Understanding Randomized Queues in Data Structures

What are randomized queues in data structures and how are they implemented?

import java.util.Random;

public class RandomizedQueue<Item> {
    private Item[] items;
    private int n;
    private Random random;

    public RandomizedQueue() {
        items = (Item[]) new Object[2];
        n = 0;
        random = new Random();
    }

    public void enqueue(Item item) {
        if (item == null) throw new IllegalArgumentException();
        if (n == items.length) resize(2 * items.length);
        items[n++] = item;
    }

    private void resize(int capacity) {
        Item[] newArray = (Item[]) new Object[capacity];
        for (int i = 0; i < n; i++) {
            newArray[i] = items[i];
        }
        items = newArray;
    }

    public Item dequeue() {
        if (isEmpty()) throw new NoSuchElementException();
        int randomIndex = random.nextInt(n);
        Item item = items[randomIndex];
        items[randomIndex] = items[n - 1];
        items[n - 1] = null;
        n--;
        if (n > 0 && n == items.length / 4) resize(items.length / 2);
        return item;
    }

    public boolean isEmpty() {
        return n == 0;
    }
}

A randomized queue is a data structure that stores a collection of items with no specific ordering, allowing items to be added and removed at random. This structure can be particularly useful in scenarios where random sampling of data is required, such as in simulations or randomized algorithms.

public Item sample() {
    if (isEmpty()) throw new NoSuchElementException();
    int randomIndex = random.nextInt(n);
    return items[randomIndex];
}

Causes

• Randomized queues provide flexibility in item retrieval—unlike traditional queues, which follow a strict FIFO (First-In-First-Out) order, randomized queues enable the removal of items in a non-deterministic manner.
• They help avoid bias in data sampling, as each item has an equal chance of being selected, which is crucial in applications like gaming and randomized testing.

Solutions

• To implement a randomized queue, you can use an array or a dynamic array, resizing it as necessary to maintain performance.
• Using a random number generator ensures that item selection during dequeue operations is uniformly distributed.

Mistake: Not handling the case when the queue is empty in dequeue samples.

Solution: Always check if the queue is empty before attempting to dequeue or sample an item.

Mistake: Improper resizing of the underlying array leading to memory wastage.

Solution: Implement checks to resize the array only when necessary to optimize memory usage.