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Program to find minimum distance that needs to be covered to meet all person in Python
Suppose we have a 2D matrix there are few values like below −
0 represents an empty cell.
1 represents a wall.
2 represents a person.
Here a person can walk any of these four directions (up, down, left and right). We have to find a cell that is not wall such that it minimizes the total travel distance each person has to walk to and finally find the distance.
So, if the input is like
| 2 | 0 | 1 | 0 |
| 1 | 0 | 1 | 2 |
| 0 | 0 | 2 | 2 |
then the output will be 7 as the best meeting point is the bottom right corner.
To solve this, we will follow these steps −
twos := a new map, costs := a new map
for each index i and row r in matrix, do
for each index j and value v in r, do
if v is same as 2, then
twos[i, j] := [i, j, 0]
costs[i, j] := make a 2D matrix of size as given matrix and fill with infinity
for each key value pair (k, q) in twos, do
seen := a new set
while q is not empty, do
(i, j, cost) := deleted first element from q
if (i, j) is in seen, then
go for the next iteration
add(i, j) into seen
costs[k, i, j] := cost
for each (di, dj) in ((1, 0), (−1, 0), (0, 1), (0, −1)), do
(ni, nj) := (i + di, j + dj)
if ni and nj are in range of matrix and matrix[ni, nj] is not 1, then
insert (ni, nj, cost + 1) at the end of q
ans := infinity
for i in range 0 to row count of matrix, do
for j in range 0 to column count of matrix, do
cur_cost := 0
for each arr in list of all values of costs, do
cur_cost := cur_cost + arr[i, j]
ans := minimum of ans and cur_cost
return ans
Let us see the following implementation to get better understanding −
Example
class Solution:
def solve(self, matrix):
twos = {}
costs = {}
for i, r in enumerate(matrix):
for j, v in enumerate(r):
if v == 2:
twos[(i, j)] = [(i, j, 0)]
costs[(i, j)] = [[1e9 for _ in matrix[0]] for _
in matrix]
for k, q in twos.items():
seen = set()
while q:
i, j, cost = q.pop(0)
if (i, j) in seen:
continue
seen.add((i, j))
costs[k][i][j] = cost
for di, dj in ((1, 0), (-1, 0), (0, 1), (0, -1)):
ni, nj = i + di, j + dj
if (ni >= 0 and nj >= 0 and ni < len(matrix) and nj < len(matrix[0]) and matrix[ni][nj] != 1):
q.append((ni, nj, cost + 1))
ans = 1e9
for i in range(len(matrix)):
for j in range(len(matrix[0])):
cur_cost = 0
for arr in costs.values():
cur_cost += arr[i][j]
ans = min(ans, cur_cost)
return ans
ob = Solution()
matrix = [
[2, 0, 1, 0],
[1, 0, 1, 2],
[0, 0, 2, 2]
]
print(ob.solve(matrix))Input
matrix = [ [2, 0, 1, 0], [1, 0, 1, 2], [0, 0, 2, 2]]
Output
7
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