feat: solve DaleStudy#286 in python

merge-k-sorted-lists/EGON.py

@@ -0,0 +1,55 @@
+from heapq import heappush, heappop
+from typing import List, Optional
+from unittest import TestCase, main
+
+
+class ListNode:
+    def __init__(self, val=0, next=None):
+        self.val = val
+        self.next = next
+
+
+class Solution:
+    def mergeKLists(self, lists: List[Optional[ListNode]]) -> Optional[ListNode]:
+        return self.solve_priority_queue(lists)
+
+    """
+    Runtime: 7 ms (Beats 100.00%)
+    Time Complexity: O(n * m)
+        - lists의 길이 k만큼 조회에 O(k)
+            - 힙의 크기가 최대 k이므로, heappush에 * O(log k)
+        - heap의 크기는 최대 k이므로, 
+            - heappop하는데 O(k * log k)
+            - heappush하는데 lists를 이루는 list를 이루는 모든 원소들의 총 갯수를 n이라 하면, O(n * log k)
+        >  O(k * log k) + O(k * log k) + O(n * log k) ~= O(max(k, n) * log k) = O(n * log k)
+
+    Memory: 19.44 MB (Beats 58.42%)
+    Space Complexity: O(k)
+        > heap의 크기는 lists의 길이 k에 비례하므로, O(k)
+    """
+    def solve_priority_queue(self, lists: List[Optional[ListNode]]) -> Optional[ListNode]:
+        root = result = ListNode(None)
+        heap = []
+
+        for i in range(len(lists)):
+            if lists[i]:
+                heappush(heap, (lists[i].val, i, lists[i]))
+
+        while heap:
+            node = heappop(heap)
+            _, idx, result.next = node
+
+            result = result.next
+            if result.next:
+                heappush(heap, (result.next.val, idx, result.next))
+
+        return root.next
+
+
+class _LeetCodeTestCases(TestCase):
+    def test_1(self):
+        self.assertEqual(True, True)
+
+
+if __name__ == '__main__':
+    main()