I'm practicing myself by doing some leetcode questions, however, I don't know why that's an overflow problem right here. I knew the way I sum the subarray was terrible, any tips for the sum of the subarray?
and the run time for this code would be forever
#include <numeric>
class Solution {
public:
int sumOddLengthSubarrays(vector<int>& arr) {
int size = arr.size();//5
int ans = 0;
int sumAll = 0;
int start = 3;
int tempsum;
for(int i =0; i< size; i++){ //sumitself
sumAll += arr[i];
}
ans = sumAll; //alreayd have the 1 index
if(size%2 == 0){//even number 6
int temp = size-1; //5
if(size == 2)
ans = sumAll;
else{
while(start <= temp){//3 < 5
for(int i = 0; i< size; i++){
for(int k =0; k< start; k++){//3
tempsum += arr[i+k];
if(i+k > temp) //reach 5
break;
}
}
start+=2;
}
}
ans+= tempsum;
}
else{//odd number
if(size == 1)
ans = sumAll;
else{
while(start < size){//3
for(int i = 0; i< size; i++){
for(int k =0; k< start; k++){//3
tempsum += arr[i+k];
if(i+k > size) //reach 5
break;
}
}
start+=2;
}
ans+= tempsum;
ans+= sumAll; //size index
}
}
return ans;
}
};
The problem is with arr[i+k]. The result of i + k can be equal to, or larger, than size. You check it after you have already gone out of bounds.
You should probably modify the inner loop condition so that never happens:
for(int k =0; k < start && (i + k) < size; k++){//3
Now you don't even need the inner check.
You can use prefix sum array technique and then for each index you can calculate the sub-array sum for each odd-length array using prefix sum array. I submitted the below solution in LeetCode and it beats runtime of 100% of submissions and memory usage of 56.95%
class Solution {
public:
int sumOddLengthSubarrays(vector<int>& arr) {
int n = arr.size();
vector<int> prefix(n+1,0);
int sum = 0;
prefix[1] = arr[0];
for(int i=1;i<n;i++)
prefix[i+1]=(arr[i]+prefix[i]);
for(int i=0;i<n;i++)
{
for(int j=i;j<n;j+=2)
sum+=prefix[j+1]-prefix[i];
}
return sum;
}
};
https://leetcode.com/problems/sum-of-all-odd-length-subarrays/discuss/1263893/Java-100-one-pass-O(n)-with-explanation
class Solution {
public int sumOddLengthSubarrays(int[] arr) {
// alt solution: O(n)
//for each i:
// if(n -1 - i) is odd, then arr[i] is counted (n-1-i)/2 + 1 times, each from 0 to i, total ((n-i)/2+1)*(i+1) times
// if(n -1 - i) is even, then arr[i] is counted (n-1-i)/2 + 1 times, if starting subseq index diff with i is even;
// (n-1-i)/2 times, if starting index diff with i s odd, total (n-i)/2 *(i+1) + (i+1)/2
// if i is even i - 1, i - 3, .. 1, total (i -2)/2 + 1 = i / 2 = (i+1) / 2
// if i is odd i-1, i-3, .., 0 total (i-1)/2 + 1 = (i+1) / 2
int total = 0;
int n = arr.length;
for(int i = 0; i < n; i++)
total += (((n - 1 - i) / 2 + 1) * (i + 1) - ((n-i) % 2)*((i+1) / 2)) * arr[i];
return total;
}
}
Related
Can someone explain how to solve the question below, Much appreciated!
Given an integer array[] of size n, your task is to count the number of magical subarrays in the arr.
