public PoolServer(string poolName, PoolType type, int speed, int delaySec, double rejectPercent, GetTargetShareHandler handler)
{
_rnd = new MersenneTwister((uint)DateTime.Now.Ticks);
Round = 1;
PoolName = poolName;
Type = type;
GHashSpeed = speed;
DelaySec = delaySec;
GetNextShare = handler;
RejectPercentile = rejectPercent;
CurrentRealShare = 0;
MyValidShare = 0;
MyLostShare = 0;
TotalScore = 0;
MyScore = 0;
MyTotalProfit = 0;
MyTotalValidShare = 0;
MyTotalShare = 0;
BaseProgress = 0;
PenaltyFactor = 1.0f;
// Initialize(initialProgress);
}
protected override void GenerateMesh()
{
foreach(Transform child in transform)
DestroyImmediate(child.gameObject);
var rand = new MersenneTwister(randomSeed);
var totalHeight = 0.0f;
var scaleDown = 1.0f;
var combineMeshInstances = new List<CombineInstance>();
for(int i = 0; i < layerCount; ++i)
{
var baseRadius = Mathf.Lerp(baseRadiusMin, baseRadiusMax, rand.NextSinglePositive()) * scaleDown;
var baseBezier = GenerateBaseBezier(baseRadius * scaleDown, rand);
var previousTotalHeight = totalHeight;
totalHeight += Mathf.Lerp(layerHeightMin, layerHeightMax, rand.NextSinglePositive()) * scaleDown;
var heightBezier = Bezier.ConstructSmoothSpline(
new Vector3[]{
Vector3.up * previousTotalHeight,
Vector3.up * totalHeight
}
);
var heightSegs = (uint)heightSegments;
var pathSegs = (uint)(radiusSegments * scaleDown);
if( heightSegs > 0 && pathSegs > 2 )
{
var combineMeshInstance = new CombineInstance();
combineMeshInstance.mesh = Loft.GenerateMesh(
heightBezier,
baseBezier,
heightSegs,
pathSegs
);
combineMeshInstances.Add(combineMeshInstance);
var topCap = new CombineInstance();
topCap.mesh = baseBezier.Triangulate(pathSegs, Vector3.up * totalHeight);
combineMeshInstances.Add(topCap);
}
scaleDown *= Mathf.Lerp(scaleDownMin, scaleDownMax, rand.NextSinglePositive());
}
var meshFilter = GetComponent<MeshFilter>();
if( meshFilter.sharedMesh )
DestroyImmediate(meshFilter.sharedMesh);
meshFilter.sharedMesh = new Mesh();
meshFilter.sharedMesh.CombineMeshes(combineMeshInstances.ToArray(), true, false);
foreach(var combineInstance in combineMeshInstances)
DestroyImmediate(combineInstance.mesh);
}
public void SampleTest()
{
var normal = new Normal(0.0, 1.0);
var rnd = new MersenneTwister();
var ms = new MetropolisSampler<double>(0.2, normal.Density, x => Normal.Sample(rnd, x, 0.1), 10);
ms.RandomSource = rnd;
double sample = ms.Sample();
}
public void MetropolisConstructor()
{
var normal = new Normal(0.0, 1.0);
var rnd = new MersenneTwister();
var ms = new MetropolisSampler<double>(0.2, normal.Density, x => Normal.Sample(rnd, x, 0.1), 10);
Assert.IsNotNull(ms.RandomSource);
ms.RandomSource = rnd;
Assert.IsNotNull(ms.RandomSource);
}
public void SampleArrayTest()
{
var normal = new Normal(0.0, 1.0);
var rnd = new MersenneTwister();
var ms = new MetropolisHastingsSampler<double>(0.2, normal.Density, (x, y) => (new Normal(x, 0.1)).Density(y), x => Normal.Sample(rnd, x, 0.1), 10)
{
RandomSource = rnd
};
ms.Sample(5);
