TheAlgorithms-C-Plus-Plus/dynamic_programming/longest_increasing_subsequence.cpp
David Leal 554919d9f5
[feat/fix/docs]: Improve the dynamic_programming/longest_increasing_subsequence.cpp file (#1504)
* [feat/fix/docs]: Improve the `dynamic_programming/longest_increasing_subsequence.cpp` file

* [test/feat]: Add self-test implementations and...

...namespace (`dynamic_programming`).
Thanks to @manncodes for the idea and help!

Co-authored-by: Mann Patel <manncodes@users.noreply.github.com>

* clang-format and clang-tidy fixes for 7d4562d6

Co-authored-by: Mann Patel <manncodes@users.noreply.github.com>
Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
2021-06-18 14:17:31 -05:00

99 lines
2.9 KiB
C++

/**
* @file
* @brief Calculate the length of the [longest increasing
* subsequence](https://en.wikipedia.org/wiki/Longest_increasing_subsequence) in
* an array
*
* @details
* In computer science, the longest increasing subsequence problem is to find a
* subsequence of a given sequence in which the subsequence's elements are in
* sorted order, lowest to highest, and in which the subsequence is as long as
* possible. This subsequence is not necessarily contiguous, or unique. Longest
* increasing subsequences are studied in the context of various disciplines
* related to mathematics, including algorithmics, random matrix theory,
* representation theory, and physics. The longest increasing subsequence
* problem is solvable in time O(n log n), where n denotes the length of the
* input sequence.
*
* @author [Krishna Vedala](https://github.com/kvedala)
* @author [David Leal](https://github.com/Panquesito7)
*/
#include <cassert> /// for assert
#include <climits> /// for std::max
#include <iostream> /// for IO operations
#include <vector> /// for std::vector
/**
* @namespace dynamic_programming
* @brief Dynamic Programming algorithms
*/
namespace dynamic_programming {
/**
* @brief Calculate the longest increasing subsequence for the specified numbers
* @param a the array used to calculate the longest increasing subsequence
* @param n the size used for the arrays
* @returns the length of the longest increasing
* subsequence in the `a` array of size `n`
*/
uint64_t LIS(const std::vector<uint64_t> &a, const uint32_t &n) {
std::vector<int> lis(n);
for (int i = 0; i < n; ++i) {
lis[i] = 1;
}
for (int i = 0; i < n; ++i) {
for (int j = 0; j < i; ++j) {
if (a[i] > a[j] && lis[i] < lis[j] + 1) {
lis[i] = lis[j] + 1;
}
}
}
int res = 0;
for (int i = 0; i < n; ++i) {
res = std::max(res, lis[i]);
}
return res;
}
} // namespace dynamic_programming
/**
* @brief Self-test implementations
* @returns void
*/
static void test() {
std::vector<uint64_t> a = {15, 21, 2, 3, 4, 5, 8, 4, 1, 1};
uint32_t n = a.size();
uint32_t result = dynamic_programming::LIS(a, n);
assert(result ==
5); ///< The longest increasing subsequence is `{2,3,4,5,8}`
std::cout << "Self-test implementations passed!" << std::endl;
}
/**
* @brief Main function
* @param argc commandline argument count (ignored)
* @param argv commandline array of arguments (ignored)
* @returns 0 on exit
*/
int main(int argc, char const *argv[]) {
uint32_t n = 0;
std::cout << "Enter size of array: ";
std::cin >> n;
std::vector<uint64_t> a(n);
std::cout << "Enter array elements: ";
for (int i = 0; i < n; ++i) {
std::cin >> a[i];
}
std::cout << "\nThe result is: " << dynamic_programming::LIS(a, n)
<< std::endl;
test(); // run self-test implementations
return 0;
}