Sliding Window Median – LeetCode Solution Java , Python 3, Python 2 , C , C++, Best and Optimal Solutions , All you need.
The median is the middle value in an ordered integer list. If the size of the list is even, there is no middle value. So the median is the mean of the two middle values.
- For examples, if
arr = [2,3,4], the median is3. - For examples, if
arr = [1,2,3,4], the median is(2 + 3) / 2 = 2.5.
You are given an integer array nums and an integer k. There is a sliding window of size k which is moving from the very left of the array to the very right. You can only see the k numbers in the window. Each time the sliding window moves right by one position.
Return the median array for each window in the original array. Answers within 10-5 of the actual value will be accepted.
Example 1:
Input: nums = [1,3,-1,-3,5,3,6,7], k = 3 Output: [1.00000,-1.00000,-1.00000,3.00000,5.00000,6.00000] Explanation: Window position Median --------------- ----- [1 3 -1] -3 5 3 6 7 1 1 [3 -1 -3] 5 3 6 7 -1 1 3 [-1 -3 5] 3 6 7 -1 1 3 -1 [-3 5 3] 6 7 3 1 3 -1 -3 [5 3 6] 7 5 1 3 -1 -3 5 [3 6 7] 6
Example 2:
Input: nums = [1,2,3,4,2,3,1,4,2], k = 3 Output: [2.00000,3.00000,3.00000,3.00000,2.00000,3.00000,2.00000]
Constraints:
1 <= k <= nums.length <= 105-231 <= nums[i] <= 231 - 1
C++ Sliding Window Median LeetCode Solution
vector<double> medianSlidingWindow(vector<int>& nums, int k) {
multiset<int> window(nums.begin(), nums.begin() + k);
auto mid = next(window.begin(), k / 2);
vector<double> medians;
for (int i=k; ; i++) {
// Push the current median.
medians.push_back((double(*mid) + *prev(mid, 1 - k%2)) / 2);
// If all done, return.
if (i == nums.size())
return medians;
// Insert nums[i].
window.insert(nums[i]);
if (nums[i] < *mid)
mid--;
// Erase nums[i-k].
if (nums[i-k] <= *mid)
mid++;
window.erase(window.lower_bound(nums[i-k]));
}
}
Java Sliding Window Median LeetCode Solution
public double[] medianSlidingWindow(int[] nums, int k) {
Comparator<Integer> comparator = (a, b) -> nums[a] != nums[b] ? Integer.compare(nums[a], nums[b]) : a - b;
TreeSet<Integer> left = new TreeSet<>(comparator.reversed());
TreeSet<Integer> right = new TreeSet<>(comparator);
Supplier<Double> median = (k % 2 == 0) ?
() -> ((double) nums[left.first()] + nums[right.first()]) / 2 :
() -> (double) nums[right.first()];
// balance lefts size and rights size (if not equal then right will be larger by one)
Runnable balance = () -> { while (left.size() > right.size()) right.add(left.pollFirst()); };
double[] result = new double[nums.length - k + 1];
for (int i = 0; i < k; i++) left.add(i);
balance.run(); result[0] = median.get();
for (int i = k, r = 1; i < nums.length; i++, r++) {
// remove tail of window from either left or right
if(!left.remove(i - k)) right.remove(i - k);
// add next num, this will always increase left size
right.add(i); left.add(right.pollFirst());
// rebalance left and right, then get median from them
balance.run(); result[r] = median.get();
}
return result;
}
Python 3 Sliding Window Median LeetCode Solution
def medianSlidingWindow(nums, k):
small, large = [], []
for i, x in enumerate(nums[:k]):
heapq.heappush(small, (-x,i))
for _ in range(k-(k>>1)):
move(small, large)
ans = [get_med(small, large, k)]
for i, x in enumerate(nums[k:]):
if x >= large[0][0]:
heapq.heappush(large, (x, i+k))
if nums[i] <= large[0][0]:
move(large, small)
else:
heapq.heappush(small, (-x, i+k))
if nums[i] >= large[0][0]:
move(small, large)
while small and small[0][1] <= i:
heapq.heappop(small)
while large and large[0][1] <= i:
heapq.heappop(large)
ans.append(get_med(small, large, k))
return ans
def move(h1, h2):
x, i = heapq.heappop(h1)
heapq.heappush(h2, (-x, i))
def get_med(h1, h2, k):
return h2[0][0] * 1. if k & 1 else (h2[0][0]-h1[0][0]) / 2.
Array-1180
String-562
Hash Table-412
Dynamic Programming-390
Math-368
Sorting-264
Greedy-257
Depth-First Search-256
Database-215
Breadth-First Search-200
Tree-195
Binary Search-191
Matrix-176
Binary Tree-160
Two Pointers-151
Bit Manipulation-140
Stack-133
Heap (Priority Queue)-117
Design-116
Graph-108
Simulation-103
Prefix Sum-96
Backtracking-92
Counting-86
Sliding Window-73
Linked List-69
Union Find-66
Ordered Set-48
Monotonic Stack-47
Recursion-43
Trie-41
Binary Search Tree-40
Divide and Conquer-40
Enumeration-39
Bitmask-37
Queue-33
Memoization-32
Topological Sort-31
Geometry-30
Segment Tree-27
Game Theory-24
Hash Function-24
Binary Indexed Tree-21
Interactive-18
Data Stream-17
String Matching-17
Rolling Hash-17
Shortest Path-16
Number Theory-16
Combinatorics-15
Randomized-12
Monotonic Queue-9
Iterator-9
Merge Sort-9
Concurrency-9
Doubly-Linked List-8
Brainteaser-8
Probability and Statistics-7
Quickselect-7
Bucket Sort-6
Suffix Array-6
Minimum Spanning Tree-5
Counting Sort-5
Shell-4
Line Sweep-4
Reservoir Sampling-4
Eulerian Circuit-3
Radix Sort-3
Strongly Connected Componen-t2
Rejection Sampling-2
Biconnected Component-1