Here any subarray array[l…r] is considered to be magical if it satisfies the magical condition.
it should contain an even number(non zero) of odd numbers
More Formally the count of odd numbers in the subarray should be even and should be greater than 0
Constraints
1<=n<=10^5
1<=array[i]<=2*(10)^5
#TestCase 1;
Input:
n=4
array[]={2,1,2,3}
output:2
the magical subarrays are: {2,1,2,3} , {1,2,3}
#Testcase 2
n=6
array[]={1,2,5,2,3,7}
output:7
the magical subarrays are:{1,2,5}, {1,2,5,2}, {2,5,2,3}, {5,2,3}, {2,3,7}, {3,7}, {1,2,5,2,3,7}
The code below gives TLE for the above constraints
long long magicalSubarrays(int n,vector<int> arr)
{
vector<int> O;
for (int i = 0; i < arr.size(); i++) {
if (arr[i] % 2 == 1) {
O.push_back(i);
}
}
int k=O.size();
long long sum = 0;
for (int i = 0; i < O.size(); i++) {
for (int j = i + 1; j < O.size(); j += 2) {
int left = (i-1<0)? O[i]: O[i]-1-O[i-1];
int right= (j+1>=k)? n-1-O[j]: O[j+1]-1-O[j];
sum += (1 + left) * (1 + right);
}
}
return sum;
}
Let O be the array that contains all indices of odd elements.
Every magical subarray must then consist of:
every number from O[i] to O[j] inclusive (where j = i+1+2*k for some k)
an arbitrary number of even items left of i
an arbitrary number of even items right of j
Or, in C++-flavored pseudocode:
int magical_subarrays(const std::vector<int>& arr) {
std::vector<size_t> O;
for (int i = 0; i < arr.length(); i++) {
if (arr[i] % 2 == 1) {
O.push_back(i);
}
}
int sum = 0;
for (int i = 0; i < O.length(); i++) {
for (int j = i + 1; j < O.length(); j += 2) {
int left = /* exercise for the reader. It involves O[i] and O[i-1]. */
int right = /* exercise for the reader. It involves O[j] and O[j+1]. */
sum += (1 + left) * (1 + right);
}
}
return sum;
}
Could you please help me out to understand why the If statement is not executing. Thank you for your time.
void allPerfects (int a, int b){
int minVal = minN(a, b);
int maxVal = maxN(a, b);
int sum =0;
vector<int> v;
while (minVal < maxVal){
v.push_back(minVal);
minVal++;
}
for (int i = 0; i < v.size() ;i++){
for (int j = 0; j < i; j++){
sum += v[j];
}
if (sum == v[i]){
printInt(v[i], false); printStr(" is a perfect number!", true);
}
}
}
Even if there aren't any bugs in your code you'll only get a print out for:
allPerfects(a, b);
if there are consecutive integers a, a + 1, ... , a + n that sum up to a + n + 1 < b.
This is only true for:
1 + 2 == 3
and
0 + 1 + 2 == 3
so a has to be 0 or 1 and b has to be greater than or equal to 4.
Edit: But this will only happen if you reset your sum:
for (int i = 0; i < v.size() ;i++){
sum = 0; // This line is missing.
for (int j = 0; j < i; j++){
sum += v[j];
}
if (sum == v[i]){
printInt(v[i], false); printStr(" is a perfect number!", true);
}
}
I faced this problem in an interview challenge
K caterpillars are eating their way through N leaves, each caterpillar
falls from leaf to leaf in a unique sequence, all caterpillars start
at a twig at position 0 and falls onto the leaves at position between
1 and N. Each caterpillar j has an associated jump number Aj. A
caterpillar with jump number j eats leaves at positions that are
multiple of j. It will proceed in the order j, 2j, 3j…. till it
reaches the end of the leaves and it stops and build its cocoon. Given
a set A of K elements , we need to determine the number
of uneaten leaves.
Constraints:
1 <= N <= 109
1 <= K <= 15
1 <= A[i] <= 109
Input format:
N = No of uneaten leaves.
K = No. of caterpillars.
