyanshu Mehta, un to atjaunināja Pranavs Singhs Sambyals>>C#

// A Javascript program for Dijkstra's single  // source shortest path algorithm.  // The program is for adjacency matrix  // representation of the graph  let V = 9; // A utility function to find the  // vertex with minimum distance  // value, from the set of vertices  // not yet included in shortest  // path tree  function minDistance(dist,sptSet) {    // Initialize min value   let min = Number.MAX_VALUE;  let min_index = -1;    for(let v = 0; v < V; v++)  {  if (sptSet[v] == false && dist[v] <= min)   {  min = dist[v];  min_index = v;  }  }  return min_index; } // A utility function to print  // the constructed distance array  function printSolution(dist) {  console.log('Vertex 		 Distance from Source ');  for(let i = 0; i < V; i++)  {  console.log(i + ' 		 ' +   dist[i] + ' ');  } } // Function that implements Dijkstra's  // single source shortest path algorithm  // for a graph represented using adjacency  // matrix representation  function dijkstra(graph, src) {  let dist = new Array(V);  let sptSet = new Array(V);    // Initialize all distances as   // INFINITE and stpSet[] as false   for(let i = 0; i < V; i++)  {  dist[i] = Number.MAX_VALUE;  sptSet[i] = false;  }    // Distance of source vertex   // from itself is always 0   dist[src] = 0;    // Find shortest path for all vertices   for(let count = 0; count < V - 1; count++)  {    // Pick the minimum distance vertex   // from the set of vertices not yet   // processed. u is always equal to   // src in first iteration.   let u = minDistance(dist, sptSet);    // Mark the picked vertex as processed   sptSet[u] = true;    // Update dist value of the adjacent   // vertices of the picked vertex.   for(let v = 0; v < V; v++)  {    // Update dist[v] only if is not in   // sptSet, there is an edge from u   // to v, and total weight of path   // from src to v through u is smaller   // than current value of dist[v]   if (!sptSet[v] && graph[u][v] != 0 &&   dist[u] != Number.MAX_VALUE &&  dist[u] + graph[u][v] < dist[v])  {  dist[v] = dist[u] + graph[u][v];  }  }  }    // Print the constructed distance array  printSolution(dist); } // Driver code let graph = [ [ 0, 4, 0, 0, 0, 0, 0, 8, 0 ],  [ 4, 0, 8, 0, 0, 0, 0, 11, 0 ],  [ 0, 8, 0, 7, 0, 4, 0, 0, 2 ],  [ 0, 0, 7, 0, 9, 14, 0, 0, 0],  [ 0, 0, 0, 9, 0, 10, 0, 0, 0 ],  [ 0, 0, 4, 14, 10, 0, 2, 0, 0],  [ 0, 0, 0, 0, 0, 2, 0, 1, 6 ],  [ 8, 11, 0, 0, 0, 0, 1, 0, 7 ],  [ 0, 0, 2, 0, 0, 0, 6, 7, 0 ] ] dijkstra(graph, 0); // This code is contributed by rag2127>

// C++ Program to find Dijkstra's shortest path using // priority_queue in STL #include  using namespace std; #define INF 0x3f3f3f3f // iPair ==>Integer Pair typedef pāris iPair; // Šī klase attēlo virzītu grafiku, izmantojot // blakus esošo sarakstu reprezentācijas klase Graph { int V; // Virsotņu skaits // Svērtajā grafikā mums ir jāsaglabā virsotnes // un svara pāris katram malu sarakstam>> adj; public: Graph(int V); // Konstruktors // funkcija malas pievienošanai grafikam void addEdge(int u, int v, int w);  // izdrukā īsāko ceļu no s void shortestPath(int s); }; // Piešķir atmiņu blakus sarakstam Graph::Graph(int V) { this->V = V;  adj = jauns saraksts [V]; } void Graph::pievienotEdge(int u, int v, int w) { adj[u].push_back(make_pair(v, w));  adj[v].push_back(make_pair(u, w)); } // Drukā īsākos ceļus no src uz visām pārējām virsotnēm void Graph::shortestPath(int src) { // Izveidojiet prioritāru rindu, lai saglabātu virsotnes, kuras // tiek iepriekš apstrādātas. Šī ir dīvaina sintakse valodā C++.  // Sīkāku informāciju par šo sintaksi skatiet tālāk norādītajā saitē // https://www.techcodeview.com priority_queue , lielāks > pq;  // Izveidojiet vektoru attālumiem un inicializējiet visus // attālumus kā bezgalīgu (INF) vektoru dist(V, INF);  // Ievietot pašu avotu prioritārā rindā un inicializēt // tā attālumu kā 0. pq.push(make_pair(0, src));  dist[src] = 0;  /* Cilpas, līdz prioritātes rinda kļūst tukša (vai visi attālumi nav pabeigti) */ while (!pq.empty()) { // Pirmā virsotne pārī ir minimālais attālums // virsotne, izņemiet to no prioritārās rindas.  // virsotnes etiķete tiek saglabāta pāra otrajā daļā (tas // ir jādara šādi, lai saglabātu virsotnes // sakārtotu attālumu (attālumam jābūt pirmajam vienumam // pārī) int u = pq.top().second; pq.pop(); // 'i' tiek izmantots, lai iegūtu visas // virsotņu saraksta blakus esošās virsotnes>::iterators i;  for (i = adj[u].begin(); i != adj[u].end(); ++i) { // Iegūt virsotnes etiķeti un strāvas svaru // blakus u.  int v = (*i).pirmais;  int svars = (*i).sekunde;  // Ja ir saīsināts ceļš uz v caur u.  if (dist[v]> dist[u] + svars) { // Atjaunināšanas attālums v dist[v] = dist[u] + svars;  pq.push(make_pair(dist[v], v));  } } } // Drukāt īsākos attālumus, kas saglabāti dist[] printf('Vertex Distance from Source
');  for (int i = 0; i< V; ++i)  printf('%d 		 %d
', i, dist[i]); } // Driver's code int main() {  // create the graph given in above figure  int V = 9;  Graph g(V);  // making above shown graph  g.addEdge(0, 1, 4);  g.addEdge(0, 7, 8);  g.addEdge(1, 2, 8);  g.addEdge(1, 7, 11);  g.addEdge(2, 3, 7);  g.addEdge(2, 8, 2);  g.addEdge(2, 5, 4);  g.addEdge(3, 4, 9);  g.addEdge(3, 5, 14);  g.addEdge(4, 5, 10);  g.addEdge(5, 6, 2);  g.addEdge(6, 7, 1);  g.addEdge(6, 8, 6);  g.addEdge(7, 8, 7);  // Function call  g.shortestPath(0);  return 0; }>

import java.util.*; class Graph {  private int V;  private List> adj;  Grafs(int V) { this.V = V;  adj = jauns ArrayList();  for (int i = 0; i< V; i++) {  adj.add(new ArrayList());  }  }  void addEdge(int u, int v, int w) {  adj.get(u).add(new iPair(v, w));  adj.get(v).add(new iPair(u, w));  }  void shortestPath(int src) {  PriorityQueue pq = new PriorityQueue(V, Comparator.comparingInt(o -> o.second));  int[] dist = jauns int[V];  Masīvi.fill(dist, Vesels skaitlis.MAX_VALUE);  pq.add(jauns iPair(0, src));  dist[src] = 0;  while (!pq.isEmpty()) { int u = pq.poll().second;  for (iPair v : adj.get(u)) { if (dist[v.first]> dist[u] + v.second) { dist[v.first] = dist[u] + v.second;  pq.add(jauns iPair(dist[v.first], v.first));  } } } System.out.println('Vertex attālums no avota');  for (int i = 0; i< V; i++) {  System.out.println(i + '		' + dist[i]);  }  }  static class iPair {  int first, second;  iPair(int first, int second) {  this.first = first;  this.second = second;  }  } } public class Main {  public static void main(String[] args) {  int V = 9;  Graph g = new Graph(V);  g.addEdge(0, 1, 4);  g.addEdge(0, 7, 8);  g.addEdge(1, 2, 8);  g.addEdge(1, 7, 11);  g.addEdge(2, 3, 7);  g.addEdge(2, 8, 2);  g.addEdge(2, 5, 4);  g.addEdge(3, 4, 9);  g.addEdge(3, 5, 14);  g.addEdge(4, 5, 10);  g.addEdge(5, 6, 2);  g.addEdge(6, 7, 1);  g.addEdge(6, 8, 6);  g.addEdge(7, 8, 7);  g.shortestPath(0);  } }>

