I'm doing an assignment which command me to count the numbers of future numbers in q elements given. A future number is a number which has all the divisors of it (1 and itself are not include) that are prime numbers. It doesn't print anything at all. Anyone have a suggestion why isn't it working ?!!
Sample input :
9
9 7 10 6 17 4 19 21 13
Sample output :
5
my code :
#include<bits/stdc++.h>
using std::cin;
using std::cout;
int i,j,count1=0,count_divisors;
int a[10005];
int prime_numbers(int n) {
if(n<2) return 0;
if(n==2||n==3) return 1;
for(i=2;i<=sqrt(n);i++)
if(n%i==0) return 0;
return 1;
}
int main() {
int q;
cin>>q;
for(i=0;i<q;i++) cin>>a[i];
for(i=0;i<q;i++) {
count_divisors=0;
for(j=2;j<=sqrt(a[i]);j++) {
if(!(a[i]%j)) {
int the=a[i]/j;
if(prime_numbers(j)==0) break;
if(prime_numbers(the)==0) break;
count_divisors+=2;
}
if(j>sqrt(a[i])&&count_divisors!=0) count1++;
}
}
cout<<count1;
}
I think the assignment is to find all numbers in an array that
a) have at least one divisor (other than 1 or the number itself) and
b) all those divisors are prime numbers.
Some examples:
9 => devisor(s): [3]; 3 is a prime number, so "9" counts.
7 => devisor(s): [none]; so "7" does NOT count.
20 => devisor(s): [2, 5, 10]; 10 is a divisor but not a prime number, so "20" does NOT count.
In the given example 5 of the numbers fulfill the criteria, namely 9, 10, 6, 4 and 21. That's 5 numbers, therefore the answer is "5".
Here is the code that does that for you:
#include <iostream>
bool is_prime_numbers(int n) {
if (n < 2) {
return false;
}
for (int i = 2; i < n; i++)
{
if (n % i == 0) {
return false;
}
}
return true;
}
int main() {
int cnt = 0;
int arr[1000];
int q;
cin >> q;
//q = 9;
for (int i = 0; i < q; i++)
cin >> arr[i];
/*arr[0] = 9;
arr[1] = 7;
arr[2] = 10;
arr[3] = 6;
arr[4] = 17;
arr[5] = 4;
arr[6] = 19;
arr[7] = 21;
arr[8] = 20;*/
for (int i = 0; i < q; i++) {
bool found_divisor_prime = false;
bool found_divisor_other = false;
int val = arr[i];
for (int div = 2; div < val; div++) {
//can "val" be devided by "div"?
if ((val % div) == 0) {
//is "div" a prime number?
if (is_prime_numbers(div))
found_divisor_prime = true;
else
found_divisor_other = true;
}
}
/* we only want to count numbers that:
* a) have a divisor other than 1 or the number itself
* b) all divisors are prime numbers
*/
if (found_divisor_prime && !found_divisor_other)
cnt++;
}
std::cout << cnt;
return 0;
}
The first bug is as AudioDroid stated that the global variable i is being changed by two functions, namely main and prime_numbers. This can be fixed by defining a local variable let's say k and looping through with it:
int k;
for (k = 2; k <= sqrt(n); k++) {
if (n % k == 0) return 0;
}
The second bug is that you wrote
if (j > sqrt(a[i]) && count_divisors != 0) count1++;
inside the inner for loop body and thus it will never be executed. It has to be taken out of there. Fix these two bugs and you will get your 5 as an answer.
However in case you have q as big as let's say 10^6 or more this solution of yours will not be very effective because for every divisor d you are looping through the range [2; sqrt(d)] instead of generating all prime numbers up to a given limit in a lookup object and just checking whether the particular divisor is a prime number or not in O(1) time.
#include <memory>
#include <bitset>
#include <iostream>
#include <vector>
int const lim = 1000000;
bool is_future(int const num, std::unique_ptr<std::bitset<lim + 1>> const& is_prime) {
int div, cnt = 0;
for (div = 2; div * div <= num; ++div) {
if (num % div == 0) {
++cnt;
if (!is_prime->test(div)) {
return false;
}
if (div * div != num) {
++cnt;
if (!is_prime->test(num / div)) {
return false;
}
}
}
}
return cnt != 0;
}
int main() {
auto sieve = std::make_unique<std::bitset<lim + 1>>();
sieve->set();
sieve->set(0, false);
sieve->set(1, false);
int i, j;
for (i = 2; i * i <= lim; ++i) {
if (sieve->test(i)) {
for (j = i * i; j <= lim; j += i) {
sieve->set(j, false);
}
}
}
int q;
std::cin >> q;
std::vector<int> seq;
seq.reserve(q);
int k, num;
for (k = 0; k != q; ++k) {
std::cin >> num;
seq.emplace_back(num);
}
int ans = 0;
for (auto const& elem : seq) {
if (is_future(elem, sieve)) {
++ans;
}
}
std::cout << ans << '\n';
return 0;
}
Set your lim constant to be high enough according to the maximum value of the number in the input. Allocate bitset on the heap via the unique_ptr sieve. Run the sieve of Eratosthenes algorithm on sieve up to lim. Read q. Define the vector seq. Use reserve to allocate memory for q elements and thus avoid preallocation. Grow seq. Initialize the counter ans with 0. Loop through seq and for every element elem call is_future with elem and sieve. is_future uses is_prime (sieve) as a lookup object to check whether the divisor div or (num/div) is prime or not in O(1) time. The counter cnt counts the number of divisors of num in the range [2; sqrt(num)]. Pay attention that I don't use std::sqrt because it is expensive If called thousands of times. If cnt is 0 the number has no divisors in the specified range so it is not future otherwise it is. If is_future returns true increment ans. Output ans.
