Random class

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A Java program that demonstrates the usage of the Random class to generate different types of random values.
public class RandomDemo {
    public static void main(String[] args) {
        // Create an instance of the Random class
        Random random = new Random();

        // Generate a random integer
        //The nextInt() method returns a random integer that can be positive, negative, or zero.
        int randomInt = random.nextInt();
        System.out.println("Random Integer: " + randomInt);

        // Generate a random integer between 0 (inclusive) and 100 (exclusive)
        int randomIntBounded = random.nextInt(100);
        System.out.println("Random Integer between 0 and 100: " + randomIntBounded);

        // Generate a random double
        // The nextDouble() method returns a random double value between 0.0 and 1.0.
        double randomDouble = random.nextDouble();
        System.out.println("Random Double: " + randomDouble);

        // Generate a random boolean
        //The nextBoolean() method returns true or false randomly.
        boolean randomBoolean = random.nextBoolean();
        System.out.println("Random Boolean: " + randomBoolean);
    }
}

Why Use the Random Class?

  1. Unpredictability in Games and Simulations:

    • Games: Think about rolling dice, shuffling cards, or generating random events in a game. These actions require randomness to make the game fair and unpredictable.
    • Simulations: When simulating real-world scenarios (like weather patterns or stock market behaviors), randomness helps create more realistic models.

  2. Testing and Validation:

    • Testing Software: When you test your code, using random inputs can help ensure that your software handles unexpected or edge-case scenarios properly. It can reveal bugs that you might not find with fixed inputs.

  3. Security:

    • Cryptography: In security, random numbers are crucial for generating secure keys, passwords, and other cryptographic elements. They help in making secure systems that are hard to predict or break into.

  4. Statistical Sampling:

    • Data Sampling: In statistics, you might need to randomly select samples from a larger dataset. This ensures that your samples are unbiased and representative of the whole dataset.

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