}
public void MetropolisHastingsConstructor()
{
var normal = new Normal(0.0, 1.0);
var rnd = new MersenneTwister();
var ms = new MetropolisHastingsSampler<double>(0.2, normal.Density, (x, y) => (new Normal(x, 0.1)).Density(y), x => Normal.Sample(rnd, x, 0.1), 10)
{
RandomSource = rnd
};
Assert.IsNotNull(ms.RandomSource);
ms.RandomSource = new Random();
Assert.IsNotNull(ms.RandomSource);
}
public Flower(int difficulty)
{
// Default values
SliceSize = 1200;
EarliHopFactor = 4.0f;
PPLNSFactor = 4.0f;
ScoreFactor = 4.0f;
Threshold = 0.43f;
PowerValue = 2.6f;
SelectType = 0;
_difficulty = difficulty;
_rnd = new MersenneTwister((uint)DateTime.Now.Ticks);
_slicedShare = new Dictionary<string, double>();
_slice = new Dictionary<string, int>();
}
public void InitializeSimutlator()
{
var seed = (uint)DateTime.Now.Ticks;
_rnd = new MersenneTwister(seed);
_currentSimGeneration = 0;
//
//
// Simulation Configuration Here //
//
//
_simConfig = new BasicSimConfig(GetNextTarget);
//_simConfig = new BclcTest(GetNextTarget);
//_simConfig = new MinecoTest(GetNextTarget);
labelAdvPerTick.Text = _simConfig.InitialSimulationSpeedUp.ToString();
SetupGeneration();
}
public RouletteRoundShare(int difficulty)
{
_difficulty = difficulty;
_rnd = new MersenneTwister((uint)DateTime.Now.Ticks);
Threshold = 0.43f;
}
public void SampleProportionalWithoutRepetitionTest() {
{
// select 1 of 100 uniformly (weights = 0)
var items = Enumerable.Range(0, 100);
var random = new MersenneTwister(31415);
var weights = Enumerable.Repeat(0.0, 100);
for (int i = 0; i < 1000; i++) {
var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 1, weights, false, false).ToArray();
Assert.AreEqual(sample.Count(), 1);
}
}
{
// select 1 of 1 uniformly (weights = 0)
var items = Enumerable.Range(0, 1);
var random = new MersenneTwister(31415);
var weights = Enumerable.Repeat(0.0, 1);
for (int i = 0; i < 1000; i++) {
var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 1, weights, false, false).ToArray();
Assert.AreEqual(sample.Count(), 1);
}
}
{
// select 1 of 2 non-uniformly (weights = 1, 2)
var items = Enumerable.Range(0, 2);
var random = new MersenneTwister(31415);
var weights = new double[] { 1.0, 2.0 };
var zeroSelected = 0;
for (int i = 0; i < 1000; i++) {
var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 1, weights, false, false).ToArray();
Assert.AreEqual(sample.Count(), 1);
if (sample[0] == 0) zeroSelected++;
}
Assert.IsTrue(zeroSelected > 0 && zeroSelected < 1000);
}
{
// select 2 of 2 non-uniformly (weights = 1, 1000)
var items = Enumerable.Range(0, 2);
var random = new MersenneTwister(31415);
var weights = new double[] { 1.0, 1000.0 };
for (int i = 0; i < 1000; i++) {
var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 2, weights, false, false).ToArray();
Assert.AreEqual(sample.Count(), 2);
Assert.AreEqual(sample.Distinct().Count(), 2);
}
}
{
// select 2 from 1 uniformly (weights = 0), this does not throw an exception but instead returns a sample with 1 element!