#Trương Quang Vinh
seq.reserve(q);
Imagine you have a vector seq that is empty. Its initial capacity is 0 which means that it has allocated a block of memory for 0 integers. If you start growing this vector let's say you want to read 10^6 integers and store them in it it has to do the following operations:
allocate a larger block of memory
copy the integers from the old block to the new one
call the destructors of the integers located on the old block
free the memory that the integers located on the old block used and return it back to the system
I made a test with 10^6 integers and under Microsoft's implementation the capacity of the vector seq changes this way:
0 -> 1 -> 2 -> 3 -> 4 -> 6 -> 9 -> 13 -> 19 -> 28 -> 42 -> 63 -> 94 -> 141 -> 211 -> 316 -> 474 -> 711 -> 1066 -> 1599 -> 2398 -> 3597 -> 5395 -> 8092 -> 12138 -> 18207 -> 27310 -> 40965 -> 61447 -> 92170 -> 138255 -> 207382 -> 311073 -> 466609 -> 699913 -> 1049869
At each of these steps where the vector seq changes its capacity the above-mentioned 4 operations will take place which of course take some time. In your case you know that you are going to store q integers in your vector and with the member function reserve you can directly allocate a block of memory that is large enough to keep them and thus avoid the above-mentioned 4 operations.
seq.emplace_back(num);
The member function emplace_back directly constructs an element from the arguments it is given instead of constructing an element from the copy created from the arguments it is given like push_back does and thus is more effective.
is_prime->test(num / div)
is_prime is an unique_ptr that points to an object of type std::bitset<lim + 1> located on the heap. The bitset class mimics the bits of a number. In your case you are going to run the sieve of Eratosthenes algorithm on this object. Let's say you want to generate all the prime numbers in the range [2; 100]. Your bitset object will initially look like that:
11111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111100
The last two bits representing the numbers 0 and 1 are set to 0 because 0 and 1 are not prime numbers. When the algorithm finishes your bitset object will look like that:
00010000000100000100010000010100010000010100000100000100010100010000010100000100010100010100010101100
The function test called via the pointer is_prime with operator-> which is the short form of (*is_prime).test will test whether the bit let's say x is 1 or 0. If it is 1 the number represented by the bit x is prime otherwise it is not.
std::unique_ptr<std::bitset<lim + 1>> const& is_prime
This is the object sieve passed as a reference to const to the function is_future which means that operations done on is_prime are done on sieve with the promise of not changing it because is_prime is a reference to const. If I don't write auto on this line
auto sieve = std::make_unique<std::bitset<lim + 1>>();
I have to write
std::unique_ptr<std::bitset<lim + 1>> sieve = std::make_unique<std::bitset<lim + 1>>();
which is where the type unique_ptr missing from main is coming from.
Related
Given an array of N numbers (not necessarily sorted). We can merge any two numbers into one and the cost of merging the two numbers is equal to the sum of the two values. The task is to find the total minimum cost of merging all the numbers.
Example:
Let the array A = [1,2,3,4]
Then, we can remove 1 and 2, add both of them and keep the sum back in array. Cost of this step would be (1+2) = 3.
Now, A = [3,3,4], Cost = 3
In second step, we can 3 and 3, add both of them and keep the sum back in array. Cost of this step would be (3+3) = 6.
Now, A = [4,6], Cost = 6
In third step, we can remove both elements from the array and keep the sum back in array again. Cost of this step would be (4+6) = 6.
Now, A = [10], Cost = 10
So, total cost turns out to be 19 (10+6+3).
We will have to pick the 2 smallest elements to minimize our total cost. A simple way to do this is using a min heap structure. We will be able to get the minimum element in O(1) and insertion will be O(log n).
The time complexity of this approach is O(n log n).
But I tried another approach, and wasn't able to find the cases where it fails. The basic idea was that the sum of two smallest elements that we will choose at any time will always be greater than the sum of the pair of elements chosen before. So the "temp" array will always be sorted, and we will be able to access the minimum elements in O(1).