A = Array of integer.
jump numbers Output:
The integer nu. Of uneaten leaves
Sample Input:
10
3
2
4
5
Output:
4
Explanation:
[2, 4, 5] is the 3-member set of jump numbers. All leaves which are multiple of 2, 4, and 5 are eaten. Only 4 leaves which are numbered 1,3,7,9 are left.
the naive approach for solving this question is have a Boolean array of all N numbers, and iterate over every caterpillar and remember the eaten leaves by it.
int uneatenusingNaive(int N, vector<int> A)
{
int eaten = 0;
vector<bool>seen(N+1, false);
for (int i = 0; i < A.size(); i++)
{
long Ai = A[i];
long j = A[i];
while (j <= N && j>0)
{
if (!seen[j])
{
seen[j] = true;
eaten++;
}
j += Ai;
}
}
return N - eaten;
}
this approach passed 8 out of 10 test cases and give wrong answer for 2 cases.
another approach using Inclusion Exclusion principle, explanation for it can be found here and here
below is my code for the second approach
int gcd(int a, int b)
{
if (b == 0)
return a;
return gcd(b, a%b);
}
int lcm(int i, int j)
{
return i*j / gcd(i, j);
}
vector<vector<int>> mixStr(vector<vector<int>> & mix, vector<int>& A, unordered_map<int, int> & maxStart)
{
vector<vector<int>> res;
if (mix.size() == 0)
{
for (int i = 0; i < A.size(); i++)
{
vector<int> tmp;
tmp.push_back(A[i]);
res.push_back(tmp);
}
return res;
}
for (int i = 0; i<mix.size(); i++)
{
int currSlotSize = mix[i].size();
int currSlotMax = mix[i][currSlotSize - 1];
for (int j = maxStart[currSlotMax]; j < A.size(); j++)
{
vector<int> tmp(mix[i]);
tmp.push_back(A[j]);
res.push_back(tmp);
}
}
return res;
}
int uneatenLeavs(int N, int k, vector<int> A)
{
int i = 0;
vector<vector<int>> mix;
bool sign = true;
int res = N;
sort(A.begin(), A.end());
unordered_map<int,int> maxStart;
for (int i = 0; i < A.size(); i++)
{
maxStart[A[i]] = i + 1;
}
int eaten = 0;
while (mix.size() != 1)
{
mix = mixStr(mix, A, maxStart);
for (int j = 0; j < mix.size(); j++)
{
int _lcm = mix[j][0];
for (int s = 1; s < mix[j].size(); s++)
{
_lcm = lcm(mix[j][s], _lcm);
}
if (sign)
{
res -= N / _lcm;
}
else
{
res += N / _lcm;
}
}
sign = !sign;
i++;
}
return res;
}
this approach passed only one 1/10 test case. and for the rest of test cases time limit exceeded and wrong answer.
Question:
What am I missing in first or second approach to be 100% correct.
Using Inclusion-Exclusion theorem is correct approach, however, your implementation seems to be too slow. We can use bitmasking technique to obtain a O(K*2^K) time complexity.
Take a look at this:
long result = 0;
for(int i = 1; i < 1 << K; i++){
long lcm = 1;
for(int j = 0; j < K; j++)
if(((1<<j) & i) != 0) //if bit j is set, compute new LCM after including A[j]
lcm *= A[j]/gcd(lcm, A[j]);
if(number of bit set in i is odd)
result += N/lcm;
else
result -= N/lcm;
}
For your first approach, an O(N*K) time complexity algorithm, with N = 10^9 and K = 15, it will be too slow, and can cause memory limit exceed/time limit exceed.
Notice that lcm can be larger than N, so, additional check is needed.
Given n items with size Ai and value Vi, and a backpack with size m. What's the maximum value can you put into the backpack?
Have you met this question in a real interview? Yes
Example
Given 4 items with size [2, 3, 5, 7] and value [1, 5, 2, 4], and a backpack with size 10. The maximum value is 9.
