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mathangpeddimathangpeddi
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Fixed lint issues
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‎Graphs/Astar.js‎

Lines changed: 189 additions & 135 deletions
Original file line numberDiff line numberDiff line change
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/**
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* @author Mathang Peddi
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* @author : Mathang Peddi
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* A* Algorithm calculates the minimum cost path between two nodes.
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* It is used to find the shortest path using heuristics.
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* It uses graph data structure.
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*/
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// Euclidean distance heuristic for 2D points
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function euclideanHeuristic(pointA, pointB) {
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const dx = pointA[0] - pointB[0];
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const dy = pointA[1] - pointB[1];
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return Math.sqrt(dx * dx + dy * dy);
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const dx = pointA[0] - pointB[0]
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const dy = pointA[1] - pointB[1]
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return Math.sqrt(dx * dx + dy * dy)
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}
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// Priority Queue (Min-Heap) implementation
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class PriorityQueue {
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constructor() {
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this.elements = []
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}
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// Priority Queue (Min-Heap) implementation
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class PriorityQueue {
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constructor() {
18-
this.elements = [];
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}
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enqueue(node, priority) {
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this.elements.push({ node, priority });
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this.bubbleUp();
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enqueue(node, priority) {
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this.elements.push({ node, priority })
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this.bubbleUp()
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}
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bubbleUp() {
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let index = this.elements.length - 1
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while (index > 0) {
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let parentIndex = Math.floor((index - 1) / 2)
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if (this.elements[index].priority >= this.elements[parentIndex].priority)
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break
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;[this.elements[index], this.elements[parentIndex]] = [
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this.elements[parentIndex],
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this.elements[index]
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]
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index = parentIndex
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}
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bubbleUp() {
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let index = this.elements.length - 1;
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while (index > 0) {
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let parentIndex = Math.floor((index - 1) / 2);
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if (this.elements[index].priority >= this.elements[parentIndex].priority) break;
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[this.elements[index], this.elements[parentIndex]] = [this.elements[parentIndex], this.elements[index]];
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index = parentIndex;
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}
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}
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dequeue() {
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if (this.elements.length === 1) {
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return this.elements.pop().node
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}
35-
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dequeue() {
37-
if (this.elements.length === 1) {
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return this.elements.pop().node;
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const node = this.elements[0].node
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this.elements[0] = this.elements.pop()
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this.sinkDown(0)
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return node
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}
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sinkDown(index) {
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const length = this.elements.length
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const element = this.elements[index]
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while (true) {
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let leftChildIndex = 2 * index + 1