As I am sorting the input array and then simply traversing the array, the complexity of my approach is O(n log n).
int minCost(vector<int>& arr) {
sort(arr.begin(), arr.end());
// temp array will contain the sum of all the pairs of minimum elements
vector<int> temp;
// index for arr
int i = 0;
// index for temp
int j = 0;
int cost = 0;
// while we have more than 1 element combined in both the input and temp array
while(arr.size() - i + temp.size() - j > 1) {
int num1, num2;
// selecting num1 (minimum element)
if(i < arr.size() && j < temp.size()) {
if(arr[i] <= temp[j])
num1 = arr[i++];
else
num1 = temp[j++];
}
else if(i < arr.size())
num1 = arr[i++];
else if(j < temp.size())
num1 = temp[j++];
// selecting num2 (second minimum element)
if(i < arr.size() && j < temp.size()) {
if(arr[i] <= temp[j])
num2 = arr[i++];
else
num2 = temp[j++];
}
else if(i < arr.size())
num2 = arr[i++];
else if(j < temp.size())
num2 = temp[j++];
// appending the sum of the minimum elements in the temp array
int sum = num1 + num2;
temp.push_back(sum);
cost += sum;
}
return cost;
}
Is this approach correct? If not, please let me know what I am missing, and the test cases in which this algorithm fails.
SPOJ Link for the same problem
The logic seems very solid to me... all the computed sums will never be decreasing and therefore you only need to add up either oldest two computed sums, next two elements or oldest sum and next element.
I would just simplify the code:
#include <vector>
#include <algorithm>
#include <stdio.h>
int hsum(std::vector<int> arr) {
int ni = arr.size(), nj = 0, i = 0, j = 0, res = 0;
std::sort(arr.begin(), arr.end());
std::vector<int> temp;
auto get = [&]()->int {
if (j == nj || (i < ni && arr[i] < temp[j])) return arr[i++];
return temp[j++];
};
while ((ni-i)+(nj-j)>1) {
int a = get(), b = get();
res += a+b;
temp.push_back(a + b); nj++;
}
return res;
}
int main() {
fprintf(stderr, "%i\n", hsum(std::vector<int>{1,4,2,3}));
return 0;
}
Very nice idea!
Another improvement is noting that the cumulative length of the two arrays being processed (the original one and the temporary one holding the sums) will decrease at every step.
Since the first step will use two input elements, the fact that the temporary array grows one element at each step will still not be enough for a "walking queue" allocated in the array itself to reach the reading pointer.
This means that there is no need of a temporary array and the space for the sums can be found in the array itself...
int hsum(std::vector<int> arr) {
int ni = arr.size(), nj = 0, i = 0, j = 0, res = 0;
std::sort(arr.begin(), arr.end());
auto get = [&]()->int {
if (j == nj || (i < ni && arr[i] < arr[j])) return arr[i++];
return arr[j++];
};
while ((ni-i)+(nj-j)>1) {
int a = get(), b = get();
res += a+b;
arr[nj++] = a + b;
}
return res;
}
About the error on SPOJ... I tried briefly to search for the problem but I didn't succeed. I tried however generating random arrays of random lengths and checking this solution with what finds a "brute-force" one implemented directly from the specs and I'm reasonably confident that the algorithm is correct.
I know at least one programming arena (Topcoder) where sometimes the problems are carefully crafted so that the computation gives correct results if using unsigned but not if using int (or if using unsigned long long but not if using long long) because of integer overflow.
I don't know if SPOJ also does this kind of nonsense(1)... may be that is the reason some hidden test case fails...
EDIT
Checking with SPOJ the algorithm passes if using long long values... this is the entry I used:
#include <stdio.h>
#include <algorithm>
#include <vector>
int main(int argc, const char *argv[]) {
int n;
scanf("%i", &n);
for (int testcase=0; testcase<n; testcase++) {
int sz; scanf("%i", &sz);
std::vector<long long> arr(sz);
for (int i=0; i<sz; i++) scanf("%lli", &arr[i]);
int ni = arr.size(), nj = 0, i = 0, j = 0;
long long res = 0;
std::sort(arr.begin(), arr.end());
auto get = [&]() -> long long {
if (j == nj || (i < ni && arr[i] < arr[j])) return arr[i++];
return arr[j++];
};
while ((ni-i)+(nj-j)>1) {
long long a = get(), b = get();
res += a+b;
arr[nj++] = a + b;
}
printf("%lli\n", res);
}
return 0;
}
PS: This very kind of computation is also what is needed to build an Huffman tree for entropy coding given the symbols frequency table and thus it's not a mere random exercise but it has practical applications.
(1) I'm saying "nonsense" because in Topcoder they never give problems that require 65 bits; thus it's not a genuine care about overflows, but just setting traps for novices.
Another that I think is a bad practice I saw on TC is that some problems are carefully designed so that the correct algorithm if using C++ will barely fit in the timeout limit: just use another language (and get e.g. a 2× slowdown) and you cannot solve the problem.
First of all, think simple!
When using a priority queue, the problem is easy!
In the first test case :
1 6 3 20
// after pushing to Q
1 3 6 20
// and sum two top items and pop and push!