var items = Enumerable.Range(0, 1);
var random = new MersenneTwister(31415);
var weights = Enumerable.Repeat(0.0, 1);
var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 2, weights, false, false).ToArray();
Assert.AreEqual(sample.Count(), 1);
}
{
// select 10 of 100 uniformly (weights = 0)
var items = Enumerable.Range(0, 100);
var random = new MersenneTwister(31415);
var weights = Enumerable.Repeat(0.0, 100);
for (int i = 0; i < 1000; i++) {
var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 10, weights, false, false).ToArray();
Assert.AreEqual(sample.Count(), 10);
Assert.AreEqual(sample.Distinct().Count(), 10);
}
}
{
// select 100 of 100 uniformly (weights = 0)
var items = Enumerable.Range(0, 100);
var random = new MersenneTwister(31415);
var weights = Enumerable.Repeat(0.0, 100);
for (int i = 0; i < 1000; i++) {
var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 100, weights, false, false).ToArray();
Assert.AreEqual(sample.Count(), 100);
Assert.AreEqual(sample.Distinct().Count(), 100);
}
}
{
// select 10 of 10 uniformly (weights = 1)
var items = Enumerable.Range(0, 10);
var random = new MersenneTwister(31415);
var weights = Enumerable.Repeat(1.0, 10);
for (int i = 0; i < 1000; i++) {
var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 10, weights, false, false).ToArray();
Assert.AreEqual(sample.Count(), 10);
Assert.AreEqual(sample.Distinct().Count(), 10);
}
}
{
// select 10 of 10 uniformly (weights = 1)
var items = Enumerable.Range(0, 10);
var random = new MersenneTwister(31415);
var weights = Enumerable.Repeat(1.0, 10);
for (int i = 0; i < 1000; i++) {
var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 10, weights, true, false).ToArray();
Assert.AreEqual(sample.Count(), 10);
Assert.AreEqual(sample.Distinct().Count(), 10);
}
}
{
// select 10 of 10 uniformly (weights = 1)
var items = Enumerable.Range(0, 10);
var random = new MersenneTwister(31415);
var weights = Enumerable.Repeat(1.0, 10);
for (int i = 0; i < 1000; i++) {
var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 10, weights, true, true).ToArray();
Assert.AreEqual(sample.Count(), 10);
Assert.AreEqual(sample.Distinct().Count(), 10);
}
}
{
// select 5 of 10 uniformly (weights = 0..n)
var items = Enumerable.Range(0, 10);
var random = new MersenneTwister(31415);
var weights = new double[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
for (int i = 0; i < 1000; i++) {
var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 5, weights, false, false).ToArray();
Assert.AreEqual(sample.Count(), 5);
Assert.AreEqual(sample.Distinct().Count(), 5);
}
}
{
// select 5 of 10 uniformly (weights = 0..n)
var items = Enumerable.Range(0, 10);
var random = new MersenneTwister(31415);
var weights = new double[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
for (int i = 0; i < 1000; i++) {
var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 5, weights, true, false).ToArray();
Assert.AreEqual(sample.Count(), 5);
Assert.AreEqual(sample.Distinct().Count(), 5);
}
}
{
// select 5 of 10 uniformly (weights = 0..n)
var items = Enumerable.Range(0, 10);
var random = new MersenneTwister(31415);
var weights = new double[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
for (int i = 0; i < 1000; i++) {
var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 5, weights, true, true).ToArray();
Assert.AreEqual(sample.Count(), 5);
Assert.AreEqual(sample.Distinct().Count(), 5);
}
}
{
// select 10 of 100 uniformly (weights = 1)
