The n
bits in_k
can be interpreted as the representation of one integer less than 2^n
.
This allows easily to work with a 1-D array (vector) d[.]
.
In practice, an interger j
corresponds to
j = in[0] + 2*in[1] + ... + 2^n-1*in[n-1]
Moreover, a direct implementation is O(NlogN). (N = 2^n)
A recursive solution is possible, for example using
f(val, n) = updown(val%2) + f(val/2, n-1) and f(val, 0) = 0.
This would correspond to a O(N) complexity, at the condition to introduce memoization, not implemented here.
Result:
0 : 0
1 : 1
2 : 1
3 : 2
4 : 1
5 : 2
6 : 2
7 : 3
8 : 1
9 : 2
10 : 2
11 : 3
12 : 2
13 : 3
14 : 3
15 : 4
#include <iostream>
#include <vector>
int up_down (int b) {
if (b) return 1;
return 0;
}
int f(int val, int n) {
if (n < 0) return 0;
return up_down (val%2) + f(val/2, n-1);
}
int main() {
const int n = 4;
int size = 1;
for (int i = 0; i < n; ++i) size *= 2;
std::vector<int> d(size, 0);
for (int i = 0; i < size; ++i) {
d[i] = f(i, n);
}
for (int i = 0; i < size; ++i) {
std::cout << i << " : " << d[i] << '
';
}
return 0;
}
As mentioned above, the recursive approach allows a O(N) complexity, at the condition to implement memoization.
Another possibility is to use a simple iterative approach, in order to get this O(N) complexity.
(here N represents to total number of data)
#include <iostream>
#include <vector>
int up_down (int b) {
if (b) return 1;
return 0;
}
int main() {
const int n = 4;
int size = 1;
for (int i = 0; i < n; ++i) size *= 2;
std::vector<int> d(size, 0);
int size_block = 1;
for (int i = 0; i < n; ++i) {
for (int j = size_block-1; j >= 0; --j) {
d[2*j+1] = d[j] + up_down(1);
d[2*j] = d[j] + up_down(0);
}
size_block *= 2;
}
for (int i = 0; i < size; ++i) {
std::cout << i << " : " << d[i] << '
';
}
return 0;
}
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