(1 + 3) 6 20 cost = 4
(4 + 6) 20 cost = 10 + 4
(10 + 20) cost = 30 + 14
30 cost = 44
#include<iostream>
#include<queue>
using namespace std;
int main()
{
int t;
cin >> t;
while (t--) {
int n;
cin >> n;
priority_queue<long long int, vector<long long int>, greater<long long int>> q;
for (int i = 0; i < n; ++i) {
int k;
cin >> k;
q.push(k);
}
long long int sum = 0;
while (q.size() > 1) {
long long int a = q.top();
q.pop();
long long int b = q.top();
q.pop();
q.push(a + b);
sum += a + b;
}
cout << sum << "\n";
}
}
Basically we need to sort the list in desc order and then find its cost like this.
A.sort(reverse=True)
cost = 0
for i in range(len(A)):
cost += A[i] * (i+1)
return cost
this function prints all triplets in an array whose product is a number k
input first line number of elements, second array elements, third target product .. passes parameters to recursive function f along with a vector that stores elements whose product might give k
thought process -> for each element we can include or exclude it to get the product k. If p > 24 or number elements multiplied > 3 we backtrack. once the prod = k, we print all numbers from vector v and pop them out and set number of elements count to 0 and product to 1 and continue
for an input of:
9
1 2 3 12 4 7 5 24 9
24
my output looks something like this :
12
2
1
9
9
9
| ->cursor justs stops here ..no further outputs...
naming scheme used :
count -> number of elements multiplied till now whose product is stored in -> p
n-> number of elements in array
k -> target pdt
i -> index of element in array currently at
code :
#include <iostream>
#include <vector>
using namespace std;
// all triplets whose product is a number k
void f(int i, int count, int p, int k, vector<int>&v, int *a, int n)
{
// success condition
if(count == 3 && p == k)
{
for(int i = 2; i >= 0; --i)
{
cout << v[i] << " " << endl;
v.pop_back();
}
p = 1;
count = 0;
}
if(count>=3 || i > n - 1 || p > k)
{
return;
}
v.push_back(a[i]);
f(i + 1, count + 1, p * a[i], k, v, a, n);
v.pop_back();
f(i + 1, count, p, k, v, a, n);
}
int main()
{
int n;
cin >> n;
int *a=new int[n];
for(int i = 0; i < n; ++i)
{
cin >> a[i];
}
int k;
cin >> k;
//int p = 1;
vector<int>v;
f(0, 0, 1, k, v, a, n);
delete[] a;
return 0;
}
Your “reset” of p and count on success is immediately strange: why does the function need to keep looking when its caller will already try the other possibilities? But that’s just a distraction from the real issue: the balanced push_back and pop_back around the first recursive call establish and rely on an invariant where every call leaves v the same length as when it started. But the success path clears the vector and leaves it shorter, so eventually you pop_back when it’s empty and—
The joys of undefined behavior (which happened to give an infinite loop here) aside, the fix is trivial: just return after printing without modifying v at all. (Then you may find some further simplifications.)
I'm making a simple program to calculate the number of pairs in an array that are divisible by 3 array length and values are user determined.
Now my code is perfectly fine. However, I just want to check if there is a faster way to calculate it which results in less compiling time?
As the length of the array is 10^4 or less compiler takes less than 100ms. However, as it gets more to 10^5 it spikes up to 1000ms so why is this? and how to improve speed?
#include <iostream>
using namespace std;
int main()
{
int N, i, b;
b = 0;
cin >> N;
unsigned int j = 0;
std::vector<unsigned int> a(N);
for (j = 0; j < N; j++) {
cin >> a[j];
if (j == 0) {
}
else {
for (i = j - 1; i >= 0; i = i - 1) {
if ((a[j] + a[i]) % 3 == 0) {
b++;
}
}
}
}
cout << b;
return 0;
}
Your algorithm has O(N^2) complexity. There is a faster way.
(a[i] + a[j]) % 3 == ((a[i] % 3) + (a[j] % 3)) % 3
Thus, you need not know the exact numbers, you need to know their remainders of division by three only. Zero remainder of the sum can be received with two numbers with zero remainders (0 + 0) and with two numbers with remainders 1 and 2 (1 + 2).
The result will be equal to r[1]*r[2] + r[0]*(r[0]-1)/2 where r[i] is the quantity of numbers with remainder equal to i.
int r[3] = {};
for (int i : a) {
r[i % 3]++;
}
std::cout << r[1]*r[2] + (r[0]*(r[0]-1)) / 2;
The complexity of this algorithm is O(N).
I've encountered this problem before, and while I don't find my particular solution, you could improve running times by hashing.
The code would look something like this:
// A C++ program to check if arr[0..n-1] can be divided
// in pairs such that every pair is divisible by k.
