# Randomness

Encryption algorithms, machine learning and making computer games less predictable all require randomness.
We can model randomness using random numbers. Java offers ready-made `Random`

class for creating random numbers.
An instance of the Random class can be used as follows:

```
import java.util.Random;
public class Raffle {
public static void main(String[] args) {
Random ladyLuck = new Random(); // create Random object ladyLuck
for (int i = 0; i < 10; i++) {
// Draw and print a random number
int randomNumber = ladyLuck.nextInt(10);
System.out.println(randomNumber);
}
}
}
```

Above we create an instance of the `Random`

class. It has `nextInt`

method, which gets an integer as a parameter. The method returns a random number between `[0, integer[`

or *0..(integer -1)*.

The program output is not always the same. One possible output is the following:

2 2 4 3 4 5 6 0 7 8

We can use the `nextInt`

method to create diverse randomness.
For example, we might need a program to give us a temperature between [-30,50].
We can do this by first creating random a number between 0 and 80 and then subtracting 30 from it.

```
Random weatherMan = new Random();
int temperature = weatherMan(81) - 30;
System.out.println(temperature);
```

A Random object can also be used to create random doubles. These can for example be used for calculating probabilities. Computers often simulate probabilities using doubles between [0..1].

The `nextDouble`

method of the Random class creates random doubles.
Let's assume the weather follows these probabilities:

- There is 0.1 probability it rains (10%)
- There is 0.3 probability it snows (30%)
- There is 0.6 probability the sun shines (60%)

Let's create a weather forecast using these probabilities.

```
import java.util.ArrayList;
import java.util.Random;
public class WeatherMan {
private Random random;
public WeatherMan() {
this.random = new Random();
}
public String forecast() {
double propability = this.random.nextDouble();
if (propability <= 0.1) {
return "It rains";
} else if (propability <= 0.4) { // 0.1 + 0.3
return "It snows";
} else { // rest, 1.0 - 0.4 = 0.6
return "The sun shines";
}
}
public int makeAForecast() {
return (int) (4 * this.random.nextGaussian() - 3);
}
}
```

The `makeAForecast`

method is interesting in many ways. The `this.random.nextGaussian()`

call is a regular method call. However what is interesting is that this method of the `Random`

class returns a normally distributed number (normally distributed numbers can be used to for example model the heights and weights of people — if you are not interested in different kinds of randomness that is OK!).

```
public int makeAForecast() {
return (int) (4 * this.random.nextGaussian() - 3);
}
```

In the previous example we use explicit type casting to convert doubles to integers `(int)`

.
We can equally convert integers to doubles with `(double) integer`

.

Let's now add a main which uses the `WeatherMan`

class.

```
// imports
public class Program {
public static void main(String[] args) {
WeatherMan forecaster = new WeatherMan();
// save days of the week to a list
ArrayList<String> days = new ArrayList<>();
days.add("Mon");
days.add("Tue");
days.add("Wed");
days.add("Thu");
days.add("Fri");
days.add("Sat");
days.add("Sun");
System.out.println("Next week's weather forecast:");
for (String day : days) {
String weatherForecast = forecaster.forecast();
int temperatureForecast = forecaster.makeAForecast();
System.out.println(day + ": " + weatherForecast + " " + temperatureForecast + " degrees.");
}
}
}
```

The program output could be:

Next week's weather forecast: Mon: It snows 1 degrees. Tue: It snows 1 degrees. Wed: The sun shines -2 degrees. Thu: The sun shines 0 degrees. Fri: It snows -3 degrees. Sat: It snows -3 degrees. Sun: The sun shines -5 degrees

Remember to check your points from the ball on the bottom-right corner of the material!