"As we can pass info to only one child at a time, I told that from any given node, we have to pass the info to that child(of this node) which has the largest subtree rooted at it. To calculate the subtree sizes, I used DFS. And then to calculate the minimum time to pass info to all the nodes, I used BFS picking the largest subtree child first at every node. I couldn't write the complete code in the given time and also made a mistake in telling the overall time complexity of my approach. I think t"
Lakshman B. - "As we can pass info to only one child at a time, I told that from any given node, we have to pass the info to that child(of this node) which has the largest subtree rooted at it. To calculate the subtree sizes, I used DFS. And then to calculate the minimum time to pass info to all the nodes, I used BFS picking the largest subtree child first at every node. I couldn't write the complete code in the given time and also made a mistake in telling the overall time complexity of my approach. I think t"See full answer
"if decreasing arr, start from end and keep checking if next element increases by 1 or not. wherever not, put that value there."
Rishabh R. - "if decreasing arr, start from end and keep checking if next element increases by 1 or not. wherever not, put that value there."See full answer
"The best and average of both the algorithms is same which is O(nlog(n)), but the worst time complexity of QuickSort is O(n^2){a case where all the elements are sorted in opposite to the fashion/order you want} while the worst TC for merge sort remains the same O(nlog(n)).
and The SC for QS=O(logn) and MS=O(n)."
The_ A. - "The best and average of both the algorithms is same which is O(nlog(n)), but the worst time complexity of QuickSort is O(n^2){a case where all the elements are sorted in opposite to the fashion/order you want} while the worst TC for merge sort remains the same O(nlog(n)).
and The SC for QS=O(logn) and MS=O(n)."See full answer
"
This is mostly correct and fairly fast.
My code has a bug somewhere where it fails on cases like the last case, where there are negative number on both ends of the array and the sums .
from collections import deque
debug = True # False
def prdbg(*x):
global debug
debug = True # False
if debug:
print(x)
else:
return
def max_sum(arr, start, end):
if type(arr) == type('''
"
Nathan B. - "
This is mostly correct and fairly fast.
My code has a bug somewhere where it fails on cases like the last case, where there are negative number on both ends of the array and the sums .
from collections import deque
debug = True # False
def prdbg(*x):
global debug
debug = True # False
if debug:
print(x)
else:
return
def max_sum(arr, start, end):
if type(arr) == type('''
"See full answer
"I walked through the code for a react.js based tic-tac-toe game written in typescript. The goal was to find ways to improve the code/ suggest improvements. I missed some areas like where state was being updated directly rather than using React's setState. There were issues around clear and maintainable logic, adherence to React best practices."
Natalie C. - "I walked through the code for a react.js based tic-tac-toe game written in typescript. The goal was to find ways to improve the code/ suggest improvements. I missed some areas like where state was being updated directly rather than using React's setState. There were issues around clear and maintainable logic, adherence to React best practices."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
"The solution produces the same result as the 'prescribed solution' yet it does not get accepted In the test results section
transcript['year'] = transcript['year'].astype(str)
df = pd.pivottable(data = transcript, index = 'studentid', columns = 'year', values = 'yearlygpa', aggfunc = 'mean').resetindex()
df = df[(df['2021'] < df['2022']) & (df['2022'] < df['2023'])]
df['average_gpa'] = df[['2021', '2022', '2023']].mean(axis=1).round(2)
return df
"
Prachi G. - "The solution produces the same result as the 'prescribed solution' yet it does not get accepted In the test results section
transcript['year'] = transcript['year'].astype(str)
df = pd.pivottable(data = transcript, index = 'studentid', columns = 'year', values = 'yearlygpa', aggfunc = 'mean').resetindex()
df = df[(df['2021'] < df['2022']) & (df['2022'] < df['2023'])]
df['average_gpa'] = df[['2021', '2022', '2023']].mean(axis=1).round(2)
return df
"See full answer