This approach involves using Breadth-First Search (BFS) to precompute the minimum Manhattan distance from each cell to any thief in the grid, and then using this information to find the maximum safeness factor for reaching the bottom-right corner.
Time Complexity: O(n2 log n), where n is the grid size, due to the Dijkstra-like process.
Space Complexity: O(n2) for storing distances and safeness factors.
1class SafestPathFinder {
2 constructor() {
3 this.directions = [[0, 1], [1, 0], [0, -1], [-1, 0]];
4 }
5
6 maxSafenessFactor(grid) {
7 const n = grid.length;
8 const distance = Array.from({ length: n }, () => Array(n).fill(Infinity));
9 const queue = [];
10
11 for (let r = 0; r < n; r++) {
12 for (let c = 0; c < n; c++) {
13 if (grid[r][c] === 1) {
14 queue.push([r, c]);
15 distance[r][c] = 0;
16 }
17 }
18 }
19
20 let idx = 0;
21 while (idx < queue.length) {
22 const [r, c] = queue[idx++];
23 for (const [dr, dc] of this.directions) {
24 const nr = r + dr, nc = c + dc;
25 if (nr >= 0 && nr < n && nc >= 0 && nc < n && distance[nr][nc] === Infinity) {
26 distance[nr][nc] = distance[r][c] + 1;
27 queue.push([nr, nc]);
28 }
29 }
30 }
31
32 const heap = new MaxPriorityQueue({ priority: x => x[2] });
33 heap.enqueue([0, 0, distance[0][0]]);
34
35 const visited = Array.from({ length: n }, () => Array(n).fill(false));
36
37 while (!heap.isEmpty()) {
38 const { element: [r, c, safeness] } = heap.dequeue();
39 if (r === n - 1 && c === n - 1) return safeness;
40 if (visited[r][c]) continue;
41 visited[r][c] = true;
42
43 for (const [dr, dc] of this.directions) {
44 const nr = r + dr, nc = c + dc;
45 if (nr >= 0 && nr < n && nc >= 0 && nc < n && !visited[nr][nc]) {
46 const newSafeness = Math.min(safeness, distance[nr][nc]);
47 heap.enqueue([nr, nc, newSafeness]);
48 }
49 }
50 }
51 return 0;
52 }
53}
54
55const spf = new SafestPathFinder();
56const grid = [[0, 0, 1], [0, 0, 0], [0, 0, 0]];
57console.log(spf.maxSafenessFactor(grid)); // Output: 2
58
JavaScript solution leverages a BFS to determine distances from each grid cell to thieves, followed by a priority-based strategy using a max-heap to compile the path with the greatest safeness factor.
This approach considers processing from both the starting and ending points in a bidirectional BFS style, potentially meeting in the middle for optimized distance calculations and safeness factor determination.
Time Complexity: O(n2) due to simultaneous BFS processing.
Space Complexity: O(n2) as distances from both ends are computed.
1#include <iostream>
2#include <vector>
3#include <queue>
4#include <climits>
5
6using namespace std;
7
8const int directions[4][2] = {{0, 1}, {1, 0}, {0, -1}, {-1, 0}};
9
10int bidirectionalSafenessFactor(vector<vector<int>>& grid) {
11 int n = grid.size();
12 vector<vector<int>> distFromStart(n, vector<int>(n, INT_MAX));
13 vector<vector<int>> distFromEnd(n, vector<int>(n, INT_MAX));
14
15 queue<pair<int, int>> startQueue;
16 startQueue.push({0, 0});
17 distFromStart[0][0] = 0;
18
19 queue<pair<int, int>> endQueue;
20 endQueue.push({n - 1, n - 1});
21 distFromEnd[n - 1][n - 1] = 0;
22
23 while (!startQueue.empty() || !endQueue.empty()) {
24 if (!startQueue.empty()) {
25 auto [r, c] = startQueue.front(); startQueue.pop();
26 for (auto& dir : directions) {
27 int nr = r + dir[0], nc = c + dir[1];
28 if (nr >= 0 && nr < n && nc >= 0 && nc < n && distFromStart[nr][nc] == INT_MAX) {
29 distFromStart[nr][nc] = distFromStart[r][c] + 1;
30 startQueue.push({nr, nc});
31 }
32 }
33 }
34
35 if (!endQueue.empty()) {
36 auto [r, c] = endQueue.front(); endQueue.pop();
37 for (auto& dir : directions) {
38 int nr = r + dir[0], nc = c + dir[1];
39 if (nr >= 0 && nr < n && nc >= 0 && nc < n && distFromEnd[nr][nc] == INT_MAX) {
40 distFromEnd[nr][nc] = distFromEnd[r][c] + 1;
41 endQueue.push({nr, nc});
42 }
43 }
44 }
45
46 // Checking overlap
47 for (int i = 0; i < n; ++i) {
48 for (int j = 0; j < n; ++j) {
49 if (distFromStart[i][j] != INT_MAX && distFromEnd[i][j] != INT_MAX) {
50 return distFromStart[i][j] + distFromEnd[i][j];
51 }
52 }
53 }
54 }
55
56 return -1;
57}
58
59int main() {
60 vector<vector<int>> grid = {{0, 0, 1}, {0, 0, 0}, {0, 0, 0}};
61 cout << bidirectionalSafenessFactor(grid) << endl; // Output: Safeness factor
62 return 0;
63}
64
The C++ code takes advantage of bidirectional BFS where BFS expansions occur simultaneously from both the beginning and ending points on the grid. This often reduces the depth of expansion and uncovers maximum converging safeness factors more rapidly.