3

I have a piece of code as follows, and the number of for loops is determined by n which is known at compile time. Each for loop iterates over the values 0 and 1. Currently, my code looks something like this

for(int in=0;in<2;in++){
    for(int in_1=0;in_1<2;in_1++){
        for(int in_2=0;in_2<2;in_2++){
          // ... n times
            for(int i2=0;i2<2;i2++){
               for(int i1=0;i1<2;i1++){
                   d[in][in_1][in_2]...[i2][i1] =updown(in)+updown(in_1)+...+updown(i1);
               }
            }
          // ...
        }
    }
}

Now my question is whether one can write it in a more compact form.

4

3 回答 3

2

You can refactor your code slightly like this:

for(int in=0;in<2;in++) {
    auto& dn = d[in];
    auto updown_n = updown(in);
    for(int in_1=0;in_1<2;in_1++) {
        // dn_1 == d[in][in_1]
        auto& dn_1 = dn[in_1];
        // updown_n_1 == updown(in)+updown(in_1)
        auto updown_n_1 = updown_n + updown(in_1);
        for(int in_2=0;in_2<2;in_2++) {
            // dn_2 == d[in][in_1][in_2]
            auto& dn_2 = dn_1[in_2];
            // updown_n_2 == updown(in)+updown(in_1)+updown(in_2)
            auto updown_n_2 = updown_n_1 + updown(in_2);
                     .
                     .
                     .
            for(int i2=0;i2<2;i1++) {
               // d2 == d[in][in_1][in_2]...[i2]
               auto& d2 = d3[i2];
               // updown_2 = updown(in)+updown(in_1)+updown(in_2)+...+updown(i2)
               auto updown_2 = updown_3 + updown(i2);
               for(int i1=0;i1<2;i1++) {
                   // d1 == d[in][in_1][in_2]...[i2][i1]
                   auto& d1 = d2[i1];
                   // updown_1 = updown(in)+updown(in_1)+updown(in_2)+...+updown(i2)+updown(i1)
                   auto updown_1 = updown_2 + updown(i1);

                   // d[in][in_1][in_2]...[i2][i1] = updown(in)+updown(in_1)+...+updown(i1);
                   d1 = updown_1;
               }
            }
        }
    }
}

And make this into a recursive function now:

template<std::size_t N, typename T>
void loop(T& d) {
    for (int i = 0; i < 2; ++i) {
        loop<N-1>(d[i], updown(i));
    }
}

template<std::size_t N, typename T, typename U>
typename std::enable_if<N != 0>::type loop(T& d, U updown_result) {
    for (int i = 0; i < 2; ++i) {
        loop<N-1>(d[i], updown_result + updown(i));
    }
}

template<std::size_t N, typename T, typename U>
typename std::enable_if<N == 0>::type loop(T& d, U updown_result) {
    d = updown_result;
}

If your type is int d[2][2][2]...[2][2]; or int*****... d;, you can also stop when the type isn't an array or pointer instead of manually specifying N (or change for whatever the type of d[0][0][0]...[0][0] is)

Here's a version that does that with a recursive lambda:

auto loop = [](auto& self, auto& d, auto updown_result) -> void {
    using d_t = typename std::remove_cv<typename std::remove_reference<decltype(d)>::type>::type;
    if constexpr (!std::is_array<d_t>::value && !std::is_pointer<d_t>::value) {
        // Last level of nesting
        d = updown_result;
    } else {
        for (int i = 0; i < 2; ++i) {
            self(self, d[i], updown_result + updown(i));
        }
    }
};
for (int i = 0; i < 2; ++i) {
    loop(loop, d[i], updown(i));
}
于 2021-01-12T13:48:31.730 回答
2

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] << '\n';
    }
    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] << '\n';
    }
    return 0;
}
于 2021-01-12T14:23:15.133 回答
0

I am assuming that it is a multi-dimensional matrix. You may have to solve it mathematically first and then write the respective equations in the program.

于 2021-01-12T14:32:39.243 回答