79. Word Search
Medium
Given an m x n
grid of characters board
and a string word
, return true
if word
exists in the grid.
The word can be constructed from letters of sequentially adjacent cells, where adjacent cells are horizontally or vertically neighboring. The same letter cell may not be used more than once.
Example 1:

Input: board = [["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]], word = "ABCCED"
Output: true
Example 2:

Input: board = [["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]], word = "SEE"
Output: true
Example 3:

Input: board = [["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]], word = "ABCB"
Output: false
Constraints:
m == board.length
n = board[i].length
1 <= m, n <= 6
1 <= word.length <= 15
board
andword
consists of only lowercase and uppercase English letters.
Follow up: Could you use search pruning to make your solution faster with a larger board
?
class Solution:
def exist(self, board: List[List[str]], word: str) -> bool:
# No Need to check for empty board since in contraints it is 1 minimum
# Word to searc is also 1 minimum
# Created visited matrix to not visit same node in dfs dual
visited = [[0 for j in range(len(board[0]))] for i in range(len(board))]
for i in range(len(board)):
for j in range(len(board[0])):
if self.dfs(board, visited, word, 0, i, j):
return True
return False
def dfs(self, board, visited, word, index, i ,j):
if len(word) == index:
# If we reach till this point it means we have matched all nodes till here
return True
if i < 0 or i >= len(board) or j < 0 or j >= len(board[0]) or visited[i][j] or word[index] != board[i][j]:
return False
visited[i][j] = 1
result = self.dfs(board, visited, word, index+1, i+1,j) or self.dfs(board, visited, word, index+1, i-1,j) or self.dfs(board, visited, word, index+1, i,j-1) or self.dfs(board, visited, word, index+1, i,j+1)
# While self unit testing found that it will fail for
# [["A","S","C","E"],["S","F","C","S"],["A","D","E","E"]]
# "ASFCCESEEDAS"
# If below unvisit not done
visited[i][j] = 0
return result
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