import heapq # iPair ==>Veselu skaitļu pāris iPair = korte # Šī klase attēlo virzītu grafiku, izmantojot # blakusesību saraksta reprezentācijas klasi Grafs: def __init__(self, V: int): # Konstruktors self.V = V self.adj = [[] for _ diapazonā(V) )] def addEdge(self, u: int, v: int, w: int): self.adj[u].append((v, w)) self.adj[v].append((u, w)) # Drukā īsākos ceļus no src uz visām pārējām virsotnēm def shortestPath(self, src: int): # Izveidojiet prioritāru rindu, lai saglabātu virsotnes, kuras # tiek iepriekš apstrādātas pq = [] heapq.heappush(pq, (0, src)) # Izveidojiet vektoru attālumiem un inicializējiet visus # attālumus kā bezgalīgus (INF) dist = [float('inf')] * self.V dist[src] = 0, kamēr pq: # Pirmā virsotne pārī ir minimālais attālums # virsotne, izņemiet to no prioritārās rindas. # virsotnes etiķete tiek saglabāta pāra d otrajā daļā, u = heapq.heappop(pq) # 'i' tiek izmantots, lai iegūtu visas blakus esošās # virsotnes virsotnes v, svars iekšā self.adj[u]: # Ja ir saīsināts ceļš uz v caur u. if dist[v]> dist[u] + svars: # Atjauno attālumu no v dist[v] = dist[u] + svars heapq.heappush(pq, (dist[v], v)) # Drukāt īsākos attālumus, kas saglabāti dist[] i diapazonā (self.V): print(f'{i} 		 {dist[i]}') # Draivera kods, ja __name__ == '__main__': # izveidojiet grafiku, kas parādīts iepriekš attēlā V = 9 g = Grafs(V) # izveidojot iepriekš parādīto grafiku g.addEdge(0, 1, 4) g.addEdge(0, 7, 8) g.addEdge(1, 2, 8) g.pievienotEdge(1, 7, 11) g.addEdge(2, 3, 7) g.addEdge(2, 8, 2) g.addEdge(2, 5, 4) g.addEdge(3, 4, 9) g.pievienotEdge(3, 5, 14) g.addEdge(4, 5, 10) g.addEdge(5, 6, 2) g.addEdge(6, 7, 1) g.addEdge(6, 8, 6) g.addEdge(7, 8, 7) g.shortestPath(0)>

using System; using System.Collections.Generic; // This class represents a directed graph using // adjacency list representation public class Graph {  private const int INF = 2147483647;  private int V;  private List [] adj;  public Graph(int V) { // Virsotņu skaits this.V = V;  // Svērtā grafikā katrai malai ir jāsaglabā virsotne // un svara pāris this.adj = new List [V];  for (int i = 0; i< V; i++)  {  this.adj[i] = new List ();  } } public void AddEdge(int u, int v, int w) { this.adj[u].Add(new int[] { v, w });  this.adj[v].Add(new int[] { u, w });  } // Drukā īsākos ceļus no src uz visām pārējām virsotnēm public void ShortestPath(int src) { // Izveidojiet prioritāru rindu, lai saglabātu virsotnes, kuras // tiek iepriekš apstrādātas.  SakārtotsSet pq = jauns SortedSet (jauns DistanceComparer());  // Izveidot masīvu attālumiem un inicializēt visus // attālumus kā bezgalīgus (INF) int[] dist = new int[V];  for (int i = 0; i< V; i++)  {  dist[i] = INF;  }  // Insert source itself in priority queue and initialize  // its distance as 0.  pq.Add(new int[] { 0, src });  dist[src] = 0;  /* Looping till priority queue becomes empty (or all  distances are not finalized) */  while (pq.Count>0) { // Pirmā virsotne pārī ir minimālais attālums // virsotne, izņemiet to no prioritārās rindas.  // virsotņu etiķete tiek saglabāta pāra otrajā daļā (tas // ir jādara šādi, lai virsotnes saglabātu // sakārtotas pēc attāluma) int[] minDistVertex = pq.Min;  pq.Remove(minDistVertex);  int u = minDistVertex[1];  // 'i' tiek izmantots, lai iegūtu visas // virsotnes blakus esošās virsotnes foreach (int[] adjVertex šajā.adj[u]) { // Iegūt virsotnes etiķeti un strāvas svaru // blakus u.  int v = adjVertex[0];  int svars = adjVertex[1];  // Ja ir īsāks ceļš uz v caur u.  if (dist[v]> dist[u] + svars) { // Atjaunināšanas attālums v dist[v] = dist[u] + svars;  pq.Add(new int[] { dist[v], v });  } } } // Drukāt īsākos attālumus, kas saglabāti