419. Battleships in a Board
Given an m x n
matrix board
where each cell is a battleship 'X'
or empty '.'
, return the number of the battleships on board
.
Battleships can only be placed horizontally or vertically on board
. In other words, they can only be made of the shape 1 x k
(1
row, k
columns) or k x 1
(k
rows, 1
column), where k
can be of any size. At least one horizontal or vertical cell separates between two battleships (i.e., there are no adjacent battleships).
Example 1:
Input: board = [["X",".",".","X"],[".",".",".","X"],[".",".",".","X"]]
Output:
Example 2:
Input: board = [["."]]
Output:
Constraints:
m == board.length
n == board[i].length
board[i][j]
is either'.'
or'X'
.
Solution
Full Solution
To count the number of battleships, we can first observe that each battleship acts the same as an island described in this problem. We can run the solution to this same problem however it's unnecessary and a much more elegant solution exists.
The problem statement mentions that each battleship will be a 1 x k
or k x 1
rectangle and no battleships are adjacent. Instead of counting battleships, we can pick a cell in each battleship that defines it. Let's call this the leader of the ship. To solve the problem, we can simply count the number of leaders. The total number of leaders we count will be the same as the number of battleships.
For each battleship, we will let the leader be the leftmost and upmost cell out of all cells in that battleship.
Example
In this specific example, there are four battleships with leaders (indicated with blue) located at (0,0)
, (2,1)
, (4,1)
, and (1,5)
.
How can we determine efficiently if a cell is a leader?
Since the leader is located in the top-left cell of a battleship and no two battleships are adjacent, we can observe that a cell with an 'X'
is a leader if the cells to the left (board[i][j-1]
) and above (board[i-1][j]
) it (if they exist) are not 'X'
cells. In addition, cells that are not leaders will always have an 'X'
cell to the left or above it so they will never be counted. To count the total number of leaders, we'll iterate through all cells and count the number of cells that satisfy the condition mentioned above.
Time Complexity
We can check if a cell is a leader in and since there are cells, our time complexity is .
Time Complexity:
Space Complexity
Since we only maintain a counter for the number of leaders, our space complexity is .
Space Complexity:
C++ Solution
class Solution {
public:
int countBattleships(vector<vector<char>>& board) {
int ans = 0;
int m = board.size();
int n = board[0].size(); // dimensions for board
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
if (board[i][j] == 'X') {
if ((i == 0 || board[i - 1][j] == '.') && (j == 0 || board[i][j - 1] == '.')) {
// check if cell is a leader
ans++;
}
}
}
}
return ans;
}
};
Java Solution
class Solution {
public int countBattleships(char[][] board) {
int ans = 0;
int m = board.length;
int n = board[0].length; // dimensions for board
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
if (board[i][j] == 'X') {
if ((i == 0 || board[i - 1][j] == '.') && (j == 0 || board[i][j - 1] == '.')) {
// check if cell is a leader
ans++;
}
}
}
}
return ans;
}
}
Python Solution
class Solution: def countBattleships(self, board: List[List[str]]) -> int: ans = 0 m = len(board) n = len(board[0]) # dimensions for board for i in range(m): for j in range(n): if board[i][j] == "X": if (i == 0 or board[i - 1][j] == ".") and ( j == 0 or board[i][j - 1] == "."): # check if cell is a leader ans += 1 return ans
Ready to land your dream job?
Unlock your dream job with a 2-minute evaluator for a personalized learning plan!
Start EvaluatorHow many times is a tree node visited in a depth first search?
Recommended Readings
LeetCode Patterns Your Personal Dijkstra's Algorithm to Landing Your Dream Job The goal of AlgoMonster is to help you get a job in the shortest amount of time possible in a data driven way We compiled datasets of tech interview problems and broke them down by patterns This way we
Recursion Recursion is one of the most important concepts in computer science Simply speaking recursion is the process of a function calling itself Using a real life analogy imagine a scenario where you invite your friends to lunch https algomonster s3 us east 2 amazonaws com recursion jpg You first
Runtime Overview When learning about algorithms and data structures you'll frequently encounter the term time complexity This concept is fundamental in computer science and offers insights into how long an algorithm takes to complete given a certain input size What is Time Complexity Time complexity represents the amount of time
Want a Structured Path to Master System Design Too? Don’t Miss This!