Last updated on October 6th, 2024 at 02:01 pm

Here, We see Find Median from Data Stream LeetCode Solution. This Leetcode problem is done in many programming languages like C++, Java, JavaScript, Python, etc. with different approaches.

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Find Median from Data Stream LeetCode Solution

Problem Statement

The median is the middle value in an ordered integer list. If the size of the list is even, there is no middle value, and the median is the mean of the two middle values.

• For example, for arr = [2,3,4], the median is 3.
• For example, for arr = [2,3], the median is (2 + 3) / 2 = 2.5.

Implement the MedianFinder class:

• MedianFinder() initializes the MedianFinder object.
• void addNum(int num) adds the integer num from the data stream to the data structure.
• double findMedian() returns the median of all elements so far. Answers within 10-5 of the actual answer will be accepted.

Example 1:Input [“MedianFinder”, “addNum”, “addNum”, “findMedian”, “addNum”, “findMedian”] [[], [1], [2], [], [3], []]
Output [null, null, null, 1.5, null, 2.0]

Explanation
MedianFinder medianFinder = new MedianFinder();
medianFinder.addNum(1); // arr = [1]
medianFinder.addNum(2); // arr = [1, 2]
medianFinder.findMedian(); // return 1.5 (i.e., (1 + 2) / 2)
medianFinder.addNum(3); // arr[1, 2, 3]
medianFinder.findMedian(); // return 2.0

1. Find Median from Data Stream LeetCode Solution C++

class MedianFinder {
public:
    priority_queue<int, vector<int>, greater<int> > minHeap;
	priority_queue<int> maxHeap;
    MedianFinder(){}
    void addNum(int num) {
        if (maxHeap.empty() or maxHeap.top() > num) {
			maxHeap.push(num);
		} else {
			minHeap.push(num);
		}
		if (maxHeap.size() > minHeap.size() + 1) {
			minHeap.push(maxHeap.top());
			maxHeap.pop();
		} else if (minHeap.size() > maxHeap.size() + 1) {
			maxHeap.push(minHeap.top());
			minHeap.pop();
		}
    }
    
    double findMedian() {
        if (maxHeap.size() == minHeap.size()) {
			if (maxHeap.empty()) {
				return 0;
			} else {
				double avg = (maxHeap.top() + minHeap.top()) / 2.0;
				return avg;
			}
		} else {
			return maxHeap.size() > minHeap.size() ? maxHeap.top() : minHeap.top();
		}
    }
};

2. Find Median from Data Stream LeetCode Solution Java

class MedianFinder {
    PriorityQueue<Integer> min = new PriorityQueue();
    PriorityQueue<Integer> max = new PriorityQueue(1000, Collections.reverseOrder());
    public void addNum(int num) {
        max.offer(num);
        min.offer(max.poll());
        if (max.size() < min.size()){
            max.offer(min.poll());
        }
    }
    public double findMedian() {
        if (max.size() == min.size()) return (max.peek() + min.peek()) /  2.0;
        else return max.peek();
    }
};

3. Find Median from Data Stream LeetCode Solution JavaScript

var MedianFinder = function() {
    this.maxHeap = new Heap(Heap.maxComparator);
    this.minHeap = new Heap(Heap.minComparator);
};
MedianFinder.prototype.addNum = function(num) {
    if(this.maxHeap.peek() === null || num < this.maxHeap.peek()) {
        this.maxHeap.add(num);
    } else {
        this.minHeap.add(num);
    }
    if(this.maxHeap.size - this.minHeap.size > 1) {
        this.minHeap.add(this.maxHeap.poll());
    } else if(this.minHeap.size - this.maxHeap.size > 1) {
        this.maxHeap.add(this.minHeap.poll());
    }
};
MedianFinder.prototype.findMedian = function() {
    if(this.maxHeap.size > this.minHeap.size) {
        return this.maxHeap.peek();
    } else if(this.maxHeap.size < this.minHeap.size) {
        return this.minHeap.peek();
    } else {
        return (this.maxHeap.peek() + this.minHeap.peek()) / 2;
    }
};

class Heap {
	constructor(comparator) {
		this.size = 0;
		this.values = [];
		this.comparator = comparator || Heap.minComparator;
	}
	add(val) {
		this.values.push(val);
		this.size ++;
		this.bubbleUp();
	}
	peek() {
		return this.values[0] || null;
	}
	poll() {
		const max = this.values[0];
		const end = this.values.pop();
		this.size --;
		if (this.values.length) {
			this.values[0] = end;
			this.bubbleDown();
		}
		return max;
	}
	bubbleUp() {
		let index = this.values.length - 1;
		let parent = Math.floor((index - 1) / 2);
		while (this.comparator(this.values[index], this.values[parent]) < 0) {
			[this.values[parent], this.values[index]] = [this.values[index], this.values[parent]];
			index = parent;
			parent = Math.floor((index - 1) / 2);
		}
	}
	bubbleDown() {
		let index = 0, length = this.values.length;
		while (true) {
			let left = null,
				right = null,
				swap = null,
				leftIndex = index * 2 + 1,
				rightIndex = index * 2 + 2;
			if (leftIndex < length) {
				left = this.values[leftIndex];
				if (this.comparator(left, this.values[index]) < 0) swap = leftIndex;
			}
			if (rightIndex < length) {
				right = this.values[rightIndex];
				if ((swap !== null && this.comparator(right, left) < 0) || (swap === null && this.comparator(right, this.values[index]))) {
					swap = rightIndex;
				}
			}
			if (swap === null) break;
			[this.values[index], this.values[swap]] = [this.values[swap], this.values[index]];
			index = swap;
		}
	}
}
Heap.minComparator = (a, b) => { return a - b; }
Heap.maxComparator = (a, b) => { return b - a; }

4. Find Median from Data Stream Solution Python

class MedianFinder:
    def __init__(self):
        self.small = []
        self.large = [] 

    def addNum(self, num):
        if len(self.small) == len(self.large):
            heappush(self.large, -heappushpop(self.small, -num))
        else:
            heappush(self.small, -heappushpop(self.large, num))

    def findMedian(self):
        if len(self.small) == len(self.large):
            return float(self.large[0] - self.small[0]) / 2.0
        else:
            return float(self.large[0])