"from collections import deque
def updateword(words, startword, end_word):
if end_word not in words:
return None # Early exit if end_word is not in the dictionary
queue = deque([(start_word, 0)]) # (word, steps)
visited = set([start_word]) # Keep track of visited words
while queue:
word, steps = queue.popleft()
if word == end_word:
return steps # Found the target word, return steps
for i in range(len(word)):
"
叶 路. - "from collections import deque
def updateword(words, startword, end_word):
if end_word not in words:
return None # Early exit if end_word is not in the dictionary
queue = deque([(start_word, 0)]) # (word, steps)
visited = set([start_word]) # Keep track of visited words
while queue:
word, steps = queue.popleft()
if word == end_word:
return steps # Found the target word, return steps
for i in range(len(word)):
"See full answer
"We can use dictionary to store cache items so that our read / write operations will be O(1).
Each time we read or update an existing record, we have to ensure the item is moved to the back of the cache. This will allow us to evict the first item in the cache whenever the cache is full and we need to add new records also making our eviction O(1)
Instead of normal dictionary, we will use ordered dictionary to store cache items. This will allow us to efficiently move items to back of the cache a"
Alfred O. - "We can use dictionary to store cache items so that our read / write operations will be O(1).
Each time we read or update an existing record, we have to ensure the item is moved to the back of the cache. This will allow us to evict the first item in the cache whenever the cache is full and we need to add new records also making our eviction O(1)
Instead of normal dictionary, we will use ordered dictionary to store cache items. This will allow us to efficiently move items to back of the cache a"See full answer
"
from typing import List
def getnumberof_islands(binaryMatrix: List[List[int]]) -> int:
if not binaryMatrix: return 0
rows = len(binaryMatrix)
cols = len(binaryMatrix[0])
islands = 0
for r in range(rows):
for c in range(cols):
if binaryMatrixr == 1:
islands += 1
dfs(binaryMatrix, r, c)
return islands
def dfs(grid, r, c):
if (
r = len(grid)
"
Rick E. - "
from typing import List
def getnumberof_islands(binaryMatrix: List[List[int]]) -> int:
if not binaryMatrix: return 0
rows = len(binaryMatrix)
cols = len(binaryMatrix[0])
islands = 0
for r in range(rows):
for c in range(cols):
if binaryMatrixr == 1:
islands += 1
dfs(binaryMatrix, r, c)
return islands
def dfs(grid, r, c):
if (
r = len(grid)
"See full answer
"A much better solution than the one in the article, below:
It looks like the ones writing articles here in Javascript do not understand the time/space complexity of javascript methods.
shift, splice, sort, etc... In the solution article you have a shift and a sort being done inside a while, that is, the multiplication of Ns.
My solution, below, iterates through the list once and then sorts it, separately. It´s O(N+Log(N))
class ListNode {
constructor(val = 0, next = null) {
th"
Guilherme F. - "A much better solution than the one in the article, below:
It looks like the ones writing articles here in Javascript do not understand the time/space complexity of javascript methods.
shift, splice, sort, etc... In the solution article you have a shift and a sort being done inside a while, that is, the multiplication of Ns.
My solution, below, iterates through the list once and then sorts it, separately. It´s O(N+Log(N))
class ListNode {
constructor(val = 0, next = null) {
th"See full answer
"This could be done using two-pointer approach assuming array is sorted: left and right pointers. We need track two sums (left and right) as we move pointers. For moving pointers we will move left to right by 1 (increment) when right sum is greater. We will move right pointer to left by 1 (decrement) when left sum is greater. at some point we will either get the sum same and that's when we exit from the loop. 0-left will be one array and right-(n-1) will be another array.
We are not going to mo"
Bhaskar B. - "This could be done using two-pointer approach assuming array is sorted: left and right pointers. We need track two sums (left and right) as we move pointers. For moving pointers we will move left to right by 1 (increment) when right sum is greater. We will move right pointer to left by 1 (decrement) when left sum is greater. at some point we will either get the sum same and that's when we exit from the loop. 0-left will be one array and right-(n-1) will be another array.