#include <bits/stdc++.h>
using namespace std;
// Returns true if arr[0..n-1] can be divided into pairs
// with sum divisible by k.
bool canPairs(int arr[], int n, int k)
{
// An odd length array cannot be divided into pairs
if (n & 1)
return false;
// Create a frequency array to count occurrences
// of all remainders when divided by k.
map<int, int> freq;
// Count occurrences of all remainders
for (int i = 0; i < n; i++)
freq[arr[i] % k]++;
// Traverse input array and use freq[] to decide
// if given array can be divided in pairs
for (int i = 0; i < n; i++)
{
// Remainder of current element
int rem = arr[i] % k;
// If remainder with current element divides
// k into two halves.
if (2*rem == k)
{
// Then there must be even occurrences of
// such remainder
if (freq[rem] % 2 != 0)
return false;
}
// If remainder is 0, then there must be two
// elements with 0 remainder
else if (rem == 0)
{
if (freq[rem] & 1)
return false;
}
// Else number of occurrences of remainder
// must be equal to number of occurrences of
// k - remainder
else if (freq[rem] != freq[k - rem])
return false;
}
return true;
}
/* Driver program to test above function */
int main()
{
int arr[] = {92, 75, 65, 48, 45, 35};
int k = 10;
int n = sizeof(arr)/sizeof(arr[0]);
canPairs(arr, n, k)? cout << "True": cout << "False";
return 0;
}
That works for a k (in your case 3)
But then again, this is not my code, but the code you can find in the following link. with a proper explanation. I didn't just paste the link since it's bad practice I think.
This question already has answers here:
How to get the least number after deleting k digits from the input number
(11 answers)
Closed 6 years ago.
I am trying to code a program that can do something like this:
in:
5 4
1 9 9 9 0
out:
9990
and i have a problem. It doesnt work on any set of numbers. For example it works for the one above, but it doesnt work for this one:
in:
15 9
2 9 3 6 5 8 8 8 8 7 2 2 8 1 4
out: 988887814
2 9 3 6 5 8 8 8 8 7 2 2 8 1 4
I did this with a vector approach and it works for any set of numbers, but i'm trying to do it a stack for a better complexity.
EDIT ---- MODIFIED FOR STD::STACK
Code for method using stack:
#include <iostream>
#include <fstream>
#include <stack>
using namespace std;
ifstream in("trompeta.in");
ofstream out("trompeta.out");
void reverseStack(stack<char> st) {
if(!st.empty())
{
char x = st.top();
st.pop();
reverseStack(st);
out<<x;
}
return;
}
int main()
{
int n,m,count=1;
stack <char> st;
char x;
in>>n>>m;
in>>x;
st.push(x);
for(int i=1; i<n; i++)
{
in>>x;
if(st.top()<x && count+n-i-1>=m)
{
st.pop();
st.push(x);
}
else
{
st.push(x);
count++;
if (count>m-1) break;
}
};
reverseStack(st);
}
Code for method using vectors:
#include <iostream>
#include <fstream>
using namespace std;
ifstream in ( "trompeta.in" );
ofstream out ( "trompeta.out" );
int main ()
{
int i = 0, N, M, max, j, p = 0, var;
in >> N >> M;
char* v = new char[N];
char* a = new char[M];
in >> v;
var = M;
max = v[0];
for ( i = 0; i < M; i++ )
{
for ( j = p ; j < N-var+1; j++ )
{
if ( v[j] > max )
{
max = v[j];
p = j;
}
}
var--;
a[i] = max;
max = v[p+1];
p = p+1;
}
for ( i = 0; i < M; i++ )
out << a[i]-'0';
}
Can any1 help me to get the STACK code working?
Using the fact that the most significant digit completely trumps all other digets except in place of a tie, I would look at the first (N-M+1) digits, find the largest single digit in that range.
If it occurs once, the first digit is locked in. Discard the digits which occur prior to that position, and you repeat for "maximum value of M-1 numbers of out N-position" to find the remaining digits of the answer. (or N-position-1, if position is zero based)
If it occurs multiple times, then recursively find "maximum value of M-1 numbers out of N-position" for each, then select the largest single result from these. There can be at most N such matches.
I forgot to mention, if N==M, you are also done.
proof of recursion:
Computing the value of the sub-match will always select M-1 digits. When M is 1, you only need to select the largest of a few positions, and have no more recursion. This is true for both cases. Also the "select from" steps always contain no more than N choices, because they are always based on selecting one most significant digit.
------------------ how you might do it with a stack ----------------
An actual implementation using a stack would be based on an object which contains the entire state of the problem, at each step, like so:
struct data { // require: n == digits.size()
int n, m;
std::string digits;
bool operator<(const data &rhs){ return digits < rhs.digits; }
};
The point of this is not just to store the original problem, but to have a way to represent any subproblem, which you can push and pop on a stack. The stack itself is not really important, here, because it is used to pick the one best result within a specific layer. Recursion handles most of the work.
