"First of all, stack and heap memory are abstraction on top of the hardware by the compiler. The hardware is not aware of stack and heap memory. There is only a single piece of memory that a program has access to. The compiler creates the concepts of stack and heap memory to run the programs efficiently.
Programs use stack memory to store local variables and a few important register values such as frame pointer and return address for program counter. This makes it easier for the compiler to gene"
Stanley Y. - "First of all, stack and heap memory are abstraction on top of the hardware by the compiler. The hardware is not aware of stack and heap memory. There is only a single piece of memory that a program has access to. The compiler creates the concepts of stack and heap memory to run the programs efficiently.
Programs use stack memory to store local variables and a few important register values such as frame pointer and return address for program counter. This makes it easier for the compiler to gene"See full answer
"function serialize(list) {
for (let i=0; i 0xFFFF) {
throw new Exception(String ${list[i]} is too long!);
}
const prefix = String.fromCharCode(length);
list[i] = ${prefix}${list[i]};
console.log(list[i])
}
return list.join('');
}
function deserialize(s) {
let i=0;
const length = s.length;
const output = [];
while (i < length) {
"
Tiago R. - "function serialize(list) {
for (let i=0; i 0xFFFF) {
throw new Exception(String ${list[i]} is too long!);
}
const prefix = String.fromCharCode(length);
list[i] = ${prefix}${list[i]};
console.log(list[i])
}
return list.join('');
}
function deserialize(s) {
let i=0;
const length = s.length;
const output = [];
while (i < length) {
"See full answer
"If 0's aren't a concern, couldn't we just
multiply all numbers.
and then divide product by each number in the list ?
if there's more than one zero, then we just return an array of 0s
if there's one zero, then we just replace 0 with product and rest 0s.
what am i missing?"
Sachin R. - "If 0's aren't a concern, couldn't we just
multiply all numbers.
and then divide product by each number in the list ?
if there's more than one zero, then we just return an array of 0s
if there's one zero, then we just replace 0 with product and rest 0s.
what am i missing?"See full answer
Software Engineer
Data Structures & Algorithms
+3 more
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"def friend_distance(friends, userA, userB):
step = 0
total_neighs = set()
llen = len(total_neighs)
total_neighs.add(userB)
while len(total_neighs)!=llen:
s = set()
step += 1
llen = len(total_neighs)
for el in total_neighs:
nes = neighbours(friends, userA, el)
if userA in nes:
return step
for p in nes:
s.add(p)
for el in s:
total_neighs.add(el)
return -1
def neighbours(A,n1, n2):
out = set()
for i in range(len(A[n2])):
if An2:
out.add(i)
return out"
Batman X. - "def friend_distance(friends, userA, userB):
step = 0
total_neighs = set()
llen = len(total_neighs)
total_neighs.add(userB)
while len(total_neighs)!=llen:
s = set()
step += 1
llen = len(total_neighs)
for el in total_neighs:
nes = neighbours(friends, userA, el)
if userA in nes:
return step
for p in nes:
s.add(p)
for el in s:
total_neighs.add(el)
return -1
def neighbours(A,n1, n2):
out = set()
for i in range(len(A[n2])):
if An2:
out.add(i)
return out"See full answer
"Approach 1: Use sorting and return the kth largest element from the sorted list. Time complexity: O(nlogn)
Approach 2: Use max heap and then select the kth largest element. time complexity: O(n+logn)
Approach 3: Quickselect. Time complexity O(n)
I explained my interviewer the 3 approaches. He told me to solve in a naive manner. Used Approach 1 had some time left so coded approach 3 also
The average time complexity of Quickselect is O(n), making it very efficient for its purpose. However, in"
GalacticInterviewer - "Approach 1: Use sorting and return the kth largest element from the sorted list. Time complexity: O(nlogn)
Approach 2: Use max heap and then select the kth largest element. time complexity: O(n+logn)
Approach 3: Quickselect. Time complexity O(n)
I explained my interviewer the 3 approaches. He told me to solve in a naive manner. Used Approach 1 had some time left so coded approach 3 also
The average time complexity of Quickselect is O(n), making it very efficient for its purpose. However, in"See full answer
"\# Definition for a binary tree node.
