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3369. Design an Array Statistics Tracker 🔒

Description

Design a data structure that keeps track of the values in it and answers some queries regarding their mean, median, and mode.

Implement the StatisticsTracker class.

  • StatisticsTracker(): Initialize the StatisticsTracker object with an empty array.
  • void addNumber(int number): Add number to the data structure.
  • void removeFirstAddedNumber(): Remove the earliest added number from the data structure.
  • int getMean(): Return the floored mean of the numbers in the data structure.
  • int getMedian(): Return the median of the numbers in the data structure.
  • int getMode(): Return the mode of the numbers in the data structure. If there are multiple modes, return the smallest one.

Note:

  • The mean of an array is the sum of all the values divided by the number of values in the array.
  • The median of an array is the middle element of the array when it is sorted in non-decreasing order. If there are two choices for a median, the larger of the two values is taken.
  • The mode of an array is the element that appears most often in the array.

 

Example 1:

Input:
["StatisticsTracker", "addNumber", "addNumber", "addNumber", "addNumber", "getMean", "getMedian", "getMode", "removeFirstAddedNumber", "getMode"]
[[], [4], [4], [2], [3], [], [], [], [], []]

Output:
[null, null, null, null, null, 3, 4, 4, null, 2]

Explanation

StatisticsTracker statisticsTracker = new StatisticsTracker();
statisticsTracker.addNumber(4); // The data structure now contains [4]
statisticsTracker.addNumber(4); // The data structure now contains [4, 4]
statisticsTracker.addNumber(2); // The data structure now contains [4, 4, 2]
statisticsTracker.addNumber(3); // The data structure now contains [4, 4, 2, 3]
statisticsTracker.getMean(); // return 3
statisticsTracker.getMedian(); // return 4
statisticsTracker.getMode(); // return 4
statisticsTracker.removeFirstAddedNumber(); // The data structure now contains [4, 2, 3]
statisticsTracker.getMode(); // return 2

Example 2:

Input:
["StatisticsTracker", "addNumber", "addNumber", "getMean", "removeFirstAddedNumber", "addNumber", "addNumber", "removeFirstAddedNumber", "getMedian", "addNumber", "getMode"]
[[], [9], [5], [], [], [5], [6], [], [], [8], []]

Output:
[null, null, null, 7, null, null, null, null, 6, null, 5]

Explanation

StatisticsTracker statisticsTracker = new StatisticsTracker();
statisticsTracker.addNumber(9); // The data structure now contains [9]
statisticsTracker.addNumber(5); // The data structure now contains [9, 5]
statisticsTracker.getMean(); // return 7
statisticsTracker.removeFirstAddedNumber(); // The data structure now contains [5]
statisticsTracker.addNumber(5); // The data structure now contains [5, 5]
statisticsTracker.addNumber(6); // The data structure now contains [5, 5, 6]
statisticsTracker.removeFirstAddedNumber(); // The data structure now contains [5, 6]
statisticsTracker.getMedian(); // return 6
statisticsTracker.addNumber(8); // The data structure now contains [5, 6, 8]
statisticsTracker.getMode(); // return 5

 

Constraints:

  • 1 <= number <= 109
  • At most, 105 calls will be made to addNumber, removeFirstAddedNumber, getMean, getMedian, and getMode in total.
  • removeFirstAddedNumber, getMean, getMedian, and getMode will be called only if there is at least one element in the data structure.

Solutions

Solution 1: Queue + Hash Table + Ordered Set

We define a queue $\textit{q}$ to store the added numbers, a variable $\textit{s}$ to store the sum of all numbers, a hash table $\textit{cnt}$ to store the occurrence count of each number, an ordered set $\textit{sl}$ to store all numbers, and an ordered set $\textit{sl2}$ to store all numbers and their occurrence counts, sorted by occurrence count in descending order and by value in ascending order.

