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RandomNumberGenerator

Inherits: RefCounted < Object

Provides methods for generating pseudo-random numbers.

Description

RandomNumberGenerator is a class for generating pseudo-random numbers. It currently uses PCG32.

Note: The underlying algorithm is an implementation detail and should not be depended upon.

To generate a random float number (within a given range) based on a time-dependent seed:

var rng = RandomNumberGenerator.new()
func _ready():
    var my_random_number = rng.randf_range(-10.0, 10.0)

Tutorials

Random number generation

Properties

int	seed	`0`
int	state	`0`

Methods

int	rand_weighted(weights: PackedFloat32Array)
float	randf()
float	randf_range(from: float, to: float)
float	randfn(mean: float = 0.0, deviation: float = 1.0)
int	randi()
int	randi_range(from: int, to: int)
void	randomize()

Property Descriptions

int seed = 0 🔗

void set_seed(value: int)
int get_seed()

Initializes the random number generator state based on the given seed value. A given seed will give a reproducible sequence of pseudo-random numbers.

Note: The RNG does not have an avalanche effect, and can output similar random streams given similar seeds. Consider using a hash function to improve your seed quality if they're sourced externally.

Note: Setting this property produces a side effect of changing the internal state, so make sure to initialize the seed before modifying the state:

Note: The default value of this property is pseudo-random, and changes when calling randomize. The 0 value documented here is a placeholder, and not the actual default seed.

var rng = RandomNumberGenerator.new()
rng.seed = hash("Redot")
rng.state = 100 # Restore to some previously saved state.

int state = 0 🔗

void set_state(value: int)
int get_state()

The current state of the random number generator. Save and restore this property to restore the generator to a previous state:

var rng = RandomNumberGenerator.new()
print(rng.randf())
var saved_state = rng.state # Store current state.
print(rng.randf()) # Advance internal state.
rng.state = saved_state # Restore the state.
print(rng.randf()) # Prints the same value as in previous.

Note: Do not set state to arbitrary values, since the random number generator requires the state to have certain qualities to behave properly. It should only be set to values that came from the state property itself. To initialize the random number generator with arbitrary input, use seed instead.

Note: The default value of this property is pseudo-random, and changes when calling randomize. The 0 value documented here is a placeholder, and not the actual default seed.

Method Descriptions

int rand_weighted(weights: PackedFloat32Array) 🔗

Returns a random index with non-uniform weights. Prints an error and returns -1 if the array is empty.

var rng = RandomNumberGenerator.new()

var my_array = ["one", "two", "three", "four"]
var weights = PackedFloat32Array([0.5, 1, 1, 2])

# Prints one of the four elements in `my_array`.
# It is more likely to print "four", and less likely to print "one".
print(my_array[rng.rand_weighted(weights)])

float randf() 🔗

Returns a pseudo-random float between 0.0 and 1.0 (inclusive).

float randf_range(from: float, to: float) 🔗

Returns a pseudo-random float between from and to (inclusive).

float randfn(mean: float = 0.0, deviation: float = 1.0) 🔗

Returns a normally-distributed, pseudo-random floating-point number from the specified mean and a standard deviation. This is also known as a Gaussian distribution.

Note: This method uses the Box-Muller transform algorithm.

int randi() 🔗

Returns a pseudo-random 32-bit unsigned integer between 0 and 4294967295 (inclusive).

int randi_range(from: int, to: int) 🔗

Returns a pseudo-random 32-bit signed integer between from and to (inclusive).

void randomize() 🔗

Sets up a time-based seed for this RandomNumberGenerator instance. Unlike the @GlobalScope random number generation functions, different RandomNumberGenerator instances can use different seeds.