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Problem solution · Python

All Nodes Distance K in Binary Tree

All Nodes Distance K in Binary Tree: a Python solution using breadth-first search. Learn the idea, check the complexity, and read the full code, with credit to Kamyu LeetCode Solutions.

Technique
Breadth-first search
Source
Kamyu LeetCode Solutions
Length
35 lines
Start with the idea.

Try the problem first. If you get stuck, read the approach below, then write your own solution. The full code is at the bottom.

Approach

Breadth-first search

For All Nodes Distance K in Binary Tree, the implementation explores reachable states in layers, which is the standard shape for unweighted shortest paths and minimum-step transitions.

  1. Model each valid configuration as a state and each legal move as an edge.
  2. Seed the queue with the starting state and mark it immediately.
  3. Expand each state once, recording distance or reachability for unseen neighbours.

Code notes

  • 35 lines of Python from the credited upstream file all-nodes-distance-k-in-binary-tree.py.
  • The implementation visibly relies on sequence storage, hash lookup, ordered lookup.
  • No explicit loop blocks detected, together with recursive traversal.

Complexity

Verify that each state and transition is processed only a bounded number of times; that determines the traversal cost.

Check the problem constraints before deciding whether this complexity will pass.

Source

Code and credit

This code comes from Kamyu LeetCode Solutions by kamyu104 and is used under the MIT licence.

Open problem ↗Original source ↗
Full codeAll Nodes Distance K in Binary Tree · PythonPython
Use this to learn the idea, then write your own version.
# Time:  O(n)# Space: O(n) import collections  class Solution(object):    def distanceK(self, root, target, K):        """        :type root: TreeNode        :type target: TreeNode        :type K: int        :rtype: List[int]        """        def dfs(parent, child, neighbors):            if not child:                return            if parent:                neighbors[parent.val].append(child.val)                neighbors[child.val].append(parent.val)            dfs(child, child.left, neighbors)            dfs(child, child.right, neighbors)         neighbors = collections.defaultdict(list)        dfs(None, root, neighbors)        bfs = [target.val]        lookup = set(bfs)        for _ in xrange(K):            bfs = [nei for node in bfs                   for nei in neighbors[node]                   if nei not in lookup]            lookup |= set(bfs)        return bfs

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