// repeat 1000000 times and calculate statistics
var items = Enumerable.Range(0, 100);
var random = new MersenneTwister(31415);
var weights = Enumerable.Repeat(1.0, 100);
var selectionCount = new int[100, 100]; // frequency of selecting item at pos
for (int i = 0; i < 1000000; i++) {
var sample = RandomEnumerable.SampleProportionalWithoutRepetition(items, random, 100, weights, false, false).ToArray();
Assert.AreEqual(sample.Count(), 100);
Assert.AreEqual(sample.Distinct().Count(), 100);
int pos = 0;
foreach (var item in sample) {
selectionCount[item, pos]++;
pos++;
}
}
var sb = new StringBuilder();
for (int item = 0; item < 100; item++) {
for (int pos = 0; pos < 100; pos++) {
sb.AppendFormat("{0} ", selectionCount[item, pos]);
}
sb.AppendLine();
}
Console.WriteLine(sb.ToString());
}
}
public void SamplesFollowsCorrectDistribution()
{
Random rnd = new MersenneTwister(1);
foreach (var dd in _discreteDistributions)
{
dd.RandomSource = rnd;
VapnikChervonenkisTest(Error, ErrorProbability, dd.Samples().Select(x => (double)x).Take(NumberOfTestSamples), dd);
}
foreach (var cd in _continuousDistributions)
{
cd.RandomSource = rnd;
VapnikChervonenkisTest(Error, ErrorProbability, cd.Samples().Take(NumberOfTestSamples), cd);
}
}
public void SampleFollowsCorrectDistribution()
{
Random rnd = new MersenneTwister(1);
foreach (var dd in _discreteDistributions)
{
dd.RandomSource = rnd;
var samples = new double[NumberOfTestSamples];
for (var i = 0; i < NumberOfTestSamples; i++)
{
samples[i] = dd.Sample();
}
VapnikChervonenkisTest(Error, ErrorProbability, samples, dd);
}
foreach (var cd in _continuousDistributions)
{
cd.RandomSource = rnd;
var samples = new double[NumberOfTestSamples];
for (var i = 0; i < NumberOfTestSamples; i++)
{
samples[i] = cd.Sample();
}
VapnikChervonenkisTest(Error, ErrorProbability, samples, cd);
}
}
public void SamplesFollowsCorrectDistribution()
{
Random rnd = new MersenneTwister();
var cd = new NormalGamma(1.0, 4.0, 3.0, 3.5);
// Sample from the distribution.
var samples = cd.Samples().Take(CommonDistributionTests.NumberOfTestSamples).ToArray();
// Extract the mean and precisions.
var means = samples.Select(mp => mp.Mean);
var precs = samples.Select(mp => mp.Precision);
var meanMarginal = cd.MeanMarginal();
var precMarginal = cd.PrecisionMarginal();
// Check the precision distribution.
CommonDistributionTests.VapnikChervonenkisTest(
CommonDistributionTests.Error,
CommonDistributionTests.ErrorProbability,
precs,
precMarginal);
// Check the mean distribution.
CommonDistributionTests.VapnikChervonenkisTest(
CommonDistributionTests.Error,
CommonDistributionTests.ErrorProbability,
means,
meanMarginal);
}
// todo: remove this
private static void SlotAdvances(BWRng pidrng, MersenneTwister ivrng)
{
pidrng.GetNext64BitNumber();
pidrng.GetNext64BitNumber();
pidrng.GetNext64BitNumber();
pidrng.GetNext64BitNumber();
pidrng.GetNext64BitNumber();
ivrng.Next();
ivrng.Next();
ivrng.Next();
ivrng.Next();
ivrng.Next();
ivrng.Next();
ivrng.Next();
ivrng.Next();
ivrng.Next();
ivrng.Next();
ivrng.Next();
ivrng.Next();
ivrng.Next();
}
private Bezier GenerateBaseBezier(float size, MersenneTwister rand)
{
var centerJitterX = Mathf.Lerp(-1.0f, 1.0f, size * centerJitterAmount * rand.NextSinglePositive());
var centerJitterZ = Mathf.Lerp(-1.0f, 1.0f, size * centerJitterAmount * rand.NextSinglePositive());
var centerJitter = new Vector3(centerJitterX, 0.0f, centerJitterZ);
var cpCount = 32;
var step = Mathf.PI * -2.0f / (float)cpCount;
