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merge_sort.cc
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/
merge_sort.cc
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/*
[ Merge sort ]
Best time complexity : O(nlog(n))
Worst time complexity : O(nlog(n))
Average time complexity : O(nlog(n))
Space complexity : O(n)
Stable : Yes
Iterator Required : Random access iterator
*/
#include <algorithm>
#include <functional>
#include <vector>
#include <iostream>
#include <ctime>
#include <Windows.h>
template <typename Iter, typename Compare>
void merge_helper(Iter first, size_t left, size_t mid, size_t right, Compare cmp)
{
typedef typename std::iterator_traits<Iter>::value_type T;
auto len1 = mid - left + 1, len2 = right - mid;
T *l = new T[len1];
T *r = new T[len2];
size_t i = 0, j = 0, z = left;
std::uninitialized_copy(first + left, first + mid + 1, l);
std::uninitialized_copy(first + mid + 1, first + right + 1, r);
while (i < len1 && j < len2)
{
*(first + z++) = cmp(r[j], l[i]) ? r[j++] : l[i++];
}
while (i < len1)
{
*(first + z++) = l[i++];
}
while (j < len2)
{
*(first + z++) = r[j++];
}
delete[]l;
delete[]r;
}
template <typename Iter, typename Compare>
void divide_helper(Iter first, size_t left, size_t right, Compare cmp)
{
if (left < right)
{
auto mid = (left + right) / 2;
divide_helper(first, left, mid, cmp);
divide_helper(first, mid + 1, right, cmp);
merge_helper(first, left, mid, right, cmp);
}
}
// merge_sort
template <typename Iter, typename Compare = std::less<>>
void merge_sort(Iter first, Iter last, Compare cmp = Compare())
{
if (first == last || first == last - 1)
return;
divide_helper(first, 0, last - first - 1, cmp);
}
// test
#define SMALL_SORT_TEST(sort, count) do { \
double t = 0.0; \
std::vector<int> v(count); \
LARGE_INTEGER t1, t2, tc; \
for (int i = 0; i < 10; ++i) { \
for (auto& it : v) it = rand(); \
QueryPerformanceFrequency(&tc); \
QueryPerformanceCounter(&t1); \
sort(v.begin(), v.end()); \
QueryPerformanceCounter(&t2); \
t += (t2.QuadPart - t1.QuadPart)*1e6 / tc.QuadPart; \
} \
t /= 10.0; \
printf(" %7d numbers cost : %fμs\n", count, t); \
} while(0)
#define LARGE_SORT_TEST(sort, count) do { \
std::vector<int> v(count); \
for (auto& it : v) it = rand(); \
LARGE_INTEGER t1, t2, tc; \
QueryPerformanceFrequency(&tc); \
QueryPerformanceCounter(&t1); \
sort(v.begin(), v.end()); \
QueryPerformanceCounter(&t2); \
printf(" %7d numbers cost : %fms\n", \
count,(t2.QuadPart - t1.QuadPart)*1e3 / tc.QuadPart); \
} while(0)
int main()
{
srand((int)time(0));
/** [ Correctness verification ] **/
std::vector<int> v = { 2,3,6,9,0,3,9,6,5,7 };
merge_sort(v.begin(), v.end(), std::greater<>());
for (auto& it : v)
std::cout << " " << it;
std::cout << "\n";
// output:
// 9 9 7 6 6 5 3 3 2 0
std::vector<int> v2(10000);
for (auto& it : v2)
it = rand();
merge_sort(v2.begin(), v2.end());
std::cout << std::boolalpha << " " << std::is_sorted(v2.begin(), v2.end()) << "\n";
// output:
// true
/** [ Small amount of data ] **/
std::cout << "\n [ Small amount of data ]\n";
SMALL_SORT_TEST(merge_sort, 16);
SMALL_SORT_TEST(merge_sort, 32);
SMALL_SORT_TEST(merge_sort, 64);
SMALL_SORT_TEST(merge_sort, 128);
SMALL_SORT_TEST(merge_sort, 512);
SMALL_SORT_TEST(merge_sort, 1024);
/** [ Large amount of data ] **/
std::cout << "\n [ Large amount of data ]\n";
LARGE_SORT_TEST(merge_sort, 10000);
LARGE_SORT_TEST(merge_sort, 100000);
LARGE_SORT_TEST(merge_sort, 1000000);
}
// I run 5 times for 'Small amount of data' and 'Large amount of data'.
// [ 1st time ]
// [ Small amount of data ]
// 16 numbers cost : 4.012178μs
// 32 numbers cost : 9.283994μs
// 64 numbers cost : 19.034521μs
// 128 numbers cost : 37.882428μs
// 512 numbers cost : 148.543906μs
// 1024 numbers cost : 288.830189μs
//
// [ Large amount of data ]
// 10000 numbers cost : 3.343171ms
// 100000 numbers cost : 33.544610ms
// 1000000 numbers cost : 351.684229ms
// [ 2nd time ]
// [ Small amount of data ]
// 16 numbers cost : 4.292098μs
// 32 numbers cost : 9.144034μs
// 64 numbers cost : 19.641013μs
// 128 numbers cost : 40.355050μs
// 512 numbers cost : 171.077419μs
// 1024 numbers cost : 297.461038μs
//
// [ Large amount of data ]
// 10000 numbers cost : 3.852158ms
// 100000 numbers cost : 32.216393ms
// 1000000 numbers cost : 329.973212ms
// [ 3rd time ]
// [ Small amount of data ]
// 16 numbers cost : 4.432057μs
// 32 numbers cost : 8.677502μs
// 64 numbers cost : 16.935125μs
// 128 numbers cost : 41.941260μs
// 512 numbers cost : 154.235601μs
// 1024 numbers cost : 308.937734μs
//
// [ Large amount of data ]
// 10000 numbers cost : 3.375828ms
// 100000 numbers cost : 32.919457ms
// 1000000 numbers cost : 339.982664ms
// [ 4th time ]
// [ Small amount of data ]
// 16 numbers cost : 4.525364μs
// 32 numbers cost : 8.910768μs
// 64 numbers cost : 18.288069μs
// 128 numbers cost : 37.882428μs
// 512 numbers cost : 149.896850μs
// 1024 numbers cost : 278.333210μs
//
// [ Large amount of data ]
// 10000 numbers cost : 3.296984ms
// 100000 numbers cost : 32.983838ms
// 1000000 numbers cost : 330.263395ms
// [ 5th time ]
// [ Small amount of data ]
// 16 numbers cost : 4.198791μs
// 32 numbers cost : 9.050728μs
// 64 numbers cost : 18.707948μs
// 128 numbers cost : 40.168437μs
// 512 numbers cost : 153.535802μs
// 1024 numbers cost : 384.376018μs
//
// [ Large amount of data ]
// 10000 numbers cost : 3.248465ms
// 100000 numbers cost : 32.323229ms
// 1000000 numbers cost : 328.938443ms
// [ average ]
// [ Small amount of data ]
// 16 numbers cost : 4.292098μs
// 32 numbers cost : 9.013405μs
// 64 numbers cost : 18.521335μs
// 128 numbers cost : 39.645921μs
// 512 numbers cost : 155.457916μs
// 1024 numbers cost : 311.587638μs
//
// [ Large amount of data ]
// 10000 numbers cost : 3.423321ms
// 100000 numbers cost : 32.797505ms
// 1000000 numbers cost : 336.168389ms