class TreeNode:
def init(self, val=0, left=None, right=None):
self.val = val
self.left = left
self.right = right
class Solution:
def maxPathSum(self, root: TreeNode) -> int:
self.max_sum = float('-inf')"
Jerry O. - "\# Definition for a binary tree node.
class TreeNode:
def init(self, val=0, left=None, right=None):
self.val = val
self.left = left
self.right = right
class Solution:
def maxPathSum(self, root: TreeNode) -> int:
self.max_sum = float('-inf')"See full answer
"Count items between indices within compartments
compartments are delineated by by: '|'
items are identified by: '*'
input_inventory = "*||||"
inputstartidxs = [1, 4, 6]
inputendidxs = [9, 5, 8]
expected_output = [3, 0, 1]
Explanation:
"*||||"
0123456789... indices
++ + # within compartments
^ start_idx = 1
^ end_idx = 9
-- - # within idxs but not within compartments
"*||||"
0123456789... indices
"
Anonymous Unicorn - "Count items between indices within compartments
compartments are delineated by by: '|'
items are identified by: '*'
input_inventory = "*||||"
inputstartidxs = [1, 4, 6]
inputendidxs = [9, 5, 8]
expected_output = [3, 0, 1]
Explanation:
"*||||"
0123456789... indices
++ + # within compartments
^ start_idx = 1
^ end_idx = 9
-- - # within idxs but not within compartments
"*||||"
0123456789... indices
"See full answer
"
import java.util.*;
class Solution {
// Time Complexity: O(n^2)
// Space Complexity: O(n)
public static List> threeSum(int[] nums) {
// Ensure that the array is sorted first
Arrays.sort(nums);
// Create the results list to return
List> results = new ArrayList();
// Iterate over the length of nums
for (int i = 0; i < nums.length-2; i++) {
// We will have the first number in"
Victor O. - "
import java.util.*;
class Solution {
// Time Complexity: O(n^2)
// Space Complexity: O(n)
public static List> threeSum(int[] nums) {
// Ensure that the array is sorted first
Arrays.sort(nums);
// Create the results list to return
List> results = new ArrayList();
// Iterate over the length of nums
for (int i = 0; i < nums.length-2; i++) {
// We will have the first number in"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
"function findPrimes(n) {
if (n < 2) return [];
const primes = [];
for (let i=2; i <= n; i++) {
const half = Math.floor(i/2);
let isPrime = true;
for (let prime of primes) {
if (i % prime === 0) {
isPrime = false;
break;
}
}
if (isPrime) {
primes.push(i);
}
}
return primes;
}
`"
Tiago R. - "function findPrimes(n) {
if (n < 2) return [];
const primes = [];
for (let i=2; i <= n; i++) {
const half = Math.floor(i/2);
let isPrime = true;
for (let prime of primes) {
if (i % prime === 0) {
isPrime = false;
break;
}
}
if (isPrime) {
primes.push(i);
}
}
return primes;
}
`"See full answer
"class ListNode:
def init(self, val=0, next=None):
self.val = val
self.next = next
def has_cycle(head: ListNode) -> bool:
slow, fast = head, head
while fast and fast.next:
slow = slow.next
fast = fast.next.next
if slow == fast:
return True
return False
debug your code below
node1 = ListNode(1)
node2 = ListNode(2)
node3 = ListNode(3)
node4 = ListNode(4)
creates a linked list with a cycle: 1 -> 2 -> 3 -> 4"
Anonymous Roadrunner - "class ListNode:
def init(self, val=0, next=None):
self.val = val
self.next = next
def has_cycle(head: ListNode) -> bool:
slow, fast = head, head
while fast and fast.next:
slow = slow.next
fast = fast.next.next
if slow == fast:
return True
return False
debug your code below
node1 = ListNode(1)
node2 = ListNode(2)
node3 = ListNode(3)
node4 = ListNode(4)
creates a linked list with a cycle: 1 -> 2 -> 3 -> 4"See full answer