In the addNumber method, we add the number to the queue $\textit{q}$, add the number to the ordered set $\textit{sl}$, then remove the number and its occurrence count from the ordered set $\textit{sl2}$, update the occurrence count of the number, and finally add the number and its updated occurrence count to the ordered set $\textit{sl2}$, and update the sum of all numbers. The time complexity is $O(\log n)$.

In the removeFirstAddedNumber method, we remove the earliest added number from the queue $\textit{q}$, remove the number from the ordered set $\textit{sl}$, then remove the number and its occurrence count from the ordered set $\textit{sl2}$, update the occurrence count of the number, and finally add the number and its updated occurrence count to the ordered set $\textit{sl2}$, and update the sum of all numbers. The time complexity is $O(\log n)$.

In the getMean method, we return the sum of all numbers divided by the number of numbers. The time complexity is $O(1)$.

In the getMedian method, we return the $\textit{len}(\textit{q}) / 2$-th number in the ordered set $\textit{sl}$. The time complexity is $O(1)$ or $O(\log n)$.

In the getMode method, we return the first number in the ordered set $\textit{sl2}$. The time complexity is $O(1)$.

In Python, we can directly access elements in an ordered set by index. In other languages, we can implement this using a heap.

The space complexity is $O(n)$, where $n$ is the number of added numbers.

  • class MedianFinder {
    private final PriorityQueue<Integer> small = new PriorityQueue<>(Comparator.reverseOrder());
    private final PriorityQueue<Integer> large = new PriorityQueue<>();
    private final Map<Integer, Integer> delayed = new HashMap<>();
    private int smallSize;
    private int largeSize;

    public void addNum(int num) {
        if (small.isEmpty() || num <= small.peek()) {
            small.offer(num);
            ++smallSize;
        } else {
            large.offer(num);
            ++largeSize;
        }
        rebalance();
    }

    public Integer findMedian() {
        return smallSize == largeSize ? large.peek() : small.peek();
    }

    public void removeNum(int num) {
        delayed.merge(num, 1, Integer::sum);
        if (num <= small.peek()) {
            --smallSize;
            if (num == small.peek()) {
                prune(small);
            }
        } else {
            --largeSize;
            if (num == large.peek()) {
                prune(large);
            }
        }
        rebalance();
    }

    private void prune(PriorityQueue<Integer> pq) {
        while (!pq.isEmpty() && delayed.containsKey(pq.peek())) {
            if (delayed.merge(pq.peek(), -1, Integer::sum) == 0) {
                delayed.remove(pq.peek());
            }
            pq.poll();
        }
    }

    private void rebalance() {
        if (smallSize > largeSize + 1) {
            large.offer(small.poll());
            --smallSize;
            ++largeSize;
            prune(small);
        } else if (smallSize < largeSize) {
            small.offer(large.poll());
            --largeSize;
            ++smallSize;
            prune(large);
        }
    }
}

class StatisticsTracker {
    private final Deque<Integer> q = new ArrayDeque<>();
    private long s;
    private final Map<Integer, Integer> cnt = new HashMap<>();
    private final MedianFinder medianFinder = new MedianFinder();
    private final TreeSet<int[]> ts
        = new TreeSet<>((a, b) -> a[1] == b[1] ? a[0] - b[0] : b[1] - a[1]);

    public StatisticsTracker() {
    }

    public void addNumber(int number) {
        q.offerLast(number);
        s += number;
        ts.remove(new int[] {number, cnt.getOrDefault(number, 0)});
        cnt.merge(number, 1, Integer::sum);
        medianFinder.addNum(number);
        ts.add(new int[] {number, cnt.get(number)});
    }

    public void removeFirstAddedNumber() {
        int number = q.pollFirst();
        s -= number;
        ts.remove(new int[] {number, cnt.get(number)});
        cnt.merge(number, -1, Integer::sum);
        medianFinder.removeNum(number);
        ts.add(new int[] {number, cnt.get(number)});
    }