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let rightChildIndex = 2 * index + 2
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let swapIndex = null
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if (
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leftChildIndex < length &&
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this.elements[leftChildIndex].priority < element.priority
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) {
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swapIndex = leftChildIndex
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}
40-
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const node = this.elements[0].node;
42-
this.elements[0] = this.elements.pop();
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this.sinkDown(0);
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return node;
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}
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47-
sinkDown(index) {
48-
const length = this.elements.length;
49-
const element = this.elements[index];
50-
while (true) {
51-
let leftChildIndex = 2 * index + 1;
52-
let rightChildIndex = 2 * index + 2;
53-
let swapIndex = null;
54-
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if (leftChildIndex < length && this.elements[leftChildIndex].priority < element.priority) {
56-
swapIndex = leftChildIndex;
57-
}
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if (rightChildIndex < length && this.elements[rightChildIndex].priority < (swapIndex === null ? element.priority : this.elements[leftChildIndex].priority)) {
60-
swapIndex = rightChildIndex;
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}
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if (swapIndex === null) break;
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[this.elements[index], this.elements[swapIndex]] = [this.elements[swapIndex], this.elements[index]];
66-
index = swapIndex;
65+
66+
if (
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rightChildIndex < length &&
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this.elements[rightChildIndex].priority <
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(swapIndex === null
70+
? element.priority
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: this.elements[leftChildIndex].priority)
72+
) {
73+
swapIndex = rightChildIndex
6774
}
75+
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if (swapIndex === null) break
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78+
;[this.elements[index], this.elements[swapIndex]] = [
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this.elements[swapIndex],
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this.elements[index]
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]
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index = swapIndex
6883
}
69-
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isEmpty() {
71-
return this.elements.length === 0;
72-
}
7384
}
74-
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function aStar(graph, src, target, points) {
76-
const openSet = new PriorityQueue(); // Priority queue to explore nodes
77-
openSet.enqueue(src, 0);
78-
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const cameFrom = Array(graph.length).fill(null); // Keep track of path
80-
const gScore = Array(graph.length).fill(Infinity); // Actual cost from start to a node
81-
gScore[src] = 0;
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const fScore = Array(graph.length).fill(Infinity); // Estimated cost from start to goal (g + h)
84-
fScore[src] = euclideanHeuristic(points[src], points[target]);
85-
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while (!openSet.isEmpty()) {
87-
// Get the node in openSet with the lowest fScore
88-
const current = openSet.dequeue();
89-
90-
// If the current node is the target, reconstruct the path and return
91-
if (current === target) {
92-
const path = [];
93-
while (cameFrom[current] !== -1) {
94-
path.push(current);
95-
current = cameFrom[current];
96-
}
97-
path.push(src);
98-
return path.reverse();
85+
86+
isEmpty() {
87+
return this.elements.length === 0
88+
}
89+
}
90+
91+
function aStar(graph, src, target, points) {
92+
const openSet = new PriorityQueue() // Priority queue to explore nodes
93+
openSet.enqueue(src, 0)
94+
95+
const cameFrom = Array(graph.length).fill(null) // Keep track of path
96+
const gScore = Array(graph.length).fill(Infinity) // Actual cost from start to a node
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gScore[src] = 0
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99+
const fScore = Array(graph.length).fill(Infinity) // Estimated cost from start to goal (g + h)
100+
fScore[src] = euclideanHeuristic(points[src], points[target])
101+
102+
while (!openSet.isEmpty()) {
103+
// Get the node in openSet with the lowest fScore
104+
const current = openSet.dequeue()
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106+
// If the current node is the target, reconstruct the path and return
107+
if (current === target) {
108+
const path = []
109+
while (cameFrom[current] !== -1) {
110+
path.push(current)
111+
current = cameFrom[current]
99112
}
100-
101-
// Explore neighbors using destructuring for cleaner code
102-
for (const [neighbor, weight] of graph[current]) {
103-
const tentative_gScore = gScore[current] + weight;
104-
105-
if (tentative_gScore < gScore[neighbor]) {
106-
cameFrom[neighbor] = current;
107-
gScore[neighbor] = tentative_gScore;
108-
const priority = gScore[neighbor] + euclideanHeuristic(points[neighbor], points[target]);
109-
fScore[neighbor] = priority;
110-
111-
openSet.enqueue(neighbor, priority);
112-
}
113+
path.push(src)
114+
return path.reverse()
115+
}
116+
117+
// Explore neighbors using destructuring for cleaner code
118+
for (const [neighbor, weight] of graph[current]) {
119+
const tentative_gScore = gScore[current] + weight