Here is the top level function which hides the data struct:
std::string select_ordered_max(int n, int m, std::string digits) {
if (n < m || (int)digits.size() != n)
return "size wrong";
data d{ n, m, digits };
data answer = select_ordered_max(d);
return answer.digits;
}
and a rough pseudocode of the recursive workhorse
data select_ordered_max(data original){
// check trivial return conditions
// determine char most_significant
// push all subproblems that satisfy most_significant
//(special case where m==1)
// pop subproblems, remembering best
return answer {original.m, original.m, std::string(1, most_significant) + best_submatch.digits };
}
String comparison works on numbers when you only compare strings of the exact same length, which is the case here.
Yes, I know having n and m is redundant with digits.size(), but I didn't want to work too hard. Including it twice simplified some recursion checks. The actual implementation only pushed a candidate to the stack if it passed the max digit check for that level of recursion. This allowed me to get the correct 9 digit answer from 15 digits of input with only 28 candidates pushed to the stack (and them popped during max-select).
Now your code has quite a few issues, but rather than focusing on those lets answer the question. Let's say that your code has been corrected to give us:
const size_t M where M is the number of digits expected in our output
const vector<int> v which is the input set of numbers of size N
You just always want to pick the highest value most significant number remaining. So we'll keep an end iterator to prevent us from picking a digit that wouldn't leave us with enough digits to finish the number, and use max_element to select:
const int pow10[] = { 1, 10, 100, 1000, 10000, 100000, 1000000, 10000000, 100000000, 1000000000 };
auto maximum = 0;
auto end = prev(cend(v), M - 1);
auto it = max_element(cbegin(v), end);
for (auto i = M - 1; i > 0; --i) {
maximum += *it * pow10[i];
advance(end, 1);
it = max_element(next(it), end);
}
maximum += *it;
Live Example
This code depends upon M being greater than 0 and less than N and less than log10(numeric_limits<int>::max())
EDIT: Sad to say this solves the consecutive digits problem, after edits the question wants subsequent digits, but not necessarily consecutive
So the little known numeric library provides inner_product which seems like just the tool for this job. Now your code has quite a few issues, but rather than focusing on those lets answer the question. Let's say that your code has been corrected to give us:
vector<int> foo(M) where M is the number of digits expected in our output
const vector<int> v which is the input set of numbers of size N
We'll use foo in the inner_product, initializing it with decreasing powers of 10:
generate(begin(foo), end(foo), [i=int{1}]() mutable {
auto result = i;
i *= 10;
return result; });
We can then use this in a loop:
auto maximum = 0;
for (auto it = prev(rend(v), size(foo) + 1); it != rbegin(v); advance(it, -1)) {
maximum = max<int>(inner_product(cbegin(foo), cend(foo), it, 0), maximum);
}
maximum = max<int>(inner_product(cbegin(foo), cend(foo), rbegin(v), 0), maximum);
Live Example
To use it's initialization requires that your initial M was smaller than N, so you may want to assert that or something.
--EDITED--
here's my suggestion with STACK based on my previous suggestion using vector
findMaxValueOutOfNDigits(stackInput, M, N)
{
// stackInput = [2, 9, 3, 6, 5, 8, 8, 8, 8, 7, 2, 2, 8, 1, 4]
// *where 4 was the first element to be inserted and 2 was the last to be inserted
// if the sequence is inverted, you can quickly fix it by doing a "for x = 0; x < stack.length; x++ { newStack.push(stack.pop()) }"
currentMaxValue = 0
for i = 0; i < (M - N + 1); i++
{
tempValue = process(stackInput, M, N)
stackInput.pop()
if (tempValue > currentMaxValue)
currentMaxValue = tempValue
}
return currentMaxValue
}
process(stackInput, M, N)
{
tempValue = stackInput.pop() * 10^(N - 1)
*howManyItemsCanILook = (M - N + 1)
for y = (N - 2); y == 0; y++
{
currentHowManyItemsCanILook = *howManyItemsCanILook
tempValue = tempValue + getValue(stackInput, *howManyItemsCanILook) * 10^(y)
*howManyItemsCanILook = *howManyItemsCanILook - 1
for x = 0; x < (currentHowManyItemsCanILook - *howManyItemsCanILook); x++
{
stackInput.pop()
}
}
return tempValue
}
getValue(stackInput, *howManyItemsCanILook)
{
currentMaxValue = stackInput.pop()
if (currentMaxValue == 9)
return 9
else
{
goUntil = *howManyItemsCanILook
for i = 0; i < goUntil; i++
{
*howManyItemsCanILook = *howManyItemsCanILook - 1
tempValue = stackInput.pop()
if (currentMaxValue < tempValue)
{
currentMaxValue = tempValue
if (currentMaxValue == 9)
return currentMaxValue
}
}
return currentMaxValue
}
}
note: where *howManyItemsCanILook is passed by reference
I hope this helps
The sequence of triangle numbers is
generated by adding the natural
numbers. So the 7th triangle number
would be 1 + 2 + 3 + 4 + 5 + 6 + 7 =
28. The first ten terms would be:
1, 3, 6, 10, 15, 21, 28, 36, 45, 55,
...
