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Given the root
of a binary tree, the value of a target node target
, and an integer k
, return an array of the values of all nodes that have a distance k
from the target node.
You can return the answer in any order.
Example 1:
Input: root = [3,5,1,6,2,0,8,null,null,7,4], target = 5, k = 2
Output: [7,4,1]
Explanation: The nodes that are a distance 2 from the target node (with value 5) have values 7, 4, and 1.
Example 2:
Input: root = [1], target = 1, k = 3
Output: []
Constraints:
[1, 500]
.0 <= Node.val <= 500
Node.val
are unique.target
is the value of one of the nodes in the tree.0 <= k <= 1000
class Solution {
public:
vector<int> ans;
map<TreeNode*, TreeNode*> parent; // son=>parent
set<TreeNode*> visit; //record visied node
void findParent(TreeNode* node){
if(!node ) return;
if( node->left ){
parent[node->left] = node;
findParent(node->left);
}
if( node->right){
parent[node->right] = node;
findParent(node->right);
}
}
vector<int> distanceK(TreeNode* root, TreeNode* target, int K) {
if( !root ) return {};
findParent(root);
dfs(target, K );
return ans;
}
void dfs( TreeNode* node, int K){
if( visit.find(node) != visit.end() )
return;
visit.insert(node);
if( K == 0 ){
ans.push_back(node->val);
return;
}
if( node->left ){
dfs(node->left, K-1);
}
if( node->right){
dfs(node->right, K-1);
}
TreeNode* p = parent[node];
if( p )
dfs(p, K-1);
}
};
def distanceK(self, root, target, K):
conn = collections.defaultdict(list)
def connect(parent, child):
# both parent and child are not empty
if parent and child:
# building an undirected graph representation, assign the
# child value for the parent as the key and vice versa
conn[parent.val].append(child.val)
conn[child.val].append(parent.val)
# in-order traversal
if child.left: connect(child, child.left)
if child.right: connect(child, child.right)
# the initial parent node of the root is None
connect(None, root)
# start the breadth-first search from the target, hence the starting level is 0
bfs = [target.val]
seen = set(bfs)
# all nodes at (k-1)th level must also be K steps away from the target node
for i in range(K):
# expand the list comprehension to strip away the complexity
new_level = []
for q_node_val in bfs:
for connected_node_val in conn[q_node_val]:
if connected_node_val not in seen:
new_level.append(connected_node_val)
bfs = new_level
# add all the values in bfs into seen
seen |= set(bfs)
return bfs
class Solution {
Map<TreeNode, Integer> map = new HashMap<>();
public List<Integer> distanceK(TreeNode root, TreeNode target, int K) {
List<Integer> res = new LinkedList<>();
find(root, target);
dfs(root, target, K, map.get(root), res);
return res;
}
// find target node first and store the distance in that path that we could use it later directly
private int find(TreeNode root, TreeNode target) {
if (root == null) return -1;
if (root == target) {
map.put(root, 0);
return 0;
}
int left = find(root.left, target);
if (left >= 0) {
map.put(root, left + 1);
return left + 1;
}
int right = find(root.right, target);
if (right >= 0) {
map.put(root, right + 1);
return right + 1;
}
return -1;
}
private void dfs(TreeNode root, TreeNode target, int K, int length, List<Integer> res) {
if (root == null) return;
if (map.containsKey(root)) length = map.get(root);
if (length == K) res.add(root.val);
dfs(root.left, target, K, length + 1, res);
dfs(root.right, target, K, length + 1, res);
}
}
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