Windowed Median
S
/**
* @file
* @brief An implementation of a median calculation of a sliding window along a
* data stream
*
* @details
* Given a stream of integers, the algorithm calculates the median of a fixed
* size window at the back of the stream. The leading time complexity of this
* algorithm is O(log(N), and it is inspired by the known algorithm to [find
* median from (infinite) data
* stream](https://www.tutorialcup.com/interview/algorithm/find-median-from-data-stream.htm),
* with the proper modifications to account for the finite window size for which
* the median is requested
*
* ### Algorithm
* The sliding window is managed by a list, which guarantees O(1) for both
* pushing and popping. Each new value is pushed to the window back, while a
* value from the front of the window is popped. In addition, the algorithm
* manages a multi-value binary search tree (BST), implemented by std::multiset.
* For each new value that is inserted into the window, it is also inserted to
* the BST. When a value is popped from the window, it is also erased from the
* BST. Both insertion and erasion to/from the BST are O(logN) in time, with N
* the size of the window. Finally, the algorithm keeps a pointer to the root of
* the BST, and updates its position whenever values are inserted or erased
* to/from BST. The root of the tree is the median! Hence, median retrieval is
* always O(1)
*
* Time complexity: O(logN). Space complexity: O(N). N - size of window
* @author [Yaniv Hollander](https://github.com/YanivHollander)
*/
#include <cassert> /// for assert
#include <cstdlib> /// for std::rand - needed in testing
#include <ctime> /// for std::time - needed in testing
#include <list> /// for std::list - used to manage sliding window
#include <set> /// for std::multiset - used to manage multi-value sorted sliding window values
#include <vector> /// for std::vector - needed in testing
/**
* @namespace probability
* @brief Probability algorithms
*/
namespace probability {
/**
* @namespace windowed_median
* @brief Functions for the Windowed Median algorithm implementation
*/
namespace windowed_median {
using Window = std::list<int>;
using size_type = Window::size_type;
/**
* @class WindowedMedian
* @brief A class to calculate the median of a leading sliding window at the
* back of a stream of integer values.
*/
class WindowedMedian {
const size_type _windowSize; ///< sliding window size
Window _window; ///< a sliding window of values along the stream
std::multiset<int> _sortedValues; ///< a DS to represent a balanced
/// multi-value binary search tree (BST)
std::multiset<int>::const_iterator
_itMedian; ///< an iterator that points to the root of the multi-value
/// BST
/**
* @brief Inserts a value to a sorted multi-value BST
* @param value Value to insert
*/
void insertToSorted(int value) {
_sortedValues.insert(value); /// Insert value to BST - O(logN)
const auto sz = _sortedValues.size();
if (sz == 1) { /// For the first value, set median iterator to BST root
_itMedian = _sortedValues.begin();
return;
}
/// If new value goes to left tree branch, and number of elements is
/// even, the new median in the balanced tree is the left child of the
/// median before the insertion
if (value < *_itMedian && sz % 2 == 0) {
--_itMedian; // O(1) - traversing one step to the left child
}
/// However, if the new value goes to the right branch, the previous
/// median's right child is the new median in the balanced tree
else if (value >= *_itMedian && sz % 2 != 0) {
++_itMedian; /// O(1) - traversing one step to the right child
}
}
/**
* @brief Erases a value from a sorted multi-value BST
* @param value Value to insert
*/
void eraseFromSorted(int value) {
const auto sz = _sortedValues.size();
/// If the erased value is on the left branch or the median itself and
/// the number of elements is even, the new median will be the right
/// child of the current one
if (value <= *_itMedian && sz % 2 == 0) {
++_itMedian; /// O(1) - traversing one step to the right child
}
/// However, if the erased value is on the right branch or the median