120+
121+
if (tentative_gScore < gScore[neighbor]) {
122+
cameFrom[neighbor] = current
123+
gScore[neighbor] = tentative_gScore
124+
const priority =
125+
gScore[neighbor] +
126+
euclideanHeuristic(points[neighbor], points[target])
127+
fScore[neighbor] = priority
128+
129+
openSet.enqueue(neighbor, priority)
113130
}
114131
}
115-
116-
return null; // Return null if there's no path to the target
117132
}
118-
119-
// Define the graph as an adjacency list
120-
const graph = [
121-
[[1, 4], [7, 8]], // Node 0 connects to node 1 (weight 4), node 7 (weight 8)
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[[0, 4], [2, 8], [7, 11]], // Node 1 connects to node 0, node 2, node 7
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[[1, 8], [3, 7], [5, 4], [8, 2]], // Node 2 connects to several nodes
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[[2, 7], [4, 9], [5, 14]], // Node 3 connects to nodes 2, 4, 5
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[[3, 9], [5, 10]], // Node 4 connects to nodes 3 and 5
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[[2, 4], [3, 14], [4, 10], [6, 2]], // Node 5 connects to several nodes
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[[5, 2], [7, 1], [8, 6]], // Node 6 connects to nodes 5, 7, 8
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[[0, 8], [1, 11], [6, 1], [8, 7]], // Node 7 connects to several nodes
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[[2, 2], [6, 6], [7, 7]] // Node 8 connects to nodes 2, 6, 7
130-
];
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// Define 2D coordinates for each node (these can be changed based on actual node positions)
133-
const points = [
134-
[0, 0], // Point for node 0
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[1, 2], // Point for node 1
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[2, 1], // Point for node 2
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[3, 5], // Point for node 3
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[4, 3], // Point for node 4
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[5, 6], // Point for node 5
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[6, 8], // Point for node 6
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[7, 10], // Point for node 7
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[8, 12] // Point for node 8
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];
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// Call the aStar function with the graph, source node (0), and target node (4)
146-
const path = aStar(graph, 0, 4, points);
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148-
console.log('Shortest path from node 0 to node 4:', path);
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150-
/**
151-
* The function returns the optimal path from the source to the target node.
152-
* The heuristic used is Euclidean distance between nodes' 2D coordinates.
153-
*/
154-
133+
134+
return null // Return null if there's no path to the target
135+
}
136+
137+
// Define the graph as an adjacency list
138+
const graph = [
139+
[
140+
[1, 4],
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[7, 8]
142+
], // Node 0 connects to node 1 (weight 4), node 7 (weight 8)
143+
[
144+
[0, 4],
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[2, 8],
146+
[7, 11]
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], // Node 1 connects to node 0, node 2, node 7
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[
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[1, 8],
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[3, 7],
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[5, 4],
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[8, 2]
153+
], // Node 2 connects to several nodes
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[
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[2, 7],
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[4, 9],
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[5, 14]
158+
], // Node 3 connects to nodes 2, 4, 5
159+
[
160+
[3, 9],
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[5, 10]
162+
], // Node 4 connects to nodes 3 and 5
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[
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[2, 4],
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[3, 14],
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[4, 10],
167+
[6, 2]
168+
], // Node 5 connects to several nodes
169+
[
170+
[5, 2],
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[7, 1],
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[8, 6]
173+
], // Node 6 connects to nodes 5, 7, 8
174+
[
175+
[0, 8],
176+
[1, 11],
177+
[6, 1],
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[8, 7]
179+
], // Node 7 connects to several nodes
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[
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[2, 2],
182+
[6, 6],
183+
[7, 7]
184+
] // Node 8 connects to nodes 2, 6, 7
185+
]
186+
187+
// Define 2D coordinates for each node (these can be changed based on actual node positions)
188+
const points = [
189+
[0, 0], // Point for node 0
190+
[1, 2], // Point for node 1
191+
[2, 1], // Point for node 2
192+
[3, 5], // Point for node 3
193+
[4, 3], // Point for node 4
194+
[5, 6], // Point for node 5
195+
[6, 8], // Point for node 6
196+
[7, 10], // Point for node 7
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[8, 12] // Point for node 8
198+
]
199+
200+
// Call the aStar function with the graph, source node (0), and target node (4)
201+
const path = aStar(graph, 0, 4, points)
202+
203+
console.log('Shortest path from node 0 to node 4:', path)
204+
205+
/**
206+
* The function returns the optimal path from the source to the target node.
207+
* The heuristic used is Euclidean distance between nodes' 2D coordinates.
208+
*/

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