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201 行
7.9 KiB

using System;
using System.Runtime.CompilerServices;
using Unity.Burst;
using Unity.Collections;
using Unity.Jobs;
using Unity.Mathematics;
namespace UnityEngine.Experimental.Perception.Randomization.Samplers
{
/// <summary>
/// A set of utility functions for defining sampler interfaces
/// </summary>
public static class SamplerUtility
{
internal const uint largePrime = 0x202A96CF;
const int k_SamplingBatchSize = 64;
/// <summary>
/// Returns the sampler's display name
/// </summary>
/// <param name="samplerType">The sampler type</param>
/// <returns>The display name</returns>
public static string GetSamplerDisplayName(Type samplerType)
{
return samplerType.Name.Replace("Sampler", string.Empty);
}
/// <summary>
/// Non-deterministically generates a random seed
/// </summary>
/// <returns>A non-deterministically generated random seed</returns>
public static uint GenerateRandomSeed()
{
return (uint)Random.Range(1, uint.MaxValue);
}
/// <summary>
/// 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
/// </summary>
/// <param name="x">Unsigned integer to hash</param>
/// <returns>The calculated hash value</returns>
[MethodImpl(MethodImplOptions.AggressiveInlining)]
static uint Hash32(uint x) {
x = ((x >> 16) ^ x) * 0x45d9f3b;
x = ((x >> 16) ^ x) * 0x45d9f3b;
x = (x >> 16) ^ x;
return x;
}
/// <summary>
/// Based on splitmix64: http://xorshift.di.unimi.it/splitmix64.c
/// </summary>
/// <param name="x">64-bit value to hash</param>
/// <returns>The calculated hash value</returns>
[MethodImpl(MethodImplOptions.AggressiveInlining)]
static ulong Hash64(ulong x) {
x = (x ^ (x >> 30)) * 0xbf58476d1ce4e5b9ul;
x = (x ^ (x >> 27)) * 0x94d049bb133111ebul;
x ^= (x >> 31);
return x;
}
/// <summary>
/// Generates new a new non-zero random state by deterministically hashing a base seed with an iteration index
/// </summary>
/// <param name="index">Usually the current scenario iteration or framesSinceInitialization</param>
/// <param name="baseSeed">The seed to be offset</param>
/// <returns>A new random state</returns>
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public static uint IterateSeed(uint index, uint baseSeed)
{
var state = (uint)Hash64(((ulong)index << 32) | baseSeed);
return state == 0u ? largePrime : state;
}
/// <summary>
/// Schedules a multi-threaded job to generate an array of samples
/// </summary>
/// <param name="sampler">The sampler to generate samples from</param>
/// <param name="sampleCount">The number of samples to generate</param>
/// <param name="jobHandle">The handle of the scheduled job</param>
/// <typeparam name="T">The type of sampler to sample</typeparam>
/// <returns>A NativeArray of generated samples</returns>
public static NativeArray<float> GenerateSamples<T>(
T sampler, int sampleCount, out JobHandle jobHandle) where T : struct, ISampler
{
var samples = new NativeArray<float>(
sampleCount, Allocator.TempJob, NativeArrayOptions.UninitializedMemory);
jobHandle = new SampleJob<T>
{
sampler = sampler,
samples = samples
}.ScheduleBatch(sampleCount, k_SamplingBatchSize);
return samples;
}
[BurstCompile]
struct SampleJob<T> : IJobParallelForBatch where T : ISampler
{
public T sampler;
public NativeArray<float> samples;
public void Execute(int startIndex, int count)
{
var endIndex = startIndex + count;
var batchIndex = startIndex / k_SamplingBatchSize;
sampler.IterateState(batchIndex);
for (var i = startIndex; i < endIndex; i++)
samples[i] = sampler.Sample();
}
}
/// <summary>
/// Source: https://www.johndcook.com/blog/csharp_phi/
/// </summary>
static float NormalCdf(float x)
{
const float a1 = 0.254829592f;
const float a2 = -0.284496736f;
const float a3 = 1.421413741f;
const float a4 = -1.453152027f;
const float a5 = 1.061405429f;
const float p = 0.3275911f;
var sign = 1;
if (x < 0)
sign = -1;
x = math.abs(x) / math.sqrt(2.0f);
var t = 1.0f / (1.0f + p*x);
var y = 1.0f - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t * math.exp(-x*x);
return 0.5f * (1.0f + sign*y);
}
/// <summary>
/// Source: https://www.johndcook.com/blog/csharp_phi_inverse/
/// </summary>
static float RationalApproximation(float t)
{
const float c0 = 2.515517f;
const float c1 = 0.802853f;
const float c2 = 0.010328f;
const float d0 = 1.432788f;
const float d1 = 0.189269f;
const float d2 = 0.001308f;
return t - ((c2*t + c1)*t + c0) / (((d2*t + d1)*t + d0)*t + 1.0f);
}
/// <summary>
/// Source: https://www.johndcook.com/blog/csharp_phi_inverse/
/// Note: generates NaN values for values 0 and 1
/// </summary>
/// <param name="uniformSample">A uniform sample value between the range (0, 1)</param>
static float NormalCdfInverse(float uniformSample)
{
return uniformSample < 0.5f
? -RationalApproximation(math.sqrt(-2.0f * math.log(uniformSample)))
: RationalApproximation(math.sqrt(-2.0f * math.log(1.0f - uniformSample)));
}
/// <summary>
/// Generates samples from a truncated normal distribution.
/// Further reading about this distribution can be found here:
/// https://en.wikipedia.org/wiki/Truncated_normal_distribution
/// </summary>
/// <param name="uniformSample">A sample value between 0 and 1 generated from a uniform distribution</param>
/// <param name="min">The minimum possible value to generate</param>
/// <param name="max">The maximum possible value to generate</param>
/// <param name="mean">The mean of the normal distribution</param>
/// <param name="stdDev">The standard deviation of the normal distribution</param>
/// <returns>A value sampled from a truncated normal distribution</returns>
/// <exception cref="ArgumentException"></exception>
public static float TruncatedNormalSample(float uniformSample, float min, float max, float mean, float stdDev)
{
if (min > max)
throw new ArgumentException("Invalid range");
if (uniformSample == 0f)
return min;
if (uniformSample == 1f)
return max;
if (stdDev == 0f)
return math.clamp(mean, min, max);
var a = NormalCdf((min - mean) / stdDev);
var b = NormalCdf((max - mean) / stdDev);
var c = math.lerp(a, b, uniformSample);
if (c == 0f)
return max;
if (c == 1f)
return min;
var stdTruncNorm = NormalCdfInverse(c);
return stdTruncNorm * stdDev + mean;
}
}
}