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Showing content from https://github.com/tucano/UnityRandom below:

tucano/UnityRandom: Random library in C# for Unity3D

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

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

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.

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.

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.

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()

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

Generation of uniform deviates in any range.

Available Transformations

• UnityRandom.Normalization.STDNORMAL with parameter: float temperature
• UnityRandom.Normalization.POWERLAW with parameter: float power

• 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)

• 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)

• 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)

• 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)

• 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)

generation of Unity Vector2 Objects.

• 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)

• 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)

generation of Unity Vector3

• 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)

• 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()

• 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)

• 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)

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()

• 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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