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Problem solution · Java

Design an Array Statistics Tracker

Design an Array Statistics Tracker: a Java solution using breadth-first search. Learn the idea, check the complexity, and read the full code, with credit to walkccc LeetCode Solutions.

Technique
Breadth-first search
Source
walkccc LeetCode Solutions
Length
58 lines
Start with the idea.

Try the problem first. If you get stuck, read the approach below, then write your own solution. The full code is at the bottom.

Approach

Breadth-first search

For Design an Array Statistics Tracker, the implementation explores reachable states in layers, which is the standard shape for unweighted shortest paths and minimum-step transitions.

  1. Model each valid configuration as a state and each legal move as an edge.
  2. Seed the queue with the starting state and mark it immediately.
  3. Expand each state once, recording distance or reachability for unseen neighbours.

Code notes

  • 58 lines of Java from the credited upstream file 3369.java.
  • The implementation visibly relies on hash lookup, ordered lookup, work queue.
  • 2 loop blocks detected.

Complexity

Verify that each state and transition is processed only a bounded number of times; that determines the traversal cost.

Check the problem constraints before deciding whether this complexity will pass.

Source

Code and credit

This code comes from walkccc LeetCode Solutions by P.-Y. Chen (walkccc) and is used under the MIT licence.

Open problem ↗Original source ↗
Full codeDesign an Array Statistics Tracker · JavaJava
Use this to learn the idea, then write your own version.
class StatisticsTracker {  public void addNumber(int number) {    q.offer(number);    count.merge(number, 1, Integer::sum);    sortedList.merge(number, 1, Integer::sum);    modeMaxHeap.offer(new Pair<>(count.get(number), number));    sum += number;  }   public void removeFirstAddedNumber() {    final int number = q.poll();    count.merge(number, -1, Integer::sum);    if (count.get(number) == 0)      count.remove(number);    sortedList.merge(number, -1, Integer::sum);    if (sortedList.get(number) == 0)      sortedList.remove(number);    sum -= number;  }   public int getMean() {    return (int) (sum / q.size());  }   public int getMedian() {    final int midIndex = q.size() / 2; // Median depends on the queue size.    int count = 0;    for (Map.Entry<Integer, Integer> entry : sortedList.entrySet()) {      count += entry.getValue();      if (count > midIndex)        return entry.getKey();    }    throw new IllegalArgumentException();  }   public int getMode() {    // Removes stale entries from the top of the heap.    while (!modeMaxHeap.isEmpty()) {      final int frequency = modeMaxHeap.peek().getKey();      final int number = modeMaxHeap.peek().getValue();      if (count.containsKey(number) && count.get(number) == frequency)        return number;      modeMaxHeap.poll();    }    throw new IllegalArgumentException();  }   private Queue<Integer> q = new LinkedList<>();  private Map<Integer, Integer> count = new HashMap<>();  private TreeMap<Integer, Integer> sortedList = new TreeMap<>();  // (frequency, number)  private PriorityQueue<Pair<Integer, Integer>> modeMaxHeap =      new PriorityQueue<>(Comparator.comparingInt(Pair<Integer, Integer>::getKey)                              .reversed()                              .thenComparingInt(Pair<Integer, Integer>::getValue));  private long sum = 0;}

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