Let us list the factors of the first
seven triangle numbers:
1: 1
3: 1,3
6: 1,2,3,6
10: 1,2,5,10
15: 1,3,5,15
21: 1,3,7,21
28: 1,2,4,7,14,28
We can see that 28 is the first
triangle number to have over five
divisors.
Given an integer n, display the first
triangle number having at least n
divisors.
Sample Input: 5
Output 28
Input Constraints: 1<=n<=320
I was obviously able to do this question, but I used a naive algorithm:
Get n.
Find triangle numbers and check their number of factors using the mod operator.
But the challenge was to show the output within 4 seconds of input. On high inputs like 190 and above it took almost 15-16 seconds. Then I tried to put the triangle numbers and their number of factors in a 2d array first and then get the input from the user and search the array. But somehow I couldn't do it: I got a lot of processor faults. Please try doing it with this method and paste the code. Or if there are any better ways, please tell me.
Here's a hint:
The number of divisors according to the Divisor function is the product of the power of each prime factor plus 1. For example, let's consider the exponential prime representation of 28:
28 = 22 * 30 * 50 * 71 * 110...
The product of each exponent plus one is: (2+1)*(0+1)*(0+1)*(1+1)*(0+1)... = 6, and sure enough, 28 has 6 divisors.
Now, consider that the nth triangular number can be computed in closed form as n(n+1)/2. We can multiply numbers written in the exponential prime form simply by adding up the exponents at each position. Dividing by two just means decrementing the exponent on the two's place.
Do you see where I'm going with this?
Well, you don't go into a lot of detail about what you did, but I can give you an optimization that can be used, if you didn't think of it...
If you're using the straightforward method of trying to find factors of a number n, by using the mod operator, you don't need to check all the numbers < n. That obviously would take n comparisons...you can just go up to floor(sqrt(n)). For each factor you find, just divide n by that number, and you'll get the conjugate value, and not need to find it manually.
For example: say n is 15.
We loop, and try 1 first. Yep, the mod checks out, so it's a factor. We divide n by the factor to get the conjugate value, so we do (15 / 1) = 15...so 15 is a factor.
We try 2 next. Nope. Then 3. Yep, which also gives us (15 / 3) = 5.
And we're done, because 4 is > floor(sqrt(n)). Quick!
If you didn't think of it, that might be something you could leverage to improve your times...overall you go from O(n) to O(sqrt (n)) which is pretty good (though for numbers this small, constants may still weigh heavily.)
I was in a programming competition way back in school where there was some similar question with a run time limit. the team that "solved" it did as follows:
1) solve it with a brute force slow method.
2) write a program to just print out the answer (you found using the slow method), which will run sub second.
I thought this was bogus, but they won.
see Triangular numbers: a(n) = C(n+1,2) = n(n+1)/2 = 0+1+2+...+n. (Formerly M2535 N1002)
then pick the language you want implement it in, see this:
"... Python
import math
def diminishing_returns(val, scale):
if val < 0:
return -diminishing_returns(-val, scale)
mult = val / float(scale)
trinum = (math.sqrt(8.0 * mult + 1.0) - 1.0) / 2.0
return trinum * scale
..."
First, create table with two columns: Triangle_Number Count_of_Factors.
Second, derive from this a table with the same columns, but consisting only of the 320 rows of the lowest triangle number with a distinct number of factors.
Perform your speedy lookup to the second table.
If you solved the problem, you should be able to access the thread on Project Euler in which people post their (some very efficient) solutions.
If you're going to copy and paste a problem, please cite the source (unless it was your teacher who stole it); and I second Wouter van Niferick's comment.
Well, at least you got a good professor. Performance is important.
Since you have a program that can do the job, you can precalculate all of the answers for 1 .. 320.
Store them in an array, then simply subscript into the array to get the answer. That will be very fast.
Compile with care, winner of worst code of the year :D
#include <iostream>
bool isPrime( unsigned long long number ){
if( number != 2 && number % 2 == 0 )
return false;
for( int i = 3;
i < static_cast<unsigned long long>
( sqrt(static_cast<double>(number)) + 1 )
; i += 2 ){
if( number % i == 0 )
return false;
}
return true;
}
unsigned int p;
unsigned long long primes[1024];
void initPrimes(){
primes[0] = 2;
primes[1] = 3;
unsigned long long number = 5;
for( unsigned int i = 2; i < 1024; i++ ){
while( !isPrime(number) )
number += 2;
primes[i] = number;
number += 2;
}
return;
}
unsigned long long nextPrime(){
unsigned int ret = p;
p++;
return primes[ret];
}
unsigned long long numOfDivs( unsigned long long number ){
p = 0;
std::vector<unsigned long long> v;
unsigned long long prime = nextPrime(), divs = 1, i = 0;
while( number >= prime ){
i = 0;
while( number % prime == 0 ){
number /= prime;
i++;
}
if( i )
v.push_back( i );
prime = nextPrime();
}
for( unsigned n = 0; n < v.size(); n++ )
divs *= (v[n] + 1);
return divs;
}
unsigned long long nextTriNumber(){
static unsigned long long triNumber = 1, next = 2;
unsigned long long retTri = triNumber;
triNumber += next;
next++;
return retTri;
}
int main()
{
initPrimes();
unsigned long long n = nextTriNumber();
unsigned long long divs = 500;
while( numOfDivs(n) <= divs )
n = nextTriNumber();
std::cout << n;
std::cin.get();
}
def first_triangle_number_with_over_N_divisors(N):
n = 4
primes = [2, 3]
fact = [None, None, {2:1}, {3:1}]
def num_divisors (x):
num = 1
for mul in fact[x].values():
num *= (mul+1)
return num
while True:
factn = {}
for p in primes:
if p > n//2: break
r = n // p
if r * p == n:
factn = fact[r].copy()
factn[p] = factn.get(p,0) + 1
if len(factn)==0:
primes.append(n)
factn[n] = 1
fact.append(factn)
(x, y) = (n-1, n//2) if n % 2 == 0 else (n, (n-1)//2)
numdiv = num_divisors(x) * num_divisors(y)
if numdiv >= N:
print('Triangle number %d: %d divisors'
%(x*y, numdiv))
break
n += 1
>>> first_triangle_number_with_over_N_divisors(500)
Triangle number 76576500: 576 divisors
Dude here is ur code, go have a look. It calculates the first number that has divisors greater than 500.