We are not going to mo"See full answer
"int main()
{
int a1[7]={1,2,3,4,5,6,7};
int a2[7]={1,9,10,11,12,13,14};
vectorv;
v.insert(v.begin(),begin(a1),end(a1));
v.insert(v.begin(),begin(a2),end(a2));
int a3[v.size()];
sort(v.begin(),v.end());
for(int i=0;i<v.size();i++)
{
a3[i]=v[i];
}
}
`"
Aryan D. - "int main()
{
int a1[7]={1,2,3,4,5,6,7};
int a2[7]={1,9,10,11,12,13,14};
vectorv;
v.insert(v.begin(),begin(a1),end(a1));
v.insert(v.begin(),begin(a2),end(a2));
int a3[v.size()];
sort(v.begin(),v.end());
for(int i=0;i<v.size();i++)
{
a3[i]=v[i];
}
}
`"See full answer
"
O(n) time, O(1) space
from typing import List
def maxsubarraysum(nums: List[int]) -> int:
if len(nums) == 0:
return 0
maxsum = currsum = nums[0]
for i in range(1, len(nums)):
currsum = max(currsum + nums[i], nums[i])
maxsum = max(currsum, max_sum)
return max_sum
debug your code below
print(maxsubarraysum([-1, 2, -3, 4]))
`"
Rick E. - "
O(n) time, O(1) space
from typing import List
def maxsubarraysum(nums: List[int]) -> int:
if len(nums) == 0:
return 0
maxsum = currsum = nums[0]
for i in range(1, len(nums)):
currsum = max(currsum + nums[i], nums[i])
maxsum = max(currsum, max_sum)
return max_sum
debug your code below
print(maxsubarraysum([-1, 2, -3, 4]))
`"See full answer
"function isPalindrome(s, start, end) {
while (s[start] === s[end] && end >= start) {
start++;
end--;
}
return end <= start;
}
function longestPalindromicSubstring(s) {
let longestPalindrome = '';
for (let i=0; i < s.length; i++) {
let j = s.length-1;
while (s[i] !== s[j] && i <= j) {
j--;
}
if (s[i] === s[j]) {
if (isPalindrome(s, i, j)) {
const validPalindrome = s.substring(i, j+1"
Tiago R. - "function isPalindrome(s, start, end) {
while (s[start] === s[end] && end >= start) {
start++;
end--;
}
return end <= start;
}
function longestPalindromicSubstring(s) {
let longestPalindrome = '';
for (let i=0; i < s.length; i++) {
let j = s.length-1;
while (s[i] !== s[j] && i <= j) {
j--;
}
if (s[i] === s[j]) {
if (isPalindrome(s, i, j)) {
const validPalindrome = s.substring(i, j+1"See full answer
"Make current as root.
2 while current is not null,
if p and q are less than current,
go left.
If p and q are greater than current,
go right.
else return current.
return null"
Vaibhav D. - "Make current as root.
2 while current is not null,
if p and q are less than current,
go left.
If p and q are greater than current,
go right.
else return current.
return null"See full answer
"#include
#include
#include
using namespace std;
void printComs(int prev, int start, int end, int target)
{
if (start >= end) return;
while (start target)
{
end--;
}
else
{
st"
Iris F. - "#include
#include
#include
using namespace std;
void printComs(int prev, int start, int end, int target)
{
if (start >= end) return;
while (start target)
{
end--;
}
else
{
st"See full answer
"The height of a binary tree is the maximum number of edges from the root node to any leaf node. To calculate the height of a binary tree, we can use a recursive approach. The basic idea is to compare the heights of the left and right subtrees of the root node, and return the maximum of them plus one."
Prashant Y. - "The height of a binary tree is the maximum number of edges from the root node to any leaf node. To calculate the height of a binary tree, we can use a recursive approach. The basic idea is to compare the heights of the left and right subtrees of the root node, and return the maximum of them plus one."See full answer