    public int getMean() {
        return (int) (s / q.size());
    }

    public int getMedian() {
        return medianFinder.findMedian();
    }

    public int getMode() {
        return ts.first()[0];
    }
}


  • class MedianFinder {
public:
    void addNum(int num) {
        if (small.empty() || num <= small.top()) {
            small.push(num);
            ++smallSize;
        } else {
            large.push(num);
            ++largeSize;
        }
        reblance();
    }

    void removeNum(int num) {
        ++delayed[num];
        if (num <= small.top()) {
            --smallSize;
            if (num == small.top()) {
                prune(small);
            }
        } else {
            --largeSize;
            if (num == large.top()) {
                prune(large);
            }
        }
        reblance();
    }

    int findMedian() {
        return smallSize == largeSize ? large.top() : small.top();
    }

private:
    priority_queue<int> small;
    priority_queue<int, vector<int>, greater<int>> large;
    unordered_map<int, int> delayed;
    int smallSize = 0;
    int largeSize = 0;

    template <typename T>
    void prune(T& pq) {
        while (!pq.empty() && delayed[pq.top()]) {
            if (--delayed[pq.top()] == 0) {
                delayed.erase(pq.top());
            }
            pq.pop();
        }
    }

    void reblance() {
        if (smallSize > largeSize + 1) {
            large.push(small.top());
            small.pop();
            --smallSize;
            ++largeSize;
            prune(small);
        } else if (smallSize < largeSize) {
            small.push(large.top());
            large.pop();
            ++smallSize;
            --largeSize;
            prune(large);
        }
    }
};

class StatisticsTracker {
private:
    queue<int> q;
    long long s = 0;
    unordered_map<int, int> cnt;
    MedianFinder medianFinder;
    set<pair<int, int>> ts;

public:
    StatisticsTracker() {}

    void addNumber(int number) {
        q.push(number);
        s += number;
        ts.erase({-cnt[number], number});
        cnt[number]++;
        medianFinder.addNum(number);
        ts.insert({-cnt[number], number});
    }

    void removeFirstAddedNumber() {
        int number = q.front();
        q.pop();
        s -= number;
        ts.erase({-cnt[number], number});
        cnt[number]--;
        if (cnt[number] > 0) {
            ts.insert({-cnt[number], number});
        }
        medianFinder.removeNum(number);
    }

    int getMean() {
        return static_cast<int>(s / q.size());
    }

    int getMedian() {
        return medianFinder.findMedian();
    }

    int getMode() {
        return ts.begin()->second;
    }
};


  • from sortedcontainers import SortedList


class StatisticsTracker:
    def __init__(self):
        self.q = deque()
        self.s = 0
        self.cnt = defaultdict(int)
        self.sl = SortedList()
        self.sl2 = SortedList(key=lambda x: (-x[1], x[0]))

    def addNumber(self, number: int) -> None:
        self.q.append(number)
        self.sl.add(number)
        self.sl2.discard((number, self.cnt[number]))
        self.cnt[number] += 1
        self.sl2.add((number, self.cnt[number]))
        self.s += number

    def removeFirstAddedNumber(self) -> None:
        number = self.q.popleft()
        self.sl.remove(number)
        self.sl2.discard((number, self.cnt[number]))
        self.cnt[number] -= 1
        self.sl2.add((number, self.cnt[number]))
        self.s -= number

    def getMean(self) -> int:
        return self.s // len(self.q)

    def getMedian(self) -> int:
        return self.sl[len(self.q) // 2]

    def getMode(self) -> int:
        return self.sl2[0][0]


# Your StatisticsTracker object will be instantiated and called as such:
# obj = StatisticsTracker()
# obj.addNumber(number)
# obj.removeFirstAddedNumber()
# param_3 = obj.getMean()
# param_4 = obj.getMedian()
# param_5 = obj.getMode()

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