Class SamplerUtility

A set of utility functions for defining sampler interfaces

Inheritance

object

SamplerUtility

Inherited Members

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Namespace: UnityEngine.Perception.Randomization.Samplers

Assembly: Unity.Perception.Runtime.dll

Syntax

public static class SamplerUtility

Fields

largePrime

A large prime number

Declaration

public const uint largePrime = 539662031

Field Value

Type	Description
uint

samplingBatchSize

The number of samples to generate per job batch in an IJobParallelForBatch job

Declaration

public const int samplingBatchSize = 64

Field Value

Type	Description
int

Methods

AnimationCurveSample(float[], float, float, float, float)

Generate samples from probability distribution derived from a given AnimationCurve.

Declaration

public static float AnimationCurveSample(float[] integratedCurve, float uniformSample, float interval, float startTime, float endTime)

Parameters

Type	Name	Description
float[]	integratedCurve	Numerical integration representing the AnimationCurve
float	uniformSample	A sample value between 0 and 1 generated from a uniform distribution
float	interval	The interval at which the original AnimationCurve was sampled in order to produce integratedCurve
float	startTime	The time attribute of the first key of the original AnimationCurve
float	endTime	The time attribute of the last key of the original AnimationCurve

Returns

Type	Description
float	The generated sample

GenerateRandomSeed()

Non-deterministically generates a non-zero random seed

Declaration

public static uint GenerateRandomSeed()

Returns

Type	Description
uint	A non-deterministically generated random seed

GetSamplerDisplayName(Type)

Returns the sampler's display name

Declaration

public static string GetSamplerDisplayName(Type samplerType)

Parameters

Type	Name	Description
Type	samplerType	The sampler type

Returns

Type	Description
string	The display name

Hash32(uint)

Hashes using constants generated from a program that maximizes the avalanche effect, independence of output bit changes, and the probability of a change in each output bit if any input bit is changed. Source: https://github.com/h2database/h2database/blob/master/h2/src/test/org/h2/test/store/CalculateHashConstant.java

Declaration

public static uint Hash32(uint x)

Parameters

Type	Name	Description
uint	x	Unsigned integer to hash

Returns

Type	Description
uint	The calculated hash value

Hash32NonZero(uint)

Generates a 32-bit non-zero hash using an unsigned integer seed

Declaration

public static uint Hash32NonZero(uint seed)

Parameters

Type	Name	Description
uint	seed	The unsigned integer to hash

Returns

Type	Description
uint	The calculated hash value

Hash64(ulong)

Based on splitmix64: http://xorshift.di.unimi.it/splitmix64.c

Declaration

public static ulong Hash64(ulong x)

Parameters

Type	Name	Description
ulong	x	64-bit value to hash

Returns

Type	Description
ulong	The calculated hash value

IntegrateCurve(float[], AnimationCurve)

Numerically integrate a given AnimationCurve using the specified number of samples. Based on https://en.wikipedia.org/wiki/Numerical_integration and http://blog.s-schoener.com/2018-05-05-animation-curves/ Using the trapezoidal rule for numerical interpolation

Declaration

public static void IntegrateCurve(float[] array, AnimationCurve curve)

Parameters

Type	Name	Description
float[]	array	The array to fill with integrated values
AnimationCurve	curve	The animation curve to sample integrate

Exceptions

Type	Condition
ArgumentException

IterateSeed(uint, uint)

Generates new a new non-zero random state by deterministically hashing a base seed with an iteration index

Declaration

public static uint IterateSeed(uint index, uint baseSeed)

Parameters

Type	Name	Description
uint	index	Usually the current scenario iteration or framesSinceInitialization
uint	baseSeed	The seed to be offset

Returns

Type	Description
uint	A new random state

TruncatedNormalSample(float, float, float, float, float)

Generates samples from a truncated normal distribution. Further reading about this distribution can be found here: https://en.wikipedia.org/wiki/Truncated_normal_distribution

Declaration

public static float TruncatedNormalSample(float uniformSample, float min, float max, float mean, float stdDev)

Parameters

Type	Name	Description
float	uniformSample	A sample value between 0 and 1 generated from a uniform distribution
float	min	The minimum possible value to generate
float	max	The maximum possible value to generate
float	mean	The mean of the normal distribution
float	stdDev	The standard deviation of the normal distribution

Returns

Type	Description
float	A value sampled from a truncated normal distribution

Exceptions

Type	Condition
ArgumentException