void main() {
long long divisors = 0;
long long nat_num = 0;
long long tri_num = 0;
int tri_sqrt = 0;
while (1) {
divisors = 0;
nat_num++;
tri_num = nat_num + tri_num;
tri_sqrt = floor(sqrt((double)tri_num));
long long i = 0;
for ( i=tri_sqrt; i>=1; i--) {
long long remainder = tri_num % i;
if ( remainder == 0 && tri_num == 1 ) {
divisors++;
}
else if (remainder == 0 && tri_num != 1) {
divisors++;
divisors++;
}
}
if (divisors >100) {
cout <<"No. of divisors: "<<divisors<<endl<<tri_num<<endl;
}
if (divisors > 500)
break;
}
cout<<"Final Result: "<<tri_num<<endl;
system("pause");
}
Boojum's answer motivated me to write this little program. It seems to work well, although it does use a brute force method of computing primes. It's neat how all the natural numbers can be broken down into prime number components.
#include <stdio.h>
#include <stdlib.h>
#include <iostream>
#include <iomanip>
#include <vector>
//////////////////////////////////////////////////////////////////////////////
typedef std::vector<size_t> uint_vector;
//////////////////////////////////////////////////////////////////////////////
// add a prime number to primes[]
void
primeAdd(uint_vector& primes)
{
size_t n;
if (primes.empty())
{
primes.push_back(2);
return;
}
for (n = *(--primes.end()) + 1; ; ++n)
{
// n is even -> not prime
if ((n & 1) == 0) continue;
// look for a divisor in [2,n)
for (size_t i = 2; i < n; ++i)
{
if ((n % i) == 0) continue;
}
// found a prime
break;
}
primes.push_back(n);
}
//////////////////////////////////////////////////////////////////////////////
void
primeFactorize(size_t n, uint_vector& primes, uint_vector& f)
{
f.clear();
for (size_t i = 0; n > 1; ++i)
{
while (primes.size() <= i) primeAdd(primes);
while (f.size() <= i) f.push_back(0);
while ((n % primes[i]) == 0)
{
++f[i];
n /= primes[i];
}
}
}
//////////////////////////////////////////////////////////////////////////////
int
main(int argc, char** argv)
{
// allow specifying number of TN's to be evaluated
size_t lim = 1000;
if (argc > 1)
{
lim = atoi(argv[1]);
}
if (lim == 0) lim = 1000;
// prime numbers
uint_vector primes;
// factors of (n), (n + 1)
uint_vector* f = new uint_vector();
uint_vector* f1 = new uint_vector();
// sum vector
uint_vector sum;
// prime factorize (n)
size_t n = 1;
primeFactorize(n, primes, *f);
// iterate over triangle-numbers
for (; n <= lim; ++n)
{
// prime factorize (n + 1)
primeFactorize(n + 1, primes, *f1);
while (f->size() < f1->size()) f->push_back(0);
while (f1->size() < f->size()) f1->push_back(0);
size_t numTerms = f->size();
// compute prime factors for (n * (n + 1) / 2)
sum.clear();
size_t i;
for (i = 0; i < numTerms; ++i)
{
sum.push_back((*f)[i] + (*f1)[i]);
}
--sum[0];
size_t numFactors = 1, tn = 1;
for (i = 0; i < numTerms; ++i)
{
size_t exp = sum[i];
numFactors *= (exp + 1);
while (exp-- != 0) tn *= primes[i];
}
std::cout
<< n << ". Triangle number "
<< tn << " has " << numFactors << " factors."
<< std::endl;
// prepare for next iteration
f->clear();
uint_vector* tmp = f;
f = f1;
f1 = tmp;
}
delete f;
delete f1;
return 0;
}