/// itself, and the number of elements is odd, the new median will be
/// the left child of the current one
else if (value >= *_itMedian && sz % 2 != 0) {
--_itMedian; // O(1) - traversing one step to the left child
}
/// Find the (first) position of the value we want to erase, and erase
/// it
const auto it = _sortedValues.find(value); // O(logN)
_sortedValues.erase(it); // O(logN)
}
public:
/**
* @brief Constructs a WindowedMedian object
* @param windowSize Sliding window size
*/
explicit WindowedMedian(size_type windowSize) : _windowSize(windowSize){};
/**
* @brief Insert a new value to the stream
* @param value New value to insert
*/
void insert(int value) {
/// Push new value to the back of the sliding window - O(1)
_window.push_back(value);
insertToSorted(value); // Insert value to the multi-value BST - O(logN)
if (_window.size() > _windowSize) { /// If exceeding size of window,
/// pop from its left side
eraseFromSorted(
_window.front()); /// Erase from the multi-value BST
/// the window left side value
_window.pop_front(); /// Pop the left side value from the window -
/// O(1)
}
}
/**
* @brief Gets the median of the values in the sliding window
* @return Median of sliding window. For even window size return the average
* between the two values in the middle
*/
float getMedian() const {
if (_sortedValues.size() % 2 != 0) {
return *_itMedian; // O(1)
}
return 0.5f * *_itMedian + 0.5f * *next(_itMedian); /// O(1)
}
/**
* @brief A naive and inefficient method to obtain the median of the sliding
* window. Used for testing!
* @return Median of sliding window. For even window size return the average
* between the two values in the middle
*/
float getMedianNaive() const {
auto window = _window;
window.sort(); /// Sort window - O(NlogN)
auto median =
*next(window.begin(),
window.size() / 2); /// Find value in the middle - O(N)
if (window.size() % 2 != 0) {
return median;
}
return 0.5f * median +
0.5f * *next(window.begin(), window.size() / 2 - 1); /// O(N)
}
};
} // namespace windowed_median
} // namespace probability
/**
* @brief Self-test implementations
* @param vals Stream of values
* @param windowSize Size of sliding window
*/
static void test(const std::vector<int> &vals, int windowSize) {
probability::windowed_median::WindowedMedian windowedMedian(windowSize);
for (const auto val : vals) {
windowedMedian.insert(val);
/// Comparing medians: efficient function vs. Naive one
assert(windowedMedian.getMedian() == windowedMedian.getMedianNaive());
}
}
/**
* @brief Main function
* @param argc command line argument count (ignored)
* @param argv command line array of arguments (ignored)
* @returns 0 on exit
*/
int main(int argc, const char *argv[]) {
/// A few fixed test cases
test({1, 2, 3, 4, 5, 6, 7, 8, 9},
3); /// Array of sorted values; odd window size
test({9, 8, 7, 6, 5, 4, 3, 2, 1},
3); /// Array of sorted values - decreasing; odd window size
test({9, 8, 7, 6, 5, 4, 5, 6}, 4); /// Even window size
test({3, 3, 3, 3, 3, 3, 3, 3, 3}, 3); /// Array with repeating values
test({3, 3, 3, 3, 7, 3, 3, 3, 3}, 3); /// Array with same values except one
test({4, 3, 3, -5, -5, 1, 3, 4, 5},
5); /// Array that includes repeating values including negatives
/// Array with large values - sum of few pairs exceeds MAX_INT. Window size
/// is even - testing calculation of average median between two middle
/// values
test({470211272, 101027544, 1457850878, 1458777923, 2007237709, 823564440,
1115438165, 1784484492, 74243042, 114807987},
6);
/// Random test cases
std::srand(static_cast<unsigned int>(std::time(nullptr)));
std::vector<int> vals;
for (int i = 8; i < 100; i++) {
const auto n =
1 + std::rand() /
((RAND_MAX + 5u) / 20); /// Array size in the range [5, 20]
auto windowSize =
1 + std::rand() / ((RAND_MAX + 3u) /
10); /// Window size in the range [3, 10]
vals.clear();
vals.reserve(n);
for (int i = 0; i < n; i++) {
vals.push_back(
rand() - RAND_MAX); /// Random array values (positive/negative)
}
test(vals, windowSize); /// Testing randomized test
}
return 0;
}