Note
You cannot divide item into small pieces and the total size of items you choose should smaller or equal to m.
int knapsack(int m, vector<int> A, vector<int> V) {
int dp[m + 1], tmp[m + 1];
for (int n = 1; n <= m; n++) {
//******the problem would disappear if i change n to start with 0
dp[n] = (n < A[0]) ? 0 : V[0] ;
tmp[n] = dp[n];
}
for (int i = 1; i < A.size(); i++) {
for (int n = 1; n <= m; n++) {
tmp[n] = dp[n];
}
for (int j = 1; j <= m; j++) {
if (j >= A[i]) {
dp[j] = max(tmp[j], (V[i] + tmp[j - A[i]]));
}
}
}
return dp[m];
}
I am failing the specific testcase and all other are fine(even larger m values)
m = 10, A = [2,3,5,7], V = [1,5,2,4]
Output: 563858905 (actually random every time) Expected: 9
I know this question is some what trivial but I'm really curious about the memory allocation process in this scenario
I'm guessing that it would be dangerous to use any array that is not initialized at the first memory location, can someone confirm with me?
I tried following code, a simpler version of yours;
#include <iostream>
using namespace std;
int knapsack(int m, int A[], int V[], int size) {
int dp[m+1], tmp[m+1];
for (int n = 1; n <= m; n++) { //*1*
dp[n] = (n < A[0]) ? 0 : V[0] ;
tmp[n] = dp[n];
}
for (int i = 1; i < 4; i++) { //*2*
for (int n = 1; n <= m; n++) { //*3*
tmp[n] = dp[n];
}
for (int j = 1; j <= m; j++) { //*4*
if (j >= A[i]) {
dp[j] = (tmp[j]> (V[i] + tmp[j - A[i]])? //*5*
tmp[j] :
(V[i] + tmp[j - A[i]])
);
}
}
}
cout << "answer:" << dp[m] << endl;
return dp[m];
}
int main(){
int a[] = {2,3,5,7};
int b[] = {1,5,2,4};
knapsack(10, a, b, 4);
return 0;
}
and got 8 as the answer, rather than a random number.
I'm not sure that my code is the correct version of yours, but I luckily noticed that the expression of V[i] + tmp[j-A[i]] at the line marked by "\\*5" accesses tmp[0] when j=2 and i=1, since A[1] == 2 and 2 >= A[1]. Thus it would not be safe without initialization of tmp[0] in this logic.
So, I guess you are right; the uninitialized value of tmp[0] may change the result value, (and in some cases the flow of the logic as well, at the conditional statement of line //*5.)
I have written a solution for the above problem but can someone please suggest an optimized way.
I have traversed through the array for count(2 to n) where count is finding subarrays of size count*count.
int n = 5; //Size of array, you may take a dynamic array as well
int a[5][5] = {{1,2,3,4,5},{2,4,7,-2,1},{4,3,9,9,1},{5,2,6,8,0},{5,4,3,2,1}};
int max = 0;
int **tempStore, size;
for(int count = 2; count < n; count++)
{
for(int i = 0; i <= (n-count); i++)
{
for(int j = 0; j <= (n-count); j++)
{
int **temp = new int*[count];
for(int i = 0; i < count; ++i) {
temp[i] = new int[count];
}
for(int k = 0; k < count; k++)
{
for(int l = 0; l <count; l++)
{
temp[k][l] = a[i+k][j+l];
}
}
//printing fetched array
int sum = 0;
for(int k = 0; k < count; k++)
{
for(int l = 0; l <count; l++)
{
sum += temp[k][l];
cout<<temp[k][l]<<" ";
}cout<<endl;
}cout<<"Sum = "<<sum<<endl;
if(sum > max)
{
max = sum;
size = count;
tempStore = new int*[count];
for(int i = 0; i < count; ++i) {
tempStore[i] = new int[count];
}
//Locking the max sum array
for(int k = 0; k < count; k++)
{
for(int l = 0; l <count; l++)
{
tempStore[k][l] = temp[k][l];
}
}
}
//printing finished
cout<<"------------------\n";
//Clear temp memory
for(int i = 0; i < size; ++i) {
delete[] temp[i];
}
delete[] temp;
}
}
}
cout<<"Max sum is = "<<max<<endl;
for(int k = 0; k < size; k++)
{
for(int l = 0; l <size; l++)
{
cout<<tempStore[k][l]<<" ";
}cout<<endl;
}cout<<"-------------------------";
//Clear tempStore memory
for(int i = 0; i < size; ++i) {
delete[] tempStore[i];
}
delete[] tempStore;
Example:
1 2 3 4 5
2 4 7 -2 1
4 3 9 9 1
5 2 6 8 0
5 4 3 2 1
Output:
Max sum is = 71
2 4 7 -2
4 3 9 9
5 2 6 8
5 4 3 2
This is a problem best solved using Dynamic Programming (DP) or memoization.
