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Add Maximum Sliding Window algorithm (#5848)
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package com.thealgorithms.others;
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import java.util.ArrayDeque;
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import java.util.Deque;
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/**
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* Maximum Sliding Window Algorithm
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*
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* This algorithm finds the maximum element in each sliding window of size k
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* in a given array of integers. It uses a deque (double-ended queue) to
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* efficiently keep track of potential maximum values in the current window.
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*
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* Time Complexity: O(n), where n is the number of elements in the input array
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* Space Complexity: O(k), where k is the size of the sliding window
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*/
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public class MaximumSlidingWindow {
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/**
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* Finds the maximum values in each sliding window of size k.
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*
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* @param nums The input array of integers
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* @param windowSize The size of the sliding window
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* @return An array of integers representing the maximums in each window
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*/
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public int[] maxSlidingWindow(int[] nums, int windowSize) {
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if (nums == null || nums.length == 0 || windowSize <= 0 || windowSize > nums.length) {
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return new int[0]; // Handle edge cases
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}
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int[] result = new int[nums.length - windowSize + 1];
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Deque<Integer> deque = new ArrayDeque<>();
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for (int currentIndex = 0; currentIndex < nums.length; currentIndex++) {
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// Remove the first element if it's outside the current window
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if (!deque.isEmpty() && deque.peekFirst() == currentIndex - windowSize) {
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deque.pollFirst();
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}
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// Remove all elements smaller than the current element from the end
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while (!deque.isEmpty() && nums[deque.peekLast()] < nums[currentIndex]) {
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deque.pollLast();
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}
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// Add the current element's index to the deque
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deque.offerLast(currentIndex);
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// If we have processed at least k elements, add to result
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if (currentIndex >= windowSize - 1) {
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result[currentIndex - windowSize + 1] = nums[deque.peekFirst()];
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}
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}
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return result;
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}
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}
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