Fixed lint issue

mathangpeddi · mathangpeddi · commit a9e17ed1caea · 2024-10-20T10:27:08.000-04:00
diff --git a/Graphs/Astar.js b/Graphs/Astar.js
@@ -7,102 +7,101 @@
  */
 
 function createGraph(V, E) {
-    // V - Number of vertices in graph
-    // E - Number of edges in graph (u,v,w)
-    const adjList = [] // Adjacency list
-    for (let i = 0; i < V; i++) {
-      adjList.push([])
-    }
-    for (let i = 0; i < E.length; i++) {
-      adjList[E[i][0]].push([E[i][1], E[i][2]])
-      adjList[E[i][1]].push([E[i][0], E[i][2]])
-    }
-    return adjList
+  // V - Number of vertices in graph
+  // E - Number of edges in graph (u,v,w)
+  const adjList = [] // Adjacency list
+  for (let i = 0; i < V; i++) {
+    adjList.push([])
   }
-  
-  // Heuristic function to estimate the cost to reach the goal
-  // You can modify this based on your specific problem, for now, we're using Manhattan distance
-  function heuristic(a, b) {
-    return Math.abs(a - b)
+  for (let i = 0; i < E.length; i++) {
+    adjList[E[i][0]].push([E[i][1], E[i][2]])
+    adjList[E[i][1]].push([E[i][0], E[i][2]])
   }
-  
-  function aStar(graph, V, src, target) {
-    const openSet = new Set([src]) // Nodes to explore
-    const cameFrom = Array(V).fill(-1) // Keep track of path
-    const gScore = Array(V).fill(Infinity) // Actual cost from start to a node
-    gScore[src] = 0
-  
-    const fScore = Array(V).fill(Infinity) // Estimated cost from start to goal (g + h)
-    fScore[src] = heuristic(src, target)
-  
-    while (openSet.size > 0) {
-      // Get the node in openSet with the lowest fScore
-      let current = -1
-      openSet.forEach((node) => {
-        if (current === -1 || fScore[node] < fScore[current]) {
-          current = node
-        }
-      })
-  
-      // If the current node is the target, reconstruct the path and return
-      if (current === target) {
-        const path = []
-        while (cameFrom[current] !== -1) {
-          path.push(current)
-          current = cameFrom[current]
-        }
-        path.push(src)
-        return path.reverse()
+  return adjList
+}
+
+// Heuristic function to estimate the cost to reach the goal
+// You can modify this based on your specific problem, for now, we're using Manhattan distance
+function heuristic(a, b) {
+  return Math.abs(a - b)
+}
+
+function aStar(graph, V, src, target) {
+  const openSet = new Set([src]) // Nodes to explore
+  const cameFrom = Array(V).fill(-1) // Keep track of path
+  const gScore = Array(V).fill(Infinity) // Actual cost from start to a node
+  gScore[src] = 0
+
+  const fScore = Array(V).fill(Infinity) // Estimated cost from start to goal (g + h)
+  fScore[src] = heuristic(src, target)
+
+  while (openSet.size > 0) {
+    // Get the node in openSet with the lowest fScore
+    let current = -1
+    openSet.forEach((node) => {
+      if (current === -1 || fScore[node] < fScore[current]) {
+        current = node
       }
-  
-      openSet.delete(current)
-  
-      // Explore neighbors
-      for (let i = 0; i < graph[current].length; i++) {
-        const neighbor = graph[current][i][0]
-        const tentative_gScore = gScore[current] + graph[current][i][1]
-  
-        if (tentative_gScore < gScore[neighbor]) {
-          cameFrom[neighbor] = current
-          gScore[neighbor] = tentative_gScore
-          fScore[neighbor] = gScore[neighbor] + heuristic(neighbor, target)
-  
-          if (!openSet.has(neighbor)) {
-            openSet.add(neighbor)
-          }
+    })
+
+    // If the current node is the target, reconstruct the path and return
+    if (current === target) {
+      const path = []
+      while (cameFrom[current] !== -1) {
+        path.push(current)
+        current = cameFrom[current]
+      }
+      path.push(src)
+      return path.reverse()
+    }
+
+    openSet.delete(current)
+
+    // Explore neighbors
+    for (let i = 0; i < graph[current].length; i++) {
+      const neighbor = graph[current][i][0]
+      const tentative_gScore = gScore[current] + graph[current][i][1]
+
+      if (tentative_gScore < gScore[neighbor]) {
+        cameFrom[neighbor] = current
+        gScore[neighbor] = tentative_gScore
+        fScore[neighbor] = gScore[neighbor] + heuristic(neighbor, target)
+
+        if (!openSet.has(neighbor)) {
+          openSet.add(neighbor)
         }
       }
     }
-  
-    return [] // Return empty path if there's no path to the target
   }
-  
-  module.exports = { createGraph, aStar }
 
-  // const V = 9
-  // const E = [
-  //   [0, 1, 4],
-  //   [0, 7, 8],
-  //   [1, 7, 11],
-  //   [1, 2, 8],
-  //   [7, 8, 7],
-  //   [6, 7, 1],
-  //   [2, 8, 2],
-  //   [6, 8, 6],
-  //   [5, 6, 2],
-  //   [2, 5, 4],
-  //   [2, 3, 7],
-  //   [3, 5, 14],
-  //   [3, 4, 9],
-  //   [4, 5, 10]
-  // ]
-  
-  // const graph = createGraph(V, E)
-  // const path = aStar(graph, V, 0, 4) // Find path from node 0 to node 4
-  // console.log(path)
-  
-  /**
-   * The function returns the optimal path from the source to the target node.
-   * The heuristic used is Manhattan distance but it can be modified.
-   */
-  
+  return [] // Return empty path if there's no path to the target
+}
+
+module.exports = { createGraph, aStar }
+
+// const V = 9
+// const E = [
+//   [0, 1, 4],
+//   [0, 7, 8],
+//   [1, 7, 11],
+//   [1, 2, 8],
+//   [7, 8, 7],
+//   [6, 7, 1],
+//   [2, 8, 2],
+//   [6, 8, 6],
+//   [5, 6, 2],
+//   [2, 5, 4],
+//   [2, 3, 7],
+//   [3, 5, 14],
+//   [3, 4, 9],
+//   [4, 5, 10]
+// ]
+
+// const graph = createGraph(V, E)
+// const path = aStar(graph, V, 0, 4) // Find path from node 0 to node 4
+// console.log(path)
+
+/**
+ * The function returns the optimal path from the source to the target node.
+ * The heuristic used is Manhattan distance but it can be modified.
+ */