Assuming n is significantly large, you will find that recalculating the sum of every possible combination of matrix will take too long, therefore if you could reuse previous calculations that would make everything much faster.
The idea is to start with the smaller matrices and calculate sum of the larger one reusing the precalculated value of the smaller ones.
long long *sub_solutions = new long long[n*n*m];
#define at(r,c,i) sub_solutions[((i)*n + (r))*n + (c)]
// Winner:
unsigned int w_row = 0, w_col = 0, w_size = 0;
// Fill first layer:
for ( int row = 0; row < n; row++) {
for (int col = 0; col < n; col++) {
at(r, c, 0) = data[r][c];
if (data[r][c] > data[w_row][w_col]) {
w_row = r;
w_col = c;
}
}
}
// Fill remaining layers.
for ( int size = 1; size < m; size++) {
for ( int row = 0; row < n-size; row++) {
for (int col = 0; col < n-size; col++) {
long long sum = data[row+size][col+size];
for (int i = 0; i < size; i++) {
sum += data[row+size][col+i];
sum += data[row+i][col+size];
}
sum += at(row, col, size-1); // Reuse previous solution.
at(row, col, size) = sum;
if (sum > at(w_row, w_col, w_size)) { // Could optimize this part if you only need the sum.
w_row = row;
w_col = col;
w_size = size;
}
}
}
}
// The largest sum is of the sub_matrix starting a w_row, w_col, and has dimensions w_size+1.
long long largest = at(w_row, w_col, w_size);
delete [] sub_solutions;
This algorithm has complexity: O(n*n*m*m) or more precisely: 0.5*n*(n-1)*m*(m-1). (Now I haven't tested this so please let me know if there are any bugs.)
Try this one (using naive approach, will be easier to get the idea):
#include <iostream>
#include<vector>
using namespace std;
int main( )
{
int n = 5; //Size of array, you may take a dynamic array as well
int a[5][5] =
{{2,1,8,9,0},{2,4,7,-2,1},{5,4,3,2,1},{3,4,9,9,2},{5,2,6,8,0}};
int sum, partsum;
int i, j, k, m;
sum = -999999; // presume minimum part sum
for (i = 0; i < n; i++) {
partsum = 0;
m = sizeof(a[i])/sizeof(int);
for (j = 0; j < m; j++) {
partsum += a[i][j];
}
if (partsum > sum) {
k = i;
sum = partsum;
}
}
// print subarray having largest sum
m = sizeof(a[k])/sizeof(int); // m needs to be recomputed
for (j = 0; j < m - 1; j++) {
cout << a[k][j] << ", ";
}
cout << a[k][m - 1] <<"\nmax part sum = " << sum << endl;
return 0;
}
With a cumulative sum, you may compute partial sum in constant time
std::vector<std::vector<int>>
compute_cumulative(const std::vector<std::vector<int>>& m)
{
std::vector<std::vector<int>> res(m.size() + 1, std::vector<int>(m.size() + 1));
for (std::size_t i = 0; i != m.size(); ++i) {
for (std::size_t j = 0; j != m.size(); ++j) {
res[i + 1][j + 1] = m[i][j] - res[i][j]
+ res[i + 1][j] + res[i][j + 1];
}
}
return res;
}
int compute_partial_sum(const std::vector<std::vector<int>>& cumulative, std::size_t i, std::size_t j, std::size_t size)
{
return cumulative[i][j] + cumulative[i + size][j + size]
- cumulative[i][j + size] - cumulative[i + size][j];
}
live example