dist[] Console.WriteLine('Vertex Distance from Source');  for (int i = 0; i< V; ++i)  Console.WriteLine(i + '	' + dist[i]);  }  private class DistanceComparer : IComparer { public int Salīdzināt(int[] x, int[] y) { if (x[0] == y[0]) { return x[1] - y[1];  } atgriešanās x[0] - y[0];  } } } public class Programma { // Draivera kods public static void Main() { // izveido diagrammu, kas norādīta iepriekš attēlā int V = 9;  Grafs g = jauns Grafs(V);  // izveidojot augstāk parādīto grafiku g.AddEdge(0, 1, 4);  g.PievienotEdge(0, 7, 8);  g.PievienotEdge(1, 2, 8);  g.PievienotEdge(1, 7, 11);  g.PievienotEdge(2, 3, 7);  g.PievienotEdge(2, 8, 2);  g.PievienotEdge(2, 5, 4);  g.PievienotEdge(3, 4, 9);  g.PievienotEdge(3, 5, 14);  g.PievienotEdge(4, 5, 10);  g.PievienotEdge(5, 6, 2);  g.PievienotEdge(6, 7, 1);  g.PievienotEdge(6, 8, 6);  g.PievienotEdge(7, 8, 7);  g.ShortestPath(0);  } } // šo kodu nodrošina bhardwajji>

// javascript Program to find Dijkstra's shortest path using // priority_queue in STL const INF = 2147483647; // This class represents a directed graph using // adjacency list representation class Graph {    constructor(V){    // No. of vertices  this.V = V;    // In a weighted graph, we need to store vertex  // and weight pair for every edge  this.adj = new Array(V);  for(let i = 0; i < V; i++){  this.adj[i] = new Array();  }  }  addEdge(u, v, w)  {  this.adj[u].push([v, w]);  this.adj[v].push([u, w]);  }  // Prints shortest paths from src to all other vertices  shortestPath(src)  {  // Create a priority queue to store vertices that  // are being preprocessed. This is weird syntax in C++.  // Refer below link for details of this syntax  // https://www.techcodeview.com  let pq = [];  // Create a vector for distances and initialize all  // distances as infinite (INF)  let dist = new Array(V).fill(INF);  // Insert source itself in priority queue and initialize  // its distance as 0.  pq.push([0, src]);  dist[src] = 0;  /* Looping till priority queue becomes empty (or all  distances are not finalized) */  while (pq.length>0) { // Pirmā virsotne pārī ir minimālais attālums // virsotne, izņemiet to no prioritārās rindas.  // virsotņu etiķete tiek saglabāta pāra otrajā daļā (tas // ir jādara šādi, lai saglabātu virsotnes // sakārtotu attālumu (attālumam jābūt pirmajam vienumam // pārī) lai u = pq[0][1]; pq.shift(); // 'i' tiek izmantots, lai iegūtu visas blakus esošās virsotnes // virsotnes for(lai i = 0; i< this.adj[u].length; i++){    // Get vertex label and weight of current  // adjacent of u.  let v = this.adj[u][i][0];  let weight = this.adj[u][i][1];  // If there is shorted path to v through u.  if (dist[v]>dist[u] + svars) { // V atjaunināšanas attālums dist[v] = dist[u] + svars;  pq.push([dist[v], v]);  pq.sort((a, b) =>{ if(a[0] == b[0]) return a[1] - b[1]; return a[0] - b[0]; });  } } } // Drukāt īsākos attālumus, kas saglabāti dist[] console.log('Vertex Distance from Source');  for (lai i = 0; i< V; ++i)  console.log(i, ' ', dist[i]);  } } // Driver's code // create the graph given in above figure let V = 9; let g = new Graph(V); // making above shown graph g.addEdge(0, 1, 4); g.addEdge(0, 7, 8); g.addEdge(1, 2, 8); g.addEdge(1, 7, 11); g.addEdge(2, 3, 7); g.addEdge(2, 8, 2); g.addEdge(2, 5, 4); g.addEdge(3, 4, 9); g.addEdge(3, 5, 14); g.addEdge(4, 5, 10); g.addEdge(5, 6, 2); g.addEdge(6, 7, 1); g.addEdge(6, 8, 6); g.addEdge(7, 8, 7); // Function call g.shortestPath(0); // The code is contributed by Nidhi goel.>