CS Fun A - Ivana

graphs/minimum_effort_path.py

@@ -1,3 +1,6 @@
+import heapq
+
+
 def min_effort_path(heights):
     """ Given a 2D array of heights, write a function to return
         the path with minimum effort.
@@ -15,4 +18,31 @@ def min_effort_path(heights):
         int
             minimum effort required to navigate the path from (0, 0) to heights[rows - 1][columns - 1]
     """
-    pass
+    if not heights:
+        return 0
+    # Get the number of rows and columns
+    rows, columns = len(heights), len(heights[0])
+
+    # Initialize the distances array with infinity
+    distances = [[float('inf')] * columns for _ in range(rows)]
+    pq = []
+
+    distances[0][0] = 0
+    # Push the starting point into the heap
+    heapq.heappush(pq, (0, 0, 0))
+
+    while pq:
+        distance, row, column = heapq.heappop(pq)
+        if row == rows - 1 and column == columns - 1:
+            return distance
+
+        # Check the four possible neighbors
+        for r, c in ((row + 1, column), (row - 1, column), (row, column + 1), (row, column - 1)):
+            # Check if the neighbor is within array
+            if 0 <= r < rows and 0 <= c < columns:
+                # Calculate the new distance
+                new_distance = max(distance, abs(heights[r][c] - heights[row][column]))
+                if new_distance < distances[r][c]:
+                    distances[r][c] = new_distance
+                    # Push the new distance into the heap
+                    heapq.heappush(pq, (new_distance, r, c))