var cps = new Vector3[cpCount];
for(int i = 0; i < cpCount; ++i)
{
var t = (float)i * step;
var x = Mathf.Sin(t) * size;
var z = Mathf.Cos(t) * size;
cps[i].x = Mathf.Lerp(0.0f, x, 1.0f - (waviness * rand.NextSinglePositive()));
cps[i].z = Mathf.Lerp(0.0f, z, 1.0f - (waviness * rand.NextSinglePositive()));
cps[i] += centerJitter;
}
return Bezier.ConstructSmoothSpline(cps, true);
}
public MersennePlusN(Int32 seed)
{
mt = new MersenneTwister(seed);
}
public MersennePlusN()
{
this.seed = new Random().Next();
mt = new MersenneTwister(seed);
}
private void Advance(BWRng pidrng, MersenneTwister ivrng, List<DreamRadarFrame.Spin> spins)
{
// first PIDRNG advance = spin
spins.Add((DreamRadarFrame.Spin) pidrng.GetNext32BitNumber(8));
pidrng.GetNext64BitNumber();
ivrng.Next();
ivrng.Next();
}
private DreamRadarFrame GeneratePokemon(BWRng pidrng2, MersenneTwister ivrng2)
{
var pidrng = new BWRng(pidrng2.Seed);
var ivrng = new MersenneTwister(ivrng2);
var frame = new DreamRadarFrame();
frame.Pid = GeneratePID(pidrng);
// two unknown advances
pidrng.GetNext64BitNumber();
pidrng.GetNext64BitNumber();
frame.Nature = pidrng.GetNext32BitNumber(25);
// IVs
frame.Hp = ivrng.Next() >> 27;
frame.Atk = ivrng.Next() >> 27;
frame.Def = ivrng.Next() >> 27;
frame.Spa = ivrng.Next() >> 27;
frame.Spd = ivrng.Next() >> 27;
frame.Spe = ivrng.Next() >> 27;
return frame;
}
public void SampleFollowsCorrectDistribution()
{
Random rnd = new MersenneTwister();
//var cd = new NormalGamma(1.0, 4.0, 3.0, 3.5);
var cd = new NormalGamma(1.0, 4.0, 7.0, 3.5);
// Sample from the distribution.
MeanPrecisionPair[] samples = new MeanPrecisionPair[CommonDistributionTests.NumberOfTestSamples];
for (int i = 0; i < CommonDistributionTests.NumberOfTestSamples; i++)
{
samples[i] = cd.Sample();
}
// Extract the mean and precisions.
var means = samples.Select(mp => mp.Mean);
var precs = samples.Select(mp => mp.Precision);
var meanMarginal = cd.MeanMarginal();
var precMarginal = cd.PrecisionMarginal();
// Check the precision distribution.
CommonDistributionTests.VapnikChervonenkisTest(
CommonDistributionTests.Error,
CommonDistributionTests.ErrorProbability,
precs,
precMarginal);
// Check the mean distribution.
CommonDistributionTests.VapnikChervonenkisTest(
CommonDistributionTests.Error,
CommonDistributionTests.ErrorProbability,
means,
meanMarginal);
}
public List<DreamRadarFrame> Generate(ulong seed, Profile profile)
{
var frames = new List<DreamRadarFrame>();
// Build the PIDRNG
uint initialFrame = Functions.initialPIDRNG(seed, profile);
var pidrng = new BWRng(seed);
pidrng.Advance(initialFrame);
// Build the MTRNG
// todo: use fast MTRNG when available
var ivrng = new MersenneTwister((uint) (seed >> 32));
// advance 8 frames for BW2
for (uint i = 0; i < 10; ++i) ivrng.Next();
// one single advancement for entering the menu
pidrng.GetNext64BitNumber();
var spins = new List<DreamRadarFrame.Spin>();
// initial advances
for (uint i = 0; i < initialFrame; ++i) Advance(pidrng, ivrng, spins);
// slot advances
// we're always doing the slot 1 advance here
pidrng.GetNext64BitNumber();
for (uint i = 1; i < TargetSlot; ++i) SlotAdvances(pidrng, ivrng);
for (uint i = initialFrame; i <= MaxFrame; ++i)
{
DreamRadarFrame frame = GeneratePokemon(pidrng, ivrng);
var arrSpins = new DreamRadarFrame.Spin[spins.Count];
spins.CopyTo(arrSpins);
frame.Spins = arrSpins;
// add checks/comparisons on the frame here
// nature/IVs
frames.Add(frame);
Advance(pidrng, ivrng, spins);
}
return frames;
}