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Random library in C# for Unity3D

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Octocat-spinner-32 Editor shuffle bag April 23, 2012
Octocat-spinner-32 ExampleFiles build April 23, 2012
Octocat-spinner-32 Samples Initial Import April 17, 2012
Octocat-spinner-32 lib the shuffle bag April 23, 2012
Octocat-spinner-32 .gitignore README PDF version April 20, 2012
Octocat-spinner-32 Example.unity
Octocat-spinner-32 README.pdf Version April 23, 2012
Octocat-spinner-32 README.textile
Octocat-spinner-32 UnityRandom.cs weighted bag April 23, 2012
README.textile

Unity Random

Version 1.0

Random Numbers for Unity3d

Homepage: http://tucanosoftware.com/projects/unityrandom

Example: http://108.166.95.149/UnityTest/UnityRandomExample/UnityRandomExample.html

FAQ

What is this?

In Unity3d there is already a Random number generator based on the platform-specific random generator.
Here we present an alternative Random library for Unity3d designed to generate uniform Pseudo-Random deviates.
The library use a fast PRNG (Mersenne-Twister) to generate: Floating Numbers in range [0-1] and in range [n-m], Vector2 and Vector3 data types.
The library comes with some special functions designed specifically for a game design framework: Shuffle Bag, Dice, Random Color.

Which kind of transformations I can apply to the random uniform deviates?

The uniform deviates can be transformed with the distributions: Standard Normal Distribution and Power-Law. In addition is possible to generate floating random deviates coming from other distributions: Poisson, Exponential and Gamma.

How I can test the random numbers?

The library add a window to the Unity3D editor that allow you to Test and Visualize your random numbers, Vector2 and Vector3. With the SAVE button, you can write the sample of random number to a txt file. This is useful if you need to analyze in deep the distribution of your random numbers with a statistical software.

Usage (C#)

Initialization
Initialization with a seed: UnityRandom urand = new UnityRandom(int seed);

Numbers
A Random number in range [0-1]: @float val = urand.Value()

Transformations
A Random number in range [0-1] with a Transformation: float val = urand.Value(UnityRandom.Normalization.STDNORMAL, 5.0f)

Vectors
A random point in a disk with R=1: Vector2 pos = urand.PointInADisk()

Colors
A random color in the range of visible light (rainbow): Color col = urand.Rainbow()

Dice
A 2D6 dice roll DiceRoll roll = urand.RollDice(2,DiceRoll.DiceType.D6)

Shuffle Bag

In games or educational programs you often don’t want real randomness. Unsurprisingly random it’s often a bit too random. You may get some items several times in a row and others too rarely. It’s either too easy or too hard. Balancing the weights is hard as well, because each run is too different. And if that wouldn’t be already bad enough; it’s also difficult to verify the results.

A shuffle Bag with values: [1,10]

float[] shufflebag = {1,2,3,4,5,6,7,8,9,10};

ShuffleBagCollection<float> thebag = _urand.ShuffleBag(shufflebag);

float randvalue = thebag.Next()

A shuffle bag with weighted values:

Dictionary wshufflebag = new Dictionary<float,int>();

wshufflebag[1] = 5;

wshufflebag[2] = 45;

wshufflebag[3] = 25;

wshufflebag[4] = 25;

@ShuffleBagCollection thebag = _urand.ShuffleBag(wshufflebag); @

float randvalue = thebag.Next()


Documentation

Initialization

  • Initialization without a seed: UnityRandom urand = new UnityRandom();
  • Initialization with a seed: UnityRandom urand = new UnityRandom(int seed);

Numbers

Generation of uniform deviates in any range.

Available Transformations

  • UnityRandom.Normalization.STDNORMAL with parameter: float temperature
  • UnityRandom.Normalization.POWERLAW with parameter: float power
Value
  • A Random number in range [0-1]: float val = urand.Value()
  • A Random number in range [0-1] with a Transformation: float val = urand.Value(UnityRandom.Normalization.STDNORMAL, 5.0f)
Range
  • A Random number in range [1-100]: float val = urand.Range(1,100)
  • A Random number in range [m-n] with a Transformation: float val = urand.Value(0,100,UnityRandom.Normalization.POWERLAW, 5.0f)
Poisson
  • The Poisson distribution (pronounced [pwasɔ̃]) is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time and/or space if these events occur with a known average rate and independently of the time since the last event. Example: float val = urand.Poisson(5.0f)
Exponential
  • The Exponential distribution describes the time between events in a Poisson process, i.e. a process in which events occur continuously and independently at a constant average rate. Example: float val = urand.Exponential(5.0f)
Gamma
  • The gamma distribution, like the lognormal distribution, is an alternative to consider for ecological variables that seem to be highly skewed. Example: float val = urand.Gamma(5.0f)

Vector2

generation of Unity Vector2 Objects.

Square
  • A random Vector2 point in a square with L=1: Vector2 pos = urand.PointInASquare()
  • A random Vector2 point in a square with L=1 normalized: Vector2 pos = urand.PointInASquare(UnityRandom.Normalization.STDNORMAL, 5.0f)
Disk/Circle
  • A random Vector2 point in a circle (in the circle perimeter) with R=1: Vector2 pos = urand.PointInACircle()
  • A random Vector2 point in a circle (in the circle perimeter) with R=1 normalized: Vector2 pos = urand.PointInACircle(UnityRandom.Normalization.STDNORMAL, 5.0f)
  • A random Vector2 point in a disk (in the circle area) with R=1: Vector2 pos = urand.PointInADisk()
  • A random Vector2 point in a circle (in the circle area) with R=1 normalized: Vector2 pos = urand.PointInADisk(UnityRandom.Normalization.STDNORMAL, 5.0f)

Vector3

generation of Unity Vector3

Cube
  • A random Vector3 point inside a cube with L=1: Vector3 pos = urand.PointInACube()
  • A random Vector3 point inside a cube with L=1 normalized: Vector3 pos = urand.PointInACube(UnityRandom.Normalization.STDNORMAL, 5.0f)
  • A random Vector3 point in the surface of a cube with L=1: Vector3 pos = urand.PointOnACube()
  • A random Vector3 point inside a cube with L=1 normalized: Vector3 pos = urand.PointOnACube(UnityRandom.Normalization.STDNORMAL, 5.0f)
Sphere
  • A random Vector3 point inside a sphere (in the sphere volume) with R=1: Vector3 pos = urand.PointInASphere()
  • A random Vector3 point in the sphere surface (in the sphere surface) with R=1: Vector3 pos = urand.PointOnASphere()

Color

  • A random Color in the range of visible light (rainbow): Color col = urand.Rainbow()
  • A random Color in the range of visible light (rainbow) normalized: Color col = urand.Rainbow(UnityRandom.Normalization.STDNORMAL, 5.0f)

Dice

  • A Dice Roll: DiceRoll roll = urand.RollDice(n,type)

Dice types:

  • DiceRoll.DiceType.D2
  • DiceRoll.DiceType.D3
  • DiceRoll.DiceType.D4
  • DiceRoll.DiceType.D6
  • DiceRoll.DiceType.D8
  • DiceRoll.DiceType.D10
  • DiceRoll.DiceType.D12
  • DiceRoll.DiceType.D20
  • DiceRoll.DiceType.D30
  • DiceRoll.DiceType.D100

Example 2D6:

DiceRoll roll = urand.RollDice(2,DiceRoll.DiceType.D6)

Shuffle Bag

From: http://kaioa.com/node/53

In games or educational programs you often don’t want real randomness. Unsurprisingly random it’s often a bit too random. You may get some items several times in a row and others too rarely. It’s either too easy or too hard. Balancing the weights is hard as well, because each run is too different. And if that wouldn’t be already bad enough; it’s also difficult to verify the results.

A shuffle Bag with values: [1,10]

float[] shufflebag = {1,2,3,4,5,6,7,8,9,10};

ShuffleBagCollection<float> thebag = _urand.ShuffleBag(shufflebag);

float randvalue = thebag.Next()

A shuffle bag with weighted values:

Dictionary wshufflebag = new Dictionary<float,int>();

wshufflebag[1] = 5;

wshufflebag[2] = 45;

wshufflebag[3] = 25;

wshufflebag[4] = 25;

@ShuffleBagCollection thebag = _urand.ShuffleBag(wshufflebag); @

float randvalue = thebag.Next()


Dev Notes

  • Clone and test
    • Fork the main project or clone: git://github.com/tucano/UnityRandom.git
    • Create an empty project in Unity
    • cd ./Assets